Bioinformatics analysis pipeline for exome sequencing data

Abstract

Next generation sequencing (NGS), also referred to as high throughput DNA sequencing (HTS), is a versatile and reliable platform for large-scale DNA sequencing, allowing single labs to sequence at a reasonable coverage the entire genome of an individual within a couple of weeks and at a comparatively low cost (1). Due to its ability to sequence massive amounts of DNA, HTS has become an extremely useful and flexible tool, enabling researches to investigate a number of questions related to the genetic sequence, including identification of known and novel single nucleotide polymorphisms (SNPs), structural variants such as deletions, insertions or inversions, or even to examine gene expression levels by first convertingmRNA to cDNAand then sequencing (RNA-seq). For these reasons, HTS has been considered the Swiss pocketknife of molecular biology (2). A key application of NGS is detection of genetic variants associated with disease phenotypes. In these analyses, the DNA sequence of a carrier of the disease-related phenotype (e.g. a tumor) is compared to reference sequence from healthy individuals to determine genetic aberrations that might be driving the disease. Although protein coding genes constitute only about 1 percent of the genome, such regions typically harbor 85 percent of themutations associatedwith diseases. This enrichment suggests that a more efficient strategy for identifying disease-related (functional) mutations is to sequence only the complete coding regions of genomes, called the exome. Efficient methods to capture and sequence the whole exome have been developed and are already having an impact in clinical diagnosis (3, 4). Here, we review the key bioinformatics steps required to process, clean and assemble the DNA sequence fromHTS whole exome sequencing data and, finally, to identify the functional mutations that might have important clinical implications in disease-specific prognosis and management.