Mineto Ota

Thrilled to share the second half of my PhD work here!

We show how data on expression quantitative trait loci (eQTL) relates to the structure of gene regulatory networks (GRN). Much of the GRN / eQTL picture is unmapped, but what we do have says a lot… (1/)

doi.org/10.1101/2025...

I'm excited to announce that I'll be starting a lab at UCSF in the @ihgatucsf.bsky.social and @ucsf-epibiostat.bsky.social in July.

We'll work at the intersection of statistical genetics, population genetics, and machine learning.

I have an opportunity to hire a staff scientist for my lab. Looking for someone with outstanding skillset in ML/statistics, genomics applications; interest in mentoring, strong publication record, PD experience required.

Email CV to me+cc my assistant (see 'contact' on my website). Ad to follow.

Whole-genome sequencing analysis of anthropometric traits in 672,976 individuals reveals convergence between rare and common genetic associations Genetic association studies have mostly focussed on common variants from genotyping arrays or rare protein-coding variants from exome sequencing. Here, we used whole-genome sequence (WGS) data in 672,...

A really nice paper by @drghawkes.bsky.social et al. argues that rare and common genetic associations converge on the same genes.

While this seems at odds with our recent work about how burden tests and GWAS prioritize different genes, our results agree (🧬🧪🧵 1/6)

www.biorxiv.org/content/10.1...

Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage Naqvi et al. reveal how DNA sequence determines the chromatin response to transcription factor (TF) dosage changes. By combining deep learning and chemical genetics, they uncover specific sequence fea...

Excited to share the peer-reviewed version of our paper on predicting the chromatin response to TF dosage using transfer learning

www.cell.com/cell-genomic...

Disease diagnostics using machine learning of B cell and T cell receptor sequences Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system’s own record of antigen ...

Disease diagnostics using machine learning of B cell and T cell receptor sequences

www.science.org/doi/10.1126/...

TL;DR: BCRs ARE ALL YOU NEED!

(Well actually .... keep reading) 1/

Japan can be a science heavyweight once more — if it rethinks funding The nation must lose its tight focus on individual disciplines if it is to keep pace with the evolving requirements of scientific enquiry.

Japan can be a science heavyweight once more — if it rethinks funding

Research leaders call for an end to substantial underfunding of interdisciplinary research in Japan.

On my current visit to 🇯🇵 I can see the country is ready for a change
#japan #academicSky 🧪

www.nature.com/articles/d41...

Thank you, Joseph! We greatly appreciate your contributions to the field and are grateful to have had the chance to work with your data!

I posted a couple days ago about our new paper on building causal graphs from genetic associations + Perturb-seq.

Here I want to expand on the value of using DIRECTIONAL information contained in LoF burden tests.🧵
[work led by @minetoota.bsky.social ]

bsky.app/profile/jkpr...

Beautifully elegant work on integrating LoF, GWAS & Perturb-seq data to build causal paths from regulators to genes / programs to phenotype. And it didn't require a foundational virtual cell model (well almost ... gene & protein embeddings r used in GeneBayes)! 😜

Great new study from @jkpritch.bsky.social’s lab, led by @minetoota.bsky.social,
combining ‘quantitative estimates of gene-trait relationships from loss-of-function burden tests with gene-regulatory connections inferred from Perturb-seq experiments in relevant cell types’ 👇

@minetoota.bsky.social set the groundwork for many ongoing projects in @jkpritch.bsky.social and Marson lab. Great to see this out!

Really nice work. And they chose one of my favorite traits to model: mean corpuscular hemoglobin.

Allows me to reuse one of my figures from a few weeks ago on a gene as old as eukaryotes, mitoferrin, which is needed to move iron into mitochondria.
@jkpritch.bsky.social

It’s been an incredibly exciting journey!
I am truly grateful to my mentors @jkpritch.bsky.social, Alex, and co-authors @jeffspence.bsky.social, @tkyzeng.bsky.social, @emmamarydann.bsky.social, as well as all the wonderful members of the Pritchard and Marson labs for their incredible support!

Thank you Jonathan for these fantastic threads about our recent work!

We dove into how we can model the gene regulatory architecture of complex traits with 1) Gene effects from LoF burden tests and 2) Perturb-seq.

Why do association studies prioritize trait-specific variants???

A quick thread about the importance of thinking about all traits at once 👇 1/6 (🧪🧬)

Specificity, length, and luck: How genes are prioritized by rare and common variant association studies Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by anal...

What do GWAS and rare variant burden tests discover, and why?

Do these studies find the most IMPORTANT genes? If not, how DO they rank genes?

Here we present a surprising result: these studies actually test for SPECIFICITY! A 🧵on what this means... (🧪🧬)

www.biorxiv.org/content/10.1...

Central control of dynamic gene circuits governs T cell rest and activation - Nature Resting and activated T cell states are established by context-specific regulators and dynamic gene circuits.

Beautiful work led by Maya Arce from Marson lab reveals a fascinating story about rewiring of a critical gene regulatory circuit in different T cell types: T effectors and Tregs
www.nature.com/articles/s41...