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breakfast: fast putative outbreak cluster and infection chain detection using SNPs

Project description

breakfast - FAST outBREAK detection and sequence clustering


breakfast is a simple and fast script developed for clustering SARS-CoV-2 genomes using precalculated sequence features (e.g. nucleotide substitutions) from covSonar or Nextclade.

This project is under development and in experimental stage


Installation using conda/mamba

Breakfast is available in bioconda. You can install it using either the conda command, or if you've installed mamba you can use that:

$ conda install breakfast
# or
$ mamba install breakfast

Installation using pip

Conda is available from PyPI and can be installed using pip:

$ pip install breakfast

Example Command Line Usage

Simple test run

breakfast \
   --input-file breakfast/test/testfile.tsv  \
   --max-dist 1 \
   --outdir test-run/

You will find your results in test-run/cluster.tsv, which should be identical to breakfast/test/expected_clusters_dist1.tsv

Using breakfast with input from covsonar

breakfast uses pre-calculated sequence features (= mutations) as input rather than raw sequences. These features can be calculated with several different programs, but the one we mainly use is covSonar. It can be used to maintain a database of mutations for a large number of sequences, which can then be easily queried.

conda activate sonar
covsonar/ add -f genomes.fasta --db mydb --cpus 8
covsonar/ match --tsv --db mydb > genomic_profiles.tsv

Clustering with a maximum SNP-distance of 1 and excluding clusters below a size of 5 sequences:

breakfast \
   --input-file genomic_profiles.tsv \
   --max-dist 1 \
   --min-cluster-size 5 \
   --outdir covsonar-breakfast-results/

Using breakfast with input from Nextclade

An alternative to covsonar that is commonly used is Nextclade CLI.

conda install -c bioconda nextclade  # If nextclade isn't already installed
nextclade dataset get --name 'sars-cov-2' --output-dir 'data/sars-cov-2'
nextclade \
   --in-order \
   --input-fasta genomes.fasta \
   --input-dataset data/sars-cov-2 \
   --output-tsv output/nextclade.tsv \
   --output-tree output/nextclade.auspice.json \
   --output-dir output/ \
   --output-basename nextclade

Alternatively, you can also use Nextclade Web to process your fasta and export the genomic profile as "nextclade.tsv".

Clustering with a maximum SNP-distance of 1 and excluding clusters below a size of 5 sequences. Since the input tsv of Nextclade looks a little different from the covSonar tsv, you need to specify the additional parameters --id-col, --clust-col and --sep2 for identifying the correct columns.

breakfast \
   --input-file output/nextclade.tsv \
   --max-dist 1 \
   --min-cluster-size 5 \
   --id-col "seqName" \
   --clust-col "substitutions" \
   --sep2 "," \
   --outdir nextclade-breakfast-results/

Sequence feature formats

Typical input data to breakfast looks something like the following table (example from covsonar with unnecessary columns removed):

accession	dna_profile
example1	C241T T606C C913T C3037T del:11288:9 C13515T
example2	C241T T606C del:1000:10
example3	C241T T606C del:1001:20

breakfast has parameters to allow the user to ignore deletions (--skip-del), insertions (--skip-ins) or mutations at the end of the sequences (which can sometimes be error-prone) when calculating the distance between sequences. To be able to do this, we need to know what kind of input is being provided (using the --var-type option), and then parse the mutations themselves. Since the format of how mutations are represented by upstream programs differs, we have implemented program-specific parsers for covsonar DNA and AA, as well as nextclade DNA and AA. Examples are shown below, in case you want to use some other program as input to breakfast you can see if the format matches one of the existing feature formats. As a fallback, you can use the "raw" format, which disables parsing and does not allow you to use breakfast's indel skipping or trimming features.


mutation type DNA (covsonar_dna) AA (covsonar_aa)
substitution C241T S:N501Y
deletion del:11288:9 ORF1:del:12:7
insertion C241TAT N:A34AK


mutation type DNA (nextclade_dna) AA (nextclade_aa)
substitution C241T S:N501Y
deletion 11288-11297 or 22492 S:V70-
insertion 273:CTTCGA (not provided)


Features will not be parsed. Skipping inserts (--skip-ins) and/or deletions (--skip-del) and the trimming options are not supported with this feature type.

Parameter description

Parameter Type Required Default Description
--input-file String 'genomic_profiles.tsv.gz' Path of the input file (in tsv format)
--max-dist Integer 1 Two sequences will be grouped together, if their pairwise edit distance does not exceed this threshold
--min-cluster-size Integer 2 Minimum number of sequences a cluster needs to include to be defined in the result file
--id-col String 'accession' Name of the sequence identifier column of the input file
--clust-col String 'dna_profile' Name of the mutation profile column of the input file
--var-type String 'covsonar_dna' Mutuation format (e.g. for DNA mutations from covsonar, use covsonar_dna). Possible values: [covsonar_dna
--sep String '\t' Input file separator
--sep2 String ' ' Secondary clustering column separator (between each mutation)
--outdir String 'output/' Path of output directory
--trim-start Integer 264 Bases to trim from the beginning
--trim-end Integer 228 Bases to trim from the end
--reference-length Integer 29903 Length of reference genome (defaults to NC_045512.2)
--skip-del Bool TRUE Deletions will be skipped for calculating the pairwise distance of your input sequences.
--skip-ins Bool TRUE Insertions will be skipped for calculating the pairwise distance of your input sequences.
--input-cache Integer None Path to import results from previous run
--output-cache String None Path to export results which can be used in the next run to decrease runtime.
--jobs Integer 1 The number of jobs (=threads) to run simultaneously
--help N/A N/A Show this help message and exit
--version N/A N/A Show version and exit


breakfast runs under Python 3.10 and later. We rely heavily on some excellent open source python libraries: networkx, pandas, numpy, scikit-learn, click, and scipy.

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