Identification of Variants is an extensive workflow to identify and predict the effect of high quality single nucleotide variants, small indels, CNVs and strutural variants from Exome or targeted resequencing data using GATK best practices. Downstream analysis such as GO and KEGG-pathway analysis can be performed on the set of interesting genes. In this workflow, parameters can also be optimized depending on datasets and analysis needs. State-of-the-art publication-quality visualization and reports of your analysis results can be easily downloaded in the form of tables, images and html.

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Pipeline

Import Disease/Normal Sample (FASTQ | SRA) Cleaned Disease/Normal (FASTQ) QC Reports Map to Reference (Disease/Normal BAM) QC Reports BAM Processing (Cleaned Disease/Normal BAM) QC Reports Indel Realignment (Cleaned Disease/Normal BAM) Base Recalibration (Cleaned Disease/Normal BAM) Structural Variants (Exome Reports) Copy Number Variants (Exome Reports) GATK Call Variants (Exome VCF) Annotate Variants (Annotated VCF) ClinChek+ Project Reports PanChek+

Import Samples

Samples are downloaded from amazon s3 bucket.

Pre-Alignment QC

Pre-alignment quality control step is performed to ensure the quality of raw data and it also provides a QC report which can spot biases in the starting library material or during sequencing. We use the FASTQC program which compiles important statistics on NGS data, such as per-base and per-sequence quality scores,GC content and checks for duplicated and overrepresented sequences within the file.

Alignment

At this stage sequencing reads are aligned to the reference genome. The mapping is performed using a fast and accurate read aligner BWA, which is based on Burrows-Wheeler Transformation algorithm. Read alignment information is reported in SAM/BAM format, which is used for calculating depth of coverage and calling variants. We get information about the location in the genome the read maps to and how confident the aligner was in mapping to that location, information about its mate read (if paired end sequenced).

Alignment QC

Alignment QC step examines the sequencing alignment data in SAM/BAM file according to the features of the mapped reads and their genomic properties and provides an overall view of the data that helps to detect biases in the sequencing and/or mapping of the data which you should be aware of before doing any further analysis. All statistics about sufficient coverage, depth of sequencing and read counts are provided at this step.

GATK processing

GATK processing step includes data cleanup operations to correct for technical biases and make the data suitable for analysis. This step involves indel cleaning, duplicate marking and base score recalibration following the GATK's latest definition of best practices.

Call variants

Calling variants using GATK follows the GATK Best Practices to identify and predict the effect of high quality single nucleotide variants and small insertions and deletions (indels) from your exome sequencing data. Likelihood data for each genotype is calculated and variants are identified with respect to the reference genome. The results are reported in the widely popular VCF format.

Structural Variants

This step identifies high quality breakpoints of large deletions, medium sized insertions, inversions, tandem duplications and other structural variants from your exome short paired-end sequencing reads using read pairs that are mapped with unexpected separation distances or orientation. It uses a pattern growth approach to identify the breakpoints of these variants from your exome data.

Copy number variants

This step identifies high quality copy number variants is performed by a pairwise comparison of read depth between the samples at each position to infer relative changes in the copy number. The output table is a set of regions with chromosome, co-ordinates, p-value and event type (amplification or deletion) of the copy number change from your exome data.

Annotate variants

Functional annotation of the high quality variants is performed to predict the effects of variants on genes (such as amino acid changes, etc.) and also it classifies the mutation in various categories like synonoumous or non-synonomous, mis-sense or silent. Novel SNVs are identified by filtering variants that are already annotated in existing publicly available databases like dbSNP. Deleterious variants are separated from the benign ones by assessing the mutagenic potential using SIFT and/or PolyPhen scoring system.

Clinchek+

After identification and annotation of high quality single nucleotide variants and small insertions and deletions (indels), these variants are prioritized on the basis of loss of function and actionable mutations. Detailed reports for Novel and Rare loss of function mutations are provided with the added information from databases like omim,clinvar,polyphen and SIFT for clinical significance.

Panchek+

PanChek+ is a proprietary computational biology module designed to sift through high-dimensional next generation sequencing data to identify and validate the presence of mutations matching with diagnostic and prognostic gene panels for disease conditions including various cancers and their subtypes.

Supported Organisms

1

Homo sapiens

2

Mus musculus

Reports

Turn your complex genomic data into a meaningful report of actionable information and insightful visualizations.
Reports are highly customizable and exportable in industry-class formats.

Read Duplication Summary

Reads Genomic Origin

Pre-filter Coverage Profile (Low)

Genomic distribution of SNPs & INDELs

Genomic distribution of SNPs

Manhattan Plot

variant summary

GO Biological Process

GO Cellular Component

GO Molecular Function

KEGG Pathway

Survival

CNV