NBC: the Naïve Bayes Classification tool webserver for taxonomic classification of metagenomic reads

By Gail L. Rosen, Erin R. Reichenberger, Aaron M. Rosenfeld. 2010

223 citations recorded by the source corpus.

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

Subfields: Information Systems and Management, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Subsequent tools aimed to classify 300-3000 base pair reads from complex mixtures of microbial genomes based on tetranucleotide frequencies, which differ across organisms [224], using supervised [225,226] or unsupervised methods [227].”
Likelihood Ratios for Out-of-Distribution Detection (2019, methodology): “Several machine learning methods have been developed to perform bacteria identiﬁcation by classifying existing known genomic sequences (Patil et al., 2011; Rosen et al., 2010), including deep learning methods (Busia et al., 2018) which are state-of-the-art.”
Machine learning for metagenomics: methods and tools (2015, methodology): “Notice however, that RDP classifier is meant for 16S data, while NBC was developed for whole genome sequencing projects.”
Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods (2017, methodology): “This method has been already used widely and successfully in many studies (Aguiar-Pulido et al. 2012; Lepora et al. 2010; Lu et al. 2010; Rosen et al. 2011; Zhang and Gao 2011).”
KrakenUniq: confident and fast metagenomics classification using unique k-mer counts (2018, methodology): “NBC: the naive Bayes classification tool webserver for taxonomic classification of metagenomic reads.”
Multi-Layer and Recursive Neural Networks for Metagenomic Classification (2015, methodology): “For example, the naïve Bayes classifier has been used by biologists for classifying sequences into an OTU using -mer frequencies [19], [20].”
CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers (2015, methodology): “A recent comparative evaluation of these tools [12] demonstrated that NBC [8] exhibits the highest accuracy and sensitivity at the genus level among [4-6,9].”
Computational meta'omics for microbial community studies (2013, methodology): “…with structured output (PhyloPythiaS (Patil et al , 2012)), interpolated Markov models (Phymm (Brady and Salzberg, 2011)), naive Bayesian classiﬁers (Rosen et al , 2011)), and Self Organizing Maps (TaxSOM (Weber et al , 2010)), or integration of intrinsic and homology-based extrinsic assignments…”