Discovering the unknown: improving detection of novel species and genera from short reads.
High-throughput sequencing technologies enable metagenome profiling, simultaneous sequencing of multiple microbial species present within an environmental sample. Since metagenomic data includes sequence fragments ('reads') from organisms that are absent from any database, new algorithms must be dev...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 495849 - 495850 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104531591&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104531591 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2011 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104531591 2011457001 NLM21541181 PMC3085467 104531591 ppf: 495849 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Discovering the unknown: improving detection of novel species and genera from short reads. aug: au: Rosen, Gail L Polikar, Robi Caseiro, Diamantino A Essinger, Steven D Sokhansanj, Bahrad A affil: Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104, USA. gailr@ece.drexel.edu sug: subj: Sequence Analysis Methods Algorithms Bacteria Resource Databases Genome Mining Genes ROC Curve Immunity Sewage ab: High-throughput sequencing technologies enable metagenome profiling, simultaneous sequencing of multiple microbial species present within an environmental sample. Since metagenomic data includes sequence fragments ('reads') from organisms that are absent from any database, new algorithms must be developed for the identification and annotation of novel sequence fragments. Homology-based techniques have been modified to detect novel species and genera, but, composition-based methods, have not been adapted. We develop a detection technique that can discriminate between 'known' and 'unknown' taxa, which can be used with composition-based methods, as well as a hybrid method. Unlike previous studies, we rigorously evaluate all algorithms for their ability to detect novel taxa. First, we show that the integration of a detector with a composition-based method performs significantly better than homology-based methods for the detection of novel species and genera, with best performance at finer taxonomic resolutions. Most importantly, we evaluate all the algorithms by introducing an 'unknown' class and show that the modified version of PhymmBL has similar or better overall classification performance than the other modified algorithms, especially for the species-level and ultrashort reads. Finally, we evaluate the performance of several algorithms on a real acid mine drainage dataset. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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