Binning contigs

Overview

Teaching: 15 min
Exercises: 60 min

Questions

• How many bins do we obtain?

Objectives

One of the drawbacks of the assembly step is that the actual genomes in the sample are usually split across several contigs. This can be a problem for analyses that benefit from having as much complete as possible genomes, such as metabolic pathways reconstruction or gene sharing analyses (check this). With binning we try to put together in a ‘bin’ all the contigs that come from the same genome, so we can have a better representation of it.

As you know from Arisdakessian et al 2021, two features are used to bin contigs: nucleotide composition and depth of coverage. You will use the CoCoNet binning software to bin the set of representative scaffolds you got in the previous lesson. For the nucleotide composition, CoCoNet computes the frequency of the k-mers (k=4 by default) in the scaffolds, taking into account both strands. For the depth of coverage, it counts the number of reads aligning to each scaffold, which at the end is a representation of the relative abundace of that sequence in the sample. This means that for the latter we need to provied CoCoNet with the short-reads aligned to the scaffolds, just as you did in the previous lesson. This time however you are not aligning all the samples altogether as if they were only one, but separately, so you should end up with 12 sorted BAM files. You can use the trick you learned in the previous lesson to process multiple samples sequentially. Don’t forget to index your sorted BAM files at the end.

# map (separately) all the sample to representative set of scaffolds
$ for sample in F*.fasta; do ... ... ... ; done

Once you have your BAM files, install and run CoCoNet with default parameters, and save the results in the 3_binning folder. It should take 5-10 minutes. Look at questions below in the meantime.

# deactivate the conda environment before running coconet
$ conda deactivate

# install coconet
$ pip install --user coconet-binning

# Run coconet. Don't forget to include your username in the command.
$ /home/<USERNAME>/.local/bin/coconet run --output 3_binning ...

Binning parameters

CoCoNet incorporates the cutoffs --min-ctg-len (minimum length of the contigs) and --min-prevalence (minimum number of samples containing a given contig). By default, the first is set to 2048 nucleotides and the second to 2 samples. How increasing or decreasing these parameters would affect the results?

Binning results should be under 3_binning/bins_0.8-0.3-0.4.csv. If you have a look at this file with head, each of the lines contains the name of a contig together with the bin identifier, which is a number. Use the script create_fasta_bins.py to create separate FASTA files for each bin, and save the results in 3_binning/fasta_bins.

# activate the environment again
$ conda activate day1_env

# download the python script
$ wget https://raw.githubusercontent.com/MGXlab/Viromics-Workshop-MGX/gh-pages/code/day1/create_fasta_bins.py

# Have a look at options
$ python create_fasta_bins.py -h

# run the script to create the FASTA bins with the correct options
$ python create_fasta_bins.py -o 3_binning/fasta_bins ...

You will be using these bins for days 3 and 4, so don’t forget where they are!

Key Points