New Peptides Under the s(ORF)ace of the Genome

Pueyo, José Ignacio; Magny, Emile G.; Couso, Juan Pablo

doi:10.1016/j.tibs.2016.05.003

Cited by 89 publications

(94 citation statements)

References 85 publications

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“…Evidence can be inferred from large-scale detection methods, either at the DNA (conservation signatures), the translation (ribosome profiling), or the protein level (mass spectrometry). And even though there is no perfect detection method for alternative proteins, one should be cognizant of each technique's strengths and pitfalls and strive to use and adapt them to better detect the entire proteomic landscape of a cell or tissue (Boekhorst et al 2011;Aspden et al 2014;Calviello et al 2016;Hellens et al 2016;Ma et al 2016;Pueyo et al 2016a;Delcourt et al 2017;Hsu and Benfey 2017;Willems et al 2017 Evidence at the genomic level An indirect but potentially powerful piece of evidence of a protein's expression is its conservation signature. Conservation signatures are already used to distinguish functional ORFs in current ORF prediction algorithms .…”

Section: Proposition Of a Novel Annotation Frameworkmentioning

confidence: 99%

“…The RefSeq database offers some more putative functional annotation (XM_, XP_ annotations), and Ensembl reports to some extent less supported transcripts' annotations (Pruitt et al 2012;Aken et al 2016). Yet, these still rely on overly restrictive criteria (one CDS per transcript, longer than 100 codons) (Chung et al 2007;Galindo et al 2007;Saghatelian and Couso 2015;Pueyo et al 2016a;Couso and Patraquim 2017). While adapting the framework of genome annotations to consider alternative ORFs will more likely yield significant advances, the need for a more flexible annotation for various purposes could be addressed (Fig.…”

Section: On the Importance Of Filtration And Curationmentioning

confidence: 99%

“…These criteria result in an important underestimation of translated ORFs in the genome (Andrews and Rothnagel 2014;Saghatelian and Couso 2015;Couso and Patraquim 2017;Plaza et al 2017). With functional evidence for previously unannotated ORFs in bacteria (Wadler and Vanderpool 2007;Hemm et al 2008Hemm et al , 2010Storz et al 2014;Lluch-Senar et al 2015;Baek et al 2017), Drosophila (Galindo et al 2007;Kondo et al 2007;Reinhardt et al 2013;Aspden et al 2014;Albuquerque et al 2015;Li et al 2016a;Pueyo et al 2016a), plants (Hanada et al 2013;Juntawong et al 2014;Hsu et al 2016;Hsu and Benfey 2017), and other eukaryotes (Oyama et al 2007;Ingolia et al 2011;Vanderperre et al 2013;Ma et al 2014), genome annotations will need to be revised.…”

mentioning

confidence: 99%

“…Recently, the development of new techniques or the optimization of existing ones has allowed for large-scale detection of alternative proteins and a more in-depth view of a cell proteomic landscape (Boekhorst et al 2011;Aspden et al 2014;Calviello et al 2016;Hellens et al 2016;Ma et al 2016;Pueyo et al 2016a;Delcourt et al 2017;Hsu and Benfey 2017;Willems et al 2017). Three detection methods-ribosome profiling, proteogenomics, and conservation signatures-have been used to identify likely translated and functional alternative ORFs, as further discussed later in this review.…”

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confidence: 99%

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Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship

et al. 2018

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Technological advances promise unprecedented opportunities for whole exome sequencing and proteomic analyses of populations. Currently, data from genome and exome sequencing or proteomic studies are searched against reference genome annotations. This provides the foundation for research and clinical screening for genetic causes of pathologies. However, current genome annotations substantially underestimate the proteomic information encoded within a gene. Numerous studies have now demonstrated the expression and function of alternative (mainly small, sometimes overlapping) ORFs within mature gene transcripts. This has important consequences for the correlation of phenotypes and genotypes. Most alternative ORFs are not yet annotated because of a lack of evidence, and this absence from databases precludes their detection by standard proteomic methods, such as mass spectrometry. Here, we demonstrate how current approaches tend to overlook alternative ORFs, hindering the discovery of new genetic drivers and fundamental research. We discuss available tools and techniques to improve identification of proteins from alternative ORFs and finally suggest a novel annotation system to permit a more complete representation of the transcriptomic and proteomic information contained within a gene. Given the crucial challenge of distinguishing functional ORFs from random ones, the suggested pipeline emphasizes both experimental data and conservation signatures. The addition of alternative ORFs in databases will render identification less serendipitous and advance the pace of research and genomic knowledge. This review highlights the urgent medical and research need to incorporate alternative ORFs in current genome annotations and thus permit their inclusion in hypotheses and models, which relate phenotypes and genotypes.

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Section: Proposition Of a Novel Annotation Frameworkmentioning

confidence: 99%

Section: On the Importance Of Filtration And Curationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship

et al. 2018

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“…However, this was not supported by Swiss-Prot statistics at that time 4 . In the intervening decade, the smORF question has surfaced regularly 22 and it now overlaps with the two closely related themes of de novo protein evolution (i.e. recent non-coding to coding transitions) 23 and ribosomal profiling experiments attempting to define the translation of novel smORFs from what was hitherto classified as non-coding RNA 24 .…”

Section: Small Proteinsmentioning

confidence: 99%

Last rolls of the yoyo: Assessing the human canonical protein count

Southan

2017

F1000Res

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In 2004, when the protein estimate from the finished human genome was only 24,000, the surprise was compounded as reviewed estimates fell to 19,000 by 2014. However, variability in the total canonical protein counts (i.e. excluding alternative splice forms) of open reading frames (ORFs) in different annotation portals persists. This work assesses these differences and possible causes. A 16-year analysis of Ensembl and UniProtKB/Swiss-Prot shows convergence to a protein number of ~20,000. The former had shown some yo-yoing, but both have now plateaued. Nine major annotation portals, reviewed at the beginning of 2017, gave a spread of counts from 21,819 down to 18,891. The 4-way cross-reference concordance (within UniProt) between Ensembl, Swiss-Prot, Entrez Gene and the Human Gene Nomenclature Committee (HGNC) drops to 18,690, indicating methodological differences in protein definitions and experimental existence support between sources. The Swiss-Prot and neXtProt evidence criteria include mass spectrometry peptide verification and also cross-references for antibody detection from the Human Protein Atlas. Notwithstanding, hundreds of Swiss-Prot entries are classified as non-coding biotypes by HGNC. The only inference that protein numbers might still rise comes from numerous reports of small ORF (smORF) discovery. However, while there have been recent cases of protein verifications from previous miss-annotation of non-coding RNA, very few have passed the Swiss-Prot curation and genome annotation thresholds. The post-genomic era has seen both advances in data generation and improvements in the human reference assembly. Notwithstanding, current numbers, while persistently discordant, show that the earlier yo-yoing has largely ceased. Given the importance to biology and biomedicine of defining the canonical human proteome, the task will need more collaborative inter-source curation combined with broader and deeper experimental confirmation in vivo and in vitro of proteins predicted in silico. The eventual closure could be well be below ~19,000.

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Application of peptide biomarkers in life analysis based on liquid chromatography–mass spectrometry technology

Han

Cong

et al. 2022

BioFactors

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Biomedicine is developing rapidly in the 21st century. Among them, the qualitative and quantitative analysis of peptide biomarkers is of considerable importance for the diagnosis and therapy of diseases and the quality evaluation of drugs and food. The identification and quantitative analysis of peptides have been going on for decades. Traditionally, immunoassays or biological assays are generally used to quantify peptides in biological matrices. However, the selectivity and sensitivity of these methods cannot meet the requirements of the application. The separation and analysis technique of liquid chromatography-mass spectrometry (LC-MS) supplies a reliable alternative. In contrast to immunoassays, LC-MS methods are capable of providing the analytical prowess necessary to satisfy the demands of peptide biomarker research in the life sciences arena. This review article provides a historical account of the in-roads made by LC-MS technology for the detection of peptide biomarkers in the past 10 years, with the focus on the qualification/quantification developments and their applications.

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New Peptides Under the s(ORF)ace of the Genome

Cited by 89 publications

References 85 publications

Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship

Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship

Last rolls of the yoyo: Assessing the human canonical protein count

Application of peptide biomarkers in life analysis based on liquid chromatography–mass spectrometry technology

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