Skip to main content

The mutation accumulation database

Project description


Build Status Coverage Status PyPI version

The mutation accumulation database

mutacc is a tool that makes it possible to create synthetic datasets to be used for quality control or benchmarking of bioinformatic tools and pipelines intended for variant calling of clinical variants. Using raw reads that supports a known variant from a real NGS data, mutacc stores the relevant reads from each case into a database. This database can then be queried to create synthetic datasets that can be used as positive controls bioinformatics pipelines.

Running the app using Docker (No installation of any software or database required)

An example containing a demo setup for the app is included in the docker-compose file. Note that this file is not intended for use in production and is only provided to illustrate how an image containing the application could be connected to a MongoDB instance and perform commands provided when running it as a container. A Docker image file for Mutacc can be pulled from Docker Hub, or can be built from the Dockerfile provided in the GitHub repository folder. Start the docker-compose demo using this command:

docker-compose up -d

What the docker-compose command does:

  • Starts the database
  • extracts the reads from a demo case (demo case resources are located under /mutacc/resources)
  • Saves them to database
  • Exports them from the database to a local file

When the above command is executed, it creates the following 4 directories: reads, imports, queries and variants in the working directory. The directory names variants contains the vcf with the variants of interest for this demo case.

After running the test, don't forget to run docker-compose to remove containers, networks, volumes and images created by docker-compose.



For installation of mutacc and the external prerequisites, this is made easy by creating conda environment

conda create -n <env_name> python=3.6 pip numpy cython

activate environment

source activate <env_name>

External Prerequisites

mutacc takes use of two external packages, seqkit>=v0.9 , and picard>=v2.18. These can be installed within a conda environment by

conda install -c bioconda picard
conda install -c bioconda seqkit

Install mutacc

Within the conda environment, do

pip install mutacc

To install from PyPI, or clone this repo and install

pip install git+


Configuration File

Some options are best passed to mutacc through a configuration file. below is an example of a config file, using the YAML format.

host: <host>                  #Defaults to 'localhost'
port: <port>                  #Defaults to 27017
database: <database_name>     #Defaults to 'mutacc'
username: <username>          
password: <password>          
root_dir: <path_to_root>  

The 'root_dir' entry specifies an existing directory in the file system, where all files generated by mutacc will be stored in corresponding subdirectories. E.g. all generated fastq files will be stored in /.../root_dir/reads/

Populate the mutacc database

To export data sets from the mutacc DB, the database must first be populated. To extract the raw reads supporting a known variant, mutacc takes use some relevant files generated from a NGS experiment up to the variant calling itself. That is the bam file, and vcf file containing only the variants of interest.

This information is specified as a 'case', represented in yaml format


    case_id: 'case123' #REQUIRED CASE_ID

  - sample_id: 'sample1' #REQUIRED
    analysis_type: 'wgs' #REQUIRED
    sex: 'male'          #REQUIRED
    mother: 'sample2'    #REQUIRED (CAN BE 0 if no mother)
    father: 'sample3'    #REQUIRED (CAN BE 0 if no father)
    bam_file: /path/to/sorted_bam #REQUIRED
    phenotype: 'affected'

  - sample_id: 'sample2'
    analysis_type: 'wgs'
    sex: 'female'        
    mother: '0' #0 if no parent            
    father: '0'         
    bam_file: /path/to/sorted_bam
    phenotype: 'unaffected'

  - sample_id: 'sample2'
    analysis_type: 'wgs'
    sex: 'male'         
    mother: '0'             
    father: '0'            
    bam_file: /path/to/sorted_bam
    phenotype: 'affected'

variants: /path/to/vcf

This will find the reads from the bam files specified for each sample. If it is desired that the reads are found from the fastq files instead, this can be done by specifying the fastq-files as such

  - sample_id: 'sample1'
    analysis_type: 'wgs'
    sex: 'male'          
    mother: 'sample2'    
    father: 'sample3'    
    bam_file: /path/to/sorted_bam
      - /path/to/fastq1
      - /path/to/fastq2
    phenotype: 'affected'

To extract the reads from the case

mutacc --config-file <config_file> extract --padding 600 --case <case_file>

the --padding option takes the number of basepairs that the desired region is padded with.

This will create a file <case_id>.json stored in the directory specified in the /.../root_dir/imports directory.

To import the case into the database

mutacc db import /.../root_dir/imports/<case_id>.json

The db command is called each time mutacc needs to do any operation against the database.

This will try to establish a connection to an instance of mongodb, by default running on 'localhost' on port 27017. If this is not wanted, it can be specified with the --host and --port options.

mutacc db -h <host> -p <port> import <case_id>.json

If authentication is required, this can be specified with the --username and --password options.

or in a configuration file e.g.

host: <host>
port: <port>
username: <username>
password: <password>
mutacc --config-file <config.yaml> db import <case_id>.json

Export datasets from the database

The datasets are exported one sample at the time. To export a synthetic dataset, the export command is used together with options.

Usage: mutacc db export [OPTIONS]

  exports dataset from DB

  -c, --case-mongo TEXT           mongodb query language json-string to query
                                  for cases in database
  -v, --variant-mongo TEXT        mongodb query language json-string to query
                                  for variants in database
  -t, --variant-type TEXT         Type of variant
  -a, --analysis [wes|wgs]        Type of analysis
  --all-variants                  Export all variants in database
  -m, --member [father|mother|child|affected]
                                  Type of sample
  -s, --sex [male|female]         Sex of sample
  --vcf-dir PATH                  Directory where vcf is created. Defaults to
  -p, --proband                   Variants from all affected samples,
                                  regardless of pedigree
  -n, --sample-name TEXT          Name of sample
  -j, --json-out                  Print results to stdout as json-string
  --help                          Show this message and exit.


mutacc --config-file <config.yaml> db export -m affected --all-variants

will find all the cases from the mutacc DB, and store this information in a file /.../root_dir/queries/sample_name_query.mutacc.

to export an entire trio, this can be done by

mutacc --config-file <config_file> db export -m child --all-variants -p -n child
mutacc --config-file <config_file> db export -m father --all-variants -n father
mutacc --config-file <config_file> db export -m mother --all-variants -n mother

This will create three files child_query_mutacc.json, father_query_mutacc.json, and mother_query_mutacc.json.

the export subcommand will also generate a truth set vcf-file for each exported samples, containing all queried variants.

To make a dataset (fastq-files) from a query file the synthesize command is used with the following options

Path to the bam file for sample to be used as background

Path to fastq file for sample to be used as background

Path to second fastq file (if paired end experiment)

Path to the query json-files created with the export command

Directory where fastq files will be stored. defaults to /.../root_dir/datasets

example, using the query files created above

mutacc --config-file <config_file> synthesize -b <bam> -f <fastq1_child> -f2 <fastq2_child> -q child_query_mutacc.json
mutacc --config-file <config_file> synthesize -b <bam> -f <fastq1_father> -f2 <fastq2_father> -q father_query_mutacc.json
mutacc --config-file <config_file> synthesize -b <bam> -f <fastq1_mother> -f2 <fastq2_mother> -q mother_query_mutacc.json

The created fastq-files will be stored in the directory /.../root_dir/datasets/ or in directory specified by ---dataset-dir

Remove case from database

To remove a case from the mutacc DB, and all the generated bam, and fastq files generated from that case from disk, the remove command is used

mutacc --config-file <config.yaml> db remove <case_id>

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mutacc-1.6.2.tar.gz (812.6 kB view hashes)

Uploaded source

Built Distribution

mutacc-1.6.2-py3-none-any.whl (820.2 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page