Classification of metagenomics data at lower taxonomic level using a robust supervised classifier

Evolutionary Bioinformatics

Volumn 11, Issue , 2015, Pages 3-10

  Hou, Tao ^a   Liu, Fu ^a   Liu, Yun ^a   Zou, Qing Yu ^a   Zhang, Xiao ^a   Wang, Ke ^a  

^a JILIN UNIVERSITY   (China)

Author keywords

Metagenomics; Outliers; Robustness; Sequencing errors; Support vector data description (SVDD); Taxonomic classification

Indexed keywords

DNA FRAGMENT;

ACCURACY; ANALYTICAL PARAMETERS; ARCHAEAL GENOME; ARTICLE; BACTERIAL GENOME; CLASSIFICATION ALGORITHM; CLASSIFIER; CONTROLLED STUDY; FEMALE; GENUS; HIDDEN MARKOV MODEL; HUMAN; INTERMETHOD COMPARISON; KERNEL METHOD; MALE; MATHEMATICAL ANALYSIS; METAGENOME; METAGENOMICS; METHOD HARMONIC MEAN; METHOD ROBUSTNESS; MICROBIAL DIVERSITY; NONHUMAN; PREDICTION; SENSITIVITY AND SPECIFICITY; SPECIES; SUPPORT VECTOR MACHINE; TAXONOMY; WEIGHTED SUPPORT VECTOR DOMAIN DESCRIPTION;

ARCHAEA; BACTERIA (MICROORGANISMS);

EID: 84921896581     PISSN: None     EISSN: 11769343     Source Type: Journal    
DOI: 10.4137/EBO.S20523     Document Type: Article

References (32)

1. Turnbaugh PJ, Gordon JI. An invitation to the marriage of metagenomics and metabolomics. Cell. 2008;134(5):708–13.
2. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10.
3. Krause L, Diaz NN, Goesmann A, et al. Phylogenetic classification of short environmental DNA fragments. Nucleic Acids Res. 2008;36(7):2230–9.
4. Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17(3):377–86.
5. Schreiber F, Gumrich P, Daniel R, Meinicke P. Treephyler: fast taxonomic profiling of metagenomes. Bioinformatics. 2010;26(7):960–1.
6. Stark M, Berger SA, Stamatakis A, von Mering C. MLTreeMap – accurate maximum likelihood placement of environmental DNA sequences into taxonomic and functional reference phylogenies. BMC Genomics. 2010;11:461.
7. Zhang Y, Sun Y. MetaDomain: a profile HMM-based protein domain classification tool for short sequences. Pac Symp Biocomput. 2012;17:271–82.
8. Campbell A, MRAzek J, Karlin S. Genome signature comparisons among prokaryote, plasmid, and mitochondrial DNA. Proc Natl Acad Sci USA. 1999;96(16):9184–9.
9. McHardy AC, Martin HG, Tsirigos A, Hugenholtz P, Rigoutsos I. Accurate phylogenetic classification of variable-length DNA fragments. Nat Methods. 2006;4(1):63–72.
10. Patil KR, Haider P, Pope PB, et al. Taxonomic metagenome sequence assignment with structured output models. Nat Methods. 2011;8(3):191–2.
11. Brady A, Salzberg SL. Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models. Nat Methods. 2009;6(9):673–6.
12. Diaz NN, Krause L, Goesmann A, Niehaus K, Nattkemper TW. TACOA – taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach. BMC Bioinformatics. 2009;10(1):56.
13. Chan CK, Hsu A, Halgamuge S, Tang SL. Binning sequences using very sparse labels within a metagenome. BMC Bioinformatics. 2008;9(1):215.
14. Rosen GL, Reichenberger ER, Rosenfeld AM. NBC: the Naive Bayes classification tool webserver for taxonomic classification of metagenomic reads. Bioinformatics. 2011;27(1):127–9.
15. Nalbantoglu OU, Way SF, Hinrichs SH, Sayood K. RAIphy: phylogenetic classification of metagenomics samples using iterative refinement of relative abundance index profiles. BMC Bioinformatics. 2011;12(1):41.
16. Zhenqiu L, Zhenqiu L, Halima B, Ming T. Efficient feature selection and multiclass classification with integrated instance and model based learning. Evol Bioinform. 2012;8:197–205.
17. Weber M, Teeling H, Huang S, et al. Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics. ISME J. 2010;5(5):918–28.
18. Leung HC, Yiu SM, Yang B, et al. A robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio. Bioinformatics. 2011;27(11):1489–95.
19. Hoff KJ. The effect of sequencing errors on metagenomic gene prediction. BMC Genomics. 2009;10(1):520.
20. Trimble WL, Keegan KP, D’Souza M, et al. Short-read reading-frame predictors are not created equal: sequence error causes loss of signal. BMC Bioinformatics. 2012;13:183.
21. Zhou F, Olman V, Xu Y. Barcodes for genomes and applications. BMC Bioinformatics. 2008;9(1):546.
22. Hou T, Liu F, Lin CX, Li DY. A new vector for identification of prokaryotes and their variable-size genomes. Curr Microbiol. 2013;66(1):96–101.
23. Wang C-D, Lai J. Position regularized support vector domain description. Pattern Recognit. 2012;46:875–84.
24. Tax DM, Duin RP. Support vector data description. Mach Learn. 2004;54(1):45–66.
25. Chor B, Horn D, Goldman N, Levy Y, Massingham T. Genomic DNA k-mer spectra: models and modalities. Genome Biol. 2009;10(10):R108.
26. Hastie T, Tibshirani R, Friedman J, Franklin J. The elements of statistical learning: data mining, inference and prediction. Math Intell. 2005;27(2):83–5.
27. Richter DC, Ott F, Auch AF, Schmid R, Huson DH. MetaSim – a sequencing simulator for genomics and metagenomics. PLoS One. 2008;3(10):e3373.
28. Qin J, Li R, Raes J, et al; MetaHIT Consortium. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59–65.
29. Li R, Zhu H, Ruan J, et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 2010;20(2):265–72.
30. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. ACM Trans Intell Sys Technol. 2011;2(3):27.
31. Frank E, Hall M, Trigg L, Holmes G, Witten IH. Data mining in bioinformatics using Weka. Bioinformatics. 2004;20(15):2479–81.
32. Vapnik V. The Nature of Statistical Learning Theory. Berlin: Springer; 2000.