VarGenius executes cohort-level DNA-seq variant calling and annotation and allows to manage the resulting data through a PostgreSQL database

Abstract

Background: Targeted resequencing has become the most used and cost-effective approach for identifying causative mutations of Mendelian diseases both for diagnostics and research purposes. Due to very rapid technological progress, NGS laboratories are expanding their capabilities to address the increasing number of analyses. Several open source tools are available to build a generic variant calling pipeline, but a tool able to simultaneously execute multiple analyses, organize, and categorize the samples is still missing.

Results: Here we describe VarGenius, a Linux based command line software able to execute customizable pipelines for the analysis of multiple targeted resequencing data using parallel computing. VarGenius provides a database to store the output of the analysis (calling quality statistics, variant annotations, internal allelic variant frequencies) and sample information (personal data, genotypes, phenotypes). VarGenius can also perform the "joint analysis" of hundreds of samples with a single command, drastically reducing the time for the configuration and execution of the analysis. VarGenius executes the standard pipeline of the Genome Analysis Tool-Kit (GATK) best practices (GBP) for germinal variant calling, annotates the variants using Annovar, and generates a user-friendly output displaying the results through a web page. VarGenius has been tested on a parallel computing cluster with 52 machines with 120GB of RAM each. Under this configuration, a 50 M whole exome sequencing (WES) analysis for a family was executed in about 7 h (trio or quartet); a joint analysis of 30 WES in about 24 h and the parallel analysis of 34 single samples from a 1 M panel in about 2 h.

Conclusions: We developed VarGenius, a "master" tool that faces the increasing demand of heterogeneous NGS analyses and allows maximum flexibility for downstream analyses. It paves the way to a different kind of analysis, centered on cohorts rather than on singleton. Patient and variant information are stored into the database and any output file can be accessed programmatically. VarGenius can be used for routine analyses by biomedical researchers with basic Linux skills providing additional flexibility for computational biologists to develop their own algorithms for the comparison and analysis of data. The software is freely available at: https://github.com/frankMusacchia/VarGenius.

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Figures

Fig. 1

VarGenius flowchart: sequential steps allowed in VarGenius to execute different pipelines. Dark gray indicates a mandatory step, medium gray an optional one and the lighter gray represents the input and output of the pipeline. This figure also shows the input and output of VarGenius

Fig. 2

VarGenius files and data management: the samples sheet data (containing FASTQ paths, analysis, samples and read files names, sequencing type, target file and user id) is imported into the database. VarGenius automatically chooses different settings for two predefined pipelines: one for exomes and the second for amplicon panels. The different tasks (quality check, alignment, refinement, variant calling, variant filtering and output production) are executed as Torque jobs using QSUB command and scheduled in the cluster

Fig. 3

VarGenius database schema: analyzes and samples information is managed at three different levels using the analyzes, samples and readfiles tables. They are also used to keep track of the steps executed. The variants table contains the information about the variants and their allelic frequencies. The statistics table contains the information about the variant for each analysis (quality score, depth, etc). The genotypes table contains the genotype information obtained with GATK HaplotypeCaller while the annotation table contains more specific information about the variants calling (gene, transcript, exon, nucleotide and aminoacidic substitutions). The last three tables (transcripts, genes and phenotypes) contain the information to build the gene annotation

Fig. 4

HTML page with results. This page is given as for example, it is the first page of the web site produced and shows how the results are organized. Links to download the output files and tables showing quality check statistics are present

Fig. 5

Example figure for global percentage of target coverage plot. This plot shows the percentage of the target covered by samples obtained with the same target file and can be used for sequencing run evaluation. This kind of plot is print by VarGenius at different levels of coverage (1X, 10X, 20X, 40X, 80X,100X). The figure shows the coverage of the target at 20X. Three different colors are used for kinship of the samples (probands, mothers and fathers)

Fig. 6

This figure details the start-and-stop method in VarGenius. At any task the input is taken from a folder belonging to the previous one. Thus, the refinement task takes the input from the alignment task and puts the output in the refine_out folder

Fig. 7

An example query to our database to identify which samples have a specific variant

Fig. 8

Example of a table of total reads on the X and Y chromosomes. This table is displayed in the HTML web page and shows, for each sample in an analysis, the total reads on both chromosome X and Y and their relative percentages

Fig. 9

An example of segregation test table. This table is displayed in the HTML web page and matches the number of variants found in a sample with a particular genotype with all other samples. It may be used to compare calls in probands and their parents