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arXiv 2020年

作者： Alser, Mohammed Rotman, Jeremy Taraszka, Kodi Shi, Huwenbo Baykal, Pelin Icer Yang, Harry Taegyun Xue, Victor Knyazev, Sergey Singer, Benjamin D. Balliu, Brunilda Koslicki, David Skums, Pavel Zelikovsky, Alex Alkan, Can Mutlu, Onur Mangul, Serghei Computer Science Department ETH Zürich Zürich8092 Switzerland Computer Engineering Department Bilkent University Bilkent Ankara 06800 Turkey Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Department of Epidemiology Harvard T.H. Chan School of Public Health MA02115 United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA02142 United States Department of Computer Science Georgia State University AtlantaGA30302 United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles Los AngelesCA90095 United States Division of Pulmonary and Critical Care Medicine Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Biochemistry & Molecular Genetics Northwestern University Feinberg School of Medicine United States Simpson Querrey Center for Epigenetics Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Computer Science and Engineering Pennsylvania State University University ParkPA16801 United States Biology Department Pennsylvania State University University ParkPA16801 United States The Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16801 United States The Laboratory of Bioinformatics I.M. Sechenov First Moscow State Medical University Moscow119991 Russia Bilkent-Hacettepe Health Sciences and Technologies Program Ankara Turkey Department of Clinical Pharmacy School of Pharmacy University of Southern California Los AngelesCA90089 United States

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to today’s diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies. Copyright © 2020, The Authors. All rights reserved.

关键词： bioinformatics

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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Research Square

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Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

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Correction to: Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes

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Genome medicine 2021年第1期13卷 139页

作者： David Z Pan Zong Miao Caroline Comenho Sandhya Rajkumar Amogha Koka Seung Hyuk T Lee Marcus Alvarez Dorota Kaminska Arthur Ko Janet S Sinsheimer Karen L Mohlke Nicholas Mancuso Linda Liliana Muñoz-Hernandez Miguel Herrera-Hernandez Maria Teresa Tusié-Luna Carlos Aguilar-Salinas Kirsi H Pietiläinen Jussi Pihlajamäki Markku Laakso Kristina M Garske Päivi Pajukanta Department of Human Genetics David Geffen School of Medicine at UCLA Los Angeles USA. Bioinformatics Interdepartmental Program UCLA Los Angeles USA. Computational and Systems Biology Interdepartmental Program UCLA Los Angeles USA. Institute of Public Health and Clinical Nutrition University of Eastern Finland Kuopio Finland. Department of Medicine David Geffen School of Medicine at UCLA Los Angeles USA. Department of Computational Medicine David Geffen School of Medicine at UCLA Los Angeles USA. Department of Genetics University of North Carolina at Chapel Hill Chapel Hill North Carolina USA. Center for Genetic Epidemiology Department of Preventative Medicine Keck School of Medicine University of Southern California Los Angeles USA. Tecnologico de Monterrey Escuela de Medicina y Ciencias de la Salud Ave. Morones Prieto 3000 Monterrey N.L. México 64710. Unidad de Investigación de Enfermedades Metabólicas Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Departamento de Endocrinología y Metabolismo del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Departamento de Cirugía Instituto Nacional de Ciencias Médicas y Nutrición Mexico City Mexico. Unidad de Biología Molecular y Medicina Genómica Instituto de Investigaciones Biomédicas UNAM/ Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico. Obesity Research Unit Research Program for Clinical and Molecular Metabolism Faculty of Medicine University of Helsinki Helsinki Finland. Obesity Center Endocrinology Abdominal Center Helsinki University Central Hospital and University of Helsinki Helsinki Finland. Department of Medicine Endocrinology and Clinical Nutrition Kuopio University Hospital Kuopio Finland. Department of Medicine University of Eastern Finland and Kuopio University Hospital Kuopio Finland. Department of Human Genetics David Geffen School of Medicine at UCLA Los Angeles USA. ppajukant

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Publisher Correction: scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

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Genome biology 2021年第1期22卷 177页

作者： Tianyi Sun Dongyuan Song Wei Vivian Li Jingyi Jessica Li Department of Statistics University of California Los Angeles CA 90095-1554 USA. Interdepartmental Program of Bioinformatics University of California Los Angeles CA 90095-7246 USA. Department of Biostatistics and Epidemiology Rutgers School of Public Health Piscataway NJ 08854 USA. li@rutgers.edu. Department of Statistics University of California Los Angeles CA 90095-1554 USA. jli@stat.ucla.edu. Department of Human Genetics University of California Los Angeles CA 90095-7088 USA. jli@stat.ucla.edu. Department of Computational Medicine University of California Los Angeles CA 90095-1766 USA. jli@stat.ucla.edu. Department of Biostatistics University of California Los Angeles CA 90095-1772 USA. jli@stat.ucla.edu.

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Publisher Correction: Accurate estimation of cell composition in bulk expression through robust integration of single-cell information

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Nature communications 2020年第1期11卷 2891页

作者： Brandon Jew Marcus Alvarez Elior Rahmani Zong Miao Arthur Ko Kristina M Garske Jae Hoon Sul Kirsi H Pietiläinen Päivi Pajukanta Eran Halperin Bioinformatics Interdepartmental Program UCLA Los Angeles CA 90095 USA. Department of Human Genetics David Geffen School ofMedicine at UCLA Los Angeles CA 90095 USA. Department of Computer Science School of Engineering UCLA Los Angeles CA 90095 USA. Department of Psychiatry and Biobehavioral Sciences UCLA Los Angeles CA 90095 USA. Obesity Research Unit Research Program for Clinical and Molecular Metabolism University of Helsinki Helsinki 00014 Finland. Obesity Center Endocrinology Abdominal Center Helsinki University Central Hospital and University of Helsinki Helsinki 00260 Finland. Bioinformatics Interdepartmental Program UCLA Los Angeles CA 90095 USA. ppajukanta@mednet.ucla.edu. Department of Human Genetics David Geffen School ofMedicine at UCLA Los Angeles CA 90095 USA. ppajukanta@mednet.ucla.edu. Institute for Precision Health School of Medicine UCLA Los Angeles CA 90095 USA. ppajukanta@mednet.ucla.edu. Department of Human Genetics David Geffen School ofMedicine at UCLA Los Angeles CA 90095 USA. ehalperin@cs.ucla.edu. Department of Computer Science School of Engineering UCLA Los Angeles CA 90095 USA. ehalperin@cs.ucla.edu. Department of Anesthesiology UCLA Health Los Angeles CA 90095 USA. ehalperin@cs.ucla.edu. Department of Computational Medicine School of Medicine UCLA Los Angeles CA 90095 USA. ehalperin@cs.ucla.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Learning binary latent variable models: A tensor eigenpair approach

arXiv

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arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Serotonin transporter inhibits antitumor immunity through regulating the intratumoral serotonin axis

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Cell 2025年

作者： Bo Li James Elsten-Brown Miao Li Enbo Zhu Zhe Li Yuning Chen Elliot Kang Feiyang Ma Jennifer Chiang Yan-Ruide Li Yichen Zhu Jie Huang Audrey Fung Quentin Scarborough Robin Cadd Jin J Zhou Arnold I Chin Matteo Pellegrini Lili Yang Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA. Electronic address: bo.li@ucla.edu. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA. Department of Materials Science and Engineering University of California Los Angeles Los Angeles CA 90095 USA. Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles CA 90095 USA. Department of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles CA USA. Department of Urology University of California Los Angeles Los Angeles CA 90095 USA Eli & Edythe Broad Center of Regenerative Medicine and Stem Cell Research University of California Los Angeles Los Angeles CA 90095 USA Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Molecular Cell and Developmental Biology University of California Los Angeles Los Angeles CA 90095 USA Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles CA 90095 USA Institute for Quantitative and Computational Biosciences-The Collaboratory University of California Los Angeles Los Angeles CA 90095 USA. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles Los Angeles CA 90095 USA Jonsson Comprehensive Cancer Center David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Bioengineering University of California Los Angeles Los Angeles CA 90095 USA Goodman-Luskin Microbiome Center University of California Los Angeles Los Angeles CA 90095 USA Molecular Biology Institute University of California Los Angeles Los Angeles CA 90095 USA Parker Institute for Cancer Immunotherapy University of California Los Angeles Los Angel

Identifying additional immune checkpoints hindering antitumor T cell responses is key to the development of next-generation cancer immunotherapies. Here, we report the induction of serotonin transporter (SERT), a regulator of serotonin levels and physiological functions in the brain and peripheral tissues, in tumor-infiltrating CD8 T cells. Inhibition of SERT using selective serotonin reuptake inhibitors (SSRIs), the most widely prescribed antidepressants, significantly suppressed tumor growth and enhanced T cell antitumor immunity in various mouse syngeneic and human xenograft tumor models. Importantly, SSRI treatment exhibited significant therapeutic synergy with programmed cell death protein 1 (PD-1) blockade, and clinical data correlation studies negatively associated intratumoral SERT expression with patient survival in a range of cancers. Mechanistically, SERT functions as a negative-feedback regulator inhibiting CD8 T cell reactivities by depleting intratumoral T cell-autocrine serotonin. These findings highlight the significance of the intratumoral serotonin axis and identify SERT as an immune checkpoint, positioning SSRIs as promising candidates for cancer immunotherapy.

关键词： PD-1 blockade SSRIs T cell antitumor immunity antidepressants cancer immunotherapy immune checkpoint intratumoral serotonin axis serotonin serotonin transporter tumor microenvironment

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Author Correction: Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative

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Genome medicine 2022年第1期14卷 128页

作者： Ruth Johnson Yi Ding Vidhya Venkateswaran Arjun Bhattacharya Kristin Boulier Alec Chiu Sergey Knyazev Tommer Schwarz Malika Freund Lingyu Zhan Kathryn S Burch Christa Caggiano Brian Hill Nadav Rakocz Brunilda Balliu Christopher T Denny Jae Hoon Sul Noah Zaitlen Valerie A Arboleda Eran Halperin Sriram Sankararaman Manish J Butte Clara Lajonchere Daniel H Geschwind Bogdan Pasaniuc Department of Computer Science University of California Los Angeles Los Angeles CA 90095 USA. ruthjohnson@g.ucla.edu. Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. ruthjohnson@g.ucla.edu. Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Bioinformatics Interdepartmental Program University of California Los Angeles Los Angeles CA 90095 USA. Department of Oral Biology School of Dentistry University of California Los Angeles Los Angeles CA 90095 USA. Institute for Quantitative and Computational Biosciences David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Medicine Division of Cardiology University of California Los Angeles Los Angeles CA 90095 USA. Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Genetics Stanford School of Medicine Stanford CA 94305 USA. Molecular Biology Institute David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Program in Neurogenetics Department of Neurology David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Department of Computer Science University of California Los Angeles Los Angeles CA 90095 USA. Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA. Division of Hematology/Oncology Department of Pediatrics Gwynne Hazen Cherry Memorial Laboratories University of California Los Angeles Los Angeles CA 90095 USA. Molecular Biology Institute University of California Los Angeles Los Angeles CA 90095 USA. Jonsson Comprehensive Cancer Center University of California Los Angeles Los Angeles CA 90095

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Publisher Correction: Partitioned polygenic risk scores identify distinct types of metabolic dysfunction-associated steatotic liver disease

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Nature medicine 2025年第2期31卷 700页

作者： Oveis Jamialahmadi Antonio De Vincentis Federica Tavaglione Francesco Malvestiti Ruifang Li-Gao Rosellina M Mancina Marcus Alvarez Kyla Gelev Samantha Maurotti Umberto Vespasiani-Gentilucci Frits Richard Rosendaal Julia Kozlitina Päivi Pajukanta François Pattou Luca Valenti Stefano Romeo Department of Molecular and Clinical Medicine Institute of Medicine Sahlgrenska Academy Wallenberg Laboratory University of Gothenburg Gothenburg Sweden. oveis.jamialahmadi@wlab.gu.se. Operative Unit of Internal Medicine Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy. Research Unit of Internal Medicine Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy. Operative Unit of Clinical Medicine and Hepatology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy. Research Unit of Clinical Medicine and Hepatology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy. Department of Pathophysiology and Transplantation Università degli Studi di Milano Milan Italy. Department of Clinical Epidemiology Leiden University Medical Center Leiden the Netherlands. Department of Molecular and Clinical Medicine Institute of Medicine Sahlgrenska Academy Wallenberg Laboratory University of Gothenburg Gothenburg Sweden. Department of Life Science Health and Health Professions Link Campus University Rome Italy. Department of Human Genetics David Geffen School of Medicine at UCLA Los Angeles CA USA. Department of Experimental and Clinical Medicine Magna Graecia University Catanzaro Italy. The Eugene McDermott Center for Human Growth and Development University of Texas Southwestern Medical Center Dallas TX USA. Bioinformatics Interdepartmental Program UCLA Los Angeles CA USA. Institute for Precision Health David Geffen School of Medicine at UCLA Los Angeles CA USA. Service de chirurgie générale et endocrinienne Centre Hospitalier Universitaire de Lille Lille France. European Genomic Institute for Diabetes UMR 1190 Translational Research for Diabetes Inserm CHU Lille University of Lille Lille France. Precision Medicine - Biological Resource Center Department of Transfusion Medicine Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico Milan Italy. Department of Molecular and Cli

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Author Correction: Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing

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Nature communications 2020年第1期11卷 4499页

作者： Igor Mandric Jeremy Rotman Harry Taegyun Yang Nicolas Strauli Dennis J Montoya William Van Der Wey Jiem R Ronas Benjamin Statz Douglas Yao Velislava Petrova Alex Zelikovsky Roberto Spreafico Sagiv Shifman Noah Zaitlen Maura Rossetti K Mark Ansel Eleazar Eskin Serghei Mangul Department of Computer Science University of California Los Angeles 404 Westwood Plaza Los Angeles CA 90095 USA. Department of Clinical Pharmacy School of Pharmacy University of Southern California 1540 Alcazar Street Los Angeles CA 90033 USA. Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles 611 Charles E. Young Drive East Los Angeles CA 90095-1570 USA. Biomedical Sciences Graduate Program University of California San Francisco 1675 Owens Street Suite 310 San Francisco CA 94143-0523 USA. Department of Molecular Cell and Developmental Biology University of California Los Angeles 610 Charles E. Young Drive South Los Angeles CA 90095 USA. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles 609 Charles E. Young Drive East Los Angeles CA 90095 USA. Program in Bioinformatics and Integrative Genomics Harvard Medical School 10 Shattuck Street Suite 514 Boston MA 02115 USA. Wellcome Trust Sanger Institute Wellcome Genome Campus Hinxton Cambridge CB10 1SA UK. Department of Computer Science Georgia State University 33 Gilmer Street SE Atlanta GA 30303 USA. The Laboratory of Bioinformatics I.M. Sechenov First Moscow State Medical University Moscow 119991 Russia. Institute for Quantitative and Computational Biosciences University of California Los Angeles 611 Charles E. Young Drive East Los Angeles CA 90095 USA. Department of Genetics The Institute of Life Sciences The Hebrew University of Jerusalem Jerusalem 9190401 Israel. Department of Medicine University of California San Francisco 533 Parnassus Avenue San Francisco CA 94143 USA. Immunogenetics Center Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles 1000 Veteran Avenue Los Angeles CA 90095-1652 USA. Sandler Asthma Basic Research Center Department of Microbiology and Immunology University of California San Francisco 513 Parnass

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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