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Proteomics. Tissue-based map of the human proteome.

1 , 2 , 3 , 4 , 2 , 5 , 2 , 4 , 6 , 4 , 4 , 7 , 2 , 8 , 9 , 2 , 4 , 9 , 9 , 2 , 4 , 9 , 9 , 10 , 9 , 2 , 2 , 9 , 2 , 2 , 2 , 2 , 2 , 11 , 12 , 4

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      Abstract

      Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

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      TopHat: discovering splice junctions with RNA-Seq

      Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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        An Integrated Encyclopedia of DNA Elements in the Human Genome

        Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
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          Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

          High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1-3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
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            Author and article information

            Affiliations
            [1 ] Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark. mathias.uhlen@scilifelab.se.
            [2 ] Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
            [3 ] Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
            [4 ] Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
            [5 ] Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
            [6 ] Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
            [7 ] Leibniz Research Centre for Working Environment and Human Factors (IfADo) at Dortmund TU, D-44139 Dortmund, Germany.
            [8 ] Lab Surgpath, Mumbai, India.
            [9 ] Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden.
            [10 ] Science for Life Laboratory, Department of Neuroscience, Karolinska Institute, SE-171 77 Stockholm, Sweden.
            [11 ] Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
            [12 ] Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark. Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
            Journal
            Science
            Science (New York, N.Y.)
            1095-9203
            0036-8075
            Jan 23 2015
            : 347
            : 6220
            347/6220/1260419 10.1126/science.1260419 25613900
            Copyright © 2015, American Association for the Advancement of Science.

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