Boston Women in Bioinformatics's Blog

A Coffee with CompBio: Behind the "We'll Be in Touch" Emails

Wed, 29 Apr 2026 00:00:00 GMT

ArkeaBio's Lauren Fitch and Kaye Guerard-Hatziioannou pull back the curtain on how bioinformatics roles actually get filled.

Ever wonder what actually happens after you submit a job application? In this episode of A Coffee with CompBio, Dina and Sharvari sit down with Lauren Fitch (Head of Computational Biology at ArkeaBio) and Kaye Guerard-Hatziioannou (HR Business Partner at ArkeaBio) to walk through the full hiring process for a Bioinformatics Software Engineer role — from resume screening to interview rounds. Lauren and Kaye share the things hiring managers wish candidates knew, offering practical advice for both early-career and mid-career professionals navigating the bioinformatics job market.

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Thanks Amulya Shastry for editing and management support, Dina Issakova for the cover art, and Valisha Shah for social media support.

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Biobank Intro Series: You Have What You Need

Tue, 21 Apr 2026 00:00:00 GMT

A wrap-up of the series and a checklist before you start your analysis

The Biobank Intro Series, visualized as a subway map: each stop builds the foundation you need before diving into biobank analysis.

If you've made it here, you've covered a lot of ground in UK Biobank and All of Us:

The research environments
The observational data
The genetic data

This series was never meant to be exhaustive, but I hope it compressed your learning curve.

Before You Start Analysis

Use this as a gut check. If you can answer yes to everything here, you're ready.

Platform

Identified the compute model you're working in (DNAnexus vs. Google Cloud)
A plan is made for saving outputs that won't disappear when your session ends

Phenotypes and Covariates

Identified the right fields or concepts for your phenotypes of interest and covariates
Checked for data quality warnings and understand what they mean for your analysis
Know which timepoints or visits have data and which one you're using (instances on UKB, enrollment/EHR timing on AoU)
Confirmed the sample size is sufficient for your question

Genetic Data

Located the genotype data format you need (unphased WGS, phased WGS, or exome sequencing)
Confirmed files are indexed (.tbi or .csi) before running any region queries
Subset to your region or variant list
Confirmed sample overlap between your phenotype and genotype data

What Comes Next

These posts will update if I find mistakes. If something looks off, feel free to reach out to me.

This series ends here, but, as the cliché goes, the journey is only beginning. Depending on your question, you might be headed toward GWAS, PheWAS, rare variant burden testing, or cross-biobank comparisons. Topics that may appear in a future series.

For now: you have the data, you know where it lives, and you know enough to not be too surprised by what you find. That's further than a lot of people get before they start. Good luck!

Reflections

None of these posts would have happened without a few people.

Thank you to Sneha Grandhi who helped me recognize my strengths in this field. The biobank work I did at Genscience with her is what this series is built on.

Almost a year ago, Lina Faller's dedication to sharing her data management work on the Boston Women in Bioinformatics website was the push I needed to finally write something of my own.

Thank you to Mitibketa Ilboudo and Sharvari Narendra for editing. Taking time to give feedback on a stranger's blog posts is not a small thing, and it's the reason I could post these with confidence.

Thank you to Lance and my family for the blind support. They may never understand what it is that I do, but I still love them.

Member Spotlight: Sharvari Narendra

Thu, 16 Apr 2026 14:00:00 GMT

Finding Her Voice, One Pipeline at a Time

From the outside, Sharvari Narendra seems like someone who has it all figured out. As a host of A Coffee with Comp Bio Season 2 Podcast, she speaks with confidence and ease, clearly grounded in her work in bioinformatics. It’s the kind of presence that makes you assume the path behind her mastery was just as steady. So when I sat down to interview her about her career journey, I expected a straightforward story. I was wrong, and I left the conversation genuinely inspired.

When Sharvari completed her Master’s in Microbiology in India, she was already on a seemingly strong, stable career path. However, stepping into an entry job, doing work that made sense on paper, she very quickly realized something important. It wasn’t the work she wanted to be doing.

She had pivoted to what she calls a “dry lab”, but not in the way we think of it now. It wasn’t biological data analysis. It was simply sitting behind a computer, doing work that didn’t feel connected to anything she cared about. She knew it wasn’t her calling, but like many people in that position, she didn’t have a clear reason to leave yet. That push came from somewhere unexpected.

In 2017, Sharvari was selected to attend the Harvard College Project for Asian and International Relations (HPAIR), an experience that would quietly change the course of her life. This global conference brings together students and young professionals to discuss major economic, political, and social issues across the Asia-Pacific region. It’s a space for future leaders, people thinking broadly about the world and their place in it. For Sharvari, it was also her first time traveling to the United States, and her first time traveling alone. She arrived in snowy Boston in the middle of winter, stepping into the cold, the unknown, and a completely new world all at once. And something shifted. It wasn’t just the setting. It was the people. The conversations. The energy in the room. As she put it, “It wasn’t the HPAIR conference per se that captured my interest, it was the attendees. They were from so many different walks of life, but all of them had one thing in common. They were all passionate about something in their lives. I wanted that.”

That moment had nothing to do with bioinformatics. It had everything to do with perspective. For the first time, she wasn’t just thinking about finding a stable job. She was thinking about what it would mean to care deeply about the work she was doing. That realization gave Sharvari the courage to make a difficult decision. When she returned to India, she left her job. Not because she had a perfect plan waiting, but because she was finally ready to search for something that felt right. What followed wasn’t a clean transition. It was a period of exploration. She tried workshops in science communication, pursued language certifications, and even considered going back into academia for a PhD in Molecular Biology. Each step didn’t necessarily bring her closer to a clear answer, but it helped her understand what didn’t fit, which turned out to be just as important.

Then she came across a 10-day workshop in bioinformatics and metagenomics. “That one stuck with me,” she reflected. This time, the interest felt different. It wasn’t entirely new, she had been introduced to bioinformatics during her microbiology training, but now she was seeing its potential up close. The idea that you could use a computer to understand complex biological systems, like the gut microbiome, opened up a completely new way of thinking. That curiosity turned into a decision. She applied to Northeastern University’s Master’s program in Bioinformatics and returned to Boston, this time with purpose. Same city, but a completely different chapter.

For Sharvari, choosing the “right” path didn’t suddenly make things easy. She explained, “I found out how much I did not know. Many times I wondered if I had done the right thing coming all the way from India.” Coding wasn’t intuitive at first. The coursework was demanding. The job search felt unpredictable. And layered on top of it all was the weight of being far from home, navigating a system that didn’t always feel built for you. It’s the kind of stretch where confidence comes and goes, sometimes hourly.

She had been looking for a sign. Fortunately, it showed up as an opportunity.

During her internship at McLean Hospital, Sharvari worked with RNA-seq data, studying how alcohol affects gene expression in the brain. For the first time, the abstract became tangible. This wasn’t just theory anymore. It was an application. “This internship reminded me why I had chosen bioinformatics. The passion to solve biological problems using a computer.” And just like that, things didn’t feel perfect, but they felt right enough to keep going.

Today, Sharvari works as a Bioinformatics Analyst at the University of Virginia, focusing on microbial genomics. Which, if you take out the technical language, means she spends her time using data to understand how microbes behave, spread, and evolve. From bacterial genomes to wastewater surveillance, her work sits at the intersection of computation and real-world impact. When she talks about it, Sharvari feels less uncertainty as she perseveres in the field, and more curiosity, “the revolution that next generation sequencing has brought to microbiology cannot be understated.” Sharvari shares as she lights up talking about how we can now track infectious diseases, identify resistance genes, and even trace the origins of microbes across time. It’s complex work, but her excitement makes it feel accessible.

Her technical experience is impressive, but what stood out most in our conversation wasn’t just the science. It was how she talked about her journey of growth. When I asked about challenges, she didn’t mention coding or technical obstacles. She said one word: agency, “I will give my opinion and if someone disagrees, I don’t really push for my opinion.” It’s the kind of thing many people experience, especially early in their careers, but rarely say out loud. Learning how to take up space, how to stand behind your ideas, how to engage instead of stepping back. These aren’t things you pick up from a textbook. They come with time, and practice, and a willingness to be uncomfortable. It’s something she’s actively working on, by stepping into leadership roles and taking ownership of her work, even when it would be easier not to.

When the conversation shifted to AI, her answer felt refreshingly balanced, “It’s so scary and so incredible all at once” Sharvari admitted. She doesn’t see it as something that replaces bioinformaticians, but something that raises the bar. Less time spent on repetitive coding, more time spent actually thinking about the biology behind the data. In a way, it’s pushing the field closer to what it was always meant to be.

Outside of her technical work, Sharvari also serves as co-chair of the Boston Women in Bioinformatics podcast. And here, her perspective becomes even clearer, “What makes a scientific conversation truly impactful is if it can reach the layman in the way it was intended.” Science isn’t meant to feel exclusive. It’s meant to be understood. And she’s intentional about creating conversations that feel accessible, not intimidating.

Toward the end of our conversation, I asked her what she would say to someone who feels curious about bioinformatics but also hesitant to step into it. Her answer had nothing to do with technical skills, “the intimidation comes from the fear of failing at something new.” And then she reframed it saying, “who is going to care if I fail? Nobody who truly cares about you is going to judge you.” It’s a perspective that feels obvious once you hear it, and surprisingly hard to internalize before you take it into action for yourself.

Last month Sharvari moderated a fantastic webinar on The Perfect Predator, a story rooted in infectious disease and real-world impact. It fits naturally into the work she’s already doing, connecting science, storytelling, and community. Check it out at this link: https://www.youtube.com/watch?v=-xvSOig0VMg&t=2901s

Looking back, her journey reads like a series of decisions made with just enough clarity to take the next step. Leaving a job. Trying something new. Moving across the world. Staying when it got hard.

And somewhere along the way, without forcing it, she built something meaningful. Not all at once. Not perfectly.

Just one step, and one pipeline, at a time.

Member Spotlight: Aysheh Alrfooh

Tue, 14 Apr 2026 14:00:00 GMT

An interview on methylation, mentorship, and movies

When I first decided to interview Aysheh Alrfooh for the BWiB Member Spotlight, I did what most of us do - I opened her LinkedIn, clicked through her webpage, and tried to piece together a sense of who she was. What I had to go on was this: she co-chairs BWiB's Career-Sponsorship Committee, and as someone who was selected as a mentor in BWiB's inaugural mentorship program this year, that alone was enough to pique my curiosity. But by the end of our conversation, I realized that her role at BWiB is just one thread in a much richer story.

Aysheh holds a PhD from the University of Iowa, where her research in epigenetics centered on identifying methylation profiles in individuals with mental health disorders - work that sits at the intersection of computation, biology, and a deeply human question: how do we better understand what's happening inside the minds of people who are suffering? After her PhD, she completed a Computational Biologist co-op at Biogen before joining Diagonal Therapeutics in Watertown, Massachusetts, where she now works as a Scientist. Diagonal is developing a new class of clustering antibody medicines aimed at addressing the root causes of severe genetic diseases.

There's a quiet stereotype that follows scientists around, especially the ones who are serious about their craft - that they are one-dimensional, that conversations with them will inevitably loop back to pipelines and p-values. I'll admit, I walked into this interview half-expecting exactly that, given the breadth of Aysheh's expertise. But what I've learned over the years is that the most accomplished people are usually the most layered. They carry within them whole worlds of curiosity, humor, conviction, and warmth that you don't see on a CV. Aysheh is one of those people. By the end of our conversation, I wasn't just impressed by her credentials - I was genuinely charmed by who she is.

Sharvari: First of all, you have such a beautiful name! What does it mean, and how do you pronounce it?
Aysheh: My name comes from an Arabic root (asha) which means 'to live,' so my name means 'alive' or 'living,' which totally describes me as a person, as I am very passionate and I want to live life to the fullest. My name is pronounced in standard Arabic as 'AA-ee-sha,' and in a simpler, less formal Arabic it is 'AH-isha'.

Sharvari: Honestly, that makes the name even more beautiful - there's something poetic about a name that captures a person so precisely. Thank you for sharing that. Speaking of how you show up in the world - you co-chair the Career-Sponsorship Committee, and BWIB just launched its inaugural mentorship program this year. What drew you to that particular work within the organization?
Aysheh: I think it chose me, not the other way around, haha! I always believe that things find me before I find them, but that doesn't mean I am depriving myself of free will. I do set intentions for causes I want to pursue, such as women's empowerment, which has always been on my mind. Just as science empowered me, I want to empower women, and the organization was the perfect fit for that. And of course, when the opportunity appeared, I applied to be co-chair, went through the interview, and got the role. I have been with the organization for over a year now and am entering my second year with them. So grateful for this opportunity.

Sharvari: And speaking as someone on the receiving end of that work - it really is making a difference. The mentorship program has been one of the most valuable learning experiences I've had this year, so thank you. On that note, here's something I personally wrestle with, and I think you'd be the perfect person to ask: if you were mentoring someone brand new to bioinformatics, what's the very first thing you'd teach them?
Aysheh: Do not be afraid to spend time writing a script from scratch and watching days go by just through learning. The learning phase is supposed to be slow, difficult, and sometimes frustrating, but that is a sign that you are truly growing. Do not be discouraged by a lack of output or low productivity in the early stages of learning. Embrace the process and be patient with yourself.

Sharvari: That's such grounding advice - and honestly, applicable far beyond bioinformatics. There's a thread of empathy that runs through everything you say, and I noticed it especially in your PhD work at the University of Iowa. Your research connects molecular biology to mental health - studying DNA methylation in bipolar disorder, schizophrenia, and suicidal behavior. How does the human side of that work shape the way you approach it?
Aysheh: It guided the project to pursue the potential of DNA methylation as a biomarker, in addition to its role as a factor contributing to the risk of suicide. The project targeted DNA methylation sequencing around SNPs that showed an association with suicide attempts in bipolar disorder, and I studied them in blood samples to determine whether we could detect a strong signal that could later be translated into a biomarker.

Sharvari: That's really powerful work. From there, you moved from epigenetics research in academia at the University of Iowa to your current role as a scientist at Diagonal Therapeutics. Looking back across that arc, what has been your most enriching professional experience so far?
Aysheh: I cannot decide between two different experiences, where one prepared me for the other, and the current one is preparing me for future opportunities. A PhD is a very individualistic experience whose main focus is learning how to pursue a research question and answer it scientifically, while also developing analytical thinking. Your first job after a PhD is where you apply what you learned, adjusting it to suit the fast pace of a corporate environment. My PhD prepared me for my current job, and my current job is preparing me for future opportunities.

Sharvari: I love that framing - every chapter doing the quiet work of setting up the next one. Sticking with that transition for a moment: you made the leap from an academic PhD lab to industry. What surprised you most about that shift?
Aysheh: Finding something interesting is not enough on its own - the real question is whether it has a practical application. That said, I have a deep love for science, and I sometimes appreciate it from a more artistic perspective, finding beauty in a discovery simply because it is fascinating. But in industry, curiosity alone is not enough; there has to be a clear path to real-world impact.

Sharvari: That tension between science as art and science as application is such a real one. Thank you for naming it so honestly. On the topic of paths and turning points - is there a moment in your career where you thought, "If I had chosen this other path, things could have been different"? How has that shaped where you are now?
Aysheh: Life has given me so many interesting and challenging options. One moment that stands out, among many, is when I got accepted into dental school to pursue a dentistry career but chose to study pharmacy instead. After graduation, I worked in a community pharmacy for one month, and on my second day I handed in my notice. And absolutely no regret!!
I believe we—most of the time—have a choice, but I realized that what matters more than the choice itself is how you deal with the unexpected. You can think about your choices a lot, study them carefully, and do your research, but if you are not ready for the unexpected by having resilience and flexibility, you will not be ready for life. Choice is a fun game, but it is not enough on its own.
That said, I recognize this is a very privileged perspective. Not everyone has the luxury of choice, and in many parts of the world people are simply fighting to survive.

Sharvari: "If you are not ready for the unexpected by having resilience and flexibility, you will not be ready for life" - I might be quoting that one back to myself for a long time. Speaking of being ready for the unexpected, AI is reshaping bioinformatics rapidly. How is it changing your day-to-day work, and what should newcomers to the field understand about that shift?
Aysheh: It is changing the field a lot, just as it is changing the whole world, and there is a lot of fear as well as excitement as the AI industry is currently navigating the 'Peak of Inflated Expectations' and beginning its descent into the 'Trough of Disillusionment' of the hype cycle. In my day to day, it is increasing my productivity by giving me faster access to answers, such as fixing a bug in my script, so the expectations are now higher for delivering more in a shorter time.
For newcomers, I would say: keep yourself updated with all the new AI tools and their latest versions that best suit and serve your role. Always double check the scripts written with AI and the outcomes generated by AI, and make sure you truly understand them. It is also worth learning about Agentic AI. Finally, work on your communication skills, because the shift in bioinformatics will place us at the interface between AI, research, and biology groups.

Sharvari: A measured and very practical take - I appreciate that you neither hype it nor dismiss it. Now, if AI is buying us back some time, here's a fun question: if you had all the time and resources in the world, what bioinformatics project would you work on?
Aysheh: It would be a project in aging research, and I will be returning to DNA methylation, but this time with a more integrated approach. DNA methylation is my first love in science, and as they say, it is very difficult to forget your first love!!

Sharvari: I'll definitely be keeping an eye out for your work - “DNA meth-aysheh-lation”, if you will. Here's one I've been excited to ask: if you ran into the 10-year-younger and the 10-year-older version of yourself, and you could only say one thing to each of them - what would it be?
Aysheh: The advice I would give my younger self is: trust the process and just keep going. I always used to put in more effort than necessary, worrying about what might go wrong and fearing I would regret not trying hard enough, which kept me in a cycle of unnecessary stress. Stress is inevitable in adult life, but it is not something young people should have to carry. I wish I had enjoyed my school years more. As for my older self, I cannot wait to meet you, and things will be great and even more exciting. It is only upward from here in every aspect, because we are ready.

Sharvari: That message to your older self genuinely gave me goosebumps. I have a feeling both versions of you would be deeply proud of who you are right now - not just for what you've achieved, but for how you've grown into yourself along the way. As someone juggling a lot of responsibilities, what's the best way for you to unwind after a tough day?
Aysheh: On a warm sunny day, I would love to go for a walk and watch the sunset. On a rainy or cold day, I would prefer staying in and watching TV. I actually have a spreadsheet of all the movies I have watched along with my ratings, if you are interested!

Sharvari: Oh, consider me very interested - I'll be following up on that. If someone could also generate a spreadsheet of all the bioinformatics tools along with their ratings, I would also be interested in that, haha..! Before we wrap, one last thing - let's play a game. Tell me two truths and a lie about yourself, and we'll leave our readers to guess which is which.
Aysheh: I completed the Everest Base Camp trek, I have 6.2 million views on my Google Maps photos, and I have 10 siblings.

Sharvari: I have my guess. Readers, I’ll let you form yours. Aysheh, thank you so much for this conversation, for the openness you brought to every question, and for the wisdom tucked into all of your answers. I hope I get the chance to talk to your 10 year older self in the future for another interview!

Biobank Intro Series: All of Us Genetic Data

Tue, 14 Apr 2026 00:00:00 GMT

Navigating All of Us genotype data for variant extraction and analysis

If UK Biobank RAP gives you a filing cabinet with labeled drawers, All of Us gives you a bucket of blobs. Technically everything you need is in there. Somewhere. In blob form. The official Google Cloud Storage documentation uses the word "blob" without apparent embarrassment. I have thoughts about this. They are not printable.

Yes, "blobs" is the technical term for flat storage objects, organized by path conventions rather than a true directory hierarchy. There are no folders, just prefixes that cosplay as folders. Until you internalize this, you may spend a lot of time fishing in the bucket.

Quick Overview: What's Available

Working with All of Us data can feel like searching a dim warehouse. Valuable datasets are everywhere, but finding the right files requires some exploration.

The All of Us documentation assures us that the data exists. Somewhere. In some format. Accessible via... environment variables? Googling variations of "all of us wgs sequencing data" will lead you to get increasingly creative with gsutil ls commands. Let me save you some time. Here's where the genotype data actually lives.

Similar to the recent WGS releases in UK Biobank, All of Us provides whole genome sequencing data in phased and unphased VCF formats. The concepts covered there, what “phased” means and when you’d choose one format over the other, carry over directly so I won’t re-explain them here. What’s different is where the files live and how you get data out of them.

Format	Notes	Path
Phased VCFs	By chromosome, easiest to access	`gs://fc-aou-datasets-controlled/v8/wgs/short_read/snpindel/aux/phasing/chr${CHROM}_AOU_v8.2_allsamples_phased.vcf.gz`
Unphased VCFs	Sharded into 20,016 files	`gs://fc-aou-datasets-controlled/v8/wgs/short_read/snpindel/exome/vcf/${BATCH}.vcf.bgz`
Hail MatrixTable	For distributed computing	`$WGS_EXOME_MULTI_HAIL_PATH`
Variant Annotation Table (VAT)	Gene-level annotations	`gs://fc-aou-datasets-controlled/v8/wgs/short_read/snpindel/aux/vat/vat_complete.bgz.tsv.gz`

Streaming VCFs from GCS

On UKB RAP, we worked around dx cat buffering problems by generating a temporary HTTPS URL with dx make_download_url and pointing bcftools at that directly (see post 02). On AoU, gsutil cat actually streams without buffering, so you can pipe it straight into bcftools. No URL workaround needed. If your environment is authenticated via GOOGLE_APPLICATION_CREDENTIALS or gcloud auth, bcftools may be able to read gs:// paths directly, though I haven't tested this.

Code

# Phased VCF
PHASED_VCF="gs://fc-aou-datasets-controlled/v8/wgs/short_read/snpindel/aux/phasing/chr${CHROM}_AOU_v8.2_allsamples_phased.vcf.gz"
gsutil -u $GOOGLE_PROJECT cat ${PHASED_VCF} \
  | bcftools view -t ${CHROM}:${REGION_START}-${REGION_END} -O z -o phased_region.vcf.gz

# Unphased VCF shard
UNPHASED_VCF="gs://fc-aou-datasets-controlled/v8/wgs/short_read/snpindel/exome/vcf/${BATCH}.vcf.bgz"
gsutil -u $GOOGLE_PROJECT cat ${UNPHASED_VCF} \
  | bcftools view -t ${CHROM}:${REGION_START}-${REGION_END} -O z -o unphased_region.vcf.gz

The phased files do come with .tbi indexes, but since piped input isn't seekable, bcftools can't use them. The -t flag streams and filters for both file types. The index is there if you ever download a file locally and query it repeatedly, but for the streaming workflow it doesn't factor in.

Finding Your Unphased Shard

The unphased VCF files live at gs://fc-aou-datasets-controlled/v8/wgs/short_read/snpindel/exome/vcf, sharded by genomic position into files numbered 0000000000 to 0000020016. There is no chromosome in the filename and no documented estimate of how many base pairs each shard covers. Each shard has a companion .interval_list file that tells you what regions it contains, which means the only way to find your region is to open interval lists until you find it.

The internet has fancy solutions for finding your regions of choice, but if you only need one region and you're willing to be a little scrappy, a manual binary search is often faster than building proper infrastructure: jump to the middle of the file range, check the interval list with gsutil, then halve the range again based on whether your region fell above or below. Looping through all 20,016 files programmatically would be painfully slow. Use the script below as a verification tool, not a search engine, unless you have a long weekend and a lot of patience.

Code

import pandas as pd
import subprocess
from tqdm import tqdm

# SET YOUR REGION OF INTEREST HERE
chrom = "11"
region_start = 47331406
region_end = 47352702

vcf_filelist = []
start = False
for i in tqdm(range(0, 20016)):
    file_int = str(i).zfill(10)
    interval_f = f"gs://fc-aou-datasets-controlled/v8/wgs/short_read/snpindel/exome/vcf/{file_int}.interval_list"
    cmd = f"gsutil -u $GOOGLE_PROJECT cat {interval_f} | grep '^chr'"
    p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
    range_df = pd.read_csv(p.stdout, header=None, sep="\t")
    range_df.columns = ['chrom', 'start', 'stop', 'strand', 'score']
    if not start:
        region_count = (
            (range_df['chrom'].str.contains(chrom)) &
            (range_df['start'] >= region_start)
        ).sum()
        if region_count > 0:
            vcf_filelist.append(file_int)
            print(file_int)
            start = True
    else:
        region_count = (
            (range_df['chrom'].str.contains(chrom)) &
            (range_df['stop'] <= region_end)
        ).sum()
        vcf_filelist.append(file_int)
        if region_count > 0:
            print(file_int)
            break

If you're planning to do this repeatedly, build an index of shard positions once and query that instead. Why one doesn't already exist is a great question.

Extracting and Merging Your Region

Once you have your shard list, stream each one through bcftools to subset to your region, then merge:

Code

# SET YOUR REGION OF INTEREST HERE
chrom="11"
region_start=47331406
region_end=47352702

shard1=0000002366  # FIRST_SHARD_NUMBER
shard2=0000002367  # LAST_SHARD_NUMBER

BASE="gs://fc-aou-datasets-controlled/v8/wgs/short_read/snpindel/exome/vcf"

# 1. Subset each shard to your region
gsutil -u $GOOGLE_PROJECT cat ${BASE}/${shard1}.vcf.bgz \
  | bcftools view -t ${chrom}:${region_start}-${region_end} -O z -o ${shard1}_region.vcf.gz

gsutil -u $GOOGLE_PROJECT cat ${BASE}/${shard2}.vcf.bgz \
  | bcftools view -t ${chrom}:${region_start}-${region_end} -O z -o ${shard2}_region.vcf.gz

# 2. Index the filtered VCFs
tabix -p vcf ${shard1}_region.vcf.gz
tabix -p vcf ${shard2}_region.vcf.gz

# 3. Concatenate
bcftools concat -a -O z \
  -o merged_AOU_v8_unphased.vcf.gz \
  ${shard1}_region.vcf.gz \
  ${shard2}_region.vcf.gz

# 4. Index the merged VCF
tabix -p vcf merged_AOU_v8_unphased.vcf.gz

# 5. Save to your workspace bucket
gsutil -u $GOOGLE_PROJECT cp merged_AOU_v8_unphased.vcf.gz $WORKSPACE_BUCKET/data/

What About Hail?

As I mentioned in post 02, Hail requires an expensive Spark cluster and is overkill for single-gene or single-region work. For those cases, bcftools is faster and cheaper. If you're doing something genuinely genome-wide that needs distributed computing, the MatrixTable is available at $WGS_EXOME_MULTI_HAIL_PATH. If you go that route, always filter at read time using the _intervals parameter. Loading the full genome MatrixTable on the default cluster will crash it.

Code

import hail as hl
import os

chrom = "11"
region_start = 47331406
region_end = 47352702

aou_wgs_mt = os.getenv("WGS_EXOME_MULTI_HAIL_PATH")

# Filter at READ time, not after loading
your_interval = hl.eval(hl.parse_locus_interval(f'{chrom}:{region_start}-{region_end}'))
mt = hl.read_matrix_table(
    aou_wgs_mt,
    _intervals=[your_interval]  # Only load this region!
)

Bonus: The Variant Annotation Table (VAT)

If you need variant annotations like gene names and predicted consequences, All of Us provides a massive TSV file you can grep by gene name:

Code

#!/bin/bash

# SET YOUR GENE OF INTEREST HERE
gene="MYBPC3"

project=$GOOGLE_PROJECT
bucket=$WORKSPACE_BUCKET
vat_f="gs://fc-aou-datasets-controlled/v8/wgs/short_read/snpindel/aux/vat/vat_complete.bgz.tsv.gz"

echo "Starting ${gene} extraction at $(date)"
gsutil -u $project cat $vat_f | gunzip | head -1 > ${gene}_vat_v8.tsv
echo "Header retrieved at $(date)"

echo "Extracting ${gene} variants..."
gsutil -u $project cat $vat_f | gunzip | grep $'\t'"${gene}"$'\t' >> ${gene}_vat_v8.tsv
echo "Done at $(date)"

wc -l ${gene}_vat_v8.tsv

echo "Uploading to workspace bucket..."
gsutil -u $project cp ${gene}_vat_v8.tsv ${bucket}/data/${gene}_vat_v8.tsv
echo "Upload complete at $(date)"

Before you run this: set your idle timeout to at least 8 hours. The file is ~150 GB compressed, grepping through it takes 45 minutes or more, and if your instance sleeps halfway through you're starting over. The output is human-readable and easy to work with, but it's not indexed, so tabix isn't an option. What you see above is about as fast as it gets without an index.

Putting It All Together

If there's a theme to this post, it's this: All of Us has enormous data and almost no indexes to help you navigate it. The unphased VCFs are sharded into 20,016 files with no chromosome in the filename. The VAT is 150 GB compressed with no tabix support. The documentation points you to environment variables and leaves the rest to you. You're not doing something wrong when it takes a while. That's just the deal. Start the script, set your idle timeout, and go do something else.

Could someone build proper indexes for all of this? Yes. Should they? Absolutely. Is that a job for a skilled computational biologist who understands the data well enough to do it right? Also yes. If you're reading this and thinking "someone should fix that," that someone could be you.

Go forth and blob on.

Biobank Intro Series: UK Biobank Genetic Data

Tue, 07 Apr 2026 00:00:00 GMT

Traversing genetic data on the UK Biobank RAP (Research Analysis Platform) environment

Genetic data. We made it. Coming from my Ph.D. in plant epigenetics, I was used to raw sequencing data from one replicate at a time. In contrast, the UK Biobank (UKB) has around 500,000 participants. The enormity of it can become an unbearable dead weight if you let it. The fix is straightforward: fit the data to your analysis, not the other way around. In my work, I've focused on a single region or a handful of variants at a time. That means streaming raw files from the data repository and filtering on the fly, rather than pulling them down in full with DNAnexus (see my previous blog on UKB hardware). The first step is knowing what genetic data UKB actually has, where it lives, and how to filter it before it becomes your problem.

The Showcase Knows About Genetics Too

In my previous post about the UKB Showcase, I focused on observational fields, but the Showcase also catalogs genetic data resources, including:

Microarray genotyping data — directly measured variants at selected positions across the genome
Imputed genotypes — variants inferred by combining array data with full-genome reference panels
Exome sequencing data — sequenced protein-coding regions (~2% of the genome)
Whole genome sequencing — full genome coverage
Phased haplotypes — estimates of which alleles sit on the same chromosome (i.e., are inherited together)

Depending on the research question, you may need multiple data types. For example, phased haplotype data provides haplotype structure but excludes rare variants, so you might pair it with whole genome sequencing calls to get both. These data types were generated using different tools and released at different times, and I find it helpful to know that context to not only pick the right data, but also to understand the publications that used UKB genetic data before me. UKB provides a comprehensive timeline of their data releases, but coming from a sequencing background, the terminology didn't sit well with me. For example, "genotyping data" actually means microarray data — not sequencing. Here are the current releases you'll most likely use:

Year	Data Type	Field ID	Participants	Notes
2017	Microarray + imputation	22418	488,377	~800K measured; ~96M imputed [^1]
2022	Exome sequencing (final)	23141	~470,000
2022	Imputation (new panels)	21007	488,377	GEL and TOPMed; now in GRCh38
2023	Whole genome sequencing	24311	~500,000	ML corrections; DRAGEN; unphased
2025	WGS (updated)	30108	~500,000	phased VCFs

And here are the earlier releases you may encounter in older publications:

Year	Data Type	Participants	Notes
2019	Exome sequencing	50,000	Alignment errors identified [^2]
2020	Exome sequencing (reprocessed)	200,000	New OQFE pipeline [^3]
2021	Exome sequencing	~454,000	[^4]
2021	Whole genome sequencing	200,000	[^5]

Where Are the Files? (The Practical Part)

The JupyterLab launcher on UKB RAP. The left sidebar gives you access to both storage spaces: your local working directory at the top (temporary, session-only) and the DNAnexus project storage below it (persistent, where Bulk data lives). Clicking "DNAnexus Notebook" in the launcher (boxed in red) opens a notebook that saves directly to project storage; no manual upload needed.

If you need a refresher on the two storage spaces, see the hardware post. The genetic data lives in data storage under a directory called "Bulk". Filenames contain field ID numbers, so you can cross-reference them with the UK Biobank Showcase. For WGS data, field ID 24311 corresponds to the ML-corrected DRAGEN release. As of the latest release, this is often the one you want.

Start broad to learn the file structure, then narrow down. Running

dx find data --name "ukb24311*.vcf.gz" --folder /Bulk | head

reveals that files follow the pattern:

/Bulk/DRAGEN WGS/ML-corrected DRAGEN population level WGS variants, pVCF format [500k release]/chr{CHROMOSOME}/ukb24311_c{CHROMOSOME}_b{BATCH}_v1.vcf.gz

Yes, the path has spaces. Always quote it. Once you know that, you can narrow the search to a specific chromosome. Even then, WGS data is split across many batch files, so | head is still warranted:

dx find data --name "ukb24311_c11_*.vcf.gz" --folder "/Bulk/DRAGEN WGS/ML-corrected DRAGEN population level WGS variants, pVCF format [500k release]/chr11" | head

Unfortunately the official documentation is no help here. The best I found was a two-year-old community forum reply suggesting each batch covers roughly 20,000 bp. Thanks, George F. You saved me more time than UKB's own docs did. Take the estimate as a ballpark, not a guarantee.

Here is the approach in practice. To find positions 47,331,406 - 47,352,702 on chromosome 11, you can divide 47,331,406 by 20,000 to derive batch 2366 as a reasonable first guess. I wrote a small script (firstpos.sh) to check the first position of any candidate file:

#!/bin/bash

CHR=$1
BATCH=$2
FILE="/Bulk/DRAGEN WGS/ML-corrected DRAGEN population level WGS variants, pVCF format [500k release]/chr${CHR}/ukb24311_c${CHR}_b${BATCH}_v1.vcf.gz"
URL=$(dx make_download_url "$FILE" --duration 1h)
bcftools query -f '%POS\n' "$URL" | head -1

bash firstpos.sh 11 2366

returns 47,319,031. This is just below my target region, which is a good sign.

Checking the next batch:

bash firstpos.sh 11 2367

returns 47,339,029, meaning batch 2366 covers 47,319,031 - 47,339,028. My region ends at 47,352,702, so I check batch 2368 to find where that's covered. Once you've identified which batches bracket your full region, you have your files. If the 20,000 bp estimate puts you nowhere near the right chromosome positions, jump by a larger increment and binary search from there. Eventually you'll find your file. Then comes the next question: what do you do with it?

What's Actually in These Files?

A VCF is a giant matrix. Lines starting with # are metadata: pipeline details, descriptions for variant annotations in the INFO column, chromosome descriptions, and so on. The last # line is the column header for the data rows below it. Each data row is one variant. The first nine columns describe that variant: chromosome, position, ID, reference allele, alternate allele, quality score, filter status, info fields, and format. Everything after column nine is per-sample genotype data. At biobank scale that's 500,000 columns. The good news is that bcftools can filter by position without loading the entire matrix into memory, which is the whole reason streaming works at scale.

Code to Stream Genetic Data in UK Biobank

That's exactly what the --regions flag does.

FILE="/Bulk/DRAGEN WGS/ML-corrected DRAGEN population level WGS variants, pVCF format [500k release]/chr11/ukb24311_c11_b2366_v1.vcf.gz"
URL=$(dx make_download_url "$FILE" --duration 1h)
bcftools view "$URL" \
  --regions 11:47331406-47352702 \
  -O z -o my_region.vcf.gz

Bcftools reads only what it needs and stops. That's the whole game: 500,000 participants, one small region, no downloading required.

The Short Version

The release timeline is worth knowing for reading older papers, but you don't need to memorize it. For most analyses, start with the DRAGEN WGS release (field ID 24311). If your analysis requires haplotype structure, reach for the phased release (field ID 30108) instead, or use both if you need rare variant calls in haplotype context. Once you've found the right batch file, dx make_download_url, hands bcftools a URL to a file that may be half a terabyte in size and lets it take only what it needs.

In the next post, I'll walk through how All of Us handles its genetic data. Spoiler: the documentation isn't better.

References

[^1]: Bycroft, C., Freeman, C., Petkova, D. et al. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 https://doi.org/10.1038/s41586-018-0579-z

[^2]: Van Hout, C. V., Tachmazidou, I., Backman, J. D., Hoffman, J. X., Ye, B., Pandey, A. K., et al. (2019). Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank. BioRxiv, 572347, https://doi.org/10.1101/572347

[^3]: Szustakowski, J. D., Balasubramanian, S., Kvikstad, E., Khalid, S., Bronson, P. G., Sasson, A., ... & Reid, J. G. (2021). Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nature genetics, 53(7), 942-948.

[^4]: Backman, J.D., Li, A.H., Marcketta, A. et al. (2021). Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628–634, https://doi.org/10.1038/s41586-021-04103-z

[^5]: Halldorsson, B.V., Eggertsson, H.P., Moore, K.H.S. et al. (2022). The sequences of 150,119 genomes in the UK Biobank. Nature 607, 732–740, https://doi.org/10.1038/s41586-022-04965-x

[^6]: The UK Biobank Whole-Genome Sequencing Consortium. (2025).Whole-genome sequencing of 490,640 UK Biobank participants. Nature 645, 692–701 https://doi.org/10.1038/s41586-025-09272-9

The Rite of Process

Thu, 02 Apr 2026 14:00:00 GMT

A Poem on Whole Genome Sequencing Analysis

You sat there, together yet all alone,

in the endless cold and dark —

waiting, for days, with bated breath,

to be rescued.

They came, promising an escape — a quick way out —

the ones in white coats made many promises,

but kept only a few.

Would you say you were rescued

if you are no longer who you used to be?

Or would you say you were elevated,

because that sounds more convincing?

They told you, when they came to save you,

that you’d be better off alone.

The journey ahead demanded individuality —

even if it meant tearing you into two,

before putting the entire picture together.

And so you agreed,

because you knew that there was only so much you could control,

especially once they began stripping you down

to see if you were fit to continue,

even if you were already in parts.

You didn’t say a word —

you just silently prayed

that you would still have enough of yourself left

when they arrogantly came to assemble you.

The white coats put you together

in a haphazard fashion —

brazen, annoyed, careless —

you weren’t perfect,

but you were suitable for their purpose.

The journey ahead demanded identity and purity.

Where you came from mattered

only in the context of who you were now.

You had started, together yet all alone,

a dozen of you,

in the endless cold and dark.

And after a sequence of events,

here you were, at the end of the road,

back with the dozen others,

hoping to be remembered

more than you waited to be rescued.

For what else was the point

of making you whole again,

if only to leave you

until you become a statistic mentioned in passing,

in the endless cold and dark.

Biobank Intro Series: All of Us Observational Data (Part II)

Tue, 31 Mar 2026 00:00:00 GMT

Loading observational data in the All of Us Researcher Workbench

The UK has a single national health system, and it shows: one table, one field ID, a coding dictionary if you needed it. The US has hundreds of hospital systems, dozens of billing standards, and its data model matches this beloved chaotic energy. In All of Us, your data is spread across clinical tables, the coded values in those tables are translated via a separate concept table, and everything traces back to person_id. More joins, more steps, but it's just what a data model built on real EHR data looks like.

With your concept IDs in hand (Part I), the workflow has three moving parts: knowing which clinical table holds your concept, joining to the concept table to decode any coded values, and merging everything back to person to build your cohort. More pieces than UKB, but engineered for the diverse US healthcare system.

The OMOP-CDM Schema

The full OMOP CDM schema is worth bookmarking. It documents every available table and its fields. Clinical tables contain records of observed medical and clinical data, where each row represents a single clinical event. In my experience, most analyses only touch a handful of clinical tables:

condition_occurrence: diagnoses observed by a provider or reported by a patient
measurement: lab values and test results (e.g., BMI, blood pressure)
observation: clinical facts that cannot be measured with a standardized test, such as medical history, family history, and lifestyle choices
drug_exposure: medication records, useful for validating diagnoses or identifying undiagnosed conditions

The person table is the anchor table of the schema. It holds one row per participant, and every clinical table links back to it via person_id. Like clinical tables, it also uses concept_id columns as foreign keys to the concept table for decoding coded values.

Query a Clinical Table

The OMOP CDM tables live in Google BigQuery. The examples below use Python's pandas.read_gbq function, which takes a SQL string and returns a dataframe. R users can likely query them with the bigrquery package. The examples assume the following setup:

Environment versions

Python 3.10.16, pandas 2.0.3, google-cloud-bigquery 2.34.4, CDR C2024Q3R9

import pandas as pd
import os
CDR = os.environ['WORKSPACE_CDR']

In Part I, we found that systolic blood pressure maps to concept ID 3004249 in the measurement table. Let's use that as our worked example:

sbp_query = f'''
    SELECT
        measurement_id,
        person_id,
        measurement_concept_id,
        measurement_type_concept_id,
        measurement_date,
        measurement_datetime,
        value_as_number
    FROM `{CDR}.measurement`
    WHERE measurement_concept_id = 3004249'''
sbp_df = pd.io.gbq.read_gbq(sbp_query, dialect='standard')
sbp_df

The SELECT statement exports these columns:

measurement_id: unique row identifier
person_id: foreign key linking the person and clinical tables
measurement_concept_id: ID of the data field
measurement_type_concept_id: ID of the data source (eg. EHR, Lab result, etc.)
measurement_date / measurement_datetime: date and time of the record
value_as_number: numeric systolic blood pressure value

The WHERE clause limits the measurement table to this field. Since we will stack multiple measurement types into a single cohort table, human-readable labels for measurement_concept_id and measurement_type_concept_id are essential for distinguishing which rows belong to which measurement.

Decode Coded Values via the `concept` Table

Coded fields across all OMOP tables store numeric IDs. To translate them, join to the concept table. The query below decodes the systolic blood pressure measurements:

sbp_query = f'''SELECT
  m.person_id,
  mc.concept_name AS measurement_name,
  mtc.concept_name AS measurement_type_name,
  m.value_as_number,
ROW_NUMBER() OVER (
    PARTITION BY m.person_id, m.measurement_concept_id
    ORDER BY m.measurement_date DESC, m.measurement_datetime DESC, m.measurement_id DESC
  ) AS recency_rank
FROM
  `{CDR}.measurement` m
LEFT JOIN `{CDR}.concept` mc ON mc.concept_id = m.measurement_concept_id
LEFT JOIN `{CDR}.concept` mtc ON mtc.concept_id = m.measurement_type_concept_id
WHERE measurement_concept_id = 3004249'''
pd.io.gbq.read_gbq(sbp_query, dialect='standard')

This JOIN pattern repeats throughout OMOP and works the same way for any coded field across any clinical table, including the person table. The query below decodes gender values:

gender_query = f'''
  SELECT
    person.person_id,
    pgc.concept_name as gender,
    person.gender_source_value,
    pgsc.concept_name as gender_source_concept_name
  FROM `{CDR}.person` person
  LEFT JOIN `{CDR}.concept` pgc ON pgc.concept_id = person.gender_concept_id
  LEFT JOIN `{CDR}.concept` pgsc ON pgsc.concept_id = person.gender_source_concept_id
'''
gender_df = pd.io.gbq.read_gbq(gender_query, dialect='standard')
gender_df

The gender_source_value column is the shortcut that feels right but isn't. It contains raw, unstandardized values from the original data source, think of it as the "close enough" column. For any real analysis, always use the decoded gender column obtained through the concept join. Keep gender_source_value around for reference, but DO NOT let it anywhere near your results.

Merge Everything into a Cohort Table

Each query produces its own dataframe. Merge them on person_id to build your cohort:

cohort_df = pd.merge(
  gender_df,
  sbp_df.sort_values('recency_rank').drop_duplicates('person_id'),
  on='person_id',
  how='left'
)

This pattern scales to any study: find your concept IDs (Part I), query the relevant OMOP tables, join to concept for readable labels, and merge the resulting dataframes in pandas.

The pandas approach above is readable and easy to follow, but it loads each table into memory separately before joining. For larger cohorts, it's more efficient to do the join entirely in SQL so BigQuery returns only the final result:

cohort_query = f'''
-- Pull one row per participant from the person table
SELECT
  p.person_id,
  pgc.concept_name AS gender,
  p.gender_source_value AS raw_gender,
  m.value_as_number AS sbp,
  mtc.concept_name AS sbp_source
FROM `{CDR}.person` p

-- Decode gender_concept_id to a human-readable label
LEFT JOIN `{CDR}.concept` pgc ON pgc.concept_id = p.gender_concept_id

-- Get the most recent SBP measurement per person
LEFT JOIN (
  SELECT
    person_id,
    value_as_number,
    measurement_concept_id,
    measurement_type_concept_id,
    ROW_NUMBER() OVER (
      PARTITION BY person_id, measurement_concept_id
      ORDER BY measurement_date DESC, measurement_datetime DESC, measurement_id DESC
    ) AS recency_rank  -- 1 = most recent
  FROM `{CDR}.measurement`
  WHERE measurement_concept_id = 3004249  -- systolic blood pressure
) m ON m.person_id = p.person_id AND m.recency_rank = 1

-- Decode measurement_concept_id and measurement_type_concept_id
LEFT JOIN `{CDR}.concept` mc ON mc.concept_id = m.measurement_concept_id
LEFT JOIN `{CDR}.concept` mtc ON mtc.concept_id = m.measurement_type_concept_id
'''
cohort_df = pd.io.gbq.read_gbq(cohort_query, dialect='standard')
cohort_df

In the above example, we are only exporting one measurement from the clinical table measurement. However, you will likely need to export multiple data fields from a single clinical table. Unfortunately, the query grows linearly with the number of measurements. A UNION_ALL approach followed by a pivot will make your query a lot cleaner. The query below exports a cohort table with gender, systolic blood pressure, and BMI (Concept ID:3038553).

cohort_query = f'''
WITH measurements AS (
  -- Stack all measurements into one table, keeping only the most recent per person
  SELECT
    m.person_id,
    m.measurement_concept_id,
    m.value_as_number,
    mtc.concept_name AS measurement_source,
    ROW_NUMBER() OVER (
      PARTITION BY m.person_id, m.measurement_concept_id
      ORDER BY m.measurement_date DESC, m.measurement_datetime DESC, m.measurement_id DESC
    ) AS recency_rank
  FROM `{CDR}.measurement` m
  -- Decode measurement_type_concept_id to a human-readable label
  LEFT JOIN `{CDR}.concept` mtc ON mtc.concept_id = m.measurement_type_concept_id
  WHERE m.measurement_concept_id IN (
    3004249,  -- systolic blood pressure
    3038553   -- BMI
  )
)
-- Pull one row per participant from the person table
SELECT
  p.person_id,
  pgc.concept_name AS gender,
  p.gender_source_value AS raw_gender,
  -- Pivot each measurement into its own column
  MAX(CASE WHEN measurement_concept_id = 3004249 THEN value_as_number END) AS sbp,
  MAX(CASE WHEN measurement_concept_id = 3038553 THEN value_as_number END) AS bmi,
  MAX(CASE WHEN measurement_concept_id = 3004249 THEN measurement_source END) AS sbp_source,
  MAX(CASE WHEN measurement_concept_id = 3038553 THEN measurement_source END) AS bmi_source
FROM `{CDR}.person` p

-- Decode gender_concept_id to a human-readable label
LEFT JOIN `{CDR}.concept` pgc ON pgc.concept_id = p.gender_concept_id

-- Join the pivoted measurements
LEFT JOIN measurements m ON m.person_id = p.person_id AND m.recency_rank = 1
GROUP BY p.person_id, pgc.concept_name, p.gender_source_value
'''
cohort_df = pd.io.gbq.read_gbq(cohort_query, dialect='standard')
cohort_df

Cohort and Dataset Builders

Tools for building datasets in All of Us. Cohort and dataset builders are easy-to-use tools but they are limited similar to a meal kit box. OMOP CDM SQL queries are flexible but more complex, like cooking from a fully stocked pantry. The CDR and other external data sources expand available data but are separate, like ingredients from an accessible garden. Image generated by ChatGPT and text modified by the author.

If you just scrolled past three SQL blocks and felt your eyes glaze over, good news: there's a meal kit version. The AoU Cohort and Dataset Builders are point-and-click tools that work in two steps: define your participant criteria (the Cohort Builder), then build your data features (the Dataset Builder). Easy to use, but limited — like a meal kit, you're working with what's in the box. I didn't find them flexible enough for my project, but they're great for exploration. And since they run SQL under the hood, you can click "Analyze" → "See Code Preview" to peek at the BigQuery query they generated. It's a handy starting point if you want to adapt it yourself.

Beyond OMOP-CDM: CDR Directory Files

Beyond observational data, note that some pre-computed data in the Researcher Workbench lives outside of OMOP-CDM data tables. If you have access to the Controlled CDR directory, you'll find pre-computed genomic data (e.g., precomputed genetic ancestry, admixture estimates, relatedness, etc.) are made readily available in tab-delimited tables. These genetically-derived estimates can complement or replace the self-reported race and ethnicity values in the BigQuery tables.

Summary

That's the full pipeline: find your concept IDs, query the right clinical table, label your coded values, and merge everything into a cohort. It takes more joins than UKB, but the logic is consistent once you've done it once. The next post moves from phenotype data into genotype data, where the complexity shifts from data models to file formats.

Biobank Intro Series: All of Us Observational Data (Part I)

Tue, 24 Mar 2026 00:00:00 GMT

Finding concept IDs for the All of Us Researcher Workbench

All of Us has returned to the chat (she made her first appearance in the hardware post) and she's bringing noble intentions and absolutely feral data provenance. UK Biobank is a well-organized remarkable resource, but with >93% European ancestry, it was never designed to represent global genetic diversity. On the other hand, All of Us was explicitly built to oversample underrepresented populations. With almost 50% of participants identifying as a racial or ethnic minority, "All of Us" isn't just a pun. It's a mission statement. (I wrote more about why that matters here).

That ambition comes with a data architecture to match. UK Biobank invited ~500,000 people to assessment centers, ran standardized measurements, and stored the results in a single curated dataset with clean field IDs. Even its hospital records flow from a single source: the UK's publicly funded National Health Service (NHS). All of Us is working with a different reality entirely. It aggregates EHR records from 50+ independent US health systems, enrollment surveys, wearables data, and biosamples, and standardizes all of it after the fact using the OMOP-CDM (Observational Medical Outcomes Partnership Common Data Model) developed by the Observational Health Data Sciences and Informatics (OHDSI) community. What gets recorded in any given participant's file follows whatever combination of ICD, LOINC, CPT, or SNOMED their health system happened to use.

This is why the workflow feels different. Instead of searching a Showcase and grabbing a field ID, you're navigating concept IDs, vocabulary mappings, and relational tables: the machinery the OMOP-CDM uses to impose order on that heterogeneity. Concept IDs are the key to unlocking most of what's available, so let's start there.

Raw hospital data is transformed into standardized OHDSI vocabularies and loaded into OMOP-CDM relational tables.

Finding Concept IDs: Three Approaches

Similar to Field IDs, a concept ID is just a number until you know what it maps to. These three tools each give you a different way to look that up, depending on how much context you need.

1. All of Us Data Browser (fastest for quick lookups)

All of Us Data Browser shows what concepts exist and how often they're used. Search "systolic blood pressure" and you'll see results under "Conditions" and "Labs & Measurements" - it can be recorded multiple ways.

Click a category to see participant counts and value distributions. For example, 225,000+ participants have concept ID 3004249 (Systolic blood pressure) with source code LOINC 8480-6.

When to use: Quick lookups, checking data availability, getting participant counts.

2. OHDSI Athena Vocabulary (richer metadata)

OHDSI Athena provides concept context such as hierarchies, standard vs. non-standard mappings, and relationships. Searching for "systolic blood pressure" returns about 78,865 items at the time of writing. However, if you look up OMOP-CDM concept ID 3004249 (which you found with the All of Us Data Browser), you'll see its vocabulary source (LOINC), domain (Measurement), and related concepts.

When to use: Detailed concept information, exploring relationships, understanding vocabulary mappings.

3. Direct SQL queries (most flexible)

Query the CONCEPT table directly to translate medical concepts into IDs:

import pandas as pd
import os

# Use environmental variable for the Controlled Tier
CDR = os.environ['WORKSPACE_CDR']

# Search by name
concept_query = f'''SELECT *
    FROM `{CDR}.concept`
    WHERE LOWER(concept_name) LIKE '%systolic blood pressure%'
    '''
sbp_query = pd.io.gbq.read_gbq(concept_query, dialect='standard')

# Or query a known concept ID directly
concept_id_query = pd.io.gbq.read_gbq(f'SELECT * FROM `{CDR}.concept` WHERE concept_id = 3004249')

Use CONCEPT_RELATIONSHIP to explore how concepts relate - for example, which ICD-10 codes map to SNOMED concept 3004249.

When to use: Complex relationship queries, programmatic vocabulary exploration.

What's Next

Finding concept IDs is the first step, but you need to navigate the relational tables to actually pull observational data. In Part II, we'll get into the messier, more interesting part.

A Coffee with CompBio: Claude Code for Bioinformatics

Mon, 23 Mar 2026 00:00:00 GMT

Can Claude Code handle real bioinformatics? Saba and Amulya put it to the test.

Claude code has taken over the tech world like a storm. But how good is Claude code when it comes to Bioinformatics and Computational Biology? In this episode of A Coffee with Compbio, Saba and Amulya take on the task of testing claude code on a simple task: performing QC on a Peripheral Blood Mononuclear Cells (PBMC) single cell dataset. They find out the dos and don’t with a tool like claude code while comparing it to how a real bioinformatician would analyze these results. Tune into this episode to learn more about Claude code for Bioinformatics.

Bonus - We share all the results, markdown files and the single cell RNA QC skills markdown file that you can try on your own!

Links from the Episode:

All files related to this episode
JournalMatch: A vibe-coded tool for finding the right match for your publication by Amulya

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Biobank Intro Series: UK Biobank Observational Data (Part II)

Mon, 16 Mar 2026 05:00:00 GMT

Loading phenotype data in the UK Biobank RAP (Research Analysis Platform) environment

When you get approved for a UK Biobank project you are gifted a VIP pass to a secure data wonderland called the UK Biobank Research Analysis Platform (UKB RAP). The UKB RAP is a cloud-based venue (built on DNAnexus infrastructure) where you can spin up coding environments (JupyterLab, RStudio, take your pick) and analyze data without the nightmare of downloading 500,000+ participant records to your poor laptop.

I enjoy working in JupyterLab, but the concepts transfer regardless of your preferred environment. Opening JupyterLab on UKB RAP is straightforward: click "Tools" in the navigation menu and hit the teal "+ New JupyterLab" button. This opens a setup GUI where you can configure your compute specs. For most analyses, the defaults work just fine (see my tips on hardware in a previous post).

In this post, we'll focus on the phenotype data. Here I define phenotype data as essentially anything that is not genetic data. This includes not just questionnaire responses, physical measurements, and hospital records, but proteomics data as well.

To query the data, you need a fully qualified dataset reference combining your project ID (your workspace on RAP) and your dispensed dataset ID (the UK Biobank data object provisioned to that project). Below I have cheatcodes for finding these values in python and BASH.

python:

import dxpy
import glob

# Get your dataset identifier
dispensed_dataset_id = dxpy.find_one_data_object(
    typename='Dataset',
    name='app*.dataset',
    folder='/',
    name_mode='glob'
)['id']

project_id = dxpy.find_one_project()["id"]
dataset = f"{project_id}:{dispensed_dataset_id}"

bash:

# Get project ID
project_id=$(dx env --json | jq -r '.project')

# Get dispensed dataset ID
dispensed_dataset_id=$(dx find data --name "app*.dataset" --brief)

# Combine them
dataset="${project_id}:${dispensed_dataset_id}"

With that in hand, let's get some data.

Step 1: Find the field names for your data

Two bowls, same feast: whether you import via Jupyter or the command line, you’re still getting the data you want.

With a list of field IDs you gathered from the UKB Showcase, your next step is to figure out their exact field names on the RAP (which aren't always identical to what the Showcase shows), and then extract the actual participant data for those fields. There are two different methods for this.

Quick lookup via terminal

When I'm in "just get it working" mode (which, let's be honest, is most of research), I found that the command-line approach is faster for quick lookups. I simply list all the field names in the terminal and grep for the ones I need.

dx extract_dataset ${dataset}  --entities participant --list-fields | grep "22420"

Dictionary approach

The UKB RAP documentation will steer you toward extracting dictionary files (*.dataset.data_dictionary.csv ), which map field IDs to field names, describe data coding schemes, and generally serve as the Rosetta Stone for the dataset. This approach is considered more "proper", and for good reason: it's reproducible, documentable, and plays nicely with notebooks.

The dictionary approach requires more setup: extracting CSVs, loading them into pandas, and writing filter logic. When you're exploring or need a quick answer, I recommend the quick and dirty command-line approach. That said, there are times when the dictionaries are nice to have. They make your code easier to comprehend and also are useful for making sense of these data tables after extraction.

Step 2: Extract the dataset

To extract the actual dataset values, use DNAnexus' extract_dataset command with the --fields flag set to the relevant field names:

dx extract_dataset <project_id>:<dispensed_dataset_id> \
  --fields participant.eid,participant.p22420_i2,participant.p22420_i3 \
  --delimiter "," \
  --output lvef_pheno.csv

Step 3: Translate the dataset values

The data you just extracted is often coded, meaning the raw values or numbers are not immediately interpretable.. Therefore, to make human analysis easier, it is sometimes helpful to translate these code to their definition.

Example: Filtering cardiomyopathy diagnoses from ICD10 codes

Working on a project to understand the genetic architecture of cardiomyopathy, I needed to identify participants with cardiomyopathy diagnoses. The Showcase told me International Classification of Disease version 10 (ICD-10) diagnosis codes were Field ID 41270.

I extracted the field names easily enough with dx commands. However, the raw data returned value codes like "I42" or "I420", meaningless without context.

Fortunately, the UKB Showcase maintains various data coding tables for each of their coded data fields. Specifically ICD-10 diagnosis codes are given coding 19 in UKB. Similarly ICD9 codes are found in coding 87.

You could also extract all the coding dictionaries once and have them ready as searchable dataframes. For example,

import subprocess
import pandas as pd

# Extract dictionaries once with -ddd flag
cmd = ["dx", "extract_dataset", dataset, "-ddd", "--delimiter", ","]
subprocess.check_call(cmd)

# Load the codings dictionary
codings_df = pd.read_csv(glob.glob("*.codings.csv")[0])

# Find which ICD10 codes mean "cardiomyopathy"
icd10_coding = codings_df[codings_df['coding_name'] == "data_coding_19"]
cardiomyopathy_codes = icd10_coding[
    icd10_coding['meaning'].str.contains('cardiomyopathy', case=False)
][["meaning", "code"]]

Now you can filter your extracted data to only participants with those specific codes, without tab-switching back to the Showcase every time you need to decode something.

The Gotchas Nobody Tells You

Authentication weirdness: Sometimes dx commands work fine in terminal but throw mysterious errors when called through subprocess in Jupyter. I've never pinned down exactly why. It possible that the Jupyter notebook does not inherit the same environment variables that your interactive terminal session has. Either way, when you hit this, just run the command in terminal instead.

Spark is not optional for big extractions: If you're pulling more than ~30 fields, you'll need a Spark cluster. The UKB RAP documentation covers this, but fair warning: Spark uses lazy evaluation, which means errors can show up way downstream from where they actually originated. Fun times.

Wrapping Up

That's the full pipeline: find your field names, extract the data, and decode any coded values. Once you've done it a couple of times, the whole process takes minutes.

In the next post, we'll look at how to do the same thing on the All of Us Researcher Workbench.

Biobank Intro Series: UK Biobank Observational Data (Part I)

Tue, 10 Mar 2026 05:00:00 GMT

An ode to the UK Biobank Showcase

The showcase returns at least three different field IDs for BMI. It is difficult to find this information in any publication.

Consider this post a love letter to the UK Biobank Showcase.

When I first started working with UK Biobank, I fell back on what I knew from graduate school. I dug through methods sections and supplemental materials to track down the features used in the study. It worked, but it was slow. Part of the problem is that papers rarely cite The UK Biobank Showcase directly. It is so foundational to the field that experienced researchers treat it as assumed knowledge. Coming from a different domain, I had no idea it existed. Once my manager pointed me to the Showcase, I discovered measurements beyond what had been published and no longer had to spend hours on detective work.

To understand why the showcase is so useful, it helps to know the scale of what UK Biobank actually contains. The UK Biobank as a resource spans clinical measurements, survey data, genomics, imaging, proteomics, metabolomics, and more. For a comprehensive overview of all available data types, see What types of data are available in UK Biobank? on the UK Biobank Community site. The Showcase is your tool for navigating all of it.

Reading Between the Data Fields: Arrays, Instances, and Codes

The Showcase doesn't just catalog measurements. It lovingly documents the shape of the data itself. On the main page for each field, the Data tab provides key details about coding, instances, and array indices that will save you real headaches downstream.

Coding

Coding is how categorical responses are stored. Rather than storing "Yes" or "No", many fields store numeric codes: 1 for "Yes", 0 for "No", and -3 for "Prefer not to answer". The Showcase provides a data coding table for each such field. More on working with complex codes in the next post.

Instances

Instances are timepoints. For example, if a measurement reports using instancing type 2, it will report measurements collected at four visits:

instance 0: the initial assessment
instance 1: the first repeat visit
instance 2: the imaging visit
instance 3: the first repeat imaging visit

For most phenotypes, Instance 0 has the largest sample size. If you need longitudinal data, expect much smaller numbers at later instances.

Arrays

Arrays are repeated measurements within a single visit. Diastolic and systolic blood pressure (4079, 4080), for example, are taken twice in one sitting. Each repeat is stored as a separate array index (0, 1). The Showcase tells you how many array values a field has so you can plan how to handle them.

The Data tab gives you the architecture of a field: how its values are structured, repeated, and encoded. What it does not tell you is whether those values are reliable, comparable, or the best available option for your phenotype. For that, you need the category-level context, and the Showcase delivers it.

Example: Not All Field IDs Are Created Equal

For example, searching "Left Ventricular Ejection Fraction" returns multiple relevant fields and three with the exact correct description (22420, 24103, and 31060). But which one should you use? This is where the Showcase becomes essential.

UK Biobank Showcase search results showing three LVEF measurement fields in different categories

Notice the three highlighted fields measure the same thing but belong to different categories. Clicking into each field reveals why this matters:

Comparison of three LVEF fields showing participant counts and category quality warnings

Field 22420 (category 133) has 39,649 measurements but includes a warning: "Quality issues may exist in this data. Researchers may wish to consider using data available in Category 157 or Category 162 as an alternative." Field 24103 (category 157) contains 80,974 measurements and references a published methodology, but warns these fields "should not be considered together" with Category 162 without quality assessment. Field 31060 (category 162) has only 4,868 participants, fewer than the flagged field 22420.

For my cardiomyopathy work (Klasfeld et al 2025), I chose field 24103 for its sample size and data quality. However, other practical information provided by the showcase includes the date of which the data was reported (Debut) and the distribution of the data (shown in the data tab in the second section of the Field ID entry).

UK Biobank Showcase Tips

After working with the Showcase on multiple projects, I've developed a workflow that catches issues before they become problems. Here's my list:

Before selecting a field:

Always check the category warnings, not just the field description
Look at the data distribution tab: Is it normally distributed? Heavy missingness? Homogeneous values? Sampling bias?
Check the total participants to plan your sample size accordingly

For reproducibility:

Note the debut date (when data became available)
Record the version date (last import/update)
Check stability rating (how data may change in future releases)

Watch out:

Like any great love, the Showcase is not perfect. Sometimes a data field has the status set to "Not available", meaning it is listed before release. If the release date is listed and it is not set in the future, reach out to UK Biobank support for clarification.
Sometimes data that appears in the Showcase is missing from your RAP workspace entirely. This can happen if your project is running an outdated version of the UK Biobank data release.
- If you are the project admin, go to the Settings page of your dispensed project and click Check for Updates in the UK Biobank section.
- If you are not the admin or the update does not resolve it, reach out to the UK Biobank support team directly. Tell them upfront if you have already searched the community forums. They are genuinely helpful and worth contacting.

The more time I spend with the Showcase, the more I appreciate what it actually is: not just a catalog, but a guide to making good decisions about your data. For features not covered here, Part III of the Showcase user guide (page 4) is worth bookmarking. In the next post, we'll dive into actually loading this data for analysis.

Biobank Intro Series: Hardware Settings

Tue, 03 Mar 2026 05:00:00 GMT

Hardware setup lessons for UK Biobank Research Analysis Platform and All of Us Workbench

In 2026 the most expensive resource isn't compute time or storage. It's your time rerunning failed analyses. After months of working across the UK Biobank RAP and All of Us Researcher Workbench, I've collected some hard-won lessons about resource management.

This post is more technical. Consider yourself warned.

Tip #1: Each Platform Has Its Own Command-Line Interface (CLI)

The CLI: a small bridge between two very different worlds. Image generated by Gemini AI.

Think of your workspace and the platform's data storage as two separate floating islands. Your code lives on one island. The massive biobank files live on the other. The CLI is the bridge between them, and each platform has its own.

UK Biobank RAP: The dx toolkit

The dx CLI is your friend for navigating the RAP filesystem:

# List files in a directory within the platform's data storage
dx ls

# Stream file contents (don't download!) from data storage
dx cat file-xxxx | bcftools view

# Upload local files from your workspace to data storage
dx upload local_file.txt --path /file/path/in/workspace/

# Download files (only if absolutely necessary) to your workspace
dx download file-xxxx --output local_file.txt

All of Us: gsutil for Google Cloud Storage

All of Us data lives in Google Cloud Storage (GCS) buckets and uses gsutil to identify, stream, and move data between your workspace and these buckets.

# List files in the controlled data-repository (CDR) bucket
gsutil ls gs://fc-aou-datasets-controlled/

# Find VCF files in the CDR bucket
gsutil ls gs://fc-aou-datasets-controlled/v7/wgs/short_read/snpindel/

# Stream directly (the right way)
gsutil cat gs://path/to/file.vcf.gz | bcftools view

Important: Add a -u flag to gsutil commands to attribute the operation to your project for proper billing and access control:

gsutil -u $GOOGLE_PROJECT [command]

Use the environment variables $GOOGLE_PROJECT and $WORKSPACE_BUCKET to avoid hardcoding paths:

# Upload local file from your workspace to your storage bucket
gsutil -u $GOOGLE_PROJECT cp local_file.txt $WORKSPACE_BUCKET/

# Download files (only if absolutely necessary) to your workspace
gsutil -u $GOOGLE_PROJECT cp gs://path/to/file.txt local_file.txt

Tip #2: Don't Bring the Cloud Home With You

Don't download massive biobank files — stream and filter directly on the platform.

Now that you know how to upload and download files, I must restate that you should not use those download commands on biobank data files. Yes, dx download and gsutil cp exist, but the data is already where it needs to be. Your job is to meet it there, not drag it to you.

Don't do this:

# UK Biobank: Copying a 500GB VCF locally
dx download file-xxxx

# All of Us: Same mistake, different platform
gsutil cp gs://path/to/huge.vcf.gz .

Do this instead:

# UK Biobank: Stream with dx
dx cat file-xxxx | bcftools view | your_analysis

# All of Us: Stream with gsutil
gsutil cat gs://path/to/file.vcf.gz | bcftools view | your_analysis

The data is already where it needs to be. It sits in the cloud, on fast storage, ready to be streamed. Copying wastes time, burns through storage quotas, and risks running out of disk space mid-analysis. Both platforms are designed for streaming access. Use it.

Tip #3: Know Your Tools and Your Files

Hail is prominently featured in All of Us documentation, which makes it tempting to reach for first. Resist that instinct and match your tool to your actual problem, not the first one you find or the most impressive-sounding one.

Why avoid Hail when possible:

Requires expensive Spark clusters
Memory-intensive operations that crash your instance
Adds complexity when simpler tools work fine

If you can use standard tools (pandas, bcftools, plink), do that instead. Save Hail for genuinely distributed computing tasks.

Regardless of which tool you choose, make sure your files are indexed (.tbi, .csi) before querying them. Without an index, tools like bcftools have to read the entire file from start to finish — region queries are no faster than loading everything.

Tip #4: Don't Let Long Jobs Catch You Off Guard

Picture this: You start a 2-hour variant annotation job, grab lunch, and return to... nothing. Just a terminated instance.

Three habits will save you from rerunning everything from scratch.

First, check your idle timeout limit before running any long job. By default, All of Us shuts down after 15 minutes of inactivity.

All of Us: Go to workspace settings → increase idle timeout to 8 hours (or your preferred duration)
UK Biobank RAP: Check instance auto-pause settings

Note: nohup or screen/tmux can keep jobs running but won't survive an instance shutdown so adjusting your timeout is still necessary.

Second, filter as early in your pipeline as possible. The less data you're carrying through each step, the faster and cheaper each step is.

Third, checkpoint intermediate results. Save outputs at meaningful stages so a crash at step 5 doesn't send you back to step 1.

None of these take more than a minute to set up. The rerun will.

The Bottom Line

When things crash:

You ran out of memory
Filter earlier in your pipeline
Checkpoint intermediate results

When things are slow:

Check if files are indexed (.tbi, .csi)
Use region queries instead of full chromosomes
Stream instead of copying

Set yourself up for success: learn your CLIs, stream your data, pick the right tool for the job, and make sure your long jobs have safety nets.

Biobank Intro Series: Getting Started

Tue, 24 Feb 2026 13:00:00 GMT

How to do biobank analysis without losing your mind

The Biobank Iceberg. Everyone knows there's more beneath the surface. You can't ignore the complexities, but you don't need to solve them all.

If I learned anything during my first few months as a bioinformatics contractor, it's that you need to know your question before you worry about the hidden complexities. Like icebergs, biobanks have immense power—and like icebergs, their foundation is made up of massive complexities beneath the surface that can sink your analysis if you're not prepared. (The Titanic had better odds.)

The first step to any biobank analysis is strategic rather than technical.

What specific research question are you trying to answer?
Who is your audience?

That's it. Two questions. Answer these before you touch a single line of code.

Consider my own work as an example:

During my postdoc: I was investigating the genetic architecture of cardiomyopathy for publication. My goal was to understand how genetic variants contribute to disease risk. A large challenge, working within the context of rare disease, is overcoming limited statistical power. Therefore, to strengthen the association signal, I restricted analyses to individuals of White British ancestry (UK Biobank data field 22006, coding 1). The healthy volunteer recruitment biases and population structure weren't just footnotes in the discussion section—they fundamentally shaped what I could and couldn't claim about cardiomyopathy genetics. I needed to understand the people making up the dataset as deeply as I understood the variants themselves.

As a contractor: I provide bioinformatics support for hypothesis-driven study design that could lead to drug development. The client wants to find out if a genetic signal associates with a phenotype strongly enough to justify further investigation. In other words, I'm running pilot analyses. I need clean cohort definitions, appropriate statistical methods, and enough power to detect effects. The recruitment biases? Important to document, but they don't invalidate finding a biological association in this specific dataset. My job is to answer the client's question efficiently and move the project forward.

In my contractor role, I've learned that curiosity must be disciplined. My job is to hit project checkpoints and answer the client's specific question, not to chase down every interesting technical rabbit hole. The data complexity matters, but staying on track isn't just good project management; it's professional integrity.

Only after you've defined your question and audience can you identify which data complexities actually matter. Every dataset comes with biases and baggage, and I fight two competing urges:

The paranoid perfectionist: Document every limitation, explore every confounding variable, and fall into a six-month rabbit hole about batch effects
The YOLO analyst: Ignore all complexity, run the analysis, get wildly misleading results, and have to redo everything anyway.

Neither instinct serves the project. The discipline I've had to learn is this: acknowledge the complexity but focus only on what affects your specific research question.

The good news is that this complexity isn't insurmountable. It just requires understanding the data first. I've already fallen into some of these rabbit holes, so hopefully you won't have to. In a series of posts, I hope to demystify UK Biobank and All of Us so you can navigate them confidently. But let me be clear, I cannot cover every nuance of these massive resources. Your specific research question may surface complexities I haven't encountered. What I can give you are foundational issues that matter across most use cases, the patterns to watch for, and questions to ask.

A Coffee with CompBio: Hacking your way into computational biology

Tue, 24 Feb 2026 00:00:00 GMT

Hackathons can be a great way to understand the trends in your field, meet new people, and network.

Hackathons are not just for coders anymore — computational biologists have made it their own with data, models, and insights! Hackathons can be a great way to understand the trends in your field, meet new people, and network. Do you want to try and participate in a hackathon this year and feel like a true hacker? The wait is over — in this episode we give you all the tea about hackathons, over a cup of coffee! Tune into our latest episode of "A Coffee with CompBio" where Sharvari Narendra and Saba Nafees talk about hackathons and more!

If you think you know some more hackathon-related resources, let us know in the comments and we will give you a shoutout in the next episode!

Links from the Episode:

Season 1 Archive
BioHackathons
nf-core Hackathon Events
UPCOMING EVENT -> nf-core Hackathon March 2026
How I Organized a Hackathon at Harvard
BIO-IT World FAIR Data Hackathon
OpenHackathons
MLH 2026 Events
CodeDay
Devpost
NCBI Codeathons

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ctDNA as an oncology endpoint

Mon, 23 Feb 2026 13:00:00 GMT

Takeaways from the FoCR symposium

I recently attended the virtual Friends of Cancer Research (FoCR) symposium on Modernizing Oncology Endpoints, focused on circulating tumor DNA (ctDNA) and AI-enabled imaging. The discussion brought together FoCR, industry, FDA, and academia, and it was energizing to see the progress so far, plus a clear path forward.

A biomarker is a measurable biological signal that helps indicate what’s happening in the body—such as the presence of disease, how aggressive it is, or whether a treatment is working. In oncology, ctDNA (circulating tumor DNA) is especially compelling because it can be measured from a simple blood draw (non-invasive) and repeated over time, enabling longitudinal monitoring of tumor burden and molecular response without relying solely on imaging or repeated tissue biopsies. That potential is exactly why ctDNA keeps coming up in conversations about endpoints: it can capture molecular change earlier, more frequently, and often more directly than traditional approaches—if we standardize how we collect, interpret, and validate it.

Key takeaways

The ctMoniTR Project showed ctDNA’s clinical signal at scale and what’s needed next.

With 20+ sponsors pooling heterogeneous trial datasets, ctMoniTR found consistent patient-level links between ctDNA reduction/clearance and patient outcomes. The next challenge is turning these signals into trial-ready and regulator-ready endpoints, especially in early-stage MRD where ctDNA levels are lowest.

Tissue availability is becoming a limiting factor.

Tumor-informed MRD assays are often preferred, but tissue is not always accessible or usable. A practical approach is to design dual-assay strategy up front: tumor-informed when feasible, with a tumor-naive backup, plus clear decision rules and an analysis plan that handles both.

Scaling requires prospective standardization.

Define clinical landmark timepoints and sampling schedules in advance, not retroactively. Real-world data can help fill natural history gaps (lead time, kinetics), but only with careful tracking of assay versions and the treatment time period.

Looking Ahead

As ctDNA endpoints evolve, they can enable earlier interventions by detecting molecular change sooner and supporting faster decisions in both clinical trials and patient care. Teams that prioritize harmonization up front will be best positioned to move quickly and confidently.

Beyond behavior: How machine learning decodes consciousness and forecasts seizures from brain activity

Wed, 11 Feb 2026 13:00:00 GMT

Reflections on a seminar exploring how AI and brain network analysis can transform patient care

Image credit: Source: Nature (2025). Adversarial testing of global neuronal workspace and integrated information theories of consciousness.

There is something genuinely exciting about starting a new semester by being reminded how much we still do not know, and how many people are actively working to figure it out.

At the first Biomedical Engineering Seminar of the spring semester at UMass Amherst's Riccio College of Engineering, Aya Khalaf, PhD, from the Yale School of Medicine presented Decoding Brain Networks for Improved Patient Quality of Life. From the beginning, it was clear this seminar would be one of those talks that stays with me long after I left the room. As someone with a background in both wet lab neuroscience research and computational modeling, seminars like this reinforce how essential computational tools and AI have become for transforming complex neural data into insights that can meaningfully impact patient care.

Studying Consciousness Beyond Behavior

At its core, the seminar focused on consciousness. What it is, how we detect it, and how it can be disrupted in neurological disease. Consciousness feels intuitive when we experience it ourselves, yet scientifically it remains one of the biggest open questions in neuroscience, with major implications for medicine, ethics, and patient care. This is a topic I am personally very interested in, especially because in the past I learned about coma states and how patients can still retain forms of consciousness even when they appear behaviorally unresponsive.

Dr. Khalaf’s work combines computational modeling, AI, electrophysiology, and neuroimaging to study how conscious perception emerges from coordinated activity across brain networks. One key idea explored was decoding the content of consciousness, not just determining whether someone is conscious at all. Rather than asking a simple yes or no question about awareness, this approach focuses on identifying what a person is actually perceiving. For example, neural activity patterns in specific brain regions can be used to distinguish whether someone is seeing a face versus an object, offering a more nuanced view of conscious experience.

Decoding conscious content using distributed brain networks across prefrontal and posterior regions (Khalaf et al. 2025).

Seeing Consciousness Hidden in Brain Signals

One moment that really stuck with me was when Dr. Khalaf discussed patients diagnosed with unresponsive wakefulness syndrome. When asked to imagine movement, patients appear behaviorally unresponsive, but neuroimaging can reveal patterns of brain activity similar to those seen in minimally conscious individuals . This highlights how behavioral tests alone may not fully capture internal awareness.

From a technical perspective, the work isolates neural signals associated with conscious perception, meaning when a stimulus is actively experienced and reportable by the individual (for example, intentionally imagining a movement). This is distinct from general stimulus processing, which refers to automatic neural responses that can occur even when a stimulus is not consciously perceived (for example, blinking without thinking about it). To study this difference, researchers examined stimulus detection networks using intracranial EEG (electroencephalogram) recordings with thousands of implanted electrodes. In this context, target stimuli were those that participants were instructed to attend to or respond to, while non-target stimuli were presented but did not require conscious attention or action. Because both types of stimuli activate the brain at some level, careful signal isolation is needed to identify neural activity that truly reflects conscious awareness rather than passive sensory processing.

The functional MRI (fMRI) analyses stood out to me because they showed how brain imaging can be used to study not just where activity occurs, but how conscious experience is coordinated across networks. By tracking changes in blood oxygen level dependent (BOLD) signals, researchers found that as participants shifted from situations where they rested and focused on a static point (fixation blocks) to circumstances where they actively processed sensory information (task blocks), they could observe how brain activity changed with conscious engagement. Using mathematical pattern recognition methods to identify groups of brain regions that consistently activated together, the study highlighted the midbrain and central thalamus as key hubs that amplify and coordinate signals across vision, hearing, and touch. While these regions have long been linked to consciousness, identifying them through data driven network analysis helped validate the approach and reinforced the idea that consciousness emerges from distributed brain systems rather than isolated regions.

Midbrain and central thalamus involvement in conscious perception across sensory modalities (Khalaf et al. 2025).

Using Machine Learning to Predict Loss of Consciousness Before a Seizure

The part of the talk that stood out to me the most focused on epilepsy and the use of machine learning to understand how seizures affect consciousness. Dr. Khalaf discussed absence seizures, a type of seizure in which patients may briefly lose awareness or responsiveness, often without obvious physical convulsions. Because these seizures can be subtle and difficult to detect behaviorally, understanding their neural signatures is especially important. Using EEG data, Dr. Khalaf presented machine learning models trained to predict whether an absence seizure would impair consciousness by comparing brain activity before a seizure begins and during the seizure itself. The models revealed clear differences in neural activity patterns between these two brain states, showing that measurable changes in network behavior emerge even before consciousness is disrupted. One key insight was that healthier brain networks tend to produce more complex neural signals, while impaired consciousness is associated with reduced signal complexity.

Technically, this approach involved extracting informative features from EEG signals that highlight differences between the brain states before and during a seizure. By applying Common Spatial Pattern analysis, the models emphasized spatial patterns of neural activity that best distinguished seizures that impaired consciousness from those that did not. Since consciousness during seizures is typically assessed through behavior alone, identifying reliable neural markers could eventually help clinicians anticipate loss of awareness and support earlier or more targeted interventions for patients. I am particularly interested in the machine learning side of this work because AI has the potential to support clinical decision making while also improving cost effectiveness. If we can rely on robust ML models to predict impaired consciousness from EEG data, it could reduce the need for extensive behavioral testing and long monitoring sessions, making epilepsy evaluation more accessible and scalable in real clinical settings.

Extracting EEG features using Common Spatial Pattern analysis to distinguish impaired and spared consciousness during seizures (Springer, Khalaf, et al. 2022).

Looking Ahead

This semester, I want to be intentional about attending seminars and writing about what I learn. Research evolves quickly, and staying curious and engaged with work happening now feels like an important part of being a student in science and engineering.

Today was a strong start.

A Coffee with CompBio: New Year Resolutions

Wed, 28 Jan 2026 00:00:00 GMT

12 New Year Resolutions For Computational Biologists

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Grab your coffee and join us for another episode of A Coffee with Comp Bio!

New year, new episode, new comp-bio goals and new hosts. But first, we wish you a very Happy New Year! Most new year's resolutions fail due to lack of clarity, so to make it easier, we begin our first episode of the season with a list that hopefully inspires you. From using AI tools to make your life easier to documenting your own code better, we are bringing resolutions every computational biologist needs this new year. Tune into the latest episode of “A Coffee with CompBio” where Sharvari Narendra and Saba Nafees present 12 awesome resolutions for the new year.

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Thanks Amulya Shastry for editing and management support and Dina Issakova for the cover art and social media support.

Support the Podcast:

We are looking for sponsors! Please get in touch if you or your business would like to help support this podcast.

Follow Sharvari and Saba on LinkedIn!

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Hosted by Ausha. See ausha.co/privacy-policy for more information

Tuesday Tactics: Build vs Buy Red Flag

Tue, 06 Jan 2026 13:00:00 GMT

Tuesday Tactics: The Excel Export Feature Request

Tue, 30 Dec 2025 13:00:00 GMT

Tuesday Tactics: Data Steward ≠ Data Janitor

Tue, 23 Dec 2025 13:00:00 GMT

The Tool Consolidation Paradox

Fri, 19 Dec 2025 13:00:00 GMT

Why Fewer Tools Often Means More Complexity

The Twelve Days of Scale-Up

Thu, 18 Dec 2025 13:00:00 GMT

A festive tune of crashed jobs caroling in the key of why.

Tuesday Tactics: The 10-Minute Test

Tue, 16 Dec 2025 13:00:00 GMT

Software Doesn't Age Like Wine

Mon, 15 Dec 2025 13:00:00 GMT

Why Your Data Tools Need Maintenance (And What That Really Means)

Interviewing Is Networking in Disguise

Wed, 10 Dec 2025 13:00:00 GMT

The real win in interviewing is the network you grow along the way.

Tuesday Tactics: Sunset Before You Scale

Tue, 09 Dec 2025 13:00:00 GMT

The Incremental Migration Pattern

Mon, 08 Dec 2025 13:00:00 GMT

How to Rebuild the Plane While Flying It

Starting a Women in Bioinformatics Chapter: A Practical Guide

Fri, 05 Dec 2025 00:00:00 GMT

"Been There, Done That" advice from established chapters

Note: This guide is based on our experience as a local volunteer group. We are now a non-profit organization and are happy to advise other groups, but we cannot share our logos or web domain due to legal considerations. Our long-term vision is to grow together: if you start a chapter and it becomes stable, we would love your leadership team to connect with ours so we can build a nationwide network.

Getting Started: The Foundation

Core Team Assembly

Start small but think strategically. Begin with 3-5 committed individuals who can each take ownership of key areas. Look for people with complementary skills: someone with event planning experience, a communications-savvy person, someone with industry connections, and ideally someone with non-profit or volunteer organization experience.

Establish clear roles early. Even before formal committees, designate who handles what to avoid overlap and ensure nothing falls through the cracks.

Legal and Organizational Structure

Research local requirements for establishing a volunteer organization
Consider whether you want to incorporate as a non-profit (this can wait until you're more established)
Create basic bylaws or operating agreements early to prevent conflicts later
Establish a simple decision-making process

BWiB BTDT advice: BWiB operated without official non-profit status for 10 years… so you don’t need to rush this.

Digital Infrastructure: Setting Up Your Online Presence

Event Management Platforms

Luma vs. Meetup: Our Experience

Meetup: Great for getting started, has built-in discovery features, costs ~$15-20/month
- Pros: Established user base, good for finding your initial community
- Cons: Limited customization, ongoing costs, platform dependency, cannot access member email addresses
Luma: Better for established groups, more professional appearance, free tier available
- Pros: More polished interface, better integration options, free for most events that we host so far, can access member email addresses
- Cons: Less discovery, need to drive your own traffic

BWiB BTDT advice: BWiB started with Meetup but we have recently transitioned to Luma.

Communication Channels

LinkedIn Group

Create a LinkedIn group early for professional networking
Post job opportunities, industry news, and event announcements
Encourage members to share their professional achievements
Use LinkedIn Events to promote your gatherings
BWiB BTDT advice: we get a lot of traffic from our LI group postings

Slack Channel

Essential for real-time communication and community building
Create channels for: #general, #jobs, #events, #resources, #introductions
Consider topic-specific channels as you grow (#r-users, #python, #career-advice)
Establish community guidelines and moderation policies from day one
BWiB BTDT advice: we have one and it’s mostly being used by the executive team, less so by the members

Email List

Don't rely solely on social platforms - build an email list
Send monthly newsletters with event updates, job postings, and community highlights
BWiB BTDT advice: we are still working on the best way to do this

Website Considerations

Start simple: a basic website with your mission, upcoming events, and contact info
Free options: GitHub Pages, Netlify, or basic WordPress
Include: About page, Events calendar, Resources section, Committee information
Make it mobile-friendly from day one
BWiB BTDT advice: it took some effort to get the first version off the ground but we like it a lot now that it’s there!

Committee Structure: Learning from Boston WiB

Based on Boston WiB's successful model, consider establishing these committees as you grow:

Essential Committees (Start Here)

Web/Digital and Communication Committee - Technical infrastructure
- Handle communications workflows
- Maintain website and online resources
- Manage digital tools and platforms
- Curate community resources
Events Committee – Programming and logistics for events

The Events Committee is responsible for:

Designing a balanced annual program (technical talks, workshops, networking, career panels, journal clubs, etc.)
Collecting ideas from the community and shaping them into concrete event formats
Handling event logistics: dates, venues (or virtual platforms), AV needs, accessibility considerations, and registration pages (e.g., Luma/Meetup)
Coordinating with speakers and panelists (outreach, confirmations, talk titles/abstracts, bios)
Ensuring each event has a clear run-of-show, roles for volunteers, and a backup plan for virtual participation where possible
Partnering with other committees (Web/Digital, Sponsorship, Communications) to promote events, capture photos/resources, and follow up with attendees
Tracking basic metrics (attendance, feedback, repeat attendees) to improve future programming and support sponsorship conversations

BWiB BTDT advice: we didn’t have official committees for the first ~9 years of the group, but once we did establish committees, we got more things done in a more organized fashion.

Event Planning: What We Wish We'd Known

Venue Selection

Free Options to Explore

University libraries and conference rooms
Hospital/medical center meeting spaces
Tech company offices (many have community programs)
Co-working spaces (often free for non-profits)
Public libraries with meeting rooms
Biotech incubators and accelerators

Pro Tips:

Always have a backup plan for virtual meetings
Test all AV equipment before events
Choose accessible locations with public transportation
Consider rotating locations to serve different geographic areas

Meeting Formats That Work

Technical workshops: Hands-on learning (R/Python tutorials, specific tools)
Career panels: Industry professionals sharing experiences
Networking mixers: Casual relationship building
Lunch meetups: Getting folks together and chat over lunch
Journal clubs: Discussing recent papers
Industry visits: Tours of biotech companies or research facilities
Skill-sharing sessions: Members teaching each other

Speaker Recruitment

Tap your local biotech and academic communities
Invite recent conference speakers (they often reuse presentations)
Consider virtual speakers to expand your options
Create a speaker database and wishlist
Offer to reciprocate speaking opportunities

Timing

During the day
- Good for virtual events
- Good for working parents
Evening
- Best for in-person events that involve networking

Funding and Sponsorship: Making It Sustainable

Free Resources to Maximize

Most successful chapters operate primarily on volunteer time and free resources for the first few years.

BWiB BTDT advice: 99% of our events are free to the public

Sponsorship Strategy

When to Start Seeking Sponsors:

Once you have 50+ regular attendees
When you have consistent programming
After establishing credibility in the community

Potential Sponsors:

Local biotech companies
Pharmaceutical companies
Academic institutions
Bioinformatics software companies
Consulting firms
Professional service providers (legal, HR, etc.)

Sponsorship Packages

Bronze ($100-500): Logo on website, mention in newsletters
Silver ($500-1500): Event speaking slot, booth at networking events
Gold ($1500+): Title sponsor of major events, annual report recognition

Grant Opportunities

Many professional organizations offer small grants for diversity initiatives
Local community foundations
Corporate diversity and inclusion grants
University community engagement funds

BWiB BTDT advice: engaging with sponsors in a meaningful way takes quite a bit of time so we have one person who is working only on that.

Community Building: The Soft Skills

Creating Inclusive Spaces

Establish and enforce a code of conduct (see ours below)
Use inclusive language in all communications
Provide multiple ways for people to engage (in-person, virtual, async)
Actively welcome newcomers and explain "inside" references
Consider childcare or timing for working parents

Sustaining Volunteer Energy

Rotate leadership responsibilities to prevent burnout
Celebrate volunteers publicly and often
Set realistic expectations and timelines
Create clear handoff procedures for roles
Host volunteer appreciation events

Measuring Success

Track metrics that matter:

Attendance at events (both unique and repeat attendees)
Engagement on digital platforms
Career advancements of members
Feedback scores from events
Diversity of speakers and attendees

BWiB BTDT advice: this is also very important for engaging with future sponsors.

BWiB Code of Conduct

This lunch event is dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices. We do not tolerate harassment in any form. Anyone violating these rules may be sanctioned or banned from attending at the discretion of the conference organizers. Please contact the organizers through meet-up messaging/Slack, if you feel someone has broken the code of conduct.

Original source and credit:

http://2012.jsconf.us/#/about & The Ada Initiative

Common Pitfalls and How to Avoid Them

Overcommitting Early

The Problem: Trying to do too much too fast leads to burnout and poor quality events.

The Solution: Start with monthly or bi-monthly events and grow from there.

Founder Dependence

The Problem: One person becomes indispensable, creating fragility.

The Solution: Distribute responsibilities and document processes from day one.

Mission Drift

The Problem: Losing focus on your core purpose as you grow.

The Solution: Regularly revisit your mission statement and evaluate activities against it.

Geographic Challenges

The Problem: Serving a spread-out community effectively.

The Solution: Embrace hybrid events and consider multiple smaller meetups.

Timeline: First Year Milestones

The timeline below is a suggestion based on our experience. If you want to grow at a different pace, you can stay in each phase as long as needed.

Months 1-3: Foundation

Assemble core team
Define mission and basic structure
Set up digital infrastructure (Meetup/Luma, LinkedIn, Slack)
Plan first event
Brainstorm the next 3 or 4 events

Months 4-6: Growth

Host 2-3 successful events
Establish committee structure
Build email list to 50+ people
Create basic website

Months 7-9: Expansion

Launch mentorship or special program
Seek first sponsorship opportunities
Establish partnerships with local organizations
Host first major event (conference, symposium)

Months 10-12: Sustainability

Develop leadership succession plan
Evaluate and refine committee structure
Plan annual programming calendar
- Document processes and create handbooks
- Contact the Boston WiB leadership team to work more closely together

Resources and Templates

Essential Tools (Free Tier)

Event Management: Luma, Meetup, Eventbrite
Communication: Slack (free up to 10,000 messages), Discord
Email Marketing: Mailchimp, ConvertKit
Project Management: Trello, Notion, Google Workspace
Design: Canva for social media graphics and flyers

Legal and Administrative

Sample bylaws and operating agreements
Code of conduct templates
Volunteer agreement forms
Event planning checklists
Financial tracking spreadsheets

BWiB BTDT advice: we started with a code of conduct… everything else came much later.

Final Words of Encouragement

Starting a Women in Bioinformatics chapter is incredibly rewarding but takes patience and persistence. Your first event might have 5 people - that's perfectly normal! Focus on providing value to your community, stay consistent with your programming, and be responsive to member needs.

Remember: every successful chapter started exactly where you are now. The bioinformatics community is generally supportive and collaborative, so don't hesitate to reach out to established chapters for advice, speaker recommendations, or partnership opportunities.

The field needs more diverse voices and inclusive spaces. By starting a local chapter, you're not just building a community - you're actively changing the landscape of bioinformatics for the better.

This guide is a living document. Please share your experiences and lessons learned to help future chapters succeed!

The Five Signals That Scream 'Hire a Data Person Now'

Thu, 04 Dec 2025 13:00:00 GMT

A gentle guide for realizing when it's time to call in the data experts and which one you actually need.

Tuesday Tactics: The "No Jira Ticket" Rule

Tue, 02 Dec 2025 13:00:00 GMT

Nobody Owns the Data Warehouse (And That's Why It's Broken)

Mon, 01 Dec 2025 13:00:00 GMT

A data warehouse only works when someone truly stewards it, or it quickly becomes a bottleneck.

Thanksgiving in Biotech: A Survival Guide

Thu, 27 Nov 2025 00:00:00 GMT

This season, the polar plunge in cross‑functional communication isn’t just survivable, it’s a chance to thrive.

Tuesday Tactics: Your First Data Hire Signal

Tue, 25 Nov 2025 00:00:00 GMT

The Explicit Out-of-Scope Section: My Secret Weapon for Project Trust

Mon, 24 Nov 2025 00:00:00 GMT

Transparency isn't just about what's included, it's about naming what's out of scope.

What Your Favorite Biobank Says About You

Fri, 21 Nov 2025 12:00:00 GMT

We asked 500,000 researchers which biobank they stan, and the results were… scientifically significant.

Disclaimer: This is satire. All biobanks are incredible resources that have advanced science immeasurably. The author uses multiple biobanks and has deep respect for all of them.

AI vs. AI Agents in Healthcare: Not the Same Thing!

Fri, 21 Nov 2025 00:00:00 GMT

The distinction between traditional AI and AI agents is crucial for understanding their impact on healthcare.

Text description of graphic

AI vs. AI Agents in Healthcare: Not the Same Thing!

Left side: AI in healthcare are depicted as assistants providing assistive intelligence. Examples include predicting patient no-shows, flagging patterns from wearables, and answering FAQs via chatbot. Human interpretation and action are still required.

Right side: AI agents are depicted as assistants with "hands to get things done," providing operational intelligence. Examples include automating data-to-system workflows, insurance verification, and patient file updates. AI agents perform multi-step tasks with minimal human intervention.

Following up on my last post about the growing role of AI Agents in Healthcare, I wanted to draw a sharp distinction: AI vs. AI Agents; they are not the same thing!

Lately, everyone’s been talking about "AI in healthcare" like it’s one big monolithic thing. But honestly, there’s a huge difference between general AI tools and AI agents, and it matters a lot for clinical workflows.

AI in healthcare (in general):

Think of this as assistive intelligence that is usually used for:

Predicting which patients might miss their appointments

Flagging abnormal patterns in wearable data

A chatbot that answers common insurance or billing questions

It’s powerful, but it still depends heavily on clinicians to interpret and act!

AI Agents in healthcare:

These are like the overachievers. They don’t just predict, they operate. Agents take in data, make decisions, and execute multi-step tasks with minimal human intervention.

For example:

An agent can automatically pull data from multiple sources, populate forms, and update the internal system eliminates the need for manual clicks.

A tool can track insurance eligibility, verify coverage, and seamlessly update the patient file in real time.

Basically:

AI = smart assistant

AI Agents = smart assistant + hands to get things done

Why it matters👇🏽

Healthcare doesn’t just need predictions; it also needs workflows that actually move.

AI agents can tackle the admin overload that burns out clinicians, while still keeping humans in the loop for real judgment calls.

We’re entering the era where hospitals won’t just ask: "Do we have AI?" They’ll ask: "Do we have agents that can actually close the loop?"

💠 Curious how this evolves in 2026 and beyond clinically, operationally, and ethically!

I Fixed the Same Bug Three Times (No One Noticed)

Thu, 20 Nov 2025 00:00:00 GMT

Tuesday Tactics: The 3-Question Requirements Filter

Tue, 18 Nov 2025 00:00:00 GMT

Work Life Decoded: Understanding Your Manager's World

Tue, 18 Nov 2025 00:00:00 GMT

How to Building a Strategic Partnership with Your Boss

Your manager isn’t your boss. They’re your business partner.

Most people see their manager as someone who assigns tasks and approves time off. But here's what changes everything: your manager is fighting for budget in meetings you'll never attend, juggling competing priorities from other departments, and trying to figure out who gets that one promotion slot when three people want it.

Once you understand their world, you can position yourself strategically.

Instead of: "I finished the project" Try: "I finished the project two days early, which means we can move the client presentation up if needed, and I documented the process for the team"

You're giving them options and solutions, not just updates.

The strategic question most people never ask: "What does success look like for YOU this quarter?"

Not the team – for them personally. Maybe they're trying to improve retention, hit a revenue target, or launch something new. Once you know their goals, you can align your work to support them.

This isn't about sucking up. It's about understanding that your manager has the authority to open doors for you – but they can't read your mind, and they're dealing with constraints you might not see.

Watch the full video where Lorena and I break down:

Understanding the hidden pressures your manager faces

Communication strategies that actually work

How to build trust and advocate for yourself strategically

Watch the full episode Work Life Decoded: Understanding Your Manager's World) on Patreon.

Write the Test First (Even for Your Science)

Mon, 17 Nov 2025 00:00:00 GMT

Why data scientists should design error messages that guide users straight to solutions.

Data Pipelines That Scientists Can Debug (Without Calling You at 9 PM)

Thu, 13 Nov 2025 00:00:00 GMT

Why data scientists should design error messages that guide users straight to solutions.

AI Agents Are Quietly Redefining Healthcare

Wed, 12 Nov 2025 00:00:00 GMT

AI agents turn healthcare data into real‑time insights, evolving with patients and providers.

Healthcare is full of data 👉🏻 labs, imaging, EHRs, vitals, even wearables, and yet most systems only react to it.

🤖AI agents are changing that! They’re designed to monitor, reason, and respond. They keep learning continuously as new data streams in.

🔸 Imagine systems that track patient vitals in real time, adjust medication alerts based on patterns, or surface the right information to clinicians before they even ask.

That’s not a futuristic dream anymore! It’s happening in early prototypes across hospitals and research networks.

The real shift isn’t just about smarter tech now, it’s about moving from static tools to adaptive systems that evolve alongside patients and providers.

💬 I’d love to hear your thoughts. Where do you think AI agents will make the biggest impact first: patient monitoring, drug discovery, or clinical decision support?

A Coffee with CompBio: Collaboration Survival Guide for CompBio

Tue, 11 Nov 2025 00:00:00 GMT

Unpacking the messy truths behind scientific teamwork.

What really happens when a wet lab scientist and a computational biologist sit down to plan an experiment? Spoiler: it's not always smooth sailing. In this episode of 'A Coffee with Compbio,' Lorena Pantano and Alex Bartlett chat with Amulya about the real talk nobody tells you about scientific collaborations.

They break down the three make-or-break moments of any project: that first meeting where you're figuring out if single-cell sequencing on mouse eyes is actually the move (hint: maybe start simpler), the data processing stage where quality issues rear their ugly head, and those uncomfortable conversations when results don't pan out.

What you'll learn:

How to redirect overambitious project plans without shutting people down

Smart ways to communicate technology limitations early

What to say when pilot data quality is... not great

Why being adaptable beats being rigid every single time

If you want to level up your collaboration game and avoid common pitfalls, grab your coffee and tune in.

Thanks Amulya Shastr for editing and management support.

Send us your comments, questions, and suggestions using this form

Listen to this podcast on other platforms:

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Apple

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We are looking for sponsors! Please get in touch if you or your business would like to help support this podcast.

Follow Lorena and Alex on LinkedIn!

If you enjoyed the episode, please subscribe and leave a review!

Hosted by Ausha. See ausha.co/privacy-policy for more information

Work Life Decoded: How to Handle Workplace Negativity (Without Becoming the Office Therapist)

Tue, 11 Nov 2025 00:00:00 GMT

Practical strategies for handling workplace negativity.

“That person is SO incompetent.” “This project is a disaster.” “Management has no idea what they’re doing.”

We’ve all worked with chronic complainers. And if you’re in any kind of leadership role—formal or informal—you’ve probably felt the pressure to fix everyone’s frustrations.

Here’s what I wish someone had told me 10 years ago: You’re not the office therapist.

In the latest episode of "Work Life Decoded", Lorena and Lina break down practical strategies for handling workplace negativity without getting dragged into the drama or becoming everyone’s emotional dumping ground.

You’ll learn:

The 3-step framework for redirecting chronic complainers toward action

How to separate legitimate work issues from gossip and personal drama

The exact language to transform negative conversations in real-time

Boundary-setting strategies that protect your own mental space

This isn’t about toxic positivity or pretending problems don’t exist. It’s about knowing when negativity is productive and when it’s destructive—and having the tools to navigate both.

Because negativity is contagious. But so is the solution-focused energy you bring to your team.

Watch the full episode Work Life Decoded: How to Handle Workplace Negativity (Without Becoming the Office Therapist) on Patreon.

The Strategic Value of Simple Solutions

Mon, 10 Nov 2025 00:00:00 GMT

The best solution is the one that solves today's problem without creating tomorrow's.

Data Stewards vs. Data Scientists

Mon, 03 Nov 2025 00:00:00 GMT

Why Biotech Needs Both (But Hires Only One)

Why Data Layer Guardrails are Key to Scalable Self-Service

Fri, 31 Oct 2025 00:00:00 GMT

What is the best kind of security for your data?

Work Life Decoded: Series Introduction

Tue, 28 Oct 2025 00:00:00 GMT

A new video series that cuts through the myths with real talk on science and careers.

Women in Bioinformatics chairs Lorena and Lina introduce their new video series tackling the career challenges that keep bioinformatics professionals up at night. With over 40 years of combined experience at Harvard, eGenesis, Ginkgo Bioworks, and other leading organizations, they're sharing the honest conversations about workplace dynamics, negotiation, and career navigation that they wish they'd had access to earlier in their careers.

Should you stay at your current job or make a move? How do you handle a colleague taking credit for your work? When should you speak up, and when should you let it go? Each video in the series addresses a specific challenge with practical, actionable advice from leaders who've been there.

This isn't about motivational platitudes—it's real talk about career strategy, advocating for yourself, supporting other minorities in the workplace, and managing the daily realities of working in science.

If you're ready for honest insights on building the career you want, join the Patreon video series, "Work Life Decoded." The first episode Welcome to Work Life Decoded is available now!

Work Life Decoded: Your Weekly Win Log – The Career Tool You Didn't Know You Needed

Tue, 28 Oct 2025 00:00:00 GMT

Why you should keep a weekly log of your wins (and how it can transform your career)

In their latest video, Women in Bioinformatics chairs Lorena and Lina tackle a deceptively simple practice that can transform your career: keeping a weekly log of your wins and quantifying your achievements.

This isn't just about surviving performance review season—though it absolutely makes that easier. A weekly win log is your defense against imposter syndrome, your foundation for a compelling resume, and your ammunition when it's time to negotiate for what you deserve. The commitment? Just five minutes every Friday.

The video walks through why this habit matters, what to track, and how to quantify your impact with concrete before/after examples. No more scrambling six months later trying to remember what you accomplished. No more underselling yourself because you forgot the numbers that prove your value.

Lorena and Lina created a one-page Quick Reference Guide with practical setup tips and real examples—available exclusively to Patreon supporters along with the full video. Ready to build this career-changing habit? Watch Your Weekly Win Log – The Career Tool You Didn't Know You Needed on Patreon and start logging this Friday. Set that 5-minute calendar reminder right now—your future self will thank you.

The Success Criteria Question: Why I Don't Start with Requirements

Mon, 27 Oct 2025 00:00:00 GMT

The difference between requirements and success criteria

Why Every Series A Biotech Hits the Same Data Wall

Thu, 23 Oct 2025 00:00:00 GMT

Avoid the common data pitfalls that slow down growing biotech companies.

Your Proof-of-Concept Is Not A Platform

Mon, 20 Oct 2025 00:00:00 GMT

The best Proof-of-Concepts teach you what to build next and then get retired.

The Universal Pattern Behind Scalable Data Systems

Thu, 16 Oct 2025 00:00:00 GMT

A Coffee with CompBio: Fail, learn, repeat, the bioinformatics way!

Tue, 14 Oct 2025 00:00:00 GMT

A coffee with Saranya Canchi

Grab your coffee and join us for another episode of A Coffee with Comp Bio!

In this episode of A Coffee with Comp Bio, hosts Alex Bartlett and Lorena Pantano sit down with Saranya Canchi, a computational biologist specializing in neuroscience. Together, they explore how to thrive as a self-directed learner in bioinformatics—tackling early challenges, learning through projects, and building problem-solving resilience. Saranya shares her journey as a self-taught bioinformatician, highlighting the importance of mastering the field’s unique language and embracing failure as part of growth. Whether you’re just starting out or looking to strengthen your learning approach, this conversation offers practical insights and inspiration for your bioinformatics journey.

Saranya's webpage: https://s-canchi.github.io/

Send us your comments, questions, and suggestions using this form

Thanks Amulya Shastr for editing and management support.

Listen to this podcast on other platforms:

 {' '}
Apple

 {' '} Spotify

We are looking for sponsors! Please get in touch if you or your business would like to help support this podcast.

Follow Lorena and Alex on LinkedIn!

If you enjoyed the episode, please subscribe and leave a review!

Hosted by Ausha. See ausha.co/privacy-policy for more information

The Sphere of Inluence in Project Management

Thu, 09 Oct 2025 00:00:00 GMT

How to focus your energy to actually make a difference

Precision Medicine, Through One Lens

Wed, 08 Oct 2025 00:00:00 GMT

Bridging Biology with Clinical Insight

Precision medicine is more than just a buzzword; it’s a shift in how we understand, treat, and even predict disease. Instead of using a “one-size-fits-all” approach, it uses data from a patient’s genome to their clinical history to design treatments that are tailored to them.

✨ While precision medicine is a broad field that also involves environmental, lifestyle, and behavioral factors, biological and clinical data play a key role in shaping how we design targeted treatments and predict patient outcomes.

#PrecisionMedicine is about asking deeper questions:

Why do two patients with the same diagnosis respond differently to the same therapy?

Which genetic signatures predict how someone will react to a drug?

How can we use molecular data to catch diseases before they appear in symptoms?

And the answer lies in #DataScience. By integrating molecular datasets (like gene expression or mutations) with clinical data (like outcomes or lab results), precision medicine connects the “why” from biology with the “what” from patient care.

This isn’t the future; it’s already reshaping how we diagnose, treat, and prevent disease.

💡 Want to see how clinical insights and biological data come together in practice?

👉🏻 I dive deeper into this idea of this integration with a real-world example here.

💭 Beyond biological and clinical data, which datasets do you believe could further advance the precision medicine revolution?

Multi-Source Data Integration

Tue, 07 Oct 2025 00:00:00 GMT

From chaos to clarity: How the medallion architecture transforms messy, multi-source data into trustworthy insights.

Managing Data Engineering Consultants Across 4 Time Zones: What Actually Worked

Thu, 02 Oct 2025 00:00:00 GMT

Strategies to management without real-time communication

The API Strategy Gap in Research

Mon, 29 Sep 2025 00:00:00 GMT

Most biotech companies build applications. Few build APIs.

Change management in small biotech

Thu, 25 Sep 2025 00:00:00 GMT

The "slow down to speed up" paradox

A Coffee with CompBio: (Dry) Lab Notebooks

Tue, 23 Sep 2025 00:00:00 GMT

The Importance of Recordkeeping in CompBio with insights from Amulya Shastry and Lina Faller.

Grab your coffee and join us for another episode of A Coffee with Comp Bio!

This time, Alexandra Bartlett and I kick things off with Amulya Shastry, a PhD student at Boston University and co-chair of Boston Women in Bioinformatics, who introduces us to llmr -- a new Tidyverse-friendly tool for connecting with LLMs like ChatGPT, Gemini, and more.

Then we sit down with Lina L. Faller, Ph.D., a veteran in bioinformatics with nearly two decades of experience bridging software engineering, research, and pharma. Lina shares why she started blogging about sustainable data systems, leadership in tech, and the very human side of computational biology. We dive into one of her favorite topics: why computational biologists should keep lab notebooks (yes, even if your "lab" is just a laptop). From reproducibility to institutional memory to the art of "forensic bioinformatics," Lina brings stories and advice that will be useful to anyone working with data.

If you’ve ever forgotten what you coded six months ago (we’ve all been there), or wondered how AI might fit into documentation and knowledge-sharing, this episode is for you.

Send us your comments, questions, and suggestions using this form

https://ellmer.tidyverse.org/articles/ellmer.html

https://linafaller.com/

Thanks Amulya Shastr for editing and management support.

Listen to this podcast on other platforms:

 {' '}
Apple

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We are looking for sponsors! Please get in touch if you or your business would like to help support this podcast.

Follow Lorena and Alex on LinkedIn!

If you enjoyed the episode, please subscribe and leave a review!

Hosted by Ausha. See ausha.co/privacy-policy for more information

Force Multiplication Through Simple Solutions

Mon, 22 Sep 2025 00:00:00 GMT

The best tech solutions aren't rocket science. They're force multipliers.

Every Quick Fix in Research Code is a Future Investment Decision

Thu, 18 Sep 2025 00:00:00 GMT

The challenge isn't eliminating quick fixes—it's being intentional about which ones you keep and how you document the journey.

The Self-Service Data Paradox

Mon, 15 Sep 2025 00:00:00 GMT

Why do good tools create more questions than they answer?

Managing Up Isn't About Office Politics

Thu, 11 Sep 2025 00:00:00 GMT

Understanding what success looks like from your manager’s perspective is key to advancing your career.

Press Play on Bioinformatics: Snakemake in Action

Tue, 09 Sep 2025 00:00:00 GMT

Ever wish running a bioinformatics pipeline was as easy as pressing “play”? With Snakemake, it can be.

🐍 Ever wish running a bioinformatics pipeline was as easy as pressing “play”?

That’s basically what Snakemake does!

Think of it like a recipe book for data analysis:

You list your ingredients (raw data)

Write down each step (rules and scripts for preprocessing, analysis, plots)

Hit “go” and it automatically cooks the entire meal for you!

The magic?

✅ No more re-running everything when just one step changes

✅ Works on your laptop or scales up to an HPC cluster

✅ Makes your analysis reproducible, so six months later (or on someone else’s machine) you get the same results

To put this into practice, I recently built a Snakemake workflow for cervical cancer gene expression analysis.

It:

🔹 Fetches data directly from GEO

🔹 Runs preprocessing + differential expression analysis

🔹 Generates a volcano plot for quick visualization

You basically write a Snakefile describing each step (preprocessing, analysis, visualization), and then run just one command:

snakemake --cores 4

That’s it! Snakemake figures out the order of tasks, runs only what’s needed, and makes sure results are reproducible.

✨ The BEST part? With the config file updated for your dataset, ANYONE can reproduce the full analysis with just that one command!

🟢 I’ve shared the pipeline on GitHub here 👉 https://lnkd.in/eS6G7W75

If you’re curious about Snakemake or just want to peek at a reproducible cancer genomics workflow, check it out!

Team Building Isn't About Trust Falls

Mon, 08 Sep 2025 00:00:00 GMT

The best technical leaders figure out what each team member wants from the project—then find ways to deliver it.

The Art of Saying No Without Losing Friends

Thu, 04 Sep 2025 00:00:00 GMT

Scope negotiation isn't about being rigid—it's about being intentional.

A Coffee with CompBio: The Spatial Transcriptomics Toolkit

Tue, 02 Sep 2025 08:00:00 GMT

Memory, Clustering, and Deconvolution

Alex Barlett and Lorena Pantano tackle the computational challenges of spatial transcriptomics. Learn how 𝗕𝗣𝗖𝗲𝗹𝗹𝘀 can help you work with millions of cells without needing terabytes of RAM, discover how 𝗕𝗮𝗻𝗸𝘀𝘆'𝘀 neighborhood-aware clustering reveals tissue architecture, and explore 𝗥𝗖𝗧𝗗'𝘀 approach to cell type deconvolution in spatially-resolved data. Plus, Lorena reviews 𝗣𝗼𝘀𝗶𝘁𝗿𝗼𝗻, the new R-friendly IDE that's catching attention in the bioinformatics community.

https://lnkd.in/eqFfkzKq

https://lnkd.in/ekrS4H5p

https://lnkd.in/e7r33PKf

https://lnkd.in/eiWqVjjK

https://lnkd.in/e7B35A7V

https://lnkd.in/eZdQ-qKV - Sean Davis

https://positron.posit.co/

Please get in touch if you or your business would like to help support this podcast. Thanks Amulya Shastr for editing and management support.

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How to Turn Stakeholders from Obstacles into Advocates

Tue, 02 Sep 2025 00:00:00 GMT

Technical skills get you the job. Stakeholder management skills make you effective in the job.

The Genomics Diversity Crisis

Mon, 01 Sep 2025 14:00:00 GMT

When 86% of genomic data comes from European ancestry, treatments built on this data will inevitably fail marginalized communities.

As diversity, equity, and inclusion (DEI) initiatives are being dismantled across US institutions, Eric Green, former Director of the National Human Genome Research Institute, opened the Festival of Genomics in Boston to make a case that DEI extends far beyond creating a better workplace culture. In genomics research, DEI is scientifically essential. DEI refers to efforts that ensure diverse representation (diversity) through fair treatment and opportunity (equity) and meaningful participation (inclusion), all grounded in respect for different communities and perspectives. Current genomic datasets overwhelmingly represent people of European ancestry, yet the insights derived from this narrow slice of humanity are being applied to diagnose, treat, and understand disease across all populations. To truly reflect human diversity, science depends on data from all communities. However, that data cannot be collected from populations who distrust the scientific establishment. Genuine commitments to equity and inclusion are the foundation needed to rebuild those critical relationships.

The Technical Problem: Sampling Bias

Sampling bias is a fundamental challenge at the heart of genomics research. This occurs when data used to build a model fails to adequately represent the study or target population due to the underrepresentation of certain groups. A classic example of sampling bias would be trying to understand human height by collecting data only from NBA players. The resulting model would drastically overestimate how tall humans are. Since sampling bias compromises generalizability, these models often produce misleading outputs. In genomics, sampling bias can lead researchers to overestimate or underestimate impacts of genetic variants or treatments.

In the field of genomics, the risk of sampling bias takes on particular urgency. In 2021, an estimated 86% of sequenced genomic data came from individuals of European ancestry [^1]. This staggering imbalance means that models trained on this data are systematically misleading and, therefore, unreliable across diverse populations. This is a profound problem because models that fail to generalize to marginalized communities will inevitably exacerbate existing health disparities.

As bioinformatics enters a new era of artificial intelligence (AI) driven discovery, the composition of our training data has never mattered more. How can we build a future of personalized medicine on a foundation that only represents less than 20% of the world's population?

The Human Problem: A Legacy of Distrust

To improve our biological models, we must prioritize diverse dataset collection. However, while the solution seems straightforward, the reality is far more complex. The scientific community's painful history of human exploitation and data misuse continues to stifle many communities' willingness to participate in research studies.

These scars run deep. From 1932 to 1972, with support from state and local governments, the US Public Health Service conducted what became known as the Tuskegee Syphilis Study, misleading impoverished Black male sharecroppers to participate in a treatment program for their "bad blood" [^2], [^3]. In reality, the program was a study of the progression of untreated syphilis. Withheld from diagnoses and available treatment, hundreds of participants lost their lives from the disease in the name of scientific advancement.

Even well-intentioned research can have ethical missteps that deepen distrust. In the 1990s, as the rate of diabetes climbed in the Havasupai tribe, an indigenous community near the Grand Canyon, around 650 members donated blood samples to Arizona State University to study genetic links to diabetes [^4]. Approximately a hundred participants signed a consent form allowing their samples to be used to "study the causes of behavior/medical disorders." However, with English as a second language for many participants and most having never completed high school, the full implications of this broad consent were at high risk of not being understood. Therefore, when researchers used the tribe's samples in studies unrelated to diabetes, it was done with disregard to the civil rights of the Havasupai tribe to self-determination. Rightfully, the Havasupai tribe sued the university and has refused to participate in any further studies.

More recently, the rise of consumer genetic testing, such as those offered by 23andMe, has made it easy to share genetic data. However, these platforms have also raised concerns about data transparency for marginalized communities [^5]. These companies tell consumers that consumers own their personal data. However, for communities who are already wary of government surveillance and over-policing, the knowledge that their genetic information could potentially be sold to pharmaceutical companies to develop medicine that they will not have access to or accessed by law enforcement in ways that could lead to wrongful convictions for them or a family member adds another layer of hesitation to research participation.

The examples listed only touch the surface of these deeply rooted issues that plague distrust of the scientific community, and I encourage readers to learn more outside the context of this article.

A Path Forward: DEI as a Framework for Rebuilding Trust

History shows that trust in science, even after profound breaches, can be rebuilt. The atrocities conducted by medical professionals in the Holocaust ruptured the relationship between science and Jewish communities [^6]. Despite this history, today the Ashkenazi Jewish population is among the most studied groups in human genomics. This reconciliation was made possible through decades of ethical reform and intentional efforts to include and support Jewish scientists.

The Nuremberg Code, a framework established in response to unethical Nazi medical experiments, set up crucial protections for research participants through informed consent and participant autonomy [^7]. However, while these protections support equitable practices, they did not insure participants have meaningful representation and decision-making power in the research itself. For the Jewish community, this gap was filled by inclusive practices like the Rockefeller Foundation's Refugee Scholar Program, which resettled Jewish scientists and supported their continued involvement in research despite widespread persecution [^8]. This combination of ethical frameworks and institutional inclusion likely contributed significantly to rebuilding trust between Jewish communities and scientific institutions.

Until recently, we were seeing similar patterns emerging through initiatives of inclusion. Researchers from communities affected by HIV/AIDS has been associated with improved rates of community engagement in research and better treatment outcomes [^9]. Similarly, Indigenous health research programs indicate that studies of their communities benefit from Indigenous leadership by increasing community engagement and fostering resources to support non-Indigenous research team members to develop cultural competency [^10]. These studies ensured that voices of the communities they were doing research on were empowered to shape the research process, from study design to data interpretation.

It must be emphasized that practicing DEI is not a quick fix. DEI practices must be sustained, proactive commitments to ensure diversity through informed consent and inclusion. As genomics continues to evolve, so must our standards for how research is conducted, whose data is included, and how the data is used. By building trust, we build better science and, in turn, better health outcomes for everyone.

As we stand at the threshold of AI-driven genomics, we have a choice to make. We can continue building models on a narrow foundation that serves only a fraction of humanity, or we can invest in the trust-building work necessary to create truly inclusive research. The technical quality of our science depends on our decision.

References:

[^1]: Fatumo, S., Chikowore, T., Choudhury, A., Ayub, M., Martin, A. R., & Kuchenbäcker, K. (2022). Diversity in genomic studies: a roadmap to address the imbalance. Nature medicine, 28(2), 243.

[^2]: Jones, J. H. (1993). Bad blood: the Tuskegee syphilis experiment. New and expanded ed. New York.

[^3]: Gray, F. (1998). The Tuskegee syphilis study: An insider’s account. Montgomery, AL: Black Belt.

[^4]: Sterling, R. L. (2011). Genetic research among the Havasupai: a cautionary tale. AMA Journal of Ethics, 13(2), 113-117.

[^5]: Raz, A. E., Niemiec, E., Howard, H. C., Sterckx, S., Cockbain, J., & Prainsack, B. (2020). Transparency, consent and trust in the use of customers' data by an online genetic testing company: an exploratory survey among 23andMe users. New Genetics and Society, 39(4), 459-482.

[^6]: Lagnado, L. M., & Dekel, S. C. (1992). Children of the flames: Dr. Josef Mengele and the untold story of the twins of Auschwitz. Penguin.

[^7]: Trials of War Criminals before the Nuremberg Military Tribunals under Control Council Law No. 10. (n.d.). Permissible medical experiments. (Vol. 2, pp. 181-182). Washington, D.C.: U.S. Government Printing Office.

[^8]: Iacobelli, T. (2021). The Rockefeller Foundation’s Refugee Scholar Program.

[^9]: Karris, M. Y., Dube, K., & Moore, A. A. (2020). What lessons it might teach us? Community engagement in HIV research. Current Opinion in HIV and AIDS, 15(2), 142-149.

[^10]: Woods, C., Settee, C., Beaucage, M., Robinson-Settee, H., Desjarlais, A., Adams, E., ... & Nahanee, D. (2023). Ensuring Indigenous co-leadership in health research: a Can-SOLVE CKD case example. International Journal for Equity in Health, 22(1), 234.

The Engineering Report That Never Gets Written

Mon, 25 Aug 2025 00:00:00 GMT

Software engineers routinely write project wrap-up reports. Bioinformaticians? Almost never.

The Post-Mortem No One Wants to Do (But Everyone Should)

Thu, 21 Aug 2025 00:00:00 GMT

Thirty minutes you spend on a post-mortem can save you thirty hours of future firefighting.

The One Question That Changed How I Build Tools

Mon, 18 Aug 2025 00:00:00 GMT

Successful tool development begins with understanding where to draw the finish line.

A Coffee with CompBio: First hand experience on transitioning to a Product Manager role

Wed, 13 Aug 2025 00:00:00 GMT

Katie Huges shares her professional journey from bioinformatics to product management with help from Lorena and Alex.

Alex Barlett and Lorena Pantano welcome Katie Hughes, their first guest, to discuss her career transition from bioinformatics to product management. Katie shares her journey from studying genetics, working in wet labs, and discovering a passion for bioinformatics, to eventually earning a master's degree in the field. She details her experience at various biotech companies, including Harvard Medical School, Moderna, Sonata Therapeutics, and Generate Biomedicines. Katie emphasizes the importance of curiosity, adaptability, and soft skills in making career transitions. She explains what a product manager does, differentiates it from similar roles, and outlines the skills and experiences that helped her succeed. The discussion also covers the day-to-day responsibilities of a product manager, the collaborative nature of the role, and advice for those interested in making a similar career shift.

Marty Cagan

How I AI podcast

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The Hidden Bottleneck in AI-Driven Drug Discovery

Mon, 11 Aug 2025 00:00:00 GMT

The real challenge in AI-drive drug discovery is the broken data infrastructure.

The Software Engineering Principle No One Teaches in Bioinformatics

Thu, 07 Aug 2025 00:00:00 GMT

Each part of your code should have one job and do it well.

Why Every Biotech Needs a Data Steward

Mon, 04 Aug 2025 00:00:00 GMT

What is a data steward? Why is important?

The Power of Listening Across Teams

Fri, 01 Aug 2025 00:00:00 GMT

Listening skills in bioinformatics is critical

A Coffee with CompBio: R You Doing It Right?

Tue, 29 Jul 2025 00:00:00 GMT

Alex and Lorena dig into the tricks and tips that'll actually make your R code work better.

Lorena Pantano and Alexandra Bartlett dig into the tricks and tips that'll actually make your R code work better. We're talking about ditching those old habits we all picked up and switching to code that works better in 2025. We cover over 10 solid habits that'll seriously boost your R game - everything from how you're reading and storing files, making plots that are publish-ready, theming, data manipulation, and setting up environments so your code works when you come back to it later. If you want to up your R skills, this one's got practical stuff you can start using right away.

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Reactive vs Proactive Bioinformatics

Mon, 28 Jul 2025 00:00:00 GMT

Bioinformatics enlightment occurs when one can regonize it has two main modes: reactive and proactive

Design Docs for Bioinformatics

Thu, 24 Jul 2025 08:00:00 GMT

Should we be designing bioinformatics projects like a software engineer?

Build vs Buy in Biotech

Mon, 21 Jul 2025 08:00:00 GMT

The hidden costs of building vs buying in biotech

Code Review Culture in Research Labs

Fri, 18 Jul 2025 08:00:00 GMT

Not enough code review and you risk irreproducible science, too much and you kill discovery momentum.

The Bioinformatics Triangle: Memory, Elegance, and Speed

Wed, 16 Jul 2025 00:00:00 GMT

How do you balance code aesthetics with performance in your bioinformatics workflows?

The Power of Strategic Data Infrastructure

Tue, 15 Jul 2025 00:00:00 GMT

Great science happens when technical infrastructure meets scientific curiosity

Why Computational Biologists Need Lab Notebooks

Fri, 11 Jul 2025 00:00:00 GMT

Somehow, this fundamental practice gets lost when we move to computational biology

A Coffee with CompBio: Fast, Private, and Publish-Ready Spatial Transcriptomics App (Without Losing Your Mind)

Thu, 10 Jul 2025 00:00:00 GMT

Alex and Lorena journey through the real-life challenges of building interactive single cell spatial data visualizations for large projects.

In this episode, we journey through the real-life challenges of building interactive single cell spatial data visualizations for large projects. Lorena shares her recent adventure turning mountains of data into a web app using tools like Python, R, and the (tricky-to-pronounce) single-cell viewer Vitessce. She discusses the hurdles of image cropping, memory limits, Python-R crossovers, and why “just putting it online” isn’t as easy as it sounds—especially when it comes to privacy, deployment, and avoiding surprise cloud bills. If you’ve ever had a collaborator say, "Can you just build me an app I can play with?", this episode is for you.

In the "Quick Sip" segment, Alex and Lorena share tips on automating code linting with GitHub Actions. Finally, in our "Brewing Up Answers" segment, we chat about managing people in academia vs. industry, and why it’s a very different ballgame on each side of the fence.

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A Coffee with CompBio: R Markdown

Thu, 26 Jun 2025 00:00:00 GMT

Alex and Lorena discuss a large bulk RNA-seq project that yielded lasting changes to their group’s everyday bioinformatics practices via the creation of parameterized R Markdown code templates.

Lorena Pantano and Alexandra Bartlett discuss a large bulk RNA-seq project that yielded lasting changes to their group’s everyday bioinformatics practices via the creation of parameterized R Markdown code templates. In the "Quick Sip" segment, they discuss reticulate for managing python environments in an R context, and in "Brewing Up Answers", they reflect on the differences between industry and academia bioinformatics.

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A Coffee with CompBio: The Thousand Dollar Alignment

Tue, 10 Jun 2025 00:00:00 GMT

From puzzlingly low mapping rates to unexpected cloud costs caused by unoptimized compute jobs, Lorena and Alex highlight how essential clear communication and bioinformatics-aware experimental design are to any successful project.

Lorena Pantano and Alexandra Bartlett share the twists and turns of realizing their methylation data wasn’t what it seemed. From puzzlingly low mapping rates to unexpected cloud costs caused by unoptimized compute jobs—thankfully caught just in time thanks to cost alarms—they highlight how essential clear communication and bioinformatics-aware experimental design are to any successful project.

In our new segments, Quick Sips and Brewing Up for Answers, we talk about PIXI for managing software environments (Thanks to Edmund Miller) and dig into the ever-present challenge of staying organized across complex projects.

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A Coffee with CompBio: Nine Samples and Zero Cells

Tue, 27 May 2025 00:00:01 GMT

Alex and Lorena dive into the messy reality of processing single-cell RNA-seq data.

In our first episode, Alex and Lorena dive into the messy reality of processing single-cell RNA-seq data. What started as a simple QC project turned into a week-long journey across compute environments, mysterious pipeline errors, and zero-cell outputs. Along the way, we troubleshoot issues with Cell Ranger, uncover strange sequencing artifacts, and reflect on lessons in data handling, pipeline reproducibility, and client communication.

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A Coffee with CompBio: Intro

Tue, 27 May 2025 00:00:00 GMT

Whether you're a researcher, student, or just bio-curious, join us for casual, insightful conversations that bridge science and real life.

In the introductory episode, Lorena and Alex introduce themselves and share how they got started in computational biology. They talk about their career paths, what drew them to bioinformatics, and some of the challenges and surprises they’ve encountered along the way. They also give a preview of the kinds of topics and practical issues they’ll be covering on the podcast, from workflow basics to troubleshooting analysis hiccups.

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Boston Women in Bioinformatics's Blog

A Coffee with CompBio: Behind the "We'll Be in Touch" Emails

Biobank Intro Series: You Have What You Need

Before You Start Analysis

What Comes Next

Reflections

Member Spotlight: Sharvari Narendra

Member Spotlight: Aysheh Alrfooh

Biobank Intro Series: All of Us Genetic Data

Quick Overview: What's Available

Streaming VCFs from GCS

Finding Your Unphased Shard

Extracting and Merging Your Region

What About Hail?

Bonus: The Variant Annotation Table (VAT)

Putting It All Together

Biobank Intro Series: UK Biobank Genetic Data

The Showcase Knows About Genetics Too

Where Are the Files? (The Practical Part)

What's Actually in These Files?

Code to Stream Genetic Data in UK Biobank

The Short Version

References

The Rite of Process

Biobank Intro Series: All of Us Observational Data (Part II)

The OMOP-CDM Schema

Query a Clinical Table

Decode Coded Values via the concept Table

Merge Everything into a Cohort Table

Cohort and Dataset Builders

Beyond OMOP-CDM: CDR Directory Files

Summary

Biobank Intro Series: All of Us Observational Data (Part I)

Finding Concept IDs: Three Approaches

1. All of Us Data Browser (fastest for quick lookups)

2. OHDSI Athena Vocabulary (richer metadata)

3. Direct SQL queries (most flexible)

What's Next

A Coffee with CompBio: Claude Code for Bioinformatics

Biobank Intro Series: UK Biobank Observational Data (Part II)

Step 1: Find the field names for your data

Quick lookup via terminal

Dictionary approach

Step 2: Extract the dataset

Step 3: Translate the dataset values

The Gotchas Nobody Tells You

Wrapping Up

Biobank Intro Series: UK Biobank Observational Data (Part I)

Reading Between the Data Fields: Arrays, Instances, and Codes

Coding

Instances

Arrays

Example: Not All Field IDs Are Created Equal

UK Biobank Showcase Tips

Biobank Intro Series: Hardware Settings

Tip #1: Each Platform Has Its Own Command-Line Interface (CLI)

Tip #2: Don't Bring the Cloud Home With You

Tip #3: Know Your Tools and Your Files

Tip #4: Don't Let Long Jobs Catch You Off Guard

The Bottom Line

Biobank Intro Series: Getting Started

A Coffee with CompBio: Hacking your way into computational biology

ctDNA as an oncology endpoint

Key takeaways

The ctMoniTR Project showed ctDNA’s clinical signal at scale and what’s needed next.

Tissue availability is becoming a limiting factor.

Scaling requires prospective standardization.

Looking Ahead

Beyond behavior: How machine learning decodes consciousness and forecasts seizures from brain activity

Studying Consciousness Beyond Behavior

Seeing Consciousness Hidden in Brain Signals

Using Machine Learning to Predict Loss of Consciousness Before a Seizure

Looking Ahead

A Coffee with CompBio: New Year Resolutions

Tuesday Tactics: Build vs Buy Red Flag

Tuesday Tactics: The Excel Export Feature Request

Tuesday Tactics: Data Steward ≠ Data Janitor

The Tool Consolidation Paradox

The Twelve Days of Scale-Up

Tuesday Tactics: The 10-Minute Test

Decode Coded Values via the `concept` Table