Publications

Our research sits at the interface of genomics, RNA biology, and machine learning, with a particular focus on understanding how sequence and chromatin architecture shape gene regulation across cells, tissues, and disease states. Across these publications, we develop computational methods and foundation models for predicting 3D genome organization and gene expression, design and interpret models of RNA splicing, build tools for long-read epigenetic analysis, and use large-scale transcriptomic and genetic data to uncover mechanisms of cancer, hematopoiesis, neurodegeneration, and other complex traits. Together, this work reflects a broad effort to turn high-dimensional genomic data into interpretable models of regulatory biology.

Highlighted

Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC

Anupama Jha, Borislav Hristov, Xiao Wang, Sheng Wang, William J. Greenleaf, Anshul Kundaje, Erez Lieberman Aiden, Alessandro Bertero, William Stafford Noble

openRxiv  ·  21 Sep 2024  ·  doi:10.1101/2024.09.16.613355

Predicts and interprets inter-chromosomal genome architecture directly from DNA sequence.

Preprint

Code

DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools

Anupama Jha, Stephanie C. Bohaczuk, Yizi Mao, Jane Ranchalis, Benjamin J. Mallory, …, Tony Li, Dale Whittington, William Stafford Noble, Andrew B. Stergachis, Mitchell R. Vollger

Genome Research  ·  07 Jun 2024  ·  doi:10.1101/gr.279095.124

Describes DNA-m6A calling and integrated long-read epigenetic analysis with fibertools.

Paper

Code

Enhanced integrated gradients: improving interpretability of deep learning models using splicing codes as a case study

Anupama Jha, J. K. Aicher, M. R. Gazzara, D. Singh, Y. Barash

Genome Biology  ·  19 Jun 2020  ·  doi:10.1186/s13059-020-02055-7

Improves deep learning interpretability with Enhanced Integrated Gradients using splicing codes as a case study.

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2025

Puget predicts gene expression across cell types using sequence and 3D chromatin organization data

Shengqi Hang, Xiao Wang, Ghulam Murtaza, Anupama Jha, Bo Wen, Tangqi Fang, Justin Sanders, Sheng Wang, William Stafford Noble

bioRxiv  ·  20 Nov 2025  ·  doi:10.1101/2025.11.19.689320

Predicts gene expression across cell types by combining sequence information with 3D chromatin organization.

Preprint

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Evo2HiC: a multimodal foundation model for integrative analysis of genome sequence and architecture

Tangqi Fang, Xiao Wang, Zhiping Xiao, Shengqi Hang, Ghulam Murtaza, Junwei Yang, Hanwen Xu, Anupama Jha, William Noble, Sheng Wang

bioRxiv  ·  19 Nov 2025  ·  doi:10.1101/2025.11.18.689171

Introduces a multimodal foundation model that jointly learns genome sequence and chromatin architecture.

Preprint

Code

Generative modeling for RNA splicing prediction and design

Di Wu, Natalie Maus, Anupama Jha, Kevin Yang, Benjamin D. Wales-McGrath, San Jewell, Anna Tangiyan, Peter Choi, Jacob R. Gardner, Yoseph Barash

openRxiv  ·  24 Jan 2025  ·  doi:10.1101/2025.01.20.633986

Uses generative modeling to predict and design RNA splicing outcomes from sequence features.

Preprint

2024

Machine learning-optimized targeted detection of alternative splicing

Kevin Yang, Nathaniel Islas, San Jewell, Di Wu, Anupama Jha, Caleb M. Radens, Jeffrey A. Pleiss, Kristen W. Lynch, Yoseph Barash, Peter S. Choi

Nucleic Acids Research  ·  27 Dec 2024  ·  doi:10.1093/nar/gkae1260

Applies machine learning to optimize targeted detection of alternative splicing events.

Paper

A generalizable Hi-C foundation model for chromatin architecture, single-cell and multi-omics analysis across species

Xiao Wang, Yuanyuan Zhang, Suhita Ray, Anupama Jha, Tangqi Fang, Shengqi Hang, Sergei Doulatov, William Stafford Noble, Sheng Wang

openRxiv  ·  20 Dec 2024  ·  doi:10.1101/2024.12.16.628821

Presents a generalizable Hi-C foundation model for chromatin architecture across species and assays.

Preprint

Code

Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC

Anupama Jha, Borislav Hristov, Xiao Wang, Sheng Wang, William J. Greenleaf, Anshul Kundaje, Erez Lieberman Aiden, Alessandro Bertero, William Stafford Noble

openRxiv  ·  21 Sep 2024  ·  doi:10.1101/2024.09.16.613355

Predicts and interprets inter-chromosomal genome architecture directly from DNA sequence.

Preprint

Code

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model

Tangqi Fang, Yifeng Liu, Addie Woicik, Minsi Lu, Anupama Jha, …, Borislav Hristov, Zixuan Liu, Hanwen Xu, William S. Noble, Sheng Wang

Bioinformatics  ·  28 Jun 2024  ·  doi:10.1093/bioinformatics/btae211

Enhances Hi-C contact matrices with a diffusion model to improve loop detection.

Paper

Code

DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools

Anupama Jha, Stephanie C. Bohaczuk, Yizi Mao, Jane Ranchalis, Benjamin J. Mallory, …, Tony Li, Dale Whittington, William Stafford Noble, Andrew B. Stergachis, Mitchell R. Vollger

Genome Research  ·  07 Jun 2024  ·  doi:10.1101/gr.279095.124

Describes DNA-m6A calling and integrated long-read epigenetic analysis with fibertools.

Paper

Code

2023

RNA splicing analysis using heterogeneous and large RNA-seq datasets

Jorge Vaquero-Garcia, Joseph K. Aicher, San Jewell, Matthew R. Gazzara, Caleb M. Radens, Anupama Jha, Scott S. Norton, Nicholas F. Lahens, Gregory R. Grant, Yoseph Barash

Nature Communications  ·  03 Mar 2023  ·  doi:10.1038/s41467-023-36585-y

Studies large heterogeneous RNA-seq datasets to improve RNA splicing analysis at scale.

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2022

Identifying common transcriptome signatures of cancer by interpreting deep learning models

Anupama Jha, Mathieu Quesnel-Vallieres, David Wang, Andrei Thomas-Tikhonenko, Kristen W. Lynch, Yoseph Barash

Genome Biology  ·  17 May 2022  ·  doi:10.1186/s13059-022-02681-3

Interprets deep learning models to identify transcriptomic signatures shared across cancers.

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2021

RNA-binding proteins PCBP1 and PCBP2 are critical determinants of murine erythropoiesis

X. Ji, Anupama Jha, J. Humenik, L. R. Ghanem, A. Kromer, C. Duncan-Lewis, …

Molecular and Cellular Biology  ·  24 Aug 2021  ·  doi:10.1128/MCB.00668-20

Shows that PCBP1 and PCBP2 are critical regulators of murine erythropoiesis.

Paper

Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits

W. P. Bone, K. M. Siewert, Anupama Jha, D. Klarin, S. M. Damrauer, K. M. Chang, …

Alzheimer's Research & Therapy  ·  04 Feb 2021  ·  doi:10.1186/s13195-021-00773-z

Identifies pleiotropic loci shared between Alzheimer’s disease and cardiometabolic traits.

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2020

Enhanced integrated gradients: improving interpretability of deep learning models using splicing codes as a case study

Anupama Jha, J. K. Aicher, M. R. Gazzara, D. Singh, Y. Barash

Genome Biology  ·  19 Jun 2020  ·  doi:10.1186/s13059-020-02055-7

Improves deep learning interpretability with Enhanced Integrated Gradients using splicing codes as a case study.

Paper

Code

2017

Integrative deep models for alternative splicing

Anupama Jha, Matthew R. Gazzara, Yoseph Barash

Bioinformatics  ·  12 Jul 2017  ·  pmc:PMC5870723

Develops integrative deep learning models for predicting alternative splicing regulation.

Paper

Code

Ancient antagonism between CELF and RBFOX families tunes mRNA splicing outcomes

Matthew R. Gazzara, Michael J. Mallory, Renat Roytenberg, John P. Lindberg, Anupama Jha, Kristen W. Lynch, Yoseph Barash

Genome Research  ·  16 May 2017  ·  doi:10.1101/gr.220517.117

Reveals how CELF and RBFOX family antagonism shapes mRNA splicing outcomes.

Paper

2011

An Optimizing Compiler for Turing Machine Description Language

P. Chakraborty, S. Taneja, N. Anand, Anupama Jha, D. Malik, A. Nayar

IUP Journal of Computer Sciences  ·  01 Jul 2011  ·  iup:tmdl-compiler

Describes an optimizing compiler for a Turing Machine Description Language.

Article