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Matthew Aguirre

@aguirre404.bsky.social
231 followers 504 following 19 posts

Postdoc @ Genentech | PhD from Stanford Biomedical Data Science.

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Reposted by Matthew Aguirre
Roshni Patel @roshnipatel.bsky.social · 20h
So excited about this work with @nasalab.bsky.social! Led by Stephanie Gogarten & with help from @daphmarts.bsky.social, we use LLMs as judges to evaluate the use of population descriptors in the entirety of the GWAS Catalog (~4,000 papers published 2007-2025) www.biorxiv.org/content/10.6...
biorxiv.org
Large language model-based bibliometric evaluation of population descriptors in human genetics
As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. M...
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Reposted by Matthew Aguirre
C. Brandon Ogbunu @cbo.bsky.social · 27/09/2026
In my latest for @undark.org, I compare AI adoption in science to the Ship of Theseus: "If you swap out every wooden plank on a ship over time, is it still the same vessel?" I argue that science derives authority from something more than data generation + publication. undark.org/2026/09/25/o...
undark.org
AI Presents Science with a ‘Ship of Theseus’ Problem
Opinion | AI can replace each part of the scientific process, leaving the enterprise recognizable but devoid of human judgment.
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Reposted by Matthew Aguirre
Jason McDermott @biodataganache.bsky.social · 25/09/2026
This is just to say... I have 'discovered' the protein that was in the phage and which you probably wanted to save for your thesis Forgive me it was uncharacterized so function and much AI
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Reposted by Matthew Aguirre
Emma Dann @emmamarydann.bsky.social · 28/08/2026
Our work on systematic perturb-seq of primary human T cells is now out in Cell 🎉 www.cell.com/cell/fulltex... It's been a privilege to work with @ronghuizhu.bsky.social between @jkpritch.bsky.social @marsonlab.bsky.social labs, with a dream-team of co-authors ❤️ Highlights in preprint thread👇
cell.com
Genome-scale perturb-seq in primary human CD4+ T cells maps context-specific regulators of T cell programs and human immune traits
A dynamic atlas of gene regulation was generated by perturbing every expressed gene across 22 million primary human CD4+ T cells under resting conditions and following re-stimulation. The resulting ma...
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Reposted by Matthew Aguirre
Yun Deng @yundeng.bsky.social · 15/08/2026
The first manuscript from my postdoc is out (doi.org/10.64898/202...)! We introduce 𝐭𝐢𝐦𝐞-𝐬𝐭𝐫𝐚𝐭𝐢𝐟𝐢𝐞𝐝 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 for studying population structure change over time, with the temporal resolution of Ancestral Recombination Graphs (ARGs). Joint with @jkpritch.bsky.social and @jeffspence.github.io. 1/n
google.com
Coalescent-Based Time-Stratified Statistics Reveal Population Structure Dynamics using the Ancestral Recombination Graph
Many questions in population genetics are concerned with reconstructing evolutionary history through time, such as inferring how population structure has changed throughout the past. Yet, many existing approaches have only an implicit temporal component, using quantities such as allele frequency or haplotype length as rough proxies for age. Recent advances in the inference of Ancestral Recombination Graphs (ARGs) have made it possible to estimate the entire sequence of local genealogies along the genome. These genealogies explicitly encode how samples are related to each other at different time points in the past, enabling the inference of how population structure has changed over time. To this end, recent work has used ARGs to define time-stratified versions of widely-used population genetics summary statistics in an attempt to capture the population structure present within a particular time window. Here, we show that naive approaches result in statistics that cannot be interpreted solely in terms of the population structure present within the time window they are targeting. To address this problem, we introduce a framework of coalescent-based time-stratified statistics, which use coalescence probabilities to partition classical summary statistics into interval-specific contributions. Using coalescent simulations, we demonstrate that these statistics accurately isolate population structure at different temporal depths and avoid spurious signals. Our results highlight the necessity of integrating coalescent theory into ARG-based temporal analyses and provide a principled and practical foundation for studying the dynamics of population structure through time. ### Competing Interest Statement The authors have declared no competing interest. National Human Genome Research Institute, https://ror.org/00baak391, R01HG014005
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Reposted by Matthew Aguirre
MwahahahahahadScientist @mads100tist.bsky.social · 14/08/2026
STOP ATTACKING BIORXIV SO I CAN DOWNLOAD PDFs OF PAPERS I WILL NEVER READ
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Reposted by Matthew Aguirre
Jeff Spence @jeffspence.github.io · 22/06/2026
Excited to see @jonj-udd.bsky.social's fantastic work out @genetics-gsa.bsky.social. Selection in _heterozygotes_ is the primary force shaping allele frequencies of loss-of-function mutations in humans, even in genes only associated with purely recessive diseases. 🧪🧬 doi.org/10.1093/gene...
doi.org
Allele Frequencies at Recessive Disease Genes are Mainly Determined by Pleiotropic Effects in Heterozygotes
Abstract. The classic theory of mutation-selection balance predicts the equilibrium frequency of genetic variation under negative selection. The model pred
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Reposted by Matthew Aguirre
Romain Lopez @biologicalml.org · 29/05/2026
We built a joint experimental and computational platform for scalable multi-modal single-cell chemical screens — profiling RNA, protein (including phospho-signaling), and chromatin accessibility responses to thousands of small molecule perturbations in parallel. www.biorxiv.org/content/10.6...
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Craig Kaplan @triggerloop.bsky.social · 28/05/2026
Complete betrayal of the country. Total Congressional abdication.
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Nikhil Milind @nikhilmilind.dev · 27/05/2026
I'm excited to share that our work studying gene dosage response curves (GDRCs) is now out in Cell Genomics (@cellpress.bsky.social). www.cell.com/cell-genomic... [1/n]
cell.com
Buffering of gene dosage response curves for human complex traits
Milind et al. explore why loss-of-function variants and duplications tend to have average effects in the same direction on 94 complex traits. Using gene dosage response curves (GDRCs), they gather evi...
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Matthew Aguirre @aguirre404.bsky.social · 23/05/2026
Happy to share that this is now out in Cell Genomics and a featured paper for Multi-Journal Submission from @cellpress.bsky.social — many thanks to the editorial team + our reviewers! Short recap + some further thoughts on the paper ⬇️ [1/7] www.cell.com/cell-genomic...
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Reposted by Matthew Aguirre
Jeff Spence @jeffspence.github.io · 31/03/2026
We are destroying species' habitats, leading to a mass extinction event. This habitat destruction also reduces the genetic diversity _within_ species. Our latest work develops quantitative models to predict how much genetic diversity has been and _will be_ lost. 🧬🧪🧵 www.pnas.org/doi/10.1073/...
pnas.org
Large future genetic diversity losses are predicted from conservation indicators even with habitat protection | PNAS
Genetic diversity within species underpins evolutionary adaptation and has recently been included as a target for protection in the United Nations’...
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Reposted by Matthew Aguirre
Huisheng (Julie) Zhu @huishengzhu.bsky.social · 30/03/2026
Why do schizophrenia GWAS signals look so flat across the genome? In our recent preprint, we explored why psychiatric disorders — and, more broadly, brain-related traits involving the central nervous system — appear to have unusual genetic architectures. 🧵1/n
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Matthew Aguirre @aguirre404.bsky.social · 22/08/2025
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...
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