Bin metagenomic contigs
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@example.com)
MetaBAT2 takes a metagenome assembly and the reads that produced the assembly and organizes the contigs into putative genomes, called "bins".
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 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
|1. contig name|
|2. contig length|
|3. total average depth (all libraries)|
|4. library 1 depth|
|5. library 1 variance|
|6. next library ... etc|
Module Commit: caf4e3c0c415602f3e11f1473734a7b682c6fdfb