Novel computational techniques for mapping and classifying Next-Generation Sequencing data

Since their emergence around 2006, Next-Generation Sequencing technologies have been revolutionizing biological and medical research. Quickly obtaining an extensive amount of short or long reads of DNA sequence from almost any biological sample enables detecting genomic variants, revealing the composition of species in a metagenome, deciphering cancer biology, decoding the evolution of living or extinct species, or understanding human migration patterns and human history in general. The pace at which the throughput of sequencing technologies is increasing surpasses the growth of storage and computer capacities, which creates new computational challenges in NGS data processing. In this thesis, we present novel computational techniques for read mapping and taxonomic classification. With more than a hundred of published mappers, read mapping might be considered fully solved. However, the vast majority of mappers follow the same paradigm and only little attention has been paid to non-standard mapping approaches. Here, we propound the so-called dynamic mapping that we show to significantly improve the resulting alignments compared to traditional mapping approaches. Dynamic mapping is based on exploiting the information from previously computed alignments, helping to improve the mapping of subsequent reads. We provide the first comprehensive overview of this method and demonstrate its qualities using Dynamic Mapping Simulator, a pipeline that compares various dynamic mapping scenarios to static mapping and iterative referencing. An important component of a dynamic mapper is an online consensus caller, i.e., a program collecting alignment statistics and guiding updates of the reference in the online fashion. We provide Ococo, the first online consensus caller that implements a smart statistics for individual genomic positions using compact bit counters. Beyond its application to dynamic mapping, Ococo can be employed as an online SNP caller in various analysis pipelines, enabling SNP calling from a stream without saving the alignments on disk. Metagenomic classification of NGS reads is another major topic studied in the thesis. Having a database with thousands of reference genomes placed on a taxonomic tree, the task is to rapidly assign a huge amount of NGS reads to tree nodes, and possibly estimate the relative abundance of involved species. In this thesis, we propose improved computational techniques for this task. In a series of experiments, we show that spaced seeds consistently improve the classification accuracy. We provide Seed-Kraken, a spaced seed extension of Kraken, the most popular classifier at present. Furthermore, we suggest ProPhyle, a new indexing strategy based on a BWT-index, obtaining a much smaller and more informative index compared to Kraken. We provide a modified version of BWA that improves the BWT-index for a quick k-mer look-up

Spaced seed design using perfect rulers

A widely used class of approximate pattern matching algorithms work in two stages, the first being a filtering stage that uses spaced seeds to quickly discards regions where a match is not likely to occur. The design of effective spaced seeds is known to be a hard problem. In this setting, we propose a family of lossless spaced seeds for matching with up to two errors based on mathematical objects known as perfect rulers. We analyze these seeds with respect to the trade-off they offer between seed weight and the minimum length of the pattern to be matched. We identify a specific property of rulers, namely their skewness, which is closely related to the minimum pattern length of the derived seeds. In this context, we study in depth the specific case of Wichmann rulers and investigate the generalization of our approach to the larger class of unrestricted rulers. Although our analysis is mainly of theoretical interest, we show that for pattern lengths of practical relevance our seeds have a larger weight, hence a better filtration efficiency, than the ones known in the literature

Spaced seed design using perfect rulers

A widely used class of approximate pattern matching algorithms work in two stages, the first being a filtering stage that uses spaced seeds to quickly discards regions where a match is not likely to occur. The design of effective spaced seeds is known to be a hard problem. In this setting, we propose a family of lossless spaced seeds for matching with up to two errors based on mathematical objects known as perfect rulers. We analyze these seeds with respect to the trade-off they offer between seed weight and the minimum length of the pattern to be matched. We identify a specific property of rulers, namely their skewness, which is closely related to the minimum pattern length of the derived seeds. In this context, we study in depth the specific case of Wichmann rulers and investigate the generalization of our approach to the larger class of unrestricted rulers. Although our analysis is mainly of theoretical interest, we show that for pattern lengths of practical relevance our seeds have a larger weight, hence a better filtration efficiency, than the ones known in the literature