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A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms

Scalzitti, Nicolas, Jeannin-Girardon, Anne, Collet, Pierre, Poch, Olivier, Thompson, Julie D.·2020·BMC Genomics
DOI 10.1186/s12864-020-6707-9PMC PMC7147072

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Abstract

Background: The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality, complex gene structures, or a lack of suitable sequences for evidence-based annotations. Results: We describe the construction of a new benchmark, called G3PO (benchmark for Gene and Protein Prediction PrOgrams), designed to represent many of the typical challenges faced by current genome annotation projects. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms, and a number of test sets are defined to evaluate the effects of different features, including genome sequence quality, gene structure complexity, protein length, etc. We used the benchmark to perform an independent comparative analysis of the most widely used ab initio gene prediction programs and identified the main strengths and weaknesses of the programs. More importantly, we highlight a number of features that could be exploited in order to improve the accuracy of current prediction tools. Conclusions: The experiments showed that ab initio gene structure prediction is a very challenging task, which should be further investigated. We believe that the baseline results associated with the complex gene test sets in G3PO provide useful guidelines for future studies.

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Research works printed but not linked: 46

  1. Matz MV. Fantastic beasts and how to sequence them: ecological genomics for obscure model organisms. Trends Genet. 2018;34:121–32. · DOI
  2. Salzberg SL. Next-generation genome annotation: we still struggle to get it right. Genome Biol. 2019;20:92 s13059–019–1715–2. · DOI · Paper page
  3. Mudge JM, Harrow J. The state of play in higher eukaryote gene annotation. Nat Rev Genet. 2016;17:758–72. · DOI
  4. Ozsolak F, Platt AR, Jones DR, Reifenberger JG, Sass LE, McInerney P, et al. Direct RNA sequencing. Nature. 2009;461:814–8. · DOI
  5. Workman RE, Tang AD, Tang PS, Jain M, Tyson JR, Zuzarte PC, et al. Nanopore native RNA sequencing of a human poly(a) transcriptome. Nat Methods. 2019; (in press). · DOI
  6. Yeh R-F, Lim LP, Burge CB. Computational inference of homologous gene structures in the human genome. Genome Res. 2001;11:803–16. · DOI
  7. Birney E. GeneWise and Genomewise. Genome Res. 2004;14:988–95. · DOI · Paper page
  8. Stanke M, Schöffmann O, Morgenstern B, Waack S. Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources. BMC Bioinform. 2006;7:62. · DOI · Paper page
  9. Kapustin Y, Souvorov A, Tatusova T, Lipman D. Splign: algorithms for computing spliced alignments with identification of paralogs. Biol Direct. 2008;3:20. · DOI
  10. Testa AC, Hane JK, Ellwood SR, Oliver RP. CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts. BMC Genomics. 2015;16:170. · DOI
  11. Cook DE, Valle-Inclan JE, Pajoro A, Rovenich H, Thomma BPHJ, Faino L. Long-read annotation: automated eukaryotic genome annotation based on long-read cDNA sequencing. Plant Physiol. 2019;179:38–54. · DOI
  12. Huang Y, Chen S-Y, Deng F. Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction. Comput Struct Biotechnol J. 2016;14:298–303. · DOI
  13. Burge C, Karlin S. Prediction of complete gene structures in human genomic DNA. J Mol Biol. 1997;268:78–94. · DOI
  14. Salzberg SL, Pertea M, Delcher AL, Gardner MJ, Tettelin H. Interpolated Markov models for eukaryotic gene finding. Genomics. 1999;59:24–31. · DOI
  15. Guigó R, Knudsen S, Drake N, Smith T. Prediction of gene structure. J Mol Biol. 1992;226:141–57. · DOI
  16. Korf I. Gene finding in novel genomes. BMC Bioinform. 2004;5:59. · DOI
  17. Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003;19(Suppl 2):ii215–25. · DOI · Paper page
  18. Lomsadze A. Gene identification in novel eukaryotic genomes by self-training algorithm. Nucleic Acids Res. 2005;33:6494–506. · DOI
  19. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2. · DOI · Paper page
  20. Drăgan M-A, Moghul I, Priyam A, Bustos C, Wurm Y. GeneValidator: identify problems with protein-coding gene predictions. Bioinformatics. 2016;32:1559–61. · DOI
  21. Nishimura O, Hara Y, Kuraku S. Evaluating genome assemblies and gene models using gVolante. In: Kollmar M, editor. Gene prediction. New York: Springer New York; 2019. p. 247–56. · DOI
  22. Kemena C, Dohmen E, Bornberg-Bauer E. DOGMA: a web server for proteome and transcriptome quality assessment. Nucleic Acids Res. 2019;47:W507–10. · DOI
  23. Delcourt V, Staskevicius A, Salzet M, Fournier I, Roucou X. Small proteins encoded by Unannotated ORFs are rising stars of the proteome, Confirming Shortcomings in Genome Annotations and Current Vision of an mRNA. Proteomics. 2018;18:1700058. · DOI
  24. Mat-Sharani S, Firdaus-Raih M. Computational discovery and annotation of conserved small open reading frames in fungal genomes. BMC Bioinform. 2019;19:551. · DOI
  25. Rajput B, Pruitt KD, Murphy TD. RefSeq curation and annotation of stop codon recoding in vertebrates. Nucleic Acids Res. 2019;47:594–606. · DOI
  26. Burset M, Guigó R. Evaluation of gene structure prediction programs. Genomics. 1996;34:353–67. · DOI
  27. Rogic S, Mackworth AK, Ouellette FBF. Evaluation of gene-finding programs on mammalian sequences. Genome Res. 2001;11:817–32. · DOI
  28. Guigo R. An assessment of gene prediction accuracy in large DNA sequences. Genome Res. 2000;10:1631–42. · DOI
  29. Goodswen SJ, Kennedy PJ, Ellis JT. Evaluating high-throughput Ab initio gene finders to discover proteins encoded in eukaryotic pathogen genomes missed by laboratory techniques. PLoS One. 2012;7:e50609. · DOI
  30. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2017;45:D158–69. · DOI · Paper page
  31. Hubbard T, Barker D, Birney E, Cameron G, Chen Y, Clark L, et al. The Ensembl genome database project. Nucleic Acids Res. 2002;30:38–41. · DOI
  32. Wilbrandt J, Misof B, Panfilio KA, Niehuis O. Repertoire-wide gene structure analyses: a case study comparing automatically predicted and manually annotated gene models. BMC Genomics. 2019;20:753. · DOI
  33. Schnoes AM, Brown SD, Dodevski I, Babbitt PC. Annotation error in public databases: Misannotation of molecular function in enzyme Superfamilies. PLoS Comput Biol. 2009;5. · DOI
  34. Yandell M, Ence D. A beginner’s guide to eukaryotic genome annotation. Nat Rev Genet. 2012;13:329–42. · DOI · Paper page
  35. Sberro H, Fremin BJ, Zlitni S, Edfors F, Greenfield N, Snyder MP, et al. Large-scale analyses of human microbiomes reveal thousands of small, novel genes. Cell. 2019;178:1245–1259.e14. · DOI · Paper page
  36. Ter-Hovhannisyan V, Lomsadze A, Chernoff YO, Borodovsky M. Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training. Genome Res. 2008;18:1979–90. · DOI
  37. Reid I, O’Toole N, Zabaneh O, Nourzadeh R, Dahdouli M, Abdellateef M, et al. SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models. BMC Bioinformatics. 2014;15:229. · DOI
  38. Hoff KJ, Lange S, Lomsadze A, Borodovsky M, Stanke M. BRAKER1: unsupervised RNA-Seq-based genome annotation with GeneMark-ET and AUGUSTUS: table 1. Bioinformatics. 2016;32:767–9. · DOI
  39. Zhang Y, Liu X, MacLeod J, Liu J. Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach. BMC Genomics. 2018;19. https://doi.org/10.1186/s12864-018-5350-1. · DOI
  40. Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, et al. Predicting splicing from primary sequence with deep learning. Cell. 2019;176:535–548.e24. · DOI · Paper page
  41. Nevers Y, Kress A, Defosset A, Ripp R, Linard B, Thompson JD, et al. OrthoInspector 3.0: open portal for comparative genomics. Nucleic Acids Res. 2019;47(Database issue):D411–8. · DOI
  42. Khenoussi W, Vanhoutrève R, Poch O, Thompson JD. SIBIS: a Bayesian model for inconsistent protein sequence estimation. Bioinformatics. 2014;30:2432–9. · DOI
  43. Rodriguez JM, Maietta P, Ezkurdia I, Pietrelli A, Wesselink J-J, Lopez G, et al. APPRIS: annotation of principal and alternative splice isoforms. Nucleic Acids Res. 2013;41(Database issue):D110–7. · DOI · Paper page
  44. Kozak M. Possible role of flanking nucleotides in recognition of the AUG initiator codon by eukaryotic ribosomes. Nucleic Acids Res. 1981;9:5233–52. · DOI · Paper page
  45. Gao K, Masuda A, Matsuura T, Ohno K. Human branch point consensus sequence is yUnAy. Nucleic Acids Res. 2008;36:2257–67. · DOI
  46. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80. · DOI · Paper page

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Metadata

Authors
Scalzitti, N., Jeannin-Girardon, A., Collet, P., Poch, O., & Thompson, J. D.
ORCID
Thompson, Julie D.
Venue
BMC Genomics
Publisher
Springer Science and Business Media LLC
Published
2020-04-09
Volume
21
Issue
1
ISSN
1471-2164
DOI
10.1186/s12864-020-6707-9
PMC
PMC7147072
Cited by
96
References
2
Language
en
Licence
Funders
  • BIONIRIA (GB)
  • Agence Nationale de la Recherche (GA-676559, ANR-18-RAR3-0006-02)

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