Author
Jean Monlong
Other affiliations: University of California, Santa Cruz, Pompeu Fabra University, Joseph Fourier University
Bio: Jean Monlong is an academic researcher from McGill University. The author has contributed to research in topics: Transcriptome & Gene. The author has an hindex of 20, co-authored 39 publications receiving 13225 citations. Previous affiliations of Jean Monlong include University of California, Santa Cruz & Pompeu Fabra University.
Papers
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Drexel University1, Yeshiva University2, Roswell Park Cancer Institute3, Virginia Commonwealth University4, Van Andel Institute5, Science Applications International Corporation6, Massachusetts Institute of Technology7, Harvard University8, University of Miami9, Icahn School of Medicine at Mount Sinai10, University of Chicago11, Howard Hughes Medical Institute12, University of Geneva13, Stanford University14, University of Oxford15, University of North Carolina at Chapel Hill16, National Institutes of Health17
TL;DR: The Genotype-Tissue Expression (GTEx) project is described, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
Abstract: Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
6,545 citations
TL;DR: The landscape of gene expression across tissues is described, thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants are cataloged, complex network relationships are described, and signals from genome-wide association studies explained by eQTLs are identified.
Abstract: Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysi...
4,418 citations
University of Geneva1, Leiden University Medical Center2, Pompeu Fabra University3, Wellcome Trust Centre for Human Genetics4, European Bioinformatics Institute5, University of Kiel6, Science for Life Laboratory7, Max Planck Society8, Broad Institute9, Harvard University10, Swiss Institute of Bioinformatics11, Stanford University12, University of Oxford13, Technische Universität München14, University of Santiago de Compostela15
TL;DR: Se sequencing and deep analysis of messenger RNA and microRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project—the first uniformly processed high-throughput RNA-sequencing data from multiple human populations with high-quality genome sequences discover extremely widespread genetic variation affecting the regulation of most genes.
Abstract: Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of messenger RNA and microRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project--the first uniformly processed high-throughput RNA-sequencing data from multiple human populations with high-quality genome sequences. We discover extremely widespread genetic variation affecting the regulation of most genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on the cellular mechanisms of regulatory and loss-of-function variation, and allows us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.
1,892 citations
TL;DR: Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples that are dominated by a relatively small number of genes, though few are exclusive to a particular tissue and vary more across tissues than individuals.
Abstract: Transcriptional regulation and posttranscriptional processing underlie many cellular and organismal phenotypes. We used RNA sequence data generated by Genotype-Tissue Expression (GTEx) project to investigate the patterns of transcriptome variation across individuals and tissues. Tissues exhibit characteristic transcriptional signatures that show stability in postmortem samples. These signatures are dominated by a relatively small number of genes—which is most clearly seen in blood—though few are exclusive to a particular tissue and vary more across tissues than individuals. Genes exhibiting high interindividual expression variation include disease candidates associated with sex, ethnicity, and age. Primary transcription is the major driver of cellular specificity, with splicing playing mostly a complementary role; except for the brain, which exhibits a more divergent splicing program. Variation in splicing, despite its stochasticity, may play in contrast a comparatively greater role in defining individual phenotypes.
1,131 citations
Alvaro N. Barbeira1, Scott P. Dickinson1, Rodrigo Bonazzola1, Jiamao Zheng1 +260 more•Institutions (43)
TL;DR: A mathematical expression is derived to compute PrediXcan results using summary data, and the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes are investigated.
Abstract: Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.
657 citations
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28 Jul 2005
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
Broad Institute1, Harvard University2, Boston Children's Hospital3, University of Washington4, University of Arizona5, Cardiff University6, Google7, Icahn School of Medicine at Mount Sinai8, Samsung Medical Center9, Vertex Pharmaceuticals10, University of Michigan11, University of Cambridge12, State University of New York Upstate Medical University13, Karolinska Institutet14, University of Eastern Finland15, Wellcome Trust Centre for Human Genetics16, University of Oxford17, Cedars-Sinai Medical Center18, University of Ottawa19, University of Pennsylvania20, University of North Carolina at Chapel Hill21, University of Helsinki22, University of California, San Diego23, University of Mississippi Medical Center24
TL;DR: The aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC) provides direct evidence for the presence of widespread mutational recurrence.
Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.
8,758 citations
TL;DR: Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases, which removes a major computational bottleneck in RNA-seq analysis.
Abstract: We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
6,468 citations
TL;DR: A significant update to one of the tools in this domain called Enrichr, a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries is presented.
Abstract: Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
6,201 citations
TL;DR: Salmon is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.
Abstract: We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA-seq reads. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. It is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which, as we demonstrate here, substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.
6,095 citations