Anupama Jha
Instructor in GeneticsAbout
Research
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Featured Publications
DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools
Jha A, Bohaczuk S, Mao Y, Ranchalis J, Mallory B, Min A, Hamm M, Swanson E, Dubocanin D, Finkbeiner C, Li T, Whittington D, Noble W, Stergachis A, Vollger M. DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools. Genome Research 2024, 34: 1976-1986. PMID: 38849157, PMCID: PMC11610455, DOI: 10.1101/gr.279095.124.Peer-Reviewed Original ResearchConceptsOxford Nanopore TechnologiesLong readsEpigenetic architectureEpigenetic dataEpigenetic analysisLong-read DNA sequencingLong-read dataLong-read sequencingSingle-nucleotide resolutionVariable genomic regionsSingle-molecule sequencingSingle-moleculeSingle-molecule resolutionLong DNA moleculesPacific BiosciencesState-of-the-art toolkitsCytosine methylationGenomic regionsSequencing platformsNanopore TechnologiesDNA sequencesGenetic analysisAccurate identificationEpigenetic studiesDNA moleculesEnhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study
Jha A, K. Aicher J, R. Gazzara M, Singh D, Barash Y. Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study. Genome Biology 2020, 21: 149. PMID: 32560708, PMCID: PMC7305616, DOI: 10.1186/s13059-020-02055-7.Peer-Reviewed Original ResearchIdentifying common transcriptome signatures of cancer by interpreting deep learning models
Jha A, Quesnel-Vallières M, Wang D, Thomas-Tikhonenko A, Lynch K, Barash Y. Identifying common transcriptome signatures of cancer by interpreting deep learning models. Genome Biology 2022, 23: 117. PMID: 35581644, PMCID: PMC9112525, DOI: 10.1186/s13059-022-02681-3.Peer-Reviewed Original ResearchConceptsCore cancer pathwaysTranscriptomic signaturesTranscriptomic featuresProtein-coding gene expressionRNA-seq samplesRNA processing genesCancer pathwaysSignatures of cancerNormal tissue typesRNA-seqAberrant splicingSplice variationCancer typesGene expressionGenomic alterationsGenesSplicingCancer biologyTranscriptomeTumor typesCell proliferationConclusionsOur resultsSolid tumor typesGene signatureTissue types
2024
Machine learning-optimized targeted detection of alternative splicing
Yang K, Islas N, Jewell S, Wu D, Jha A, Radens C, Pleiss J, Lynch K, Barash Y, Choi P. Machine learning-optimized targeted detection of alternative splicing. Nucleic Acids Research 2024, 53: gkae1260. PMID: 39727154, PMCID: PMC11797022, DOI: 10.1093/nar/gkae1260.Peer-Reviewed Original ResearchConceptsRNA-seq dataRNA-seqQuantification of alternative splicingDetection of alternative splicingGTEx RNA-seq dataRNA-seq methodJunction-spanning readsMultiplex reverse transcriptionPool of primersSequencing depthSplicing eventsPrimer sequencesAlternative splicingTranscriptome analysisRNA sequencingPrimersVariation sequencingReverse transcriptionSequenceSplicingHigh-throughputComprehensive detectionTranscriptomeTranscriptionRNAEnhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model
Fang T, Liu Y, Woicik A, Lu M, Jha A, Wang X, Li G, Hristov B, Liu Z, Xu H, Noble W, Wang S. Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model. Bioinformatics 2024, 40: i471-i480. PMID: 38940142, PMCID: PMC11211821, DOI: 10.1093/bioinformatics/btae211.Peer-Reviewed Original ResearchConceptsHigh-coverage dataChromatin featuresHi-CResolution enhancement methodExperimental Hi-C dataHi-C dataChromatin structure analysisContact matricesF1 scoreEnhancement methodDNA sequencesMachine learning modelsMean square errorNatural imagesChromatinSource codeLoop detectionLearning modelsModel backboneResolution enhancementThree-dimensional architectureGenomeComputational methodsStructural analysisCapricorn
2023
RNA splicing analysis using heterogeneous and large RNA-seq datasets
Vaquero-Garcia J, Aicher J, Jewell S, Gazzara M, Radens C, Jha A, Norton S, Lahens N, Grant G, Barash Y. RNA splicing analysis using heterogeneous and large RNA-seq datasets. Nature Communications 2023, 14: 1230. PMID: 36869033, PMCID: PMC9984406, DOI: 10.1038/s41467-023-36585-y.Peer-Reviewed Original ResearchConceptsRNA-seqRNA splicing analysisRNA-seq datasetsRNA-seq dataTranscriptome complexityGTEx v8Splicing regulationSplicing analysisRNA splicingBiological replicatesSplice variationMAJIQSplice variantsSplicingRNASuite of algorithmsTranscriptomeBenchmark datasetsBrain subregionsSynthetic dataDatasetVariantsReplicationRegulation
2021
RNA-Binding Proteins PCBP1 and PCBP2 Are Critical Determinants of Murine Erythropoiesis
Ji X, Jha A, Humenik J, Ghanem L, Kromer A, Duncan-Lewis C, Traxler E, Weiss M, Barash Y, Liebhaber S. RNA-Binding Proteins PCBP1 and PCBP2 Are Critical Determinants of Murine Erythropoiesis. Molecular And Cellular Biology 2021, 41: e00668-20. PMID: 34180713, PMCID: PMC8384066, DOI: 10.1128/mcb.00668-20.Peer-Reviewed Original ResearchConceptsRNA-binding proteinsRNA-binding protein PCBP1Erythroid lineageBlood formationExon splicingComplex phenotypesPrimary erythroid progenitorsPCBP1Mouse developmentGene expressionPCBP2Fetal demisePeri-implantitisErythroid progenitorsMurine erythropoiesisLociMouse embryosErythropoietic differentiationLineagesProteinEmbryosMidgestationDifferentiationInactivationMiceMOCCASIN: a method for correcting for known and unknown confounders in RNA splicing analysis
Slaff B, Radens C, Jewell P, Jha A, Lahens N, Grant G, Thomas-Tikhonenko A, Lynch K, Barash Y. MOCCASIN: a method for correcting for known and unknown confounders in RNA splicing analysis. Nature Communications 2021, 12: 3353. PMID: 34099673, PMCID: PMC8184769, DOI: 10.1038/s41467-021-23608-9.Peer-Reviewed Original ResearchMulti-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits
Bone W, Siewert K, Jha A, Klarin D, Damrauer S, Chang K, Tsao P, Assimes T, Ritchie M, Voight B. Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits. Alzheimer's Research & Therapy 2021, 13: 34. PMID: 33541420, PMCID: PMC7860582, DOI: 10.1186/s13195-021-00773-z.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociGenotype-Tissue ExpressionPleiotropic lociCausal genesAssociation studiesCardiometabolic traitsAlzheimer's diseaseBivariate genome-wide association studyGenome-wide association studiesComplex genetic relationshipsQuantitative trait lociGTEx eQTL dataEtiology of ADHuman genetic evidenceAD lociEQTL dataGenetic risk factorsTrait lociAlternative splicingGenetic evidenceTrait associationsGenetic relationshipsRisk of ADAD riskLoci
2017
Integrative deep models for alternative splicing
Jha A, Gazzara M, Barash Y. Integrative deep models for alternative splicing. Bioinformatics 2017, 33: i274-i282. PMID: 28882000, PMCID: PMC5870723, DOI: 10.1093/bioinformatics/btx268.Peer-Reviewed Original ResearchConceptsAlternative splicingCLIP-seqExon skipping eventsRelevant genomic dataTranscriptome complexitySplicing outcomesGenome sequenceBioinformatics OnlineSplicing regulationSequencing technologiesGenomic dataSplicing codeSupplementary dataRNA-seqSkipping eventsSplicing factorsExpression dataExpression experimentsBiological insightsDeep neural networksAS predictionSplicingRegulatory mechanismsRegulatory factorsEffects of datasets
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