ParallNormal: An Efficient Variant Calling Pipeline for Unmatched Sequencing Data

Nowadays, next generation sequencing is closer to clinical application in the field of oncology. Indeed, it allows the identification of tumor-specific mutations acquired during cancer development, progression and resistance to therapy. In parallel with an evolving sequencing technology, novel computational approaches are needed to cope with the requirement of a rapid processing of sequencing data into a list of clinically-relevant genomic variants. Since sequencing data from both tumors and their matched normal samples are not always available (unmatched data), there is a need of a computational pipeline leading to variants calling in unmatched data. Despite the presence of many accurate and precise variant calling algorithms, an efficient approach is still lacking. Here, we propose a parallel pipeline (ParallNormal) designed to efficiently identify genomic variants from whole- exome sequencing data, in absence of their matched normal. ParallNormal integrates well-known algorithms such as BWA and GATK, a novel tool for duplicate removal (DuplicateRemove), and the FreeBayes variant calling algorithm. A re-engineered implementation of FreeBayes, optimized for execution on modern multi-core architectures is also proposed. ParallNormal was applied on whole-exome sequencing data of pancreatic cancer samples without considering their matched normal. The robustness of ParallNormal was tested using results of the same dataset analyzed using matched normal samples and considering genes involved in pancreatic carcinogenesis. Our pipeline was able to confirm most of the variants identified using matched normal data.