2024
GENCODE 2025: reference gene annotation for human and mouse
Mudge J, Carbonell-Sala S, Diekhans M, Martinez J, Hunt T, Jungreis I, Loveland J, Arnan C, Barnes I, Bennett R, Berry A, Bignell A, Cerdán-Vélez D, Cochran K, Cortés L, Davidson C, Donaldson S, Dursun C, Fatima R, Hardy M, Hebbar P, Hollis Z, James B, Jiang Y, Johnson R, Kaur G, Kay M, Mangan R, Maquedano M, Gómez L, Mathlouthi N, Merritt R, Ni P, Palumbo E, Perteghella T, Pozo F, Raj S, Sisu C, Steed E, Sumathipala D, Suner M, Uszczynska-Ratajczak B, Wass E, Yang Y, Zhang D, Finn R, Gerstein M, Guigó R, Hubbard T, Kellis M, Kundaje A, Paten B, Tress M, Birney E, Martin F, Frankish A. GENCODE 2025: reference gene annotation for human and mouse. Nucleic Acids Research 2024, gkae1078. PMID: 39565199, DOI: 10.1093/nar/gkae1078.Peer-Reviewed Original ResearchGene annotationLong-read transcriptome sequencingMulti-genome alignmentsRibo-Seq experimentsUCSC Genome BrowserState-of-the-art proteomicsGenome browserRibo-seqSpecies genomesMouse genomeTranscriptome sequencingGENCODEGenomeAnnotation workflowAnnotationSequencePangenomeMiceGenesetsState-of-the-artUCSCProteomicsTranscriptionGenesSpeciesFast, sensitive detection of protein homologs using deep dense retrieval
Hong L, Hu Z, Sun S, Tang X, Wang J, Tan Q, Zheng L, Wang S, Xu S, King I, Gerstein M, Li Y. Fast, sensitive detection of protein homologs using deep dense retrieval. Nature Biotechnology 2024, 1-13. PMID: 39123049, DOI: 10.1038/s41587-024-02353-6.Peer-Reviewed Original ResearchProtein language modelsRemote homologsProtein homologsProtein sequence comparisonsAlignment-based approachesWell-characterized proteinsPSI-BLASTSuperfamily levelProtein evolutionSequence comparisonProtein sequencesHomologyProteinSensitivity compared to previous methodsSensitive detectionHMMERSuperfamilyStructural informationSequenceLeveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach
Frank M, Ni P, Jensen M, Gerstein M. Leveraging a large language model to predict protein phase transition: A physical, multiscale, and interpretable approach. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2320510121. PMID: 39110734, PMCID: PMC11331094, DOI: 10.1073/pnas.2320510121.Peer-Reviewed Original ResearchConceptsProtein phase transitionsAssociated with reduced gene expressionProtein structure predictionAlzheimer's disease-related proteinsDisease-related proteinsAlzheimer's diseaseProtein sequencesSequence variantsStructure predictionAmyloid aggregatesProtein designGene expressionAge-related diseasesNatural defense mechanismsSoluble stateProteinDefense mechanismsBiophysical featuresAlzheimerSequenceAmyloidVariantsExpressionLanguage modelComputational frameworkMassively parallel characterization of regulatory elements in the developing human cortex
Deng C, Whalen S, Steyert M, Ziffra R, Przytycki P, Inoue F, Pereira D, Capauto D, Norton S, Vaccarino F, Pollen A, Nowakowski T, Ahituv N, Pollard K, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, Bhattacharya A, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clarke D, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Fullard J, Galani K, Galeev T, Gandal M, Gaynor S, Gerstein M, Geschwind D, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Haroutunian V, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Hoffman G, Huang Y, Huuki-Myers L, Hwang A, Hyde T, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, Khullar S, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu C, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Margolis M, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollard K, Pollen A, Pratt H, Przytycki P, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Roussos P, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Sestan N, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Voloudakis G, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Weinberger D, Wen C, Weng Z, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Massively parallel characterization of regulatory elements in the developing human cortex. Science 2024, 384: eadh0559. PMID: 38781390, DOI: 10.1126/science.adh0559.Peer-Reviewed Original ResearchConceptsGene regulatory elementsRegulatory elementsRegulation of enhancer activityCharacterization of regulatory elementsCis-regulatory activityNeuronal developmentPrimary cellsEnhanced activityGene regulationHuman neuronal developmentNucleotide changesEnhancer sequencesSequence basisUpstream regulatorComprehensive catalogHuman cellsDeveloping cortexSequenceVariantsOrganoidsCellsCerebral organoidsCortexHuman cortexNucleotideUsing a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements
Pratt H, Andrews G, Shedd N, Phalke N, Li T, Pampari A, Jensen M, Wen C, Consortium P, Gandal M, Geschwind D, Gerstein M, Moore J, Kundaje A, Colubri A, Weng Z. Using a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements. Science Advances 2024, 10: eadj4452. PMID: 38781344, PMCID: PMC11114231, DOI: 10.1126/sciadv.adj4452.Peer-Reviewed Original ResearchConceptsEpigenetic dataCell-type-specific gene regulationCis-regulatory elementsComprehensive atlasGenetic variants associated with psychiatric disordersLineage-specific transcription factorsBrain cell typesMammalian elementsPsychENCODE ConsortiumNoncoding regionsEvolutionary historyGene regulationRegulatory elementsSequence mutationsTranscription factorsSequence syntaxRegulatory informationPrimate-specific sequencesBinding sitesHuman traitsCell typesFunctional implicationsPsychiatric disordersSequenceFetal brain development
2009
PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls
Rozowsky J, Euskirchen G, Auerbach RK, Zhang ZD, Gibson T, Bjornson R, Carriero N, Snyder M, Gerstein MB. PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nature Biotechnology 2009, 27: 66-75. PMID: 19122651, PMCID: PMC2924752, DOI: 10.1038/nbt.1518.Peer-Reviewed Original Research