MetaCon: unsupervised clustering of metagenomic contigs with probabilistic k-mers statistics and coverage.

ABSTRACT:

Motivation

Sequencing technologies allow the sequencing of microbial communities directly from the environment without prior culturing. Because assembly typically produces only genome fragments, also known as contigs, it is crucial to group them into putative species for further taxonomic profiling and down-streaming functional analysis. Taxonomic analysis of microbial communities requires contig clustering, a process referred to as binning, that is still one of the most challenging tasks when analyzing metagenomic data. The major problems are the lack of taxonomically related genomes in existing reference databases, the uneven abundance ratio of species, sequencing errors, and the limitations due to binning contig of different lengths.

Results

In this context we present Met

SUBMITTER: Qian J 

PROVIDER: S-EPMC6873667 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature