Low Coverage Thoughts

We’re well into the winter break, and I’ve been keeping myself busy by building a pipeline to calculate 10x Visium spot uncertainty scores (will be posted to my github soon). It’s been much more time-consuming than I initially thought, with new concerns seeming to appear following each small step forward. But that’s science I guess; every answer produces another question.  One such concern that popped into my head is that spatial transcriptomics might never be widespread enough to make this pipeline particularly useful. But since I’m already 3 weeks into the project, I’m just going to argue against this concern by reviewing the history of omics technologies. Today, you can sequence multiple human genomes with good depth for a couple hundred dollars, but when it first came out in 2006, Illumina’s DNA sequencer cost $300,000 for a single human genome. I’m sure a lot of professionals back then assumed that the cost made Illumina sequencing unusable for drug development. However, early...

The other day, I read a comment joking that everyone in science is either an egoist or has imposter syndrome. It was obviously satirical, but it lingered with me, prompting me to wonder why it feels uncomfortably close to the truth. The joke assumes a binary, that you can only think that you’re the best scientist or the worst. But speaking from my own experience, I’d argue that most scientists are forced to adopt the external projection of egoism while still internally feeling imposter syndrome at the same time. I often feel like the academic system favors having an ego, whether intentionally or not. Bold claims in high-impact journals, confident grant applications, and assertive presentations at conferences all signal authority, which tends to be rewarded. Metrics like citations, h-index, and journal prestige favor those who can make their work highly visible, while leadership roles and networking opportunities often go to the most outspoken or self-assured. Now, I’m not saying that ...

Looking Beyond Mice: Why Biology Needs Stranger Model Organisms It’s been almost a month since my last post, with finals, Thanksgiving, and friends visiting Boston keeping me completely busy. While diving into papers for my final projects, a concept that I’d thought about before but never really looked into has been brought back to my attention. I’ve always wondered why academics don’t put a greater focus on studying organisms outside the usual B6 mice, zebrafish, fruit flies, etc. These organisms are great for modeling human diseases, but not all medical breakthroughs are achieved directly from human cellular biology. If you look at some of the most impactful medical breakthroughs in the past 20 years, many of them stem from non-traditional model organisms, despite the majority of biomedical research focusing directly on humans via model organisms. Just off the top of my head, discoveries such as GFP thanks to the crystal jellyfish, neuronal memory storage thanks to sea slugs, and CRI...

     Whether you’re on board with them or not, LLMs are taking over our education system, workplaces, and everyday lives. I consider myself fortunate that I’ve gone through the majority of education without the presence of LLMs. I can only imagine how many corners I might’ve cut during my intro CS and software engineering classes back in undergrad if I could’ve just asked Claude to fix my code. But today’s students face that constant neon glow in the back of their minds urging them to take the easy way out, and I suspect the same dynamic is creeping into the workplace as well. That’s exactly why it feels worth talking about how bioinformaticians can use LLMs not to sidestep the learning process, but to actually deepen our understanding of concepts and accelerate our workflows.       In my experience, learning with LLMs is actually much easier than working with them. Being able to quickly acquire specific and generally accurate information while studyin...

Imagine a world where you could pay to find out what your DNA says about the likelihood that you drive for leisure, or how fast you tend to walk. Well, that world already exists. For around $300, you can discover your genetic probability for everything from your addiction likelihood, to your “probability for older age of first sexual intercourse.” As strange as it sounds, this is a real service that I paid for, backed by real data and real studies—like the Mills et al. paper that identified 371 genetic variants linked to age at first sex and birth, many associated with externalizing behavior.   Last summer I sent in a cheek swab to Nebula Genomics, and 4 months later, I received a full mutation analysis of my genome along with some other information about ancestry and microbiome. I was ecstatic about getting the results, and in all honesty didn’t care if I found out anything negative. I’ve since made changes to my vitamins, supplements, and lifestyle choices. The SNP rs1172362...

Aside from my bioinformatics journey, I work as a “brand ambassador”, which is a glamorous way of saying that I set up corporate networking events. Last night I had an event in downtown Boston for a bunch of board members, and got to listen to them all talk about what their companies were up to (sadly there were no biotech companies). At a certain point in the night, I got bored of hearing about “smart, scalable data-driven solutions to next-gen problems backed by AI blockchain models,” and decided to play buzzword bingo with myself to pass the time. I’m sure you can probably guess what word that I checked off over and over and over again. The AI craze is absolutely real, but I wonder how many of the executives dumping money into it actually understand what they are implementing it for?  It seems like since covid, the surge of AI has created a corporate hysteria, driven by grossly exaggerated headlines, and overall lack of understanding. Articles warn of AI revolutionizing every in...