CONCOCT’s documentation¶
CONCOCT “bins” metagenomic contigs. Metagenomic binning is the process of clustering sequences into clusters corresponding to operational taxonomic units of some level.
Features¶
CONCOCT does unsupervised binning of metagenomic contigs by using nucleotide composition - kmer frequencies - and coverage data for multiple samples. CONCOCT can accurately (up to species level) bin metagenomic contigs. For optimal performance:
- Map several samples against your assembled contigs.
- Cut longer contigs into 10 - 20 kb pieces prior to mapping.
- Evaluate your bins using single copy genes.
Installation¶
For a comprehensive guide on how to install CONCOCT and all its dependencies, see Installation.
Known Issues¶
- Contig names consisting of digits only are not allowed. Please rename your contigs in both the fasta and the coverage table before proceeding.
- Contig sequences can only contain letters A,C,G or T. For example Ns are currently not allowed.
- Contigs need to be cut up prior to binning. This is covered in the Basic Usage page.
For a more up to date list of reported issues, check the issue tracker: https://github.com/BinPro/CONCOCT/issues
Licence¶
FreeBSD
Contents:¶
Installation¶
With Bioconda [Recommended]¶
The easiest and recommended way to install concoct is through Bioconda and conda in an isolated environment:
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda create -n concoct_env python=3 concoct
Note for Mac OSX users¶
Currently concoct on Mac OSX can only run in single threaded mode, which drastically increases the runtime. However, the Mac OSX installation of concoct can still be useful for testing purposes and is possible to install through conda as shown above.
Manual Installation¶
The conda installation should be enough for most users. However, if you want to modify the source code, a manual installation might be needed. An example of a manual installation on an Ubuntu system can be seen in the Travis CI config file.
Using Docker¶
We provide a Docker image: binpro/concoct_latest which contains CONCOCT and its dependencies for a basic workflow.
Assuming DOcker is installed, the following command will then download the image from the Docker image index, map the Data folder to the image and log you into the docker image.
docker run -v /home/USER/Data:/opt/Data -i -t binpro/concoct_latest bash
To test concoct you can then do:
$ cd /opt/CONCOCT_latest
$ nosetests
Which should execute all tests without errors.
Basic Usage¶
This guide assumes you have your original contigs assembled into a file original_contigs.fa
and that you have mapped reads from several samples to these contigs into .bam
files.
Note that the assembly can be constructed using either one single sample or several (usually all) samples.
In either case, all sample reads should be mapped against the assembly to achieve the best binning performance.
The next step is then to cut contigs into smaller parts:
cut_up_fasta.py original_contigs.fa -c 10000 -o 0 --merge_last -b contigs_10K.bed > contigs_10K.fa
Generate table with coverage depth information per sample and subcontig. This step assumes the directory ‘mapping’ contains sorted and indexed bam files where each sample has been mapped against the original contigs:
concoct_coverage_table.py contigs_10K.bed mapping/Sample*.sorted.bam > coverage_table.tsv
Run concoct:
concoct --composition_file contigs_10K.fa --coverage_file coverage_table.tsv -b concoct_output/
Merge subcontig clustering into original contig clustering:
merge_cutup_clustering.py concoct_output/clustering_gt1000.csv > concoct_output/clustering_merged.csv
Extract bins as individual FASTA:
mkdir concoct_output/fasta_bins
extract_fasta_bins.py original_contigs.fa concoct_output/clustering_merged.csv --output_path concoct_output/fasta_bins
These bins should now be evaluated and filtered for completeness and contamination using for example CheckM or BUSCO.
Command Line Options¶
CONCOCT uses several command line options to control the clustering, here is a
complete documentation of these. These can also be viewed by typing concoct
-h
on the command line:
usage: - [-h] [--coverage_file COVERAGE_FILE]
[--composition_file COMPOSITION_FILE] [-c CLUSTERS] [-k KMER_LENGTH]
[-t THREADS] [-l LENGTH_THRESHOLD] [-r READ_LENGTH]
[--total_percentage_pca TOTAL_PERCENTAGE_PCA] [-b BASENAME] [-s SEED]
[-i ITERATIONS] [--no_cov_normalization] [--no_total_coverage]
[--no_original_data] [-o] [-d] [-v]
optional arguments:
-h, --help show this help message and exit
--coverage_file COVERAGE_FILE
specify the coverage file, containing a table where
each row correspond to a contig, and each column
correspond to a sample. The values are the average
coverage for this contig in that sample. All values
are separated with tabs.
--composition_file COMPOSITION_FILE
specify the composition file, containing sequences in
fasta format. It is named the composition file since
it is used to calculate the kmer composition (the
genomic signature) of each contig.
-c CLUSTERS, --clusters CLUSTERS
specify maximal number of clusters for VGMM, default
400.
-k KMER_LENGTH, --kmer_length KMER_LENGTH
specify kmer length, default 4.
-t THREADS, --threads THREADS
Number of threads to use
-l LENGTH_THRESHOLD, --length_threshold LENGTH_THRESHOLD
specify the sequence length threshold, contigs shorter
than this value will not be included. Defaults to
1000.
-r READ_LENGTH, --read_length READ_LENGTH
specify read length for coverage, default 100
--total_percentage_pca TOTAL_PERCENTAGE_PCA
The percentage of variance explained by the principal
components for the combined data.
-b BASENAME, --basename BASENAME
Specify the basename for files or directory where
outputwill be placed. Path to existing directory or
basenamewith a trailing '/' will be interpreted as a
directory.If not provided, current directory will be
used.
-s SEED, --seed SEED Specify an integer to use as seed for clustering. 0
gives a random seed, 1 is the default seed and any
other positive integer can be used. Other values give
ArgumentTypeError.
-i ITERATIONS, --iterations ITERATIONS
Specify maximum number of iterations for the VBGMM.
Default value is 500
--no_cov_normalization
By default the coverage is normalized with regards to
samples, then normalized with regards of contigs and
finally log transformed. By setting this flag you skip
the normalization and only do log transorm of the
coverage.
--no_total_coverage By default, the total coverage is added as a new
column in the coverage data matrix, independently of
coverage normalization but previous to log
transformation. Use this tag to escape this behaviour.
--no_original_data By default the original data is saved to disk. For big
datasets, especially when a large k is used for
compositional data, this file can become very large.
Use this tag if you don't want to save the original
data.
-o, --converge_out Write convergence info to files.
-d, --debug Debug parameters.
-v, --version show program's version number and exit
CONCOCT Scripts¶
CONCOCT ships with some additional scripts which are very useful to e.g. create input files and to extract output fastas for concoct. These scripts are:
cut_up_fasta.py
concoct_coverage_table.py
merge_cutup_clustering.py
extract_fasta_bins.py
The repository CONCOCT contains additional scripts in the CONCOCT/scripts
directory which are not fully maintained.
They implement methods that we apply after binning with CONCOCT and it might be useful as a starting point or inspiration when creating your own scripts for downstream processing of the output files.
Out of these scripts, the ones documented here are:
dnadiff_dist_matrix.py
extract_scg_bins.py
[Deprecated]
Contents:
cut_up_fasta.py¶
Usage¶
The usage and help documentation of cut_up_fasta.py
can be seen by
running cut_up_fasta.py -h
:
usage: - [-h] [-c CHUNK_SIZE] [-o OVERLAP_SIZE] [-m] [-b BEDFILE]
contigs [contigs ...]
Cut up fasta file in non-overlapping or overlapping parts of equal length.
Optionally creates a BED-file where the cutup contigs are specified in terms
of the original contigs. This can be used as input to concoct_coverage_table.py.
positional arguments:
contigs Fasta files with contigs
optional arguments:
-h, --help show this help message and exit
-c CHUNK_SIZE, --chunk_size CHUNK_SIZE
Chunk size
-o OVERLAP_SIZE, --overlap_size OVERLAP_SIZE
Overlap size
-m, --merge_last Concatenate final part to last contig
-b BEDFILE, --bedfile BEDFILE
BEDfile to be created with exact regions of the
original contigs corresponding to the newly created
contigs
Example¶
An example of how to run cut_up_fasta.py
:
cut_up_fasta.py original_contigs.fa -c 10000 -o 0 --merge_last -b contigs_10K.bed > contigs_10K.fa
This creates a fasta file and a BED file.
The fasta file contigs_10K.fa
contains the original contigs cut up into parts of length exactly 10K, except for the last contig part which is between 10K and 20K long.
The BED file contigs_10K.bed
contains a list of the contig parts created with coordinates in the original contigs.
concoct_coverage_table.py¶
Usage¶
The usage and help documentation of concoct_coverage_table.py
can be seen by
running concoct_coverage_table.py -h
:
usage: - [-h] [--samplenames SAMPLENAMES] bedfile bamfiles [bamfiles ...]
A script to generate the input coverage table for CONCOCT using a BEDFile.
Output is written to stdout. The BEDFile defines the regions used as
subcontigs for concoct. This makes it possible to get the coverage for
subcontigs without specifically mapping reads against the subcontigs. @author:
inodb, alneberg
positional arguments:
bedfile Contigs BEDFile with four columns representing:
'Contig ID, Start Position, End Position and SubContig
ID' respectively. The Subcontig ID must contain the
pattern 'concoct_part_[0-9]*' while the contigs which
are not cutup cannot contain this pattern. This file
can be generated by the cut_up_fasta.py script.
bamfiles BAM files with mappings to the original contigs.
optional arguments:
-h, --help show this help message and exit
--samplenames SAMPLENAMES
File with sample names, one line each. Should be same
nr of bamfiles. Default sample names used are the file
names of the bamfiles, excluding the file extension.
Example¶
An example of how to run concoct_coverage_table.py
:
concoct_coverage_table.py contigs_10K.bed mapping/Sample*.sorted.bam > coverage_table.tsv
This creates a coverage table suitable as input for concoct as the coverage_file parameter.
The contigs_10K.bed
file is created from the cut_up_fasta.py
script and the bam
-files needs to be sorted and indexed.
merge_cutup_clustering.py¶
Usage¶
The usage and help documentation of merge_cutup_clustering.py
can be seen by
running merge_cutup_clustering.py -h
:
usage: - [-h] cutup_clustering_result
With contigs cutup with cut_up_fasta.py as input, sees to that the consequtive
parts of the original contigs are merged. prints result to stdout. @author:
alneberg
positional arguments:
cutup_clustering_result
Input cutup clustering result.
optional arguments:
-h, --help show this help message and exit
Example¶
An example of how to run merge_cutup_clustering.py
:
merge_cutup_clustering.py concoct_output/clustering_gt1000.csv > concoct_output/clustering_merged.csv
This merges the clustering clustering_gt1000.csv
created by concoct by looking at cluster assignments per contig part and assigning a concensus cluster for the original contig.
The output clustering_merged.csv contains a header line and contig_id and cluster_id per line, separated by a comma.
extract_fasta_bins.py¶
Usage¶
The usage and help documentation of extract_fasta_bins.py
can be seen by
running extract_fasta_bins.py -h
:
usage: - [-h] [--output_path OUTPUT_PATH] fasta_file cluster_file
extract_fasta_bins.py Extract a fasta file for each cluster from a concoct
result file.
positional arguments:
fasta_file Input Fasta file.
cluster_file Concoct output cluster file
optional arguments:
-h, --help show this help message and exit
--output_path OUTPUT_PATH
Directory where files will be printed
Example¶
An example of how to run extract_fasta_bins.py
:
mkdir concoct_output/fasta_bins
extract_fasta_bins.py original_contigs.fa concoct_output/clustering_merged.csv --output_path concoct_output/fasta_bins
This creates a fasta file for each cluster assigned by concoct. The clusters assigned need not to be complete or uncontaminated and should be investigated closer with e.g. CheckM.
dnadiff_dist_matrix.py¶
Usage¶
The usage and help documentation of dnadiff_dist_matrix.py
can be seen by
running pyhton dnadiff_dist_matrix -h
:
usage: - [-h] [--min_coverage MIN_COVERAGE] [--fasta_names FASTA_NAMES]
[--plot_image_extension PLOT_IMAGE_EXTENSION] [--skip_dnadiff]
[--skip_matrix] [--skip_plot] [--cluster-threshold CLUSTER_THRESHOLD]
output_folder fasta_files [fasta_files ...]
Output distance matrix between fasta files using dnadiff from MUMmer. Generates
dnadiff output files in folders:
output_folder/fastaname1_vs_fastaname2/
output_folder/fastaname1_vs_fastaname3/
etc
where fastaname for each fasta file can be supplied as an option to the script.
Otherwise they are just counted from 0 to len(fastafiles)
The distance between each bin is computed using the 1-to-1 alignments of the
report files (not M-to-M):
1 - AvgIdentity if min(AlignedBases) >= min_coverage. Otherwise distance is 1.
Or 0 to itself.
Resulting matrix is printed to stdout and to output_folder/dist_matrix.tsv. The
rows and columns of the matrix follow the order of the supplied fasta files. The
names given to each fasta file are also outputted to the file
output_folder/fasta_names.tsv
A hierarchical clustering of the distance using euclidean average linkage
clustering is plotted. This can be deactivated by using --skip_plot. The
resulting heatmap is in output_folder/hclust_heatmap.pdf or
output_folder/hclust_dendrogram.pdf and the resulting clustering is presented
in output_folder/clustering.tsv. The image extension can be changed.
positional arguments:
output_folder Output folder
fasta_files fasta files to compare pairwise using MUMmer's dnadiff
optional arguments:
-h, --help show this help message and exit
--min_coverage MIN_COVERAGE
Minimum coverage of bin in percentage to calculate
distance otherwise distance is 1. Default is 50.
--fasta_names FASTA_NAMES
File with names for fasta file, one line each. Could
be sample names, bin names, genome names, whatever you
want. The names are used when storing the MUMmer
dnadiff results as in
output_folder/fastaname1_vs_fastaname2/. The names are
also used for the plots.
--plot_image_extension PLOT_IMAGE_EXTENSION
Type of image to plotted e.g. pdf, png, svg.
--skip_dnadiff Skips running MUMmer and uses output_folder as given
input to calculate the distance matrix. Expects
dnadiff output as
output_folder/fastaname1_vs_fastaname2/out.report
--skip_matrix Skips Calculating the distance matrix.
--skip_plot Skips plotting the distance matrix. By default the
distance matrix is clustered hierarchically using
euclidean average linkage clustering. This step
requires seaborn and scipy.
--cluster-threshold CLUSTER_THRESHOLD
The maximum within cluster distance allowed.
Example¶
An example of how to run dnadiff_dist_matrix
on the test data:
cd CONCOCT/scripts
python dnadiff_dist_matrix.py test_dnadiff_out tests/test_data/bins/sample*.fa
This results in the following output files in the folder test_dnadiff_out/
:
dist_matrix.stv
The distance matrixfasta_names.tsv
The names given to each bin (or fasta file)clustering.tsv
This file will give a cluster assignment for each bin (or fasta file)hcust_dendrogram.pdf
Dendrogram of the clustering (click for example)hcust_heatmap.pdf
Heatmap of the clustering (click for example)
Then there is also for each pairwise dnadiff
alignment the following output
files in a subfolder fastaname1_vs_fastaname2/
:
out.1coords
out.1delta
out.cmd
out.delta
out.mcoords
out.mdelta
out.qdiff
out.rdiff
out.report
out.snps
out.unqry
out.unref
See MUMmer’s own manual for an explanation of each file with dnadiff --help
.
[Deprecated] extract_scg_bins.py¶
Usage¶
The usage and help documentation of extract_scg_bins.py
can be seen by
running pyhton extract_scg_bins -h
:
usage: - [-h] --output_folder OUTPUT_FOLDER --scg_tsvs SCG_TSVS [SCG_TSVS ...]
--fasta_files FASTA_FILES [FASTA_FILES ...] --names NAMES [NAMES ...]
[--groups GROUPS [GROUPS ...]] [--max_missing_scg MAX_MISSING_SCG]
[--max_multicopy_scg MAX_MULTICOPY_SCG]
Extract bins with given SCG (Single Copy genes) criteria. Criteria can be set
as a combination of the maximum number of missing SCGs and the maximum number
of multicopy SCGs. By default the script selects from pairs of scg_tsvs and
fasta_files, the pair that has the highest number of approved bins. In case
there are multiple with the max amount of approved bins, it takes the one that
has the highest sum of bases in those bins. If that is the same, it selects the
first one passed as argument.
One can also group the pairs of scg_tsvs and fasta_files with the --groups
option so one can for instance find the best binning per sample.
optional arguments:
-h, --help show this help message and exit
--output_folder OUTPUT_FOLDER
Output folder
--scg_tsvs SCG_TSVS [SCG_TSVS ...]
Single Copy Genes (SCG) tsvs as outpututted by
COG_table.py. Should have the same ordering as
fasta_files.
--fasta_files FASTA_FILES [FASTA_FILES ...]
Fasta files. Should have the same ordering as scg_tsvs
--names NAMES [NAMES ...]
Names for each scg_tsv and fasta_file pair. This is
used as the prefix for the outputted bins.
--groups GROUPS [GROUPS ...]
Select the best candidate for each group of scg_tsv
and fasta_file pairs. Number of group names given
should be equal to the number of scg_tsv and
fasta_file pairs. Identical group names indicate same
groups.
--max_missing_scg MAX_MISSING_SCG
--max_multicopy_scg MAX_MULTICOPY_SCG
Example¶
An example of how to run extract_scg_bins
on the test data:
cd CONCOCT/scripts/tests/test_data
python extract_scg_bins.py \
--output_folder test_extract_scg_bins_out \
--scg_tsvs tests/test_data/scg_bins/sample0_gt300_scg.tsv \
tests/test_data/scg_bins/sample0_gt500_scg.tsv \
--fasta_files tests/test_data/scg_bins/sample0_gt300.fa \
tests/test_data/scg_bins/sample0_gt500.fa \
--names sample0_gt300 sample0_gt500 \
--max_missing_scg 2 --max_multicopy_scg 4 \
--groups gt300 gt500
This results in the following output files in the folder test_extraxt_scg_bins_out/
:
$ ls test_extract_scg_bins_out/
sample0_gt300_bin2.fa sample0_gt500_bin2.fa
Only bin2 satisfies the given criteria for both binnings. If we want to get the
best binning of the two, one can remove the --groups
parameter (or give
them the same group id). That would only output sample0_gt500_bin2.fa
,
because the sum of bases in the approved bins of sample0_gt500
is higher
than that of sample0_gt300
.