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Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences
MetaInformatics Laboratory, Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh, India.
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2015 (English)In: Genomics, ISSN 0888-7543, E-ISSN 1089-8646, Vol. 106, no 1, p. 1-6Article in journal (Refereed) Published
Abstract [en]

Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annotate and classify proteins are extremely slow. Therefore, to achieve faster and accurate functional annotation, we have developed an orthology-based functional classifier 'Woods' by using a combination of machine learning and similarity-based approaches. Woods displayed a precision of 98.79% on independent genomic dataset, 96.66% on simulated metagenomic dataset and >97% on two real metagenomic datasets. In addition, it performed >87 times faster than BLAST on the two real metagenomic datasets. Woods can be used as a highly efficient and accurate classifier with high-throughput capability which facilitates its usability on large metagenomic datasets.

Place, publisher, year, edition, pages
Academic Press, 2015. Vol. 106, no 1, p. 1-6
Keywords [en]
Functional annotation, Machine learning, Metagenome, Random Forest
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:kth:diva-258004DOI: 10.1016/j.ygeno.2015.04.001ISI: 000355674900001PubMedID: 25863333Scopus ID: 2-s2.0-84930542687OAI: oai:DiVA.org:kth-258004DiVA, id: diva2:1350773
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QC 20190913

Available from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-09-13Bibliographically approved

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