Chanjo: Clincal grade sequence coverage analysis

Implementation

Chanjo is written in Python (3.2+). It follows UNIX conventions and is built around text streams that can be incorporated into pipelines (Figure 1). Chanjo is distributed via GitHub, installation is simple and robust thanks to extensive tests. Chanjo loads output files from Sambamba depth⁷ and stores coverage related statistics, e.g. average coverage and completeness in a SQL database. Completeness is defined as the percentage of bases meeting a user-defined coverage threshold for each genomic interval (Figure 2). Chanjo does not aim to analyze the whole genome, but will limit the analyses to predefined genomic regions of interest defined in BED format.

Figure 1. Chanjo + Sambamba workflow.

Figure 2. Completeness description.

Supported features

Chanjo supports any genomic intervals as long as they adhere to the BED format with two optional columns for linking exons to transcripts and genes. Hence, it is easy to set-up independent databases using different gene and transcripts definitions, e.g. ccds, refseq or ensembl. When the genomic intervals are defined and added to the database, it is simple to add additional samples.

API

Chanjo uses a predefined database schema where exons are organized into transcripts and genes (Figure 3). Extracting basic coverage metrics such as “average coverage”, “overall completeness”, etc. for different transcripts and genes is easily done through the Python application programing interface (API). Setting up a database with the “init” subcommand only needs to be done once. After the basic structure of the database is in place, the user can add an arbitrary number of samples to the database and include or exclude samples, as preferred from the downstream analysis. The SQL schema has been designed to be a powerful tool on its own for studying coverage. It allows for quick aggregate metrics across multiple samples and can be used as a general coverage API for accompanying tools. One example of such a tool is Chanjo-Report, a clinical-grade coverage report generator for Chanjo output developed to be used in a clinical setting. The report can be tailored to include any number of samples, genes or transcripts (Figure 4). The report can be exported as a PDF and is assembled via a web interface using Chanjo-Report together with a Chanjo database. This works as a powerful bridge between bioinformaticians and professionals that work with analysis who often lack programming skills.

Figure 3. Chanjo database schema.

Figure 4. Chanjo Report example.

Operation

Chanjo requires a installation of Python version 3.2 or above. In a production setting, a mySQL database is preferred, however it is possible to use sqlite, which comes with the python installation. Chanjo has been installed and tested on Linux and Mac OS environments while there should not be any problems to install on Windows. Performance wise, a standard computer will be sufficient to run Chanjo.

Workflow

It is straight forward to setup a working demo of Chanjo to test how to use the tool:

chanjo init --demo ./ chanjo - demo
for file in *. coverage .bed
do
    echo "${ file }"
    chanjo load --group group1 "${ file }"
done
chanjo calculate mean -- pretty

The first command initializes a SQL database with tables, it will also use a reduced bed file to link exons, transcripts, and genes according to the definitions in “hgnc.min.bed”. Chanjo uses output generated by “sambamba depth” which includes average coverage and completeness data for each exon defined in “hgnc.min.bed”. The for loop loads data for 3 samples into the database for persistent storage. Finally, the CLI can be used to execute simple queries and output the results in JSON format.

   chanjo calculate mean sample_1
{
     ’metrics ’: {
          ’ completeness_ 10’: 90.38,
          ’ completeness_ 20’: 90.92,
          ’mean_coverage ’: 193.85
          },
          ’sample_id ’: ’sample_ 1’
}

Chanjo is intended to be used with a central database where samples are continuously added over time. This facilitates aggregate statistics, e.g. trending coverage metrics, poorly covered regions of the genome and comparing gene panel coverage across samples.

Chanjo report

In many settings the workflow for a sample starts in the lab where DNA is prepared and sequenced. After that bioinformaticians prepare the output for analysis and hand it over to a researcher or clinician. Chanjo-Report is developed as a tool to present data generated by the bioinformaticians to the end-user. Here one can specify a subset of regions, in many cases a gene panel that is specific for a disease group, and get a well structured report of the fraction of transcripts/genes that are fully covered and which are not. This has proven essential when performing clinical tests with MPS data.