The DOI matches its Crossref record; 76 of the 77 references it cites were found in a registry or an index.
Shotgun proteomics aids discovery of novel protein-coding genes, alternative splicing, and “resurrected” pseudogenes in the mouse genome
Open accessAbstract
Recent advances in proteomic mass spectrometry (MS) offer the chance to marry high-throughput peptide sequencing to transcript models, allowing the validation, refinement, and identification of new protein-coding loci. We present a novel pipeline that integrates highly sensitive and statistically robust peptide spectrum matching with genome-wide protein-coding predictions to perform large-scale gene validation and discovery in the mouse genome for the first time. In searching an excess of 10 million spectra, we have been able to validate 32%, 17%, and 7% of all protein-coding genes, exons, and splice boundaries, respectively. Moreover, we present strong evidence for the identification of multiple alternatively spliced translations from 53 genes and have uncovered 10 entirely novel protein-coding genes, which are not covered in any mouse annotation data sources. One such novel protein-coding gene is a fusion protein that spans the Ins2 and Igf2 loci to produce a transcript encoding the insulin II and the insulin-like growth factor 2–derived peptides. We also report nine processed pseudogenes that have unique peptide hits, demonstrating, for the first time, that they are not just transcribed but are translated and are therefore resurrected into new coding loci. This work not only highlights an important utility for MS data in genome annotation but also provides unique insights into the gene structure and propagation in the mouse genome. All these data have been subsequently used to improve the publicly available mouse annotation available in both the Vega and Ensembl genome browsers (http://vega.sanger.ac.uk).
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“Initial efforts to assess the contribution of alternative splicing to proteomic composition and diversity have focused exclusively on the identification of proteins derived from alternatively spliced transcripts in a steady-state system (Blakeley et al., 2010, Brosch et al., 2011, Ezkurdia et al., 2012, Lander et al., 2001, Leoni et al., 2011, Tress et al., 2008, Xing et al., 2011, Zhou et al., 2”
From Impact of Alternative Splicing on the Human Proteome (2017) · cites this paper as Brosch et al., 2011
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Initial sequencing and analysis of the human genome
International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216
Self-citationStatistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Perkins, D. N., Pappin, D., Creasy, D. M., & Cottrell, J. S. (1999) · Electrophoresis · doi:10.1002/(sici)1522-2683(19991201)20:18 · Cited by 8,264
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223
Self-citationAlternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
Keller, A., Nesvizhskii, A., Kolker, E., & Aebersold, R. (2002) · Analytical Chemistry · doi:10.1021/ac025747h · Cited by 4,970
Prediction of complete gene structures in human genomic DNA
Burge, C., & Karlin, S. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1997.0951 · Cited by 4,211
Roepstorff, P., & Fohlman, J. (1984) · Journal of Mass Spectrometry · doi:10.1002/bms.1200111109 · Cited by 2,705
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2007) · Nature Methods · doi:10.1038/nmeth1113 · Cited by 2,424
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Protein Analysis by Shotgun/Bottom-up Proteomics
Zhang, Y., Fonslow, B. R., Shan, B., Baek, M.-C., & Yates, J. R. (2013) · Chemical Reviews · doi:10.1021/cr3003533 · Cited by 1,491
Next-generation proteomics: towards an integrative view of proteome dynamics
Altelaar, M. A. F., Muñoz, J., & Heck, A. J. R. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3356 · Cited by 802
Proteogenomics: concepts, applications and computational strategies
Nesvizhskii, A. I. (2014) · Nature Methods · doi:10.1038/nmeth.3144 · Cited by 797
Multiple evidence strands suggest that there may be as few as 19 000 human protein-coding genes
Ezkurdia, I., Juan, D., Rodrı́guez, J. M., Frankish, A., Diekhans, M., Harrow, J., Vázquez, J., Valencia, A., & Tress, M. L. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu309 · Cited by 583
Self-citationOvercoming challenges and dogmas to understand the functions of pseudogenes
Cheetham, S. W., Faulkner, G. J., & Dinger, M. E. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0196-1 · Cited by 294
Metadata
- Authors
- Brosch, M., Saunders, G. I., Frankish, A., Collins, M. O., Yu, L., Wright, J., Verstraten, R., Adams, D. J., Harrow, J., Choudhary, J. S., & Hubbard, T.
- Affiliations
- Wellcome Sanger Institute (GB)
- ORCID
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2011-04-01
- Volume
- 21
- Issue
- 5
- Pages
- 756-767
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC3083093
- OpenAlex
- W1997509288
- Cited by
- 130
- References
- 77
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 5 · 2022: 2 · 2021: 9 · 2020: 3 · 2019: 8 · 2018: 4 · 2017: 14 · 2016: 9 · 2015: 21 · 2014: 19 · 2013: 11 · 2012: 13
- Type
- posted content
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- Genome Research (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Wellcome Trust (077198)
- Cancer Research UK (13031)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- shotgun proteomics, peptide-spectrum matching, novel protein-coding genes, alternative splicing, processed pseudogenes, gene validation, insulin-like growth factor II, genome annotation pipeline
- MeSH terms
- Animals, Genes, Peptides, Pseudogenes, Genome, Alternative Splicing, Genomics, Proteomics, Mice, Tandem Mass Spectrometry
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