Binning metagenomic contigs¶

Assembled contigs can be grouped into so called ‘genome bins’ using information about their nucleotide composition and their abundance profiles in several samples. The rationale is that contigs from the same genome will have similar nucleotide composition and will show up in similar abundances across samples.

There are several programs that perform unsupervised clustering of contigs based on composition and coverage. Currently, this workflow uses MaxBin2 and/or CONCOCT. The quality and phylogeny of bins can be estimated using CheckM.

Important

MaxBin2 uses conserved marker genes for prokaryotes to identify seed contigs. This means that it will not be able to bin eukaryotic contigs. In order to properly bin eukaryotic contigs please use CONCOCT.

Important

The minimum required version of CONCOCT is 1.0.0 which only works on Linux so far. However, if you manage to compile CONCOCT on OSX you can use it with the workflow by installing it into the nbis-meta environment path and then omit the --use-conda flag to snakemake.

To perform the binning step of the workflow run the following:

snakemake --use-conda --configfile <your-config-file> -p binning

Settings¶

maxbin: Set to True to use MaxBin2.

concoct: Set to True to use CONCOCT.

min_contig_length: Minimum contig length to include in binning. Shorter contigs will be ignored. By setting multiple values here you can make the workflow run the binning steps multiple times with different minimum lengths. Output is stored under results/maxbin/<min_contig_length>/ and results/concoct/<min_contig_length>/.

maxbin_threads: Number of threads to use for the MaxBin2 step.

concoct_threads: Number of threads to use for the CONCOCT step.

Note

If there are not enough contigs meeting the minimum contig length cutoff the workflow will exit with an error. Either decrease the length cutoff or see if you can improve the assembly somehow.

Binning tutorial¶

When the binning step is included in the workflow all assembly groups specified in the sample annotation file will be binned and for each assembly the abundances of contigs will be calculated by cross-mapping all samples in the sample annotation file.

This means that if you have many assemblies and/or samples there will be a lot of jobs to run which can be a bit overwhelming. If you want you may first run the assembly step and then bin individual assemblies.

Let’s try this out using some supplied example data. The configuration file examples/binning/config.yaml is set up to use four mock communities created with this metagenomic-mocks repository. The samples each contain 100,000 read-pairs sampled from the same 10 genomes but in varying proportions, roughly simulating the proportions of the genomes in different body sites.

Have a look at the jobs that will be run in this example by doing a dry-run:

snakemake --use-conda --configfile examples/binning/config.yaml -np

Results will be placed under results/examples/.

Summary statistics for generated bins are written to a file summary_stats.tsv inside every bin directory (e.g. results/examples/concoct/stool/1000/summary_stats.tsv in the example above).

Splitting up the example¶

If you want, you can first generate the assemblies which will be used for binning. Simply run:

snakemake -j 4 --configfile examples/binning/config.yaml -p assembly

Then run the binning steps:

snakemake --use-conda -j 4 --configfile examples/binning/config.yaml -p binning