Continuous distributed representation of biological sequences for deep proteomics and genomics

PLoS ONE

Volumn 10, Issue 11, 2015, Pages

  Asgari, Ehsaneddin ^a   Mofrad, Mohammad R K ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

3 PHOSPHOSHIKIMATE 1 CARBOXYVINYLTRANSFERASE; ADENOSINE TRIPHOSPHATASE; ALDEHYDE DEHYDROGENASE; ANKYRIN; BRCA1 PROTEIN; CHAPERONIN CONTAINING TCP1; GLYCINE; NUCLEOPORIN; PHENYLALANINE; PROTON TRANSPORTING ADENOSINE TRIPHOSPHATE SYNTHASE; REDUCED NICOTINAMIDE ADENINE DINUCLEOTIDE; RIBOSOMAL PROTEIN L10E; RIBOSOMAL PROTEIN L17; RIBOSOMAL PROTEIN L19; RIBOSOMAL PROTEIN L20; RIBOSOMAL PROTEIN L23; RIBOSOMAL PROTEIN L3; RIBOSOMAL PROTEIN L33; RIBOSOMAL PROTEIN L35; RIBOSOMAL PROTEIN S10P; RIBOSOMAL PROTEIN S11; RIBOSOMAL PROTEIN S18; RIBOSOMAL PROTEIN S2; RIBOSOMAL PROTEIN S20E; RIBOSOMAL PROTEIN S23; RIBOSOMAL PROTEIN S4; RIBOSOME PROTEIN; RNA POLYMERASE BETA SUBUNIT; TRANSFER RNA; UNCLASSIFIED DRUG; UNINDEXED DRUG; INTRINSICALLY DISORDERED PROTEIN; PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOINFORMATICS; CLASSIFIER; GENE VECTOR; GENOMICS; HUMAN; HYDROPHOBICITY; NUCLEIC ACID DATABASE; PROTEIN ANALYSIS; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; PROTEIN VECTOR; PROTEOMICS; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; SUPPORT VECTOR MACHINE; BIOLOGY; CHEMISTRY; CLASSIFICATION; NATURAL LANGUAGE PROCESSING; PROCEDURES; PROTEIN DATABASE; PROTEIN SECONDARY STRUCTURE;

AMINO ACID SEQUENCE; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; GENOMICS; INTRINSICALLY DISORDERED PROTEINS; NATURAL LANGUAGE PROCESSING; NUCLEAR PORE COMPLEX PROTEINS; PROTEIN STRUCTURE, SECONDARY; PROTEINS; PROTEOMICS; SUPPORT VECTOR MACHINE;

EID: 84953217068     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0141287     Document Type: Article

References (39)

1. Yandell MD, Majoros WH. Genomics and natural language processing. Nature Reviews Genetics. 2002; 3(8):601-610.
2. Searls DB. The language of genes. Nature. 2002; 420(6912):211-217. doi: 10.1038/nature01255
3. Motomura K, Fujita T, Tsutsumi M, Kikuzato S, Nakamura M, Otaki JM. Word decoding of protein amino acid sequences with availability analysis: A linguistic approach. PloS one. 2012; 7(11):e50039. doi: 10.1371/journal.pone.0050039
4. Cai Y, Lux MW, Adam L, Peccoud J. Modeling structure-function relationships in synthetic DNA sequences using attribute grammars. PLoS Comput Biol. 2009; 5(10):e1000529. doi: 10.1371/journal. pcbi.1000529
5. Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural processing letters. 1999; 9(3):293-300. doi: 10.1023/A:1018628609742
6. Hinton GE. Distributed representations. School of Computer Science at Carnegie Mellon University. 1984
7. Lasko TA, Denny JC, Levy MA. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PloS one. 2013; 8(6):e66341. doi: 10.1371/journal. pone.0066341
8. Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RK, et al. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015; 347(6218):1254806. doi: 10.1126/science.1254806
9. Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. The Journal of Machine Learning Research. 2011; 12:2493-2537.
10. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems; 2013. p. 3111-3119.
11. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:13013781. 2013
12. Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic acids research. 2002; 30(7):1575-1584. doi: 10.1093/nar/30.7.1575
13. Bork P, Dandekar T, Diaz-Lazcoz Y, Eisenhaber F, Huynen M, Yuan Y. Predicting function: from genes to genomes and back. Journal of molecular biology. 1998; 283(4):707-725. doi: 10.1006/jmbi.1998. 2144
14. Remmert M, Biegert A, Hauser A, Söding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nature methods. 2012; 9(2):173-175. doi: 10.1038/nmeth.1818
15. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: The protein families database. Nucleic acids research. 2013;p. gkt1223.
16. Cai C, Han L, Ji ZL, Chen X, Chen YZ. SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic acids research. 2003; 31(13):3692-3697. doi: 10.1093/nar/gkg600
17. Leslie CS, Eskin E, Noble WS. The spectrum kernel: A string kernel for SVM protein classification. In: Pacific symposium on biocomputing. vol. 7. World Scientific; 2002. p. 566-575.
18. Huynen M, Snel B, Lathe W, Bork P. Predicting protein function by genomic context: quantitative evaluation and qualitative inferences. Genome research. 2000; 10(8):1204-1210. doi: 10.1101/gr.10.8.1204
19. Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: A structural classification of proteins database for the investigation of sequences and structures. Journal of molecular biology. 1995; 247(4):536-540. doi: 10.1016/S0022-2836(05)80134-2
20. Aragues R, Sali A, Bonet J, Marti-Renom MA, Oliva B. Characterization of protein hubs by inferring interacting motifs from protein interactions. PloS Computational Biology. 2007; 3.9:e178. doi: 10.1371/journal.pcbi.0030178
21. Dunker AK, Silman I, Uversky VN, Sussman JL. Function and structure of inherently disordered proteins. Current opinion in structural biology. 2008; 18(6):756-764. doi: 10.1016/j.sbi.2008.10.002
22. Dyson HJ, Wright PE. Intrinsically unstructured proteins and their functions. Nature reviews Molecular cell biology. 2005; 6(3):197-208. doi: 10.1038/nrm1589
23. Sugase K, Dyson HJ, Wright PE. Mechanism of coupled folding and binding of an intrinsically disordered protein. Nature. 2007; 447(7147):1021-1025. doi: 10.1038/nature05858
24. He B, Wang K, Liu Y, Xue B, Uversky VN, Dunker AK. Predicting intrinsic disorder in proteins: An overview. Cell research. 2009; 19(8):929-949. doi: 10.1038/cr.2009.87
25. Jamali T, Jamali Y, Mehrbod M, Mofrad M. Nuclear pore complex: biochemistry and biophysics of nucleocytoplasmic transport in health and disease. Int Rev Cell Mol Biol. 2011; 287:233-286. doi: 10. 1016/B978-0-12-386043-9.00006-2
26. Sickmeier M, Hamilton JA, LeGall T, Vacic V, Cortese MS, Tantos A, et al. DisProt: The database of disordered proteins. Nucleic acids research. 2007; 35(suppl 1):D786-D793. doi: 10.1093/nar/gkl893
27. Ando D, Colvin M, Rexach M, Gopinathan A. Physical motif clustering within intrinsically disordered nucleoporin sequences reveals universal functional features. PloS one. 2013; 8(9):e73831. doi: 10. 1371/journal.pone.0073831
28. Azimi M, Mofrad MR. Higher Nucleoporin-Importinβ Affinity at the Nuclear Basket Increases Nucleocytoplasmic Import. PloS one. 2013; 8(11):e81741. doi: 10.1371/journal.pone.0081741
29. Peyro M, Soheilypour M, Lee BL, Mofrad M. Evolutionary conserved sequence features optimizes nucleoporins behavior for cargo transportation through nuclear pore complex. Scientific Reports. In press 2015;.
30. Procter JB, Thompson J, Letunic I, Creevey C, Jossinet F, Barton GJ. Visualization of multiple alignments, phylogenies and gene family evolution. Nature methods. 2010; 7:S16-S25. doi: 10.1038/nmeth.1434
31. Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Rajandream MA, et al. Artemis: sequence visualization and annotation. Bioinformatics. 2000; 16(10):944-945. doi: 10.1093/bioinformatics/16.10.944
32. Ganapathiraju M, Weisser D, Rosenfeld R, Carbonell J, Reddy R, Klein-Seetharaman J. Comparative n-gram analysis of whole-genome protein sequences. In: Proceedings of the second international conference on Human Language Technology Research. Morgan Kaufmann Publishers Inc.; 2002. p. 76-81.
33. Srinivasan SM, Vural S, King BR, Guda C. Mining for class-specific motifs in protein sequence classification. BMC bioinformatics. 2013; 14(1):96. doi: 10.1186/1471-2105-14-96
34. Vries JK, Liu X. Subfamily specific conservation profiles for proteins based on n-gram patterns. BMC bioinformatics. 2008; 9(1):72. doi: 10.1186/1471-2105-9-72
35. Goldberg Y, Levy O. word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:14023722. 2014;.
36. Van der Maaten L, Hinton G. Visualizing data using t-SNE. Journal of Machine Learning Research. 2008; 9(2579-2605):85.
37. McGregor E. Proteins and proteomics: A laboratory manual. Journal of Proteome Research. 2004; 3 (4):694-694. doi: 10.1021/pr040022a
38. Rose PW, Bi C, Bluhm WF, Christie CH, Dimitropoulos D, Dutta S, et al. The RCSB Protein Data Bank: new resources for research and education. Nucleic acids research. 2013; 41(D1):D475-D482. doi: 10. 1093/nar/gks1200
39. Platzer A. Visualization of SNPs with t-SNE. PloS one. 2013; 8(2):e56883. doi: 10.1371/journal.pone. 0056883