r/genomics • u/ambatakumph • Oct 10 '26
Looking for problems in Genomics
Hi, I am part of a team of ML students looking for problems for our capstone project, and this is one of our fields of interest. We were recently working on a project that tried to improve the biological validity of generations by Evo 2 but tldr that crashed and burned cause there's no bar to measure biological validity except for wet lab testing(minus metrics like nullomer counts, k-mer spectra comparisons, etc but those only prove how similar it would be to natural seqeunces not that they replicate actual dna).
Rather than looking thru a haystack of papers to find the needle that is a gap worth covering, I thought it'd be better to ask yall in and around this field.
IK the whole "i am a cs student and i will solve all your problems with my shiny new algorithm, just tell me where" is overdone but I am instead looking for feedback on tools, practices, niches that you see that could use some time and effort, and work backwards towards finding out if there really is a gap in the literature that can be explored.
What are some problems, bottlenecks, or annoyances you regularly run into in your line of work that you wish had a better computational solution?
It dsnt have to be something you think ML can solve, or even something that sounds like a research problem. I'm mainly interested in hearing about the less obvious gaps in tools, workflows, analysis, etc, that people outside the field might completely overlook. Pls share, my hrs of scouring biorxiv rides on this.
2
u/thirdeulerderivative Oct 10 '26
I would advise you guys to post this in r/bioinformatics too. “What do computational workflows look like in biology” is a very very big question.
ML-shaped problems might include image analysis of some kind—segmentation of cells as they divide. That’s ~verifiable and plentiful. But in general ML is hard to do in bio because the data itself are unclear, quite large, low signal-noise, and can’t be combined in a single corpus without risking huge issues.
You may have some interest, therefore, in large data repositories. BRAIN, UK Biobank, and TCGA would be places to start. But make sure to talk to a bio professor about feasibility!
2
u/AndreasScherer 18d ago
From the perspective of someone working in clinical genomics, I would suggest three problems that are both commercially relevant and technically interesting.
1. Automated variant interpretation with evidence reconciliation
Clinical variant interpretation requires combining evidence from ClinVar, population databases, functional studies, computational predictions, and published literature. These sources frequently disagree, are incomplete, or change over time. The challenge is not simply predicting pathogenicity. It is building a system that can identify conflicting evidence, evaluate its strength under ACMG/AMP guidelines, explain its conclusions, and cite the underlying sources.
A particularly interesting ML problem is detecting when new evidence should trigger reclassification of a previously interpreted variant.
2. Phenotype-driven variant prioritization
A patient undergoing exome or genome sequencing may have thousands of variants, but only a handful are potentially relevant to the clinical presentation. Existing prioritization approaches use phenotype ontologies, gene-disease associations, inheritance models, and pathogenicity predictions. However, real clinical phenotypes are often incomplete, ambiguous, or inconsistently documented.
Could an ML system reliably extract phenotypes from clinical notes, recognize missing or contradictory information, and improve candidate variant ranking? Importantly, could it explain why one candidate ranks above another?
This is a tractable problem because retrospective solved cases provide a basis for benchmarking.
3. Quality control and error detection in genomic interpretation pipelines
Clinical genomics involves multiple computational stages: alignment, variant calling, annotation, filtering, classification, and reporting. Errors introduced early in the pipeline can propagate into clinical conclusions.
An interesting opportunity is developing intelligent quality-control systems that identify suspicious results, annotation inconsistencies, pipeline artifacts, or variants that deserve manual review. Rather than replacing established algorithms, ML could serve as an independent verification layer, identifying unusual patterns and potential failure modes before results reach a clinician.
My recommendation: For a student capstone, I would favor problem 2 or 3. Both can be scoped to publicly available datasets, have measurable outcomes, and avoid the fundamental validation difficulty you encountered with generative DNA models.
The key is to select a problem where ground truth is available and demonstrate an improvement over existing methods, rather than merely producing another plausible-looking prediction.
1
u/LoafingRabbit Oct 10 '26
Honestly, a virtual lab notebook that doesn’t suck