mGene: Accurate SVM-based gene finding with an application to nematode genomes

Genome Research

Volumn 19, Issue 11, 2009, Pages 2133-2143

  Schweikert, Gabriele ^a,b,c   Zien, Alexander ^a,d   Zeller, Georg ^a,c   Behr, Jonas ^a   Dieterich, Christoph ^c,e   Cheng, Soon Ong ^a,b,f   Philips, Petra ^a   De Bona, Fabio ^a   Hartmann, Lisa ^a   Bohlen, Anja ^a   Krüger, Nina ^a   Sonnenburg, Sören ^a,d   Rätsch, Gunnar ^a  

^a FRIEDRICH MIESCHER LABORATORY OF THE MAX PLANCK SOCIETY   (Germany)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^c MAX PLANCK INSTITUTE FOR DEVELOPMENTAL BIOLOGY   (Germany)

^d FRAUNHOFER FIRST   (Germany)

^e MAX DELBRÜCK CENTER FOR MOLECULAR MEDICINE   (Germany)

^f ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

NUCLEOTIDE;

ARTICLE; CAENORHABDITIS ELEGANS; CONTROLLED STUDY; EUKARYOTIC CELL; EXON; GENE SEQUENCE; GENETIC TRANSCRIPTION; GENOME; NEMATODE; NONHUMAN; PRIORITY JOURNAL; PROBABILITY; REVERSE TRANSCRIPTION POLYMERASE CHAIN REACTION; UNTRANSLATED REGION;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; CAENORHABDITIS; CAENORHABDITIS ELEGANS; COMPUTATIONAL BIOLOGY; GENES, HELMINTH; GENOME, HELMINTH; GENOMICS; REPRODUCIBILITY OF RESULTS; REVERSE TRANSCRIPTASE POLYMERASE CHAIN REACTION; RNA SPLICE SITES; SEQUENCE ANALYSIS, DNA; TRANSCRIPTION INITIATION SITE;

CAENORHABDITIS ELEGANS; EUKARYOTA; NEMATODA;

EID: 70350708134     PISSN: 10889051     EISSN: 15495469     Source Type: Journal    
DOI: 10.1101/gr.090597.108     Document Type: Article

References (54)

1. Alexeyenko A, Tamas I, Liu G, Sonnhammer EL. 2006. Automatic clustering of orthologs and inparalogs shared by multiple proteomes. Bioinformatics 22: e9-e15. (Pubitemid 44288267)
2. Allen JE, Majoros WH, Pertea M, Salzberg SL. 2006. JIGSAW, GeneZilla, and GlimmerHMM: Puzzling out the features of human genes in the encode regions. Genome Biol 7: S9. doi: 10.1186/gb-2006-7-sl-s9.
3. Altun Y, Tsochantaridis I, Hofmann T. 2003. Hidden Markov support vector machines. Scientific Commons, St. Gallen, Switzerland.
4. Ben-Hur A, Ong CS, Sonnenburg S, Scholköpf B, Rätsch G. 2008. Support vector machines and kernels for computational biology. PLoS Comput Biol 4: e1000173. doi: 10.1371/journal.pcbi.1000173.
5. Bernal A, Crammer K, Hatzigeorgiou A, Pereira F. 2007. Global discriminative learning for higher-accuracy computational gene prediction. PLoS Comput Biol, 3: e54. doi: 10.1371/journal.pcbi.0030054.
6. Brent MR. 2008. Steady progress and recent breakthroughs in the accuracy of automated genome annotation. Nat Rev Genet 9: 62-73.
7. Brown RH, Gross SS, Brent MR. 2005. Begin at the beginning: Predicting genes with 59 UTRs. Genome Res 15: 742-747.
8. Burge CB, Karlin S. 1998. Finding the genes in genomic DNA. Curr Opin Struct Biol 8: 346-354.
9. The C. elegans Sequencing Consortium. 1998. Genome sequence of the nematode C. elegans: A platform for investigating biology. Science 282: 2012-2018.
10. Coghlan A, Fiedler TJ, McKay SJ, Flicek P, Harris TW, Blasiar D. 2008. nGASP-the nematode genome annotation assessment project. BMC Bioinformatics 9: 549. doi: 10.1186/1471-2105-9-549.
11. Cortes C, Vapnik VN. 1995. Support vector networks. Mach Learn 20: 273-297.
12. DeCaprio D, Vinson JP, Pearson MD, Montgomery P, Doherty M, Galagan JE. 2007. Conrad: Gene prediction using conditional random fields. Genome Res 17: 1389-1398. (Pubitemid 47360913)
13. Dieterich C, Clifton SW, Schuster LN, Chinwalla A, Delehaunty K, Dinkelacker I, Fulton L, Fulton R, Godfrey J, Minx P, et al. 2008. The Pristionchus pacificus genome provides a unique perspective on nematode lifestyle and parasitism. Nat Genet 40: 1193-1198.
14. Foissac S, Schiex T. 2005. Integrating alternative splicing detection into gene prediction. BMC Bioinformatics 6: 25 doi: 10. 1186/1471-2105-6-25. (Pubitemid 41357989)
15. Gerischer U, editor. 2008. Acinetobacter molecular biology. Caister Academic Press, Norwich, UK.
16. Gerstein MB, Bruce C, Rozowsky JS, Zheng D, Du J, Korbel JO, Emanuelsson O, Zhang ZD, Weissman S, Snyder M. 2007. What is a gene, post- ENCODE? History and updated definition. Genome Res 17: 669-681. (Pubitemid 46919705)
17. Graber JH, Salisbury J, Hutchins LN, Blumenthal T. 2007. C. elegans sequences that control trans-splicing and operon pre-mRNA processing. RNA 13: 1409-1426. (Pubitemid 47347412)
18. Gross SS, Brent MR. 2006. Using multiple alignments to improve gene prediction. J Comput Biol 13: 379-393.
19. Gross SS, Do CB, Sirota M, Batzoglou S. 2007. CONTRAST: A discriminative, phylogeny-free approach to multiple informant de novo gene prediction. Genome Biol 8: R269. doi: 10.1186gb-12-r269. (Pubitemid 351917508)
20. Guigó R, Flicek JF, Abril P, Reymond A, Lagarde J, Denoeud F, Antonarakis S, Ashburner M, Bajic VB, Birney E, et al. 2006. EGASP: The human ENCODE genome annotation assessment project. Genome Biol 7: s1. doi: 10.1186/gb-2006-7-s1-s2.
21. Hettich R, Kortanek KO. 1993. Semi-infinite programming: Theory, methods and applications. SIAM Rev 3: 380-429.
22. Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, Bork P, Das U, Daugherty L, Duquenne L, et al. 2008. InterPro: The integrative protein signature database. Nucleic Acids Res 37: D211-D215.
23. Keerthi SS, Sundararajan S. 2007. CRF versus SVM-struct for sequence labeling. Technical report, Yahoo Research. http://www.keerthis.com/crf- comparison-keerthi-07.pdf.
24. Kent WJ. 2002. BLAT-the BLAST-like alignment tool. Genome Res 12: 656-664.
25. Kiontke K, Sudhaus W. 2006. Ecology of Caenorhabditis species. WormBook 9: 1-14. doi: 10.1895/wormbook.l.37.1.
26. Korf I. Gene finding in novel genomes. 2004. BMC Bioinformatics 5: 59. doi: 10.1186/1471-2105-5-59.
27. Kulp D, Haussler D, Reese MG, Eeckman FH. 1996. A generalized hidden Markov model for the recognition of human genes in DNA. Proc Int Conf Intell Syst Mol Biol 4: 134-142.
28. Leslie C, Eskin E, Noble WS. 2002. The spectrum kernel: A string kernel for SVM protein classification. InWorld Scientific (eds. RB Altman et al.), pp. 564-575. Pacific Symposium on Biocomputing, River Edge, NJ.
29. Liu H, Han H, Li J, Wong L. 2003. An in silico method for prediction of polyadenylation signals in human sequences. Genome Inform 14: 84-93.
30. Ng AY, Jordan MI. 2002. On discriminative vs. generative classifiers: A comparison of logistic regression and naïve Bayes. In Advances in neural information processing systems 14, Vols. 1 and 2 (eds. TG Dietterich et al.), pp. 841-848. MIT Press, Cambridge, MA.
31. Rätsch G. 2001. "Robust boosting via convex optimization." PhD thesis, University of Potsdam, Germany.
32. Rätsch G, Sonnenburg S. 2007. Large-scale hidden semi-Markov SVMs. In Advances in neural information processing systems, Vol.19 (eds. B Schölkopf et al.), pp. 1161-1168, MIT Press, Cambridge, MA.
33. Rätsch G, Sonnenburg S, Schölkopf B. 2005. RASE: Recognition of alternatively spliced exons in C. elegans. Bioinformatics (Suppl. 1) 21: i369-i377.
34. Rätsch G, Sonnenburg S, Schäfer C. 2006. Learning interpretable SVMs for biological sequence classification. BMC Bioinformatics (Suppl. 1) 7: S9. doi: 10.1186/1471-2105-7-S1-S9. (Pubitemid 46437493)
35. Rätsch G, Sonnenburg S, Srinivasan J, Witte H, Müller K-R, Sommer R, Schökopf B. 2007. Improving the Caenorhabditis elegans genome annotation using machine learning. PLoS Comput Biol 3: e20. doi: 10.1371/journal.pcbi.0030020.
36. Reese MG, Hartzell G, Harris NL, Ohler U, Abril JF, Lewis SE. 2000. Genome annotation assessment in Drosophila melanogaster. Genome Res 10: 483-501. (Pubitemid 30259078)
37. Remm M, Storm CE, Sonnhammer EL. 2001. Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. J Mol Biol 314: 1041-1052. (Pubitemid 34073068)
38. Salamov AA, Solovyev VV. 2000. Ab initio gene finding in Drosophila genomic DNA. Genome Res 10: 516-522. (Pubitemid 30259081)
39. Sarawagi S, Cohen WW. 2004. Semi-Markov conditional random fields for information extraction. In Advances in neural information processing systems; Proceedings of the 2004 conference, Vol.17 (eds. LK Saul. et al.). MIT Press, Cambridge, MA.
40. Schölkopf B, Smola AJ. 2002. Learning with kernels. MIT Press, Cambridge, MA.
41. Schweikert G, Behr J, Zien A, Zeller G, Ong CS, Sonnenburg S, Raetsch G. 2009. mGene.web: A web service for accurate computational gene finding. Nucleic Acids Res 37: W312-W316.
42. Siepel A, Diekhans M, Brejova B, Langton L, Stevens M, Comstock CLG, Davis C, Ewing B, Oommen S, Lau C, et al. 2007. Targeted discovery of novel human exons by comparative genomics. Genome Res 17: 1763-1773. (Pubitemid 351360000)
43. Sonnenburg S, Zien A, Rätsch G. 2006. ARTS: Accurate recognition of transcription starts in human. Bioinformatics 22: e472-e480.
44. Sonnenburg S, Rätsch G, Rieck K. 2007a. Large-scale learning with string kernels. In Large-scale kernel machines, Chapter 4 (eds. L Bottou et al.), pp. 73-104. MIT Press, Cambridge, MA.
45. Sonnenburg S, Schweikert G, Philips P, Behr J, Rätsch G. 2007b. Accurate splice site prediction using support vector machines. BMC Bioinformatics 8: doi: 10.1186/1471-2105-8-S10-S7.
46. Stanke M, Schoffmann O, Morgenstern B, Waack S. 2006. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinformatics 7: 62. doi: 10.1186/1471- 2105-7-62.
47. Stein LD, Bao Z, Blasiar D, Blumenthal T, Brent MR, Chen N, Chinwalla A, Clarke L, Clee C, Coghlan A, et al. 2003. The genome sequence of Caenorhabditis briggsae: A platform for comparative genomics. PLoS Biol 1: E45. doi: 10.1371/journal.pbio.0000045. (Pubitemid 39112161)
48. Sternberg PW, Waterston RH, Speith J, Eddy SR, Wilson RK. 2003. Genome sequence of additional Caenorhabditis species: Enhancing the utility of C. elegans as a model organism. National Human Genome Research Institute. http://www.genome.gov/Pages/Research/Sequencing/ SeqProposals/CaenorhabditisSEQ. pdf.
49. Stormo GD, Haussler D. 1994. Optimally parsing a sequence into different classes based on multiple types of evidence. Proc Int Conf Intell Mol Biol 2: 369-375.
50. Tsochantaridis I, Hofmann T, Joachims T, Altun Y. 2004. Support vector machine learning for interdependent and structured output spaces. In ACM international conference proceedings series; Proceedings of the twentyfirst international conference on machine learning. ACM, Alberta, Canada.
51. Tsochantaridis I, Hofmann T, Joachims T, Altun Y. 2005. Large-margin methods for structured output spaces. J Mach Learn Res 6: 1453-1484.
52. Wu CH, Apweiler R, Bairoch A, Natale DA, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, et al. 2006. The universal protein resource (uniprot): An expanding universe of protein information. Nucleic Acids Res 34: D187-D191.
53. Zdobnov EM, Apweiler R. 2001. InterProScan-an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17: 847-848. (Pubitemid 32970486)
54. Zien A, Rätsch G, Mika S, Schölkopf B, Lengauer T, Müller K-R. 2000. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 16: 799-807.