De novo derivation of proteomes from transcriptomes for transcript and protein identification

Evans, Vanessa; Barker, G. L.; Heesom, Kate J; Fan, Jun; Bessant, Conrad; Matthews, David A.

doi:10.1038/nmeth.2227

Cited by 156 publications

(188 citation statements)

References 34 publications

(36 reference statements)

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“…The PIT strategy derives the customized protein sequence database queried for protein identification directly from RNA-seq data of the same or a similar sample (Evans et al 2012). It therefore limits protein products in the custom database to those resulting from the three-or six-frame translation of the assembled transcripts that are indeed expressed in the organ, tissue, or cell type of interest.…”

Section: Proteogenomic Approaches Applied To Spermatogenesismentioning

confidence: 99%

Linking transcriptomics and proteomics in spermatogenesis

Chalmel¹,

Rolland²

2015

Reproduction

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Spermatogenesis is a complex and tightly regulated process leading to the continuous production of male gametes, the spermatozoa. This developmental process requires the sequential and coordinated expression of thousands of genes, including many that are testisspecific. The molecular networks underlying normal and pathological spermatogenesis have been widely investigated in recent decades, and many high-throughput expression studies have studied genes and proteins involved in male fertility. In this review, we focus on studies that have attempted to correlate transcription and translation during spermatogenesis by comparing the testicular transcriptome and proteome. We also discuss the recent development and use of new transcriptomic approaches that provide a better proxy for the proteome, from both qualitative and quantitative perspectives. Finally, we provide illustrations of how testis-derived transcriptomic and proteomic data can be integrated to address new questions and how the 'proteomics informed by transcriptomics' technique, by combining RNA-seq and MS-based proteomics, can contribute significantly to the discovery of new protein-coding genes or new protein isoforms expressed during spermatogenesis.Reproduction (2015) 150 R149-R157

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Section: Proteogenomic Approaches Applied To Spermatogenesismentioning

confidence: 99%

Linking transcriptomics and proteomics in spermatogenesis

Chalmel¹,

Rolland²

2015

Reproduction

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“…These include reducing a database to only include sequences with transcript expression evidence (40), including fusion or chimeric sequences (44), incorporating nonsynonymous single nucleotide polymorphism (SNP) or SNV sequences (40), and, for non-model systems, building a proteomic database from de novo assembled transcripts (45,46). The advent of next generation proteomics will most certainly arrive when all these sources of transcriptomic variation can be seamlessly incorporated into sample-specific proteomic databases.…”

mentioning

confidence: 99%

Discovery and Mass Spectrometric Analysis of Novel Splice-junction Peptides Using RNA-Seq

Sheynkman

Shortreed

Frey

et al. 2013

Molecular & Cellular Proteomics

120

205

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Human proteomic databases required for MS peptide identification are frequently updated and carefully curated, yet are still incomplete because it has been challenging to acquire every protein sequence from the diverse assemblage of proteoforms expressed in every tissue and cell type. In particular, alternative splicing has been shown to be a major source of this cell-specific proteomic variation. Many new alternative splice forms have been detected at the transcript level using next generation sequencing methods, especially RNA-Seq, but it is not known how many of these transcripts are being translated. Leveraging the unprecedented capabilities of next generation sequencing methods, we collected RNA-Seq and proteomics data from the same cell population (Jurkat cells) and created a bioinformatics pipeline that builds customized databases for the discovery of novel splicejunction peptides. Eighty million paired-end Illumina reads and ϳ500,000 tandem mass spectra were used to identify 12,873 transcripts (19,320 including isoforms) and 6810 proteins. We developed a bioinformatics workflow to retrieve high-confidence, novel splice junction sequences from the RNA data, translate these sequences into the analogous polypeptide sequence, and create a customized splice junction database for MS searching. Based on the RefSeq gene models, we detected 136,123 annotated and 144,818 unannotated transcript junctions. Of those, 24,834 unannotated junctions passed various quality filters (e.g. minimum read depth) and these entries were translated into 33,589 polypeptide sequences and used for database searching. We discovered 57 splice junction peptides not present in the Uniprot-Trembl proteomic database comprising an array of different splicing events, including skipped exons, alternative donors and acceptors, and noncanonical transcriptional start sites. To our knowledge this is the first example of using sample-specific RNA-Seq data to create a splice-junction database and discover new peptides resulting from alternative splicing. Mass spectrometry-based proteomics relies on accurate databases to identify and quantify proteins, including those derived from splice variants, indels, and single nucleotide variants (SNVs) 1 (1). Most computational search algorithms detect peptides by scoring the degree of similarity between in silico derived and experimental peptide spectra, and thus can only identify peptides that are present in the proteomic database. If the polypeptide sequence is not present in the database used for searching, even if the peptide is present in the sample, it will fail to be detected.Human proteomic databases used for mass spectrometric peptide identification are frequently updated and carefully curated, yet are still incomplete. Despite efforts to comprehensively annotate every gene product, there are still many undiscovered proteoforms (2) because the complete human proteome-the aggregate of all protein products expressed in every tissue, cell, and cellular state-turns out to be vastly more complex than was...

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“…Ideally, this type of study would entail transcriptome sequencing and ribosome profiling of relevant cells followed by MS analyses in which MIP spectra are assigned by searching the six-frame in silico translation of transcriptomic reads. This strategy has been successfully used for identification of proteins in non-model species as well as higher eukaryotes [46,65,66].…”

Section: Cryptic Mipsmentioning

confidence: 99%