Dataset Information

GSTaxClassifier: a genomic signature based taxonomic classifier for metagenomic data analysis.

ABSTRACT: GSTaxClassifier (Genomic Signature based Taxonomic Classifier) is a program for metagenomics analysis of shotgun DNA sequences. The program includes a simple but effective algorithm, a modification of the Bayesian method, to predict the most probable genomic origins of sequences at different taxonomical ranks, on the basis of genome databases;a function to generate genomic profiles of reference sequences with tri-, tetra-, penta-, and hexa-nucleotide motifs for setting a user-defined database; two different formats (tabular- and tree-based summaries) to display taxonomic predictions with improved analytical methods; and effective ways to retrieve, search, and summarize results by integrating the predictions into the NCBI tree-based taxonomic information.GSTaxClassifier takes input nucleotide sequences and using a modified Bayesian model evaluates the genomic signatures between metagenomic query sequences and reference genome databases. The simulation studies of a numerical data sets showed that GSTaxClassifier could serve as a useful program for metagenomics studies, which is freely available at http://helix2.biotech.ufl.edu:26878/metagenomics/.

SUBMITTER: Yu F

PROVIDER: S-EPMC2770370 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GSTaxClassifier: a genomic signature based taxonomic classifier for metagenomic data analysis.

Yu Fahong F Sun Yijun Y Liu Li L Farmerie William W

Bioinformation 20090820 1

GSTaxClassifier (Genomic Signature based Taxonomic Classifier) is a program for metagenomics analysis of shotgun DNA sequences. The program includes a simple but effective algorithm, a modification of the Bayesian method, to predict the most probable genomic origins of sequences at different taxonomical ranks, on the basis of genome databases;a function to generate genomic profiles of reference sequences with tri-, tetra-, penta-, and hexa-nucleotide motifs for setting a user-defined database; t ...[more]

PMID: 20011152

Similar Datasets

Project description:BackgroundAn open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them.ResultsTo test the proposed pipeline, we simulated both SG and AMP short-reads, from 1000 16S full-length sequences. Then, we adopted a k-mer representation to map sequences as vectors into a numerical space. Finally, we trained two different deep learning architecture, i.e., convolutional neural network (CNN) and deep belief network (DBN), obtaining a trained model for each taxon. We tested our proposed methodology to find the best parameters configuration, and we compared our results against the classification performances provided by a reference classifier for bacteria identification, known as RDP classifier. We outperformed the RDP classifier at each taxonomic level with both architectures. For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data.ConclusionsIn this work, we proposed a 16S short-read sequences classification technique based on k-mer representation and deep learning architecture, in which each taxon (from phylum to genus) generates a classification model. Experimental results confirm the proposed pipeline as a valid approach for classifying bacteria sequences; for this reason, our approach could be integrated into the most common tools for metagenomic analysis. According to obtained results, it can be successfully used for classifying both SG and AMP data.

Dataset Information

GSTaxClassifier: a genomic signature based taxonomic classifier for metagenomic data analysis.

Publications

GSTaxClassifier: a genomic signature based taxonomic classifier for metagenomic data analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets