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

Read alignment using deep neural networks

2019 Spring.Includes bibliographical references.Read alignment is the process of mapping short DNA sequences into the reference genome. With the advent of consecutively evolving "next generation" sequencing technologies, the need for sequence alignment tools appeared. Many scientific communities and the companies marketing the sequencing technologies developed a whole spectrum of read aligners/mappers for different error profiles and read length characteristics. Among the most recent successfully marketed sequencing technologies are Oxford Nanopore and PacBio SMRT sequencing, which are considered top players because of their extremely long reads and low cost. However, the reads may contain error up to 20% that are not generally uniformly distributed. To deal with that level of error rate and read length, proximity preserving hashing techniques, such as Minhash and Minimizers, were utilized to quickly map a read to the target region of the reference sequence. Subsequently, a variant of global or local alignment dynamic programming is then used to give the final alignment. In this research work, we train a Deep Neural Network (DNN) to yield a hashing scheme for the highly erroneous long reads, which is deemed superior to Minhash for mapping the reads. We implemented that idea to build a read alignment tool: DNNAligner. We evaluated the performance of our aligner against the popular read aligners in the bioinformatics community currently — minimap2, bwa-mem and graphmap. Our results show that the performance of DNNAligner is comparable to other tools without any code optimization or integration of other advanced features. Moreover, DNN exhibits superior performance in comparison with Minhashon neighborhood classification

Technology dictates algorithms: Recent developments in read alignment

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to todays diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies

Functional characterization and annotation of trait-associated genomic regions by transcriptome analysis

In this work, two novel implementations have been presented, which could assist in the design and data analysis of high-throughput genomic experiments. An efficient and flexible tiling probe selection pipeline utilizing the penalized uniqueness score has been implemented, which could be employed in the design of various types and scales of genome tiling task. A novel hidden semi-Markov model (HSMM) implementation is made available within the Bioconductor project, which provides a unified interface for segmenting genomic data in a wide range of research subjects.In dieser Arbeit werden zwei neuartige Implementierungen präsentiert, die im Design und in der Datenanalyse von genomischen Hochdurchsatz-Experiment hilfreich sein könnten. Die erste Implementierung bildet eine effiziente und flexible Auswahl-Pipeline für Tiling-Proben, basierend auf einem Eindeutigkeitsmaß mit einer Maluswertung. Als zweite Implementierung wurde ein neuartiges Hidden-Semi-Markov-Modell (HSMM) im Bioconductor Projekt verfügbar gemacht

Dynamic read mapping and online consensus calling for better variant detection

Variant detection from high-throughput sequencing data is an essential step in identification of alleles involved in complex diseases and cancer. To deal with these massive data, elaborated sequence analysis pipelines are employed. A core component of such pipelines is a read mapping module whose accuracy strongly affects the quality of resulting variant calls.We propose a dynamic read mapping approach that significantly improves read alignment accuracy. The general idea of dynamic mapping is to continuously update the reference sequence on the basis of previously computed read alignments. Even though this concept already appeared in the literature, we believe that our work provides the first comprehensive analysis of this approach.To evaluate the benefit of dynamic mapping, we developed a software pipeline (http://github.com/karel-brinda/dymas) that mimics different dynamic mapping scenarios. The pipeline was applied to compare dynamic mapping with the conventional static mapping and, on the other hand, with the so-called iterative referencing – a computationally expensive procedure computing an optimal modification of the reference that maximizes the overall quality of all alignments. We conclude that in all alternatives, dynamic mapping results in a much better accuracy than static mapping, approaching the accuracy of iterative referencing.To correct the reference sequence in the course of dynamic mapping, we developed an online consensus caller named Ococo (http://github.com/karel-brinda/ococo). Ococo is the first consensus caller capable to process input reads in the online fashion.Finally, we provide conclusions about the feasibility of dynamic mapping and discuss main obstacles that have to be overcome to implement it. We also review a wide range of possible applications of dynamic mapping with a special emphasis on variant detection

Cgaln: fast and space-efficient whole-genome alignment

<p>Abstract</p> <p>Background</p> <p>Whole-genome sequence alignment is an essential process for extracting valuable information about the functions, evolution, and peculiarities of genomes under investigation. As available genomic sequence data accumulate rapidly, there is great demand for tools that can compare whole-genome sequences within practical amounts of time and space. However, most existing genomic alignment tools can treat sequences that are only a few Mb long at once, and no state-of-the-art alignment program can align large sequences such as mammalian genomes directly on a conventional standalone computer.</p> <p>Results</p> <p>We previously proposed the CGAT (Coarse-Grained AlignmenT) algorithm, which performs an alignment job in two steps: first at the block level and then at the nucleotide level. The former is "coarse-grained" alignment that can explore genomic rearrangements and reduce the sizes of the regions to be analyzed in the next step. The latter is detailed alignment within limited regions. In this paper, we present an update of the algorithm and the open-source program, Cgaln, that implements the algorithm. We compared the performance of Cgaln with those of other programs on whole genomic sequences of several bacteria and of some mammalian chromosome pairs. The results showed that Cgaln is several times faster and more memory-efficient than the best existing programs, while its sensitivity and accuracy are comparable to those of the best programs. Cgaln takes less than 13 hours to finish an alignment between the whole genomes of human and mouse in a single run on a conventional desktop computer with a single CPU and 2 GB memory.</p> <p>Conclusions</p> <p>Cgaln is not only fast and memory efficient but also effective in coping with genomic rearrangements. Our results show that Cgaln is very effective for comparison of large genomes, especially of intact chromosomal sequences. We believe that Cgaln provides novel viewpoint for reducing computational complexity and will contribute to various fields of genome science.</p

Next-Generation Sequencing: Acquisition, Analysis, and Assembly

The process of sequencing a genome involves many steps, and accordingly, this project contains work from each of those steps. Genome sequencing begins with acquisition of sequence data, therefore, a novel biochemistry was utilized and optimized for the Sequencing By Ligation (SBL) process. A cyclic SBL protocol was created that could be utilized to extend sequencing reads in both the 5\u27 and 3\u27 directions, for an increase in read length and thru-put. After sequence acquisition, there is the process of data analysis, and the focus shifted to creating software that could take sequence information and match up the individual reads to a reference genome with greater speed and efficiency than other commonly-used software. The Sequence Analysis Workbench Tool, SAWTooth, was written and shown to outperform contemporaries NOVOAlign and BOWTIE. Finally, the last aspect of genome sequencing is de novo assembly, prompting a comparative analysis of three assemblers: CLC Genomics Workbench, Velvet Assembler, and MIRA. Results were generated using Mauve to assess the general effects of different sequencing platforms on the final assembly

SHRiMP: Accurate Mapping of Short Color-space Reads

The development of Next Generation Sequencing technologies, capable of sequencing hundreds of millions of short reads (25–70 bp each) in a single run, is opening the door to population genomic studies of non-model species. In this paper we present SHRiMP - the SHort Read Mapping Package: a set of algorithms and methods to map short reads to a genome, even in the presence of a large amount of polymorphism. Our method is based upon a fast read mapping technique, separate thorough alignment methods for regular letter-space as well as AB SOLiD (color-space) reads, and a statistical model for false positive hits. We use SHRiMP to map reads from a newly sequenced Ciona savignyi individual to the reference genome. We demonstrate that SHRiMP can accurately map reads to this highly polymorphic genome, while confirming high heterozygosity of C. savignyi in this second individual. SHRiMP is freely available at http://compbio.cs.toronto.edu/shrimp