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Adam He

@missingarib.bsky.social
68 followers 88 following 38 posts

Genomics, transcription regulation, and machine learning.

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Reposted by Adam He
Evgeny Kvon @evgenykvon.bsky.social · 02/07/2025
Our paper describing the Range Extender element which is required and sufficient for long-range enhancer activation at the Shh locus is now available at @nature.com. Congrats to @gracebower.bsky.social who led the study. Below is a brief summary of the main findings www.nature.com/articles/s41... 1/
nature.com
Range extender mediates long-distance enhancer activity - Nature
The REX element is associated with long-range enhancer–promoter interactions.
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Reposted by Adam He
Nikolai Slavov @slavov-n.bsky.social · 05/06/2025
A case study on the challenges of evaluating AI predictions in biology and the implications for published results. 1/2 rachel.fast.ai/posts/2025-0...
rachel.fast.ai
Rachel Thomas, PhD - Deep learning gets the glory, deep fact checking gets ignored
an AI researcher going back to school for immunology
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Reposted by Adam He
AI x Bio Discovery @aixbiobot.bsky.social · 26/05/2025
Frustratingly easy domain adaptation for cross-speciestranscription factor binding prediction [new] Predicts TF binding in target species via aligned sequence data distribution moments for cross-species generalization.
Figure 1Figure 2Figure 3Figure 4
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Reposted by Adam He
NE Ohio Regional Sewer District @neorsd.org · 08/05/2025
breaking news, white steam emerges from the Autoclave
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Reposted by Adam He
Lars Velten @larsplus.bsky.social · 08/05/2025
Out in Cell @cp-cell.bsky.social: Design principles of cell-state-specific enhancers in hematopoiesis 🧬🩸 screen of fully synthetic enhancers in blood progenitors 🤖 AI that creates new cell state specific enhancers 🔍 negative synergies between TFs lead to specificity! www.cell.com/cell/fulltex... 🧵
cell.com
Design principles of cell-state-specific enhancers in hematopoiesis
Screen of minimalistic enhancers in blood progenitor cells demonstrates widespread dual activator-repressor function of transcription factors (TFs) and enables the model-guided design of cell-state-sp...
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Reposted by Adam He
Gagneur lab @gagneurlab.bsky.social · 05/05/2025
Many of you enjoy our sequence-based model of single-cell RNA and ATAC data scooby... Don't miss Laura Marten's talk at the upcoming Kipoi seminar about it this Wed! @lauradmartens.bsky.social @johahi.bsky.social @kipoizoo.bsky.social Last preprint version: www.biorxiv.org/content/10.1...
biorxiv.org
scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution
Understanding how regulatory DNA elements shape gene expression across individual cells is a fundamental challenge in genomics. Joint RNA-seq and epigenomic profiling provides opportunities to build u...
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Reposted by Adam He
Yoav Gilad @ygilad.bsky.social · 05/05/2025
www.biorxiv.org/content/10.1...
biorxiv.org
Disease-associated loci share properties with response eQTLs under common environmental exposures
Many of the genetic loci associated with disease are expected to have context-dependent regulatory effects that are underrepresented in the transcriptomes of healthy, steady-state adult tissues. To un...
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Adam He @missingarib.bsky.social · 05/05/2025
Finally finished porting our CLIPNET models to PyTorch. I've released the code for loading the TF models into PT as part our PersonalBPNet package, which also contains ...
github.com
GitHub - adamyhe/PersonalBPNet: A small modification to bpnetlite's BPNet to accomodate large validation datasets.
A small modification to bpnetlite's BPNet to accomodate large validation datasets. - adamyhe/PersonalBPNet
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Reposted by Adam He
Xavier Grau-Bové 🌾 @xgrau.bsky.social · 19/03/2025
New preprint from the @arnausebe.bsky.social lab! 💐 Here @crisnava.bsky.social, @seanamontgomery.bsky.social & collaborators develop a novel ChIPseq protocol, and demonstrate its huge potential to study the evolution of chromatin function and regulation across the eukaryotic tree of life.
Figure 1 from the paper, with two panels. Panel a shows a schematic cladogram of the eukaryotic tree of life with an adjacent table showing the presence/absence of various histone post-translational modifications in various lineages. Panel b is a summary of the multiplexing strategy for ChIP-seq experiments developed in the paper.
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Reposted by Adam He
Jacob Schreiber @jmschreiber91.bsky.social · 24/04/2025
Our preprint on designing and editing cis-regulatory elements using Ledidi is out! Ledidi turns *any* ML model (or set of models) into a designer of edits to DNA sequences that induce desired characteristics. Preprint: www.biorxiv.org/content/10.1... GitHub: github.com/jmschrei/led...
biorxiv.org
Programmatic design and editing of cis-regulatory elements
The development of modern genome editing tools has enabled researchers to make such edits with high precision but has left unsolved the problem of designing these edits. As a solution, we propose Ledi...
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Adam He @missingarib.bsky.social · 10/04/2025
www.biorxiv.org/content/10.1...
biorxiv.org
High-resolution reconstruction of cell-type specific transcriptional regulatory processes from bulk sequencing samples
Biological systems exhibit remarkable heterogeneity, characterized by intricate interplay among diverse cell types. Resolving the regulatory processes of specific cell types is crucial for delineating developmental mechanisms and disease etiologies. While single-cell sequencing methods such as scRNA-seq and scATAC-seq have revolutionized our understanding of individual cellular functions, adapting bulk genome-wide assays to achieve single-cell resolution of other genomic features remains a significant technical challenge. Here, we introduce Deep-learning-based DEconvolution of Tissue profiles with Accurate Interpretation of Locus-specific Signals (DeepDETAILS), a novel quasi-supervised framework to reconstruct cell-type-specific genomic signals with base-pair precision. DeepDETAILS’ core innovation lies in its ability to perform cross-modality deconvolution using scATAC-seq reference libraries for other bulk datasets, benefiting from the affordability and availability of scATAC-seq data. DeepDETAILS enables high-resolution mapping of genomic signals across diverse cell types, with great versatility for various omics datasets, including nascent transcript sequencing (such as PRO-cap and PRO-seq) and ChIP-seq for chromatin modifications. Our results demonstrate that DeepDETAILS significantly outperformed traditional statistical deconvolution methods. Using DeepDETAILS, we developed a comprehensive compendium of high-resolution nascent transcription and histone modification signals across 39 diverse human tissues and 86 distinct cell types. Furthermore, we applied our compendium to fine-map risk variants associated with Primary Sclerosing Cholangitis (PSC), a progressive cholestatic liver disorder, and revealed a potential etiology of the disease. Our tool and compendium provide invaluable insights into cellular complexity, opening new avenues for studying biological processes in various contexts. ### Competing Interest Statement The authors have declared no competing interest.
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Reposted by Adam He
Michael Guertin @guertin.bsky.social · 08/04/2025
Our latest work indicates that termination of paused RNA polymerase is its most likely fate, while attempting to reconcile disparate estimates of relative rates and pause residency times from previous studies: www.biorxiv.org/content/10.1...
biorxiv.org
Genome-wide dynamic nascent transcript profiles reveal that most paused RNA polymerases terminate
We present a simple model for analyzing and interpreting data from kinetic experiments that measure engaged RNA polymerase occupancy. The framework represents the densities of nascent transcripts with...
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Adam He @missingarib.bsky.social · 17/03/2025
Does anyone know if the ATAC-seq bam files on ENCODE have had their tags shifted? and if so, by the more common +4/-5 or by +4/-4?
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Reposted by Adam He
Kipoi @kipoizoo.bsky.social · 01/03/2025
Join us for our next Kipoi Seminar with with Alexander Sasse @lxsasse.bsky.social @zmbh.uni-heidelberg.de 👉Advanced training strategies for genomic sequence-to-function models 📅 Wed March 5, 5:30pm CET 🧬 kipoi.org/seminar/ 🦋 @kipoizoo.bsky.social
kipoi.org
Kipoi
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Jacob Schreiber @jmschreiber91.bsky.social · 23/02/2025
Accurate de novo transcription unit annotation from run-on and sequencing data @charlesdanko.bsky.social www.biorxiv.org/content/10.1...
biorxiv.org
Accurate de novo transcription unit annotation from run-on and sequencing data
Functional element annotations are critical tools used to provide insight into the molecular processes governing cell development, differentiation, and disease. Run-on and sequencing assays measure th...
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Reposted by Adam He
AI x Bio Discovery @aixbiobot.bsky.social · 22/02/2025
ralphi: a deep reinforcement learning framework for haplotype assembly [new] Deep reinforcement learning accurately partitions reads into haplotype sets. It uses fragment graphs and the max-cut problem for the reward objective.
ralphi: a deep reinforcement learning framework for haplotype assemblyFigure 1Figure 2Figure 3
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Reposted by Adam He
AI x Bio Discovery @aixbiobot.bsky.social · 22/02/2025
A scalable approach to investigating sequence-to-expression prediction from personal genomes [new] Models fail to gen. w/ individual var., personal genome training helps some individuals only.
A scalable approach to investigating sequence-to-expression prediction from personal genomesFigure 1Figure 2Figure S1
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Reposted by Adam He
Andrew Marderstein @amarderstein.bsky.social · 19/02/2025
New preprint w/ @soumyakundu.bsky.social @sbmontgom.bsky.social @anshulkundaje.bsky.social ! Using deep learning & scATAC-seq, we studied context-specific variants in disease & evolution, and introduce FLARE for de novo mutations—w/ application to autism-affected families. doi.org/10.1101/2025...
biorxiv.org
Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart
Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal...
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Reposted by Adam He
AI x Bio Discovery @aixbiobot.bsky.social · 16/02/2025
Refining sequence-to-expression modelling with chromatin accessibility [new] Chromatin accessibility enhances sequence-to-expression models by focusing on open regions. Incorporating it improves predictions and reduces bias.
Refining sequence-to-expression modelling with chromatin accessibilityFigure 1Figure 3Figure 2
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Reposted by Adam He
AI x Bio Discovery @aixbiobot.bsky.social · 13/02/2025
Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human Genetics [new] TraitGym benchmarks reveal model-specific strengths in causal variant prediction for Mendelian/complex traits.
Benchmarking DNA Sequence Models for Causal Regulatory Variant Prediction in Human GeneticsFigure 1Figure 3Figure 4
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Reposted by Adam He
Jacob Schreiber @jmschreiber91.bsky.social · 12/02/2025
Iterative improvement of deep learning models using synthetic regulatory genomics www.biorxiv.org/content/10.1...
biorxiv.org
Iterative improvement of deep learning models using synthetic regulatory genomics
Generative deep learning models can accurately reconstruct genome-wide epigenetic tracks from the reference genome sequence alone. But it is unclear what predictive power they have on sequence divergi...
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Reposted by Adam He
Peter Koo @pkoo562.bsky.social · 05/02/2025
[SAVE THE DATE] MLCB 2025 is happening Sept 10-11 at the NY Genome Center in NYC! Attend the premier conference at the intersection of ML & Bio, share your research and make lasting connections! Submission deadline: June 1 More details: mlcb.github.io Help spread the word—please RT! #MLCB2025
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Adam He @missingarib.bsky.social · 01/02/2025
www.biorxiv.org/content/10.1... Another genomic FM benchmark 💀
biorxiv.org
Genomic Foundationless Models: Pretraining Does Not Promise Performance
The success of Large Language Models has inspired the development of Genomic Foundation Models (GFMs) through similar pretraining techniques. However, the relationship between pretraining performance ...
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Adam He @missingarib.bsky.social · 01/02/2025
www.biorxiv.org/content/10.1... Might explain some of the discrepancies between QTL effect & personalized gene expression prediction performance by S2F models
biorxiv.org
Haplotype rather than single causal variants effects contribute to regulatory gene expression associations in human myeloid cells
Genome-wide association studies typically identify hundreds to thousands of loci, many of which harbor multiple independent peaks, each parsimoniously assumed to be due to the activity of a single cau...
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Reposted by Adam He
AI x Bio Discovery @aixbiobot.bsky.social · 29/01/2025
Enformation Theory: A Blueprint for Evaluating Deep Learning Models in Genomics [updated] Framework for evaluating genomic DL models like Enformer/Borzoi. Uses benchmarks, embeddings, & decomposition to understand capabilities.
Enformation Theory: A Blueprint for Evaluating Deep Learning Models in GenomicsFigure 2Figure 1Figure 3
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Reposted by Adam He
AI x Bio Discovery @aixbiobot.bsky.social · 28/01/2025
Neur-Ally: A deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders [new]
Neur-Ally: A deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disordersFigure 1Figure 3Figure 4
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Adam He @missingarib.bsky.social · 27/01/2025
Had a great time at this last year!
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Reposted by Adam He
AI x Bio Discovery @aixbiobot.bsky.social · 17/01/2025
GenVarLoader: An accelerated dataloader for applying deep learning to personalized genomics [new] Deep learning hindered by I/O; new tool speeds data load via mem-mapped storage, incr. throughput & compression.
GenVarLoader: An accelerated dataloader for applying deep learning to personalized genomicsFigure 1Figure 2
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Anshul Kundaje @anshulkundaje.bsky.social · 25/12/2024
Our ChromBPNet preprint out! www.biorxiv.org/content/10.1... Huge congrats to Anusri! This was quite a slog (for both of us) but we r very proud of this one! It is a long read but worth it IMHO. Methods r in the supp. materials. Bluetorial coming soon below 1/
biorxiv.org
ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants
Despite extensive mapping of cis-regulatory elements (cREs) across cellular contexts with chromatin accessibility assays, the sequence syntax and genetic variants that regulate transcription factor (T...
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Reposted by Adam He
Andrea Bernardini @bernardini-andrea.bsky.social · 22/12/2024
Brief thread on our opinion article "Q-rich activation domains: flexible ‘rulers’ for transcription start site selection?". Transcription people are surely aware of the important reports on the DNA sequence determinants of start site (TSS) selection in the human genome that came out this year. 1/15
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