mRNA variant detection workflow identifies transcriptomic variants from your RNA-seq 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.

Try it in Genome Explorer®

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) GATK Call Variants (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

In this step sequencing reads are aligned to the reference genome. The mapping is performed using an ultrafast, memory-efficient read aligner Tophat 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.

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 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 transcriptome variants from your RNA-seq 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.

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.

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