Jonathan Warrell
Associate Research ScientistCards
About
Research
Publications
2024
1230 Design of enhanced TCR against cancer antigens using an AI system
Min M, Onoguchi K, Li T, Mori D, Warrell J, Machart P, Moesch A, Meiser A, Pait I, Okamura A, Muraoka D, Matsushita H, Bendjama K. 1230 Design of enhanced TCR against cancer antigens using an AI system. 2024, a1371-a1371. DOI: 10.1136/jitc-2024-sitc2024.1230.Peer-Reviewed Original ResearchA variational graph-partitioning approach to modeling protein liquid-liquid phase separation
Wang G, Warrell J, Zheng S, Gerstein M. A variational graph-partitioning approach to modeling protein liquid-liquid phase separation. Cell Reports Physical Science 2024, 5: 102292. DOI: 10.1016/j.xcrp.2024.102292.Peer-Reviewed Original ResearchPredicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs
Song T, Cosatto E, Wang G, Kuang R, Gerstein M, Min M, Warrell J. Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs. Bioinformatics 2024, 40: ii111-ii119. PMID: 39230702, PMCID: PMC11373608, DOI: 10.1093/bioinformatics/btae383.Peer-Reviewed Original ResearchConceptsGene expressionSpatial gene expressionSpatial transcriptomics technologiesTissue histology imagesExpressed genesGene activationTranscriptomic technologiesMolecular underpinningsGraph neural networksState-of-the-artSpatial expressionGenesTissue architectureExpressionHistological imagesNeural networkSpatially Informed Gene Signatures for Response to Immunotherapy in Melanoma.
Aung T, Warrell J, Martinez-Morilla S, Gavrielatou N, Vathiotis I, Yaghoobi V, Kluger H, Gerstein M, Rimm D. Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma. Clinical Cancer Research 2024, 30: 3520-3532. PMID: 38837895, PMCID: PMC11326985, DOI: 10.1158/1078-0432.ccr-23-3932.Peer-Reviewed Original ResearchGene signatureResistance to immunotherapyResponse to immunotherapyPrediction of treatment outcomeResistant to treatmentAccurate prediction of treatment outcomePredictive of responseImmunotherapy outcomesMelanoma patientsMelanoma specimensValidation cohortPatient stratificationDiscovery cohortTreatment outcomesImmunotherapyMelanomaTumorPatientsCohortS100BOutcomesGene expression dataGenesCD68+macrophagesExpression dataSingle-cell genomics and regulatory networks for 388 human brains
Emani P, Liu J, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee C, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken T, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard J, Gancz M, Garrido-MartÃn D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman G, Huang A, Jiang Y, Jin T, Jorstad N, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran J, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan A, Riesenmy T, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini K, Wamsley B, Wang G, Xia Y, Xiao S, Yang A, Zheng S, Gandal M, Lee D, Lein E, Roussos P, Sestan N, Weng Z, White K, Won H, Girgenti M, Zhang J, Wang D, Geschwind D, Gerstein M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, Brennand K, Capauto D, Champagne F, Chatzinakos C, Chen H, Cheng L, Chess A, Chien J, 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, Duong D, Eagles N, Edelstein J, Galani K, Girdhar K, Goes F, Greenleaf W, Guo H, Guo Q, Hadas Y, Hallmayer J, Han X, Haroutunian V, He C, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hyde T, Iatrou A, Inoue F, Jajoo A, Jiang L, Jin P, Jops C, Jourdon A, Kellis M, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Li J, Li M, Lin X, Liu S, Liu C, Loupe J, Lu D, Ma L, Mariani J, Martinowich K, Maynard K, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Mukamel E, Nairn A, Nemeroff C, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Pinto D, Pochareddy S, Pollard K, Pollen A, Przytycki P, Purmann C, Qin Z, Qu P, Raj T, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, 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, Wang T, Wang S, Wang Y, Wei Y, Weimer A, Weinberger D, Wen C, Whalen S, Willsey A, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang Y, Ziffra R, Zeier Z, Zintel T. Single-cell genomics and regulatory networks for 388 human brains. Science 2024, 384: eadi5199. PMID: 38781369, PMCID: PMC11365579, DOI: 10.1126/science.adi5199.Peer-Reviewed Original ResearchConceptsSingle-cell genomicsSingle-cell expression quantitative trait locusExpression quantitative trait lociDrug targetsQuantitative trait lociPopulation-level variationSingle-cell expressionCell typesDisease-risk genesTrait lociGene familyRegulatory networksGene expressionCell-typeMultiomics datasetsSingle-nucleiGenomeGenesCellular changesHeterogeneous tissuesExpressionCellsChromatinLociMultiomicsLatent evolutionary signatures: a general framework for analysing music and cultural evolution
Warrell J, Salichos L, Gancz M, Gerstein M. Latent evolutionary signatures: a general framework for analysing music and cultural evolution. Journal Of The Royal Society Interface 2024, 21: 20230647. PMID: 38503341, PMCID: PMC10950459, DOI: 10.1098/rsif.2023.0647.Peer-Reviewed Original ResearchConceptsDomain of musicModeling musical styleCultural processes of changeChord transitionsMusical corporaMusical piecesMusical stylesGenre predictionPrinciples of organizationSongMusicCultural processesCultural evolutionProcess of changeRepresentationGenreDeep generative architecturePiecesStyleHarmonyGenerator architectureEvolutionary spaceLatent embeddingsLatent spaceVariational autoencoder
2022
Insights from incorporating quantum computing into drug design workflows
Lau B, Emani P, Chapman J, Yao L, Lam T, Merrill P, Warrell J, Gerstein M, Lam H. Insights from incorporating quantum computing into drug design workflows. Bioinformatics 2022, 39: btac789. PMID: 36477833, PMCID: PMC9825754, DOI: 10.1093/bioinformatics/btac789.Peer-Reviewed Original ResearchConceptsQuantum machine learningComputer-aided drug designMachine-learning moduleQuantum computing methodsCommercial quantum computersMachine learningJupyter notebooksNeural networkComputing methodClassical baselinesDesign workflowQML modelsQuantum hardwarePython codeAcademic useSupplementary dataQuantum computerWorkflowJudicious partitioningModuleHardwareGitHubClassical counterpartCase studyComputer127 Spatial-specific gene signatures outperform bulk-mRNA signatures to define resistance to immunotherapy in melanoma patients
Aung T, Warrell J, Martinez-Morilla S, Gavrielatou N, Vathiotis L, Chan N, Kluger H, Rimm D. 127 Spatial-specific gene signatures outperform bulk-mRNA signatures to define resistance to immunotherapy in melanoma patients. 2022, a140-a140. DOI: 10.1136/jitc-2022-sitc2022.0127.Peer-Reviewed Original Research133 Spatially defined gene signatures uncover the association of extracellular matrix genes with immunotherapy resistance in head and neck squamous cell carcinoma
Gavrielatou N, Warrell J, Aung T, Vathiotis L, Economopoulou P, Burtness B, Psyrri A, Rimm D. 133 Spatially defined gene signatures uncover the association of extracellular matrix genes with immunotherapy resistance in head and neck squamous cell carcinoma. 2022, a146-a146. DOI: 10.1136/jitc-2022-sitc2022.0133.Peer-Reviewed Original Research
2021
Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH
Savulescu A, Brackin R, Bouilhol E, Dartigues B, Warrell J, Pimentel M, Beaume N, Fortunato I, Dallongeville S, Boulle M, Soueidan H, Agou F, Schmoranzer J, Olivo-Marin J, Franco C, Gomes E, Nikolski M, Mhlanga M. Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH. Cell Reports Methods 2021, 1: 100068. PMID: 35474672, PMCID: PMC9017151, DOI: 10.1016/j.crmeth.2021.100068.Peer-Reviewed Original ResearchConceptsSubcellular localizationSingle-molecule RNA fluorescenceSingle-cell RNA sequencingNumber of transcriptsSmFISH dataRNA fluorescenceRNA sequencingHybridization dataBiological insightsLocalization patternsSubcellular distributionPatterns of clusteringCellular architectureRNACellular subtypesSkeletal muscle fibersCell micropatterningProtein immunolabelingPhysiological systemsCellsLocalizationBasis of quantificationSpatial distributionTranscriptsSequencing