Large disclosing the nature of computational tools for the analysis of next generation sequencing data

Abstract

Next-generation sequencing (NGS) technologies are rapidly changing the approach to complex genomic studies, opening the way to personalized drugs development and personalized medicine. NGS technologies are characterized by a massive throughput for relatively short-sequences (30-100), and they are currently the most reliable and accurate method for grouping individuals on the basis of their genetic profiles. The first and crucial step in sequence analysis is the conversion of millions of short sequences (reads) into valuable genetic information by their mapping to a known (reference) genome. New computational methods, specifically designed for the type and the amount of data generated by NGS technologies, are replacing earlier widespread genome alignment algorithms which are unable to cope with such massive amount of data. This review provides an overview of the bioinformatics techniques that have been developed for the mapping of NGS data onto a reference genome, with a special focus on polymorphism rate and sequence error detection. The different techniques have been experimented on an appropriately defined dataset, to investigate their relative computational costs and usability, as seen from an user perspective. Since NGS platforms interrogate the genome using either the conventional nucleotide space or the more recent color space, this review does consider techniques both in nucleotide and color space, emphasizing similarities and diversities.