CHESS: a new human gene catalog curated from thousands of large-scale RNA sequencing experiments reveals extensive transcriptional noise

Pertea, Mihaela; Shumate, Alaina; Pertea, Geo; Varabyou, Ales; Breitwieser, Florian P.; Chang, Yu Chi; Madugundu, Anil K.; Pandey, Akhilesh; Salzberg, Steven L.

doi:10.1186/s13059-018-1590-2

Cited by 296 publications

(290 citation statements)

References 57 publications

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“…Additionally, about 5,700-5,800 genes in human and 2,600-3,900 in mouse produce more than one protein in those conditions. These estimates are considerably lower than what is generally predicted from RNA expression (Pertea et al 2018). This may be explained by the limited coverage of Ribo-seq reads but may be also due to the fact that RNA-seq artificially amplifies fragments of unproductive RNAs leading to many false positives.…”

Section: Discussionmentioning

confidence: 61%

“…Differential production of transcript isoforms, especially through the mechanism of alternative splicing, is crucial in multiple biological processes such as cell differentiation, acquisition of tissuespecific functions, and DNA repair (Fiszbein and Kornblihtt 2017;Baralle and Giudice 2017;Shkreta and Chabot 2015), as well as in multiple pathologies (Ward and Cooper 2010;Singh and Eyras 2017;Cummings et al 2017). Although analysis of RNA sequencing (RNA-seq) data from multiple samples has indicated a large diversity of transcript molecules (Pertea et al 2018), genes express mostly one single isoform in any given condition and this isoform may change across conditions (Gonzàlez-Porta et al 2013;Sebestyén et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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Ribosome profiling at isoform level reveals an evolutionary conserved impact of differential splicing on the proteome

Reixachs‐Solé

Ruiz-Orera

Albà

et al. 2019

Preprint

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AbstractThe differential production of transcript isoforms from gene loci is a key cellular mechanism. Yet, its impact in protein production remains an open question. Here, we describe ORQAS (ORF quantification pipeline for alternative splicing), a new pipeline for the translation quantification of individual transcript isoforms using ribosome-protected mRNA fragments (Ribosome profiling). We found evidence of translation for 40-50% of the expressed transcript isoforms in human and mouse, with 53% of the expressed genes having more than one translated isoform in human, 33% in mouse. Differential analysis revealed that about 40% of the splicing changes at RNA level were concordant with changes in translation, with 21.7% of changes at RNA level and 17.8% at translation level conserved between human and mouse. Furthermore, orthologous cassette exons preserving the directionality of the change were conserved between human and mouse and enriched in microexons in a comparison between glia and glioma. ORQAS leverages ribosome profiling to uncover a widespread and evolutionary conserved impact of differential splicing on the translation of isoforms and, in particular, of microexon-containing isoforms. ORQAS is available at https://github.com/comprna/orqas

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Section: Discussionmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Ribosome profiling at isoform level reveals an evolutionary conserved impact of differential splicing on the proteome

Reixachs‐Solé

Ruiz-Orera

Albà

et al. 2019

Preprint

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“…To predict whether ERV-ORFs are expressed as proteins, we performed comparative analyses using a wide-range of transcriptome and histone mark data generated by next-generation sequencing. We first compared observed ERV-ORFs from our study with quantified transcriptome data derived from 31 human tissues generated in the GTEx study (Carithers et al 2015) from the CHESS database (Pertea et al 2018). We found that a total of 279 ERV-ORFs overlapped with transcripts in the CHESS database.…”

Section: Omics Analyses Of Erv-orfsmentioning

confidence: 99%

“…One of the most comprehensive RNA-Seq datasets was generated by the genotype-tissue expression (GTEx) study using 31 human tissues (Carithers et al 2015). The data was used to assemble a similar pipeline in the Comprehensive Human Expressed SequenceS (CHESS) project and served to generate 20,352 potential protein-coding genes, and 116,156 novel transcripts in the human genome (Pertea et al 2018). A comparative analysis study focusing on the protein-coding region of ERVs using the new transcript data, in combination with cap analysis of gene expression (CAGE) data (Lizio et al 2015), and epigenomic data will provide new insight into the transcriptional potential, and functionality of these regions.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive genomic analysis reveals dynamic evolution of endogenous retroviruses that code for retroviral-like protein domains

Ueda

Kryukov

Mitsuhashi

et al. 2019

Preprint

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Endogenous retroviruses (ERVs) are remnants of ancient retroviral infections of mammalian germline cells. A large proportion of ERVs lose their open reading frames (ORFs), while others retain them and become exapted by the host species. However, it remains unclear what proportion of ERVs possess ORFs (ERV-ORFs), become transcribed, and serve as candidates for co-opted genes. Hence, we investigated characteristics of 176,401 ERV-ORFs containing retroviral-like protein domains (gag, pro, pol, and env) in 19 mammalian genomes. The fractions of ERVs possessing ORFs were overall small (~0.15%) although they varied depending on domain types as well as species. The observed divergence of ERV-ORF from their consensus sequences suggested that a large proportion of ERV-ORFs either recently or anciently inserted themselves into mammalian genomes. Alternatively, very few ERVs lacking ORFs were found to exhibit similar divergence patterns. To identify ERV-ORFs transcribed as proteins, we compared ERV-ORFs with various multi-omics data including transcriptome data, trimethylation at histone H3 lysine 36, and transcription initiation sites from 2,834 cell types, and found 408and 752 ERV-ORFs, accounting for 2-3% of all ERV-ORFs, with high transcriptional potential in humans and mice, respectively. Moreover, many of these ERV-ORFs with transcriptional potential were lineage-specific sequences exhibiting tissue-specific expression. These results suggest a possibility for the expression of uncharacterized functional genes containing ERV-ORFs hidden within mammalian genomes. Together, our analyses suggest that more ERV-ORFs may be co-opted in a host-species specific manner than we currently know, which are likely to have contributed to mammalian evolution and diversification.

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“…ClusterBurden was devised to be suitable for scanning large-scale whole-exome sequencing projects designed to identify novel pathogenic genes for rare Mendelian diseases. The combination of FE and BIN-test to model clustering and burden minimizes the computation overhead required to calculate p-values making analyses of >20,000 genes [40] in hundreds of thousands of cases and controls, practical in terms of execution time and computer memory requirements. For genes with a significant BIN-test 'cluster' p-value, ClusterBurden calculated considerably lower p-values that the traditional Fisher's exact 'burden' test, implying an increase in statistical power (Table 1).…”

Section: Clusterburdenmentioning

confidence: 99%

Data-driven modelling of mutational hotspots and in-silico predictors in hypertrophic cardiomyopathy

Waring

Harper

Salatino

et al. 2019

Preprint

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Although rare missense variants underlying a number of Mendelian diseases have been noted to cluster in specific regions of proteins, this information may be underutilized when evaluating the pathogenicity of a gene or variant. We introduce ClusterBurden and GAMs, two methods for rapid association testing and predictive modelling, respectively, that combine variant burden and amino-acid residue clustering, in casecontrol studies. We show that ClusterBurden increases statistical power to identify disease genes driven by missense variants, in simulated and experimental 34-gene panel for hypertrophic cardiomyopathy. We then demonstrate that GAMs can be used to apply the ACMG criteria PM1 and PP3 quantitatively, and resolve a wide range of pathogenicity potential amongst variants of uncertain significance. An R package is available for association testing using ClusterBurden, and a web application (Pathogenicity_by_Position) is available for missense variant risk prediction using GAMs for six sarcomeric genes. In conclusion, the inclusion of amino-acid residue positional information enhances the accuracy of gene and rare variant pathogenicity interpretation. Author SummaryTwo statistical methods have been developed that utilize signal in the residue position of missense variants. The first is a rapid association method that tests the joint hypothesis of an excess of rare-variants and rarevariant clustering. The method, ClusterBurden, is powerful when rare-missense variants cluster in discrete pathogenic regions of the protein. It can be applied to exome-scans to discover novel Mendelian diseasegenes, that may not be identified by classic burden testing. The second method is a statistical model for rare-missense variant interpretation. It provides superior predictive performance compared to generic in silico predictors by training on our large case-control dataset. The method represents a data-driven quantitative approach to apply hotspot and in-silico prediction criteria from the ACMG variant interpretation guidelines.

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CHESS: a new human gene catalog curated from thousands of large-scale RNA sequencing experiments reveals extensive transcriptional noise

Cited by 296 publications

References 57 publications

Ribosome profiling at isoform level reveals an evolutionary conserved impact of differential splicing on the proteome

Ribosome profiling at isoform level reveals an evolutionary conserved impact of differential splicing on the proteome

Comprehensive genomic analysis reveals dynamic evolution of endogenous retroviruses that code for retroviral-like protein domains

Data-driven modelling of mutational hotspots and in-silico predictors in hypertrophic cardiomyopathy

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