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      A deep proteome and transcriptome abundance atlas of 29 healthy human tissues

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          Abstract

          Genome‐, transcriptome‐ and proteome‐wide measurements provide insights into how biological systems are regulated. However, fundamental aspects relating to which human proteins exist, where they are expressed and in which quantities are not fully understood. Therefore, we generated a quantitative proteome and transcriptome abundance atlas of 29 paired healthy human tissues from the Human Protein Atlas project representing human genes by 18,072 transcripts and 13,640 proteins including 37 without prior protein‐level evidence. The analysis revealed that hundreds of proteins, particularly in testis, could not be detected even for highly expressed mRNAs, that few proteins show tissue‐specific expression, that strong differences between mRNA and protein quantities within and across tissues exist and that protein expression is often more stable across tissues than that of transcripts. Only 238 of 9,848 amino acid variants found by exome sequencing could be confidently detected at the protein level showing that proteogenomics remains challenging, needs better computational methods and requires rigorous validation. Many uses of this resource can be envisaged including the study of gene/protein expression regulation and biomarker specificity evaluation.

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          Most cited references31

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          Proteogenomics: concepts, applications and computational strategies.

          Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to help identify novel peptides (not present in reference protein sequence databases) from mass spectrometry-based proteomic data; in turn, the proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. In recent years, owing to the emergence of new sequencing technologies such as RNA-seq and dramatic improvements in the depth and throughput of mass spectrometry-based proteomics, the pace of proteogenomic research has greatly accelerated. Here I review the current state of proteogenomic methods and applications, including computational strategies for building and using customized protein sequence databases. I also draw attention to the challenge of false positive identifications in proteogenomics and provide guidelines for analyzing the data and reporting the results of proteogenomic studies.
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            A “Proteomic Ruler” for Protein Copy Number and Concentration Estimation without Spike-in Standards*

            Absolute protein quantification using mass spectrometry (MS)-based proteomics delivers protein concentrations or copy numbers per cell. Existing methodologies typically require a combination of isotope-labeled spike-in references, cell counting, and protein concentration measurements. Here we present a novel method that delivers similar quantitative results directly from deep eukaryotic proteome datasets without any additional experimental steps. We show that the MS signal of histones can be used as a “proteomic ruler” because it is proportional to the amount of DNA in the sample, which in turn depends on the number of cells. As a result, our proteomic ruler approach adds an absolute scale to the MS readout and allows estimation of the copy numbers of individual proteins per cell. We compare our protein quantifications with values derived via the use of stable isotope labeling by amino acids in cell culture and protein epitope signature tags in a method that combines spike-in protein fragment standards with precise isotope label quantification. The proteomic ruler approach yields quantitative readouts that are in remarkably good agreement with results from the precision method. We attribute this surprising result to the fact that the proteomic ruler approach omits error-prone steps such as cell counting or protein concentration measurements. The proteomic ruler approach is readily applicable to any deep eukaryotic proteome dataset—even in retrospective analysis—and we demonstrate its usefulness with a series of mouse organ proteomes.
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              Long noncoding RNAs are rarely translated in two human cell lines

              Data from the Encyclopedia of DNA Elements (ENCODE) project show over 9640 human genome loci classified as long noncoding RNAs (lncRNAs), yet only ∼100 have been deeply characterized to determine their role in the cell. To measure the protein-coding output from these RNAs, we jointly analyzed two recent data sets produced in the ENCODE project: tandem mass spectrometry (MS/MS) data mapping expressed peptides to their encoding genomic loci, and RNA-seq data generated by ENCODE in long polyA+ and polyA− fractions in the cell lines K562 and GM12878. We used the machine-learning algorithm RuleFit3 to regress the peptide data against RNA expression data. The most important covariate for predicting translation was, surprisingly, the Cytosol polyA− fraction in both cell lines. LncRNAs are ∼13-fold less likely to produce detectable peptides than similar mRNAs, indicating that ∼92% of GENCODE v7 lncRNAs are not translated in these two ENCODE cell lines. Intersecting 9640 lncRNA loci with 79,333 peptides yielded 85 unique peptides matching 69 lncRNAs. Most cases were due to a coding transcript misannotated as lncRNA. Two exceptions were an unprocessed pseudogene and a bona fide lncRNA gene, both with open reading frames (ORFs) compromised by upstream stop codons. All potentially translatable lncRNA ORFs had only a single peptide match, indicating low protein abundance and/or false-positive peptide matches. We conclude that with very few exceptions, ribosomes are able to distinguish coding from noncoding transcripts and, hence, that ectopic translation and cryptic mRNAs are rare in the human lncRNAome.
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                Author and article information

                Contributors
                gagneur@in.tum.de
                hannes.hahne@omicscouts.com
                kuster@tum.de
                Journal
                Mol Syst Biol
                Mol. Syst. Biol
                10.1002/(ISSN)1744-4292
                MSB
                msb
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                1744-4292
                18 February 2019
                February 2019
                : 15
                : 2 ( doiID: 10.1002/msb.v15.2 )
                : e8503
                Affiliations
                [ 1 ] Chair of Proteomics and Bioanalytics Technische Universität München Freising Germany
                [ 2 ] Computational Biology Department of Informatics Technical University of Munich Garching bei München Germany
                [ 3 ] Department of Biochemistry Quantitative Biosciences Munich Gene Center Ludwig Maximilian Universität München Germany
                [ 4 ] OmicScouts GmbH Freising Germany
                [ 5 ] Science for Life Laboratory KTH ‐ Royal Institute of Technology Stockholm Sweden
                [ 6 ] Science for Life Laboratory Department of Immunology, Genetics and Pathology Uppsala University Uppsala Sweden
                [ 7 ] JPT Peptide Technologies GmbH Berlin Germany
                [ 8 ] Center for Integrated Protein Science Munich (CIPSM) Munich Germany
                Author notes
                [*] [* ] Corresponding author. Tel: +49 89 289 19411; E‐mail: gagneur@ 123456in.tum.de

                Corresponding author. Tel: +49 8161 976289 0; Fax: +49 8161 976289 1; E‐mail: hannes.hahne@ 123456omicscouts.com

                Corresponding author. Tel: +49 8161 71 5696; Fax: +49 8161 71 5931; E‐mail: kuster@ 123456tum.de

                [†]

                These authors contributed equally to this work

                Author information
                https://orcid.org/0000-0002-4402-0690
                https://orcid.org/0000-0002-6651-1773
                https://orcid.org/0000-0003-0703-3940
                https://orcid.org/0000-0002-8924-8365
                https://orcid.org/0000-0003-3601-0051
                https://orcid.org/0000-0002-9094-1677
                Article
                MSB188503
                10.15252/msb.20188503
                6379049
                30777892
                01be7b02-1581-4e83-a9f1-ecdc7bdc42fa
                © 2019 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 20 June 2018
                : 01 January 2019
                : 08 January 2019
                Page count
                Figures: 7, Tables: 0, Pages: 16, Words: 13318
                Funding
                Funded by: Center for Integrated Protein Analysis Munich (CIPSM)
                Funded by: Knut and Alice Wallenberg Foundation
                Categories
                Article
                Articles
                Custom metadata
                2.0
                msb188503
                February 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.5.9 mode:remove_FC converted:18.02.2019

                Quantitative & Systems biology
                human proteome,human transcriptome,proteogenomics,quantitative mass spectrometry,rna‐seq,genome-scale & integrative biology,methods & resources

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