MetaBAT2 clusters metagenomic contigs into different "bins", each of which should correspond to a putative genome.

MetaBAT2 uses nucleotide composition information and source strain abundance (measured by depth-of-coverage by aligning the reads to the contigs) to perform binning.

Implemented for KBase by Jeff Froula([email protected])

MetaBAT2 takes a metagenome assembly and the reads that produced the assembly and organizes the contigs into putative genomes, called "bins".

Configuration:

Assembly Object: The Assembly object is a collection of assembled genome fragments, called "contigs". These are the items that MetaBAT2 will bin. Currently only a single Assembly object is accepted by the MetaBAT2 App.

BinnedContig Object Name: The BinnedContig Object represents the directory of binned contigs created by MetaBAT2. This object can be used for downstream analysis

Read Library Object: The read libraries are aligned to the assembly using bbmap, and provide the abundance information for each contig that roughly follows the species abundance.

Minimum Contig Length: Contigs that are too short may slow down analysis and not give statistically meaningful nucleotide composition profiles. A value of 1000 - 2500 bp is a reasonable cutoff.

Output:

Output Object:The BinnedContig Object represents the directory of binned contigs created by MetaBAT2. This object can be used for downstream analysis.

Output Bin Summary Report:The number of bins produced, the number of contigs that were binned and the total number of contigs in the assembly.

Downloadable files: The enitre output of the MetaBAT2 run may be downloaded as a zip file. This zip file also contains a table of read-depth coverage per contig ("*.depth.txt")

column definitions for *.depth.txt file