Interpretation, stratification and evidence for sequence variants affecting mRNA splicing in complete human genome...

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Information theory methods accurately predict non-coding mutation effects in Mendelian diseases.
• Predicting splice site alterations is crucial for understanding genetic disease mechanisms.

Purpose of the Study:

• To introduce the Shannon pipeline software for genome-scale mutation analysis.
• To demonstrate the software's capability in predicting variants affecting mRNA splicing.

Main Methods:

• Utilizing information theory to compare reference and variant splice site information content.
• Implementing the Shannon pipeline on the CLC-Bio Genomics platform for variant annotation.
• Analyzing mutations and SNPs in cancer cell line genomes (U2OS, U251, A431).

Main Results:

• The Shannon pipeline successfully predicted variants affecting mRNA splicing, including novel and rare SNPs.
• Identified potential natural and cryptic mRNA splicing variants, distinguishing null from leaky mutations.
• Validated predictions through RNA-seq and expression microarray analyses in cancer and HapMap cell lines.

Conclusions:

• The Shannon pipeline software provides a valuable tool for genome-scale mutation analysis, specifically for predicting splicing alterations.
• The software effectively identifies and prioritizes potentially deleterious variants for laboratory investigation.
• Predicted functional variants in cancer cell lines included inactivating, leaky, and cryptic splicing mutations.