Compressed Spaced Suffix Arrays

Spaced seeds are important tools for similarity search in bioinformatics, and using several seeds together often significantly improves their performance. With existing approaches, however, for each seed we keep a separate linear-size data structure, either a hash table or a spaced suffix array (SSA). In this paper we show how to compress SSAs relative to normal suffix arrays (SAs) and still support fast random access to them. We first prove a theoretical upper bound on the space needed to store an SSA when we already have the SA. We then present experiments indicating that our approach works even better in practice

Compressed Spaced Suffix Arrays

As a first step in designing relatively-compressed data structures---i.e., such that storing an instance for one dataset helps us store instances for similar datasets---we consider how to compress spaced suffix arrays relative to normal suffix arrays and still support fast access to them. This problem is of practical interest when performing similarity search with spaced seeds because using several seeds in parallel significantly improves their performance, but with existing approaches we keep a separate linear-space hash table or spaced suffix array for each seed. We first prove a theoretical upper bound on the space needed to store a spaced suffix array when we already have the suffix array. We then present experiments indicating that our approach works even better in practice.Peer reviewe

Multiple seeds sensitivity using a single seed with threshold

Spaced seeds are a fundamental tool for similarity search in biosequences. The best sensitivity/selectivity trade-offs are obtained using many seeds simultaneously: This is known as the multiple seed approach. Unfortunately, spaced seeds use a large amount of memory and the available RAM is a practical limit to the number of seeds one can use simultaneously. Inspired by some recent results on lossless seeds, we revisit the approach of using a single spaced seed and considering two regions homologous if the seed hits in at least t sufficiently close positions. We show that by choosing the locations of the don't care symbols in the seed using quadratic residues modulo a prime number, we derive single seeds that when used with a threshold t > 1 have competitive sensitivity/selectivity trade-offs, indeed close to the best multiple seeds known in the literature. In addition, the choice of the threshold t can be adjusted to modify sensitivity and selectivity a posteriori, thus enabling a more accurate search in the specific instance at issue. The seeds we propose also exhibit robustness and allow flexibility in usage

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