Improving GENCODE reference gene annotation using a high-stringency proteogenomics workflow

Nature Communications

Volumn 7, Issue , 2016, Pages

  Wright, James C ^a   Mudge, Jonathan ^a   Weisser, Hendrik ^a   Barzine, Mitra P ^b   Gonzalez, Jose M ^a   Brazma, Alvis ^b   Choudhary, Jyoti S ^a   Harrow, Jennifer ^a  

^a WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^b EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEOME; PROTEIN;

GENE; GENE EXPRESSION; GENOME; NUMERICAL MODEL; PEPTIDE; PROTEIN; PROTEOMICS;

ARTICLE; FILTRATION; GENCODE CONSORTIUM; GENETIC MODEL; HUMAN; HUMAN GENOME; HUMAN TISSUE; LARGE SCALE PRODUCTION; MOLECULAR GENETICS; PEPTIDE ANALYSIS; PROTEOGENOMICS; PROTEOMICS; PSEUDOGENE; REFERENCE DATABASE; AMINO ACID SEQUENCE; GENE EXPRESSION REGULATION; GENE ONTOLOGY; GENETICS; METABOLISM; OPEN READING FRAME; PROCEDURES; STATISTICS AND NUMERICAL DATA;

AMINO ACID SEQUENCE; GENE EXPRESSION REGULATION; GENE ONTOLOGY; GENOME, HUMAN; HUMANS; MOLECULAR SEQUENCE ANNOTATION; OPEN READING FRAMES; PROTEINS; PROTEOGENOMICS; PSEUDOGENES;

EID: 84973345496     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms11778     Document Type: Article

References (57)

1. Harrow, J. et al. GENCODE: producing a reference annotation for ENCODE. Genome Biol. 7(Suppl 1): S41-S49 (2006).
2. Harrow, J. et al. Identifying protein-coding genes in genomic sequences. Genome Biol. 10, 201 (2009).
3. Lin, M. F., Jungreis, I. & Kellis, M. PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions. Bioinformatics 27, i275-i282 (2011).
4. Stadler, C. et al. Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells. Nat. Methods 10, 315-323 (2013).
5. Ahn, J. M. et al. Proteogenomic analysis of human chromosome 9-encoded genes from human samples and lung cancer tissues. J. Proteome Res. 13, 137-146 (2014).
6. Khatun, J. et al. Whole human genome proteogenomic mapping for ENCODE cell line data: identifying protein-coding regions. BMC Genomics 14, 141 (2013).
7. Woo, S. et al. Advanced proteogenomic analysis reveals multiple peptide mutations and complex immunoglobulin peptides in colon cancer. J. Proteome Res. 14, 3555-3567 (2015).
8. Ezkurdia, I. et al. Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum. Mol. Genet. 23, 5866-5878 (2014).
9. Tanner, S. et al. Improving gene annotation using peptide mass spectrometry. Genome Res. 17, 231-239 (2007).
10. Brosch, M. et al. Shotgun proteomics aids discovery of novel protein-coding genes, alternative splicing, and 'resurrected' pseudogenes in the mouse genome. Genome Res. 21, 756-767 (2011).
11. Beck, M., Claassen, M. & Aebersold, R. Comprehensive proteomics. Curr. Opin. Biotechnol. 22, 3-8 (2011).
12. Neuhauser, N. et al. High performance computational analysis of large-scale proteome data sets to assess incremental contribution to coverage of the human genome. J. Proteome Res. 12, 2858-2868 (2013).
13. Omenn, G. S. et al. Metrics for the human proteome project 2015: progress on the human proteome and guidelines for high-confidence protein identification. J. Proteome Res. 14, 3452-3460 (2015).
14. Kim, M. S. et al. A draft map of the human proteome. Nature 509, 575-581 (2014).
15. Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582-587 (2014).
16. Pandey, A. & Pevzner, P. A. Proteogenomics. Proteomics 14, 2631-2632 (2014).
17. Nesvizhskii, A. I. Proteogenomics: concepts, applications and computational strategies. Nat. Methods 11, 1114-1125 (2014).
18. Blakeley, P., Overton, I. M. & Hubbard, S. J. Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies. J. Proteome Res. 11, 5221-5234 (2012).
19. Zhang, K. et al. A note on the false discovery rate of novel peptides in proteogenomics. Bioinformatics 31, 3249-3253 (2015).
20. Savitski, M. M., Wilhelm, M., Hahne, H., Kuster, B. & Bantscheff, M. A scalable approach for protein false discovery rate estimation in large proteomic data sets. Mol. Cell. Proteomics 14, 2394-2404 (2015).
21. Gupta, N., Bandeira, N., Keich, U. & Pevzner, P. A. Target-decoy approach and false discovery rate: when things may go wrong. J. Am. Soc. Mass Spectrom. 22, 1111-1120 (2011).
22. Kall, L., Canterbury, J. D., Weston, J., Noble, W. S. & MacCoss, M. J. Semi-supervised learning for peptide identification from shotgun proteomics data sets. Nat. Methods 4, 923-925 (2007).
23. Armengaud, J. et al. Non-model organisms, a species endangered by proteogenomics. J. Proteomics 105, 5-18 (2014).
24. Kucharova, V. & Wiker, H. G. Proteogenomics in microbiology: taking the right turn at the junction of genomics and proteomics. Proteomics 14, 2360-2675 (2014).
25. Branca, R. M. et al. HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nat. Methods 11, 59-62 (2014).
26. Zhang, Y. et al. Tissue-based proteogenomics reveals that human testis endows plentiful missing proteins. J. Proteome Res. 14, 3583-3594 (2015).
27. Uhlen, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
28. Deutsch, E. W. The peptideatlas project. Methods Mol. Biol. 604, 285-296 (2010).
29. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621-628 (2008).
30. Takahashi, H., Kato, S., Murata, M. & Carninci, P. CAGE (cap analysis of gene expression): a protocol for the detection of promoter and transcriptional networks. Methods Mol. Biol. 786, 181-200 (2012).
31. Derti, A. et al. A quantitative atlas of polyadenylation in five mammals. Genome Res. 22, 1173-1183 (2012).
32. Uhlen, M. et al. Towards a knowledge-based Human Protein Atlas. Nat. Biotechnol. 28, 1248-1250 (2010).
33. Vizcaino, J. A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063-D1069 (2013).
34. Desiere, F. et al. The PeptideAtlas project. Nucleic Acids Res. 34, D655-D658 (2006).
35. Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918-920 (2012).
36. Kohlbacher, O. et al. TOPP - the OpenMS proteomics pipeline. Bioinformatics 23, e191-e197 (2007).
37. Harrow, J. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760-1774 (2012).
38. UniProt, C. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204-D212 (2015).
39. Stanke, M., Steinkamp, R., Waack, S. & Morgenstern, B. AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res. 32, W309-W312 (2004).
40. Guttman, M. et al. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat. Biotechnol. 28, 503-510 (2010).
41. Djebali, S. et al. Landscape of transcription in human cells. Nature 489, 101-108 (2012).
42. Rice, P., Longden, I. & Bleasby, A. EMBOSS: the European molecular biology open software suite. Trends Genet. 16, 276-277 (2000).
43. Kim, S. & Pevzner, P. A. MS-GF+ makes progress towards a universal database search tool for proteomics. Nat. Commun. 5, 5277 (2014).
44. Brosch, M., Yu, L., Hubbard, T. & Choudhary, J. Accurate and sensitive peptide identification with Mascot Percolator. J. Proteome Res. 8, 3176-3181 (2009).
45. Wright, J. C. et al. Enhanced peptide identification by electron transfer dissociation using an improved Mascot Percolator. Mol. Cell. Proteomics 11, 478-491 (2012).
46. Granholm, V. et al. Fast and accurate database searches with MS-GF+Percolator. J. Proteome Res. 13, 890-897 (2014).
47. Spivak, M., Weston, J., Bottou, L., Kall, L. & Noble, W. S. Improvements to the percolator algorithm for peptide identification from shotgun proteomics data sets. J. Proteome Res. 8, 3737-3745 (2009).
48. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).
49. Pruitt, K. D. et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 42, D756-D763 (2014).
50. Lane, L. et al. neXtProt: a knowledge platform for human proteins. Nucleic Acids Res. 40, D76-D83 (2012).
51. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
52. Anders, S., Pyl, P. T. & Huber, W. HTSeq - a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166-169 (2015).
53. Flicek, P. et al. Ensembl 2014. Nucleic Acids Res. 42, D749-D755 (2014).
54. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 43, D6-17 (2015).
55. FANTOM Consortium et al. A promoter-level mammalian expression atlas. Nature 507, 462-470 (2014).
56. Fagerberg, L. et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol. Cell. Proteomics 13, 397-406 (2014).
57. Hezroni, H. et al. Principles of long non-coding RNA evolution derived from direct comparison of transcriptomes in 17 species. Cell Rep. 11, 1110-1122 (2015).