@somet3000

PhD Student @MSKCancerCenter @cbm_student | @kanishkadey and @thenormanlab labs

Joined July 2020
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  • Followers126
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Happy to share that the paper on enhancer interactions led by @zrcjessica and I from my time in the @GrahamMcVicker lab is now published in Cell Genomics! cell.com/cell-genomics/fullt… A thread on some of the new analyses below: (1/n)

Analysis of single-cell CRISPR perturbations indicates that enhancers predominantly act multiplic...

Zhou and Guruvayurappan et al. present a statistical framework for quantifying the joint activity of multiple enhancers from single-cell CRISPR perturbation experiments. They find that pairs of...

cell.com
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Brad Balderson@BradBalderson3Oct 22
#ASHG26 #ASHG2026 room 511/Level 5 at 8am to see @GrahamMcVicker present DNACipher, a model that combines DNA sequence & omic-signal imputation, to comprehensibly predict variant effects across cell types/assays, & interpret GWAS hits. preprint: biorxiv.org/content/10.1101/…
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Super excited to share what we have been working on at the intersection of human genetics and Perturb-seq! Check out Tom's tweetorial below for more details!
Thomas Norman@thenormanlabOct 13
1/ Most genes and variants matter somewhere but look silent in any one cell model. Can we build conditions that reveal phenotypes? With @kanishkadey, we engineer cell states to expose hidden disease biology and trace how genetic risk reaches disease genes. biorxiv.org/content/10.64898…
Visual abstract: activating TFs singly and in pairs engineers new cell states, and the Mosaic model learns how TFs combine. Findings: hidden AMD risk variants, state-dependent convergence, and chromatin priming.

ALT Visual abstract: activating TFs singly and in pairs engineers new cell states, and the Mosaic model learns how TFs combine. Findings: hidden AMD risk variants, state-dependent convergence, and chromatin priming.

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Han Yuan@HY3952Oct 5
Saturation mutagenesis of long-context seq2fun models requires millions of evaluations per gene. We introduce Multi-ISM to make this mapping routine: biorxiv.org/content/10.64898…

Scalable saturation mutagenesis reveals gene regulatory architecture and rare variant effects

Long-context sequence-to-function models enable nucleotide-resolution prediction of regulatory variant effects, motivating comprehensive mutational interrogation across broad genomic contexts. Yet...

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Rahul Satija@satijalabOct 9
Delighted to share RNA fingerprinting, out today in @CellCellPress! Together with @david_a_knowles , we use Perturb-seq experiments—where the causal perturbation is known—to infer what drives cellular responses in new datasets: cell.com/cell/abstract/S0092…

Mapping transcriptional responses to cellular perturbations with RNA fingerprinting

RNA fingerprinting is a statistical framework that infers causal regulators from single-cell experiments by mapping their transcriptional responses onto reference perturbation dictionaries.

cell.com
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Jay Shendure@JShendureOct 8
Exciting day for DNA Typewriter w/ 2 papers from the lab & SeaHub out today :) 1. Mouse lineage from E0 to E13.5 science.org/doi/10.1126/scie… 2. A fluctuation test for gastruloid heterogeneity cell.com/cell/fulltext/S0092… Paper #1 covered in quoted thread. More on paper #2 here. 1/n

A DNA Typewriter records the cell lineage history of a mouse, from zygote to late organogenesis

Mammalian biology unfolds over time, within tissues and organs opaque to our eyes and instruments. We applied DNA Typewriter, a sequential molecular recorder, to record the cell lineage of a mouse...

science.org
Jay Shendure@JShendureJul 31
Thrilled to post thread re: new single-cell lineage of mouse embryo reconstructed w/ DNA Typewriter. One animal, zygote to late organogenesis (E13.5). Tree has 1,340,794 transcriptionally profiled, annotated tips (cells), 1,142,588 dated internal nodes, rooted at zygote 1/n
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Luke Koblan@LukeKoblanOct 8
Comprehensive fate mapping of C. elegans transformed our understanding of development by revealing how coordinated fate decisions generate a complex organism. In our paper out today in @CellCellPress , we take a major step toward extending this vision to mammalian development. sciencedirect.com/science/ar…
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Jacob Schreiber@jmschreiber91Oct 8
After a long wait, tangermeme is out @naturemethods! tangermeme implements the primitives of genomic sequence-to-function modeling with efficient, tested, and composable functions. I use it every day in my work, and I hope you find it useful too. nature.com/articles/s41592-0…

Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models

Nature Methods - The tangermeme software package is a comprehensive, flexible and efficient Swiss Army knife for deep learning-based cis-regulatory pattern identification and analysis.

nature.com
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David Kelley@drkllyOct 9
This week we're releasing a preprint exploring state space models for long-sequence genomic prediction, alongside Cerberus, a set of open PyTorch models built on this work. biorxiv.org/content/10.64898…

Cerberus: bidirectional state space blocks improve accuracy and efficiency of regulatory sequence...

Sequence-to-function models predict regulatory activity and variant effects from DNA sequence alone, and leading models such as Borzoi compute long-range interactions with self-attention. The...

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Xiangmeng(Shawn) Cai@XiangmengCaiOct 9
Have you ever looked at a gene in IGV or a genome browser and wondered “What are all those (ATAC-seq/H3K27ac/CTCF ChIP-seq/…) peaks doing?” 🤔 I’m thrilled to share that we developed a new technology called Swap-seq to answer that exact question. biorxiv.org/content/10.64898…

Systematic mapping of enhancers, silencers, and topological elements with prime editing deletion...

Noncoding regulatory elements in the human genome can control gene expression through distinct mechanisms, including acting as enhancers, silencers, or topological elements. However, we lack tools to...

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Masahiro Kanai@masakanaiSep 30
Our single-nucleus immune multiome atlas is now out in @Nature! 🧬 10M PBMCs from 1,108 @FinnGen_FI donors recruited by @FRCBSresearch, profiled at @broadinstitute KCO with chromatin accessibility and gene expression in the same nuclei, to trace how disease variants act 🧵👇
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Hyejung Won@hyejung_wonSep 23
1/ I’m excited to share our new work, now out in @NatureNeuro! This was a wonderful collaboration with my long-term collaborator @jsteinlab, led by two fantastic trainees, @NanaMatoba and Jessica McAfee. nature.com/articles/s41593-0…

Massively parallel assessment of gene regulatory activity at human cortical-structure-associated...

Nature Neuroscience - A large-scale functional screen identified noncoding variants that regulate gene activity during human brain development, providing insights into the molecular mechanisms that...

nature.com
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Feiyang Huang@fyng_hSep 18
We often frame cancer treatment as an risk-benefit tradeoff. In reality, we characterize benefit far better than risk. Why? because adverse event data, systematically collected across cancer types and treatments, did not exist until now. The playbook:
Ed Reznik@EdReznikSep 18
Incredibly excited to report today on the determinants of toxicity to systemic therapy in cancer patients. Led by many including Alex Guo, Feiyang Huang, Saksham Mohan, River Walser, and the fearless @ZiadBakouny. Co-led by Wes Tansey and Jian Carrot-Zhang medrxiv.org/content/10.64898…

Population-Scale Precision Safety in Oncology Reveals Clinical and Genetic Determinants of Systemic...

Treatment toxicity constrains the use of effective cancer therapies, but its clinical and genetic determinants remain poorly defined. We developed a large language model-based approach to produce...

medrxiv.org
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Hoyin Chu@hoyin_chuSep 9
Excited to share my first protein design story is now fully published at @natBME ! Very fortunate to have been able to work along the very talented @arthurwchow and Betty Li, who showed some the most jaw-dropping in vivo data I have ever seen! Come check out our paper!
Caleb Lareau
@CalebLareauSep 9
Three years ago, my lab at @MSKCancerCenter set out to see if we could harness AI for new therapeutic proteins. Today in @natBME, our first step in developing these AI-designed molecules as anti-tumor therapies is online. Read more here: 1/n mskcc.org/news/using-ai-to-d…
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Sherwood Lab@SherwoodLabSep 2
LDLR variants are the most common cause of inherited high cholesterol, but we don’t know which are harmful. We used prime editing to measure >5,000, out now in @CircAHA, led by @phillipzhou24, Minja Velimirovic & @TiannnYuu with @CassaLabBWH 🧵 ahajournals.org/doi/10.1161/… (1/15)

LDLR Variant Classification Through Activity-Normalized Prime Editing Screening | Circulation

BACKGROUND: Inherited variants in the LDL (low-density lipoprotein) receptor (LDLR) gene are the most common cause of familial hypercholesterolemia, significantly increasing coronary artery disease...

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Xuanyao Liu@XuanyaoLiuAug 11
Our paper is out today in Cell, "Trans-regulatory gene mapping prioritizes disease drivers in asthma", cell.com/cell/fulltext/S0092… 🎉 GWAS hands us thousands of disease-linked variants, but the genes that actually drive the disease often aren't the ones sitting at those variants.
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Junhong Choi@_Choi_JunhongAug 11
Excited to share the first preprint from our lab! We combined a DNA Typewriter lineage recorder with clone-resolved founder profiling to find what makes metastatic potential heritable in lung cancer xenografts. 1/13 biorxiv.org/content/10.64898…

Time-resolved lineage recording reveals a pre-existing, heritable cell state underlying metastatic...

Metastasis causes most cancer deaths[1][1],[2][2], yet no recurrent mutation specifically drives it[3][3],[4][4], raising the possibility that metastatic potential is a non-genetic yet heritable cell...

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Anshul Kundaje@anshulkundajeAug 5
Very excited to announce ENCODE GRAMMAR (Genomic Regulatory Atlas of sequence Models, Motifs, Annotations & Rules): 3,865 experiment-specific deep learning model sets and sequence annotations for decoding human regulatory DNA. 1/
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Quaid Morris@quaidmorrisJul 21
Can we predict which T cells recognize which peptides? A lot of the signal is hiding in AlphaFold3's hallucinations. enFoldX: instead of one predicted structure, we use the whole noisy ensemble to predict TCR:pMHC binding. 💻github.com/jonlevi/enFoldX biorxiv.org/content/10.64898…
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Andreas Gschwind@argschwindJul 15
Our paper is now out in Nature! 🎉 Expanded to 92M+ enhancer–gene links across 1,458 biosamples, with new benchmarks and analyses. "An encyclopedia of human enhancer–gene regulatory interactions": nature.com/articles/s41586-0… Grateful to the whole team of this massive effort!

An encyclopedia of human enhancer–gene regulatory interactions

Nature - An encyclopedia of more than 92 million enhancer–gene regulatory interactions created as part of the ENCODE4 project provides a valuable resource for future studies of gene...

nature.com
Andreas Gschwind@argschwind14 Nov 2023
Excited to share our latest work on bioRxiv! biorxiv.org/content/10.1101/… A highly collaborative effort with @ksmualim @karbalayghareh, @mayayayas, @kanishkadey, @evelynjagoda,@WangXijhu, @LarsMSteinmetz, @anshulkundaje, @jengreitz and many others from @ENCODE_NIH! Thread👇

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease[1][1]–[6][2]. Here we create and...

biorxiv.org
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Jason Tan@yxjtanJun 30
Does every enhancer work with every promoter? With @jengreitz and @WJGreenleaf, we revisit this long-debated question and resolve an outstanding contradiction in the field. A tour 🧵👇 biorxiv.org/content/10.64898…

Intrinsic promoter responsiveness dictates sensitivity to transcriptional activation by enhancers

Enhancers activate specific target promoters, but whether intrinsic enhancer-promoter compatibility contributes to this specificity is debated. Recent studies using different reporter assays have...

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