Short prokaryotic DNA fragment binning using a hierarchical classifier based on linear discriminant analysis and principal component analysis.
Metagenomics is an emerging field in which the power of genomic analysis is applied to an entire microbial community, bypassing the need to isolate and culture individual microbial species. Assembling of metagenomic DNA fragments is very much like the overlap-layout-consensus procedure for assembling isolated genomes, but is augmented by an additional binning step to differentiate scaffolds, contigs and unassembled reads into various taxonomic groups. In this paper, we employed n-mer oligonucleotide frequencies as the features and developed a hierarchical classifier (PCAHIER) for binning short (≤ 1,000 bps) metagenomic fragments. The principal component analysis was used to reduce the high dimensionality of the feature space. The hierarchical classifier consists of four layers of local classifiers that are implemented based on the linear discriminant analysis. These local classifiers are responsible for binning prokaryotic DNA fragments into superkingdoms, of the same superkingdom into phyla, of the same phylum into genera, and of the same genus into species, respectively. We evaluated the performance of the PCAHIER by using our own simulated data sets as well as the widely used simHC synthetic metagenome data set from the IMG/M system. The effectiveness of the PCAHIER was demonstrated through comparisons against a non-hierarchical classifier, and two existing binning algorithms (TETRA and Phylopythia).
School of Electrical and Computer Engineering, Georgia Institute of Technology, 210 Technology Circle, Savannah, GA 31407, USA. hzheng7@gatech.edu
Hao
H
Zheng
Hongwei
H
Wu
D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D004247:DNA / Q000145:classification* / Q000235:genetics*; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D016002:Discriminant Analysis; D015723:Gene Library; D022761:Gene Transfer, Horizontal; D005827:Genetics, Microbial; D054892:Metagenome; D056186:Metagenomics / Q000706:statistics & numerical data*; D025341:Principal Component Analysis; D017422:Sequence Analysis, DNA
D004247:DNA