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CLC Bio launches Shannon Human Splicing Pipeline developed by OGI PBDF funded company Cytognomix

The splicing mutation pipeline plugin finds and interprets genomic variants that alter mRNA splicing in human exome, targeted sequencing, or complete genome data.

Cytognomix genome-scale human splicing mutation analysis.
Shannon pipeline from Cytognomix was initially created to address the vexing problem of assessing the many variants of unknown significance that are detected in cancer genetic testing. The Shannon human splicing mutation plug-in uses a proven approach to predict mRNA splicing abnormalities based on single nucleotide variant data from human sequencing. The Cytognomix plugin can be deployed on CLC Genomics workbench either as a standalone application or to CLC Genomics server using the Genomics Workbench client.

Features
The Cytognomix Shannon plugin identifies splicing mutations with industry-leading sensitivity and specificity. It is based on a legacy of peer-reviewed literature confirming validated mutation predictions using our patented information theory based technology. Changes in information content in splice sites correspond to actual differences in binding affinity. Input can either be CLC bio SNP objects, in standard VCF, or an indexed simple SNP format. Results are based on sensible default parameters, are sortable and intuitive, and exportable to spreadsheets or genome browsers.

Benefits
•Hones in on a limited number of variants that potentially affect mRNA splicing
•Variants categorized by whether they fully or partially inactivate natural splice sites or activate cryptic sites
•Predicts variants missed by other bioinformatic methods
•Changes displayed graphically as Manhattan-like plots or custom tracks
•Mutations are sortable according to information content, proximity to natural splice site, relative strength of cryptic vs natural site, gene, and coordinate
•Variants affecting SNPs are annotated and can be filtered by allele frequency
•Rapid results generated from complete genome-wide, variant analysis of thousands of SNPs

For more information, please click here.