Jha Lab

Publications

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Highlighted

Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC
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 i fibertools i
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
Tool
Code
Enhanced integrated gradients: improving interpretability of deep learning models using splicing codes as a case study
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

All

2025

Puget predicts gene expression across cell types using sequence and 3D chromatin organization data
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
Code
Evo2HiC: a multimodal foundation model for integrative analysis of genome sequence and architecture
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
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
Code

2024

Machine learning-optimized targeted detection of alternative splicing
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
Tool
Code
A generalizable Hi-C foundation model for chromatin architecture, single-cell and multi-omics analysis across species
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
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
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 i fibertools i
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
Tool
Code

2023

RNA splicing analysis using heterogeneous and large RNA-seq datasets
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.
Paper
Code

2022

Identifying common transcriptome signatures of cancer by interpreting deep learning models
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.
Paper
Code

2021

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

2020

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