Journal article
NBC: the Naïve Bayes Classification tool webserver for taxonomic classification of metagenomic reads
Bioinformatics (Oxford, England), v 27(1), pp 127-129
01 Jan 2011
PMID: 21062764
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Motivation:
Datasets from high-throughput sequencing technologies have yielded a vast amount of data about organisms in environmental samples. Yet, it is still a challenge to assess the exact organism content in these samples because the task of taxonomic classification is too computationally complex to annotate all reads in a dataset. An easy-to-use webserver is needed to process these reads. While many methods exist, only a few are publicly available on webservers, and out of those, most do not annotate all reads.
Results:
We introduce a webserver that implements the naïve Bayes classifier (NBC) to classify all metagenomic reads to their best taxonomic match. Results indicate that NBC can assign next-generation sequencing reads to their taxonomic classification and can find significant populations of genera that other classifiers may miss.
Availability:
Publicly available at:
http://nbc.ece.drexel.edu
.
Contact:
gailr@ece.drexel.edu
Metrics
Details
- Title
- NBC: the Naïve Bayes Classification tool webserver for taxonomic classification of metagenomic reads
- Creators
- Gail L Rosen - Department of Electrical and Computer EngineeringErin R Reichenberger - Department of Electrical and Computer EngineeringAaron M Rosenfeld - Department of Electrical and Computer Engineering
- Publication Details
- Bioinformatics (Oxford, England), v 27(1), pp 127-129
- Publisher
- Oxford University Press
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000285626300017
- Scopus ID
- 2-s2.0-78650549608
- Other Identifier
- 991014877719504721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability