WOODSTOCC: Extracting Latent Parallelism from a DNA Sequence Aligner on a GPU

An exponential increase in the speed of DNA sequencing over the past decade has driven demand for fast, space-efficient algorithms to process the resultant data. The first step in processing is alignment of many short DNA sequences, or reads, against a large reference sequence. This work presents WOODSTOCC, an implementation of short-read alignment designed for Graphics Processing Unit (GPU) architectures. WOODSTOCC translates a novel CPU implementation of gapped short-read alignment, which has guaranteed optimal and complete results, to the GPU. Our implementation combines an irregular trie search with dynamic programming to expose regularly structured parallelism. We first describe this implementation, then discuss its port to the GPU. WOODSTOCC’s GPU port exploits three generally useful techniques for extracting regular parallelism from irregular computations: dynamic thread mapping with a worklist, kernel stage decoupling, and kernel slicing. We discuss the performance impact of these techniques and suggest further opportunities for improvement

NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language

Background: Shotgun metagenomes contain a sample of all the genomic material in an environment, allowing for the characterization of a microbial community. In order to understand these communities, bioinformatics methods are crucial. A common first step in processing metagenomes is to compute abundance estimates of different taxonomic or functional groups from the raw sequencing data. Given the breadth of the field, computational solutions need to be flexible and extensible, enabling the combination of different tools into a larger pipeline. Results: We present NGLess and NG-meta-profiler. NGLess is a domain specific language for describing next-generation sequence processing pipelines. It was developed with the goal of enabling user-friendly computational reproducibility. It provides built-in support for many common operations on sequencing data and is extensible with external tools with configuration files. Using this framework, we developed NG-meta-profiler, a fast profiler for metagenomes which performs sequence preprocessing, mapping to bundled databases, filtering of the mapping results, and profiling (taxonomic and functional). It is significantly faster than either MOCAT2 or htseq-count and (as it builds on NGLess) its results are perfectly reproducible. Conclusions: NG-meta-profiler is a high-performance solution for metagenomics processing built on NGLess. It can be used as-is to execute standard analyses or serve as the starting point for customization in a perfectly reproducible fashion. NGLess and NG-meta-profiler are open source software (under the liberal MIT license) and can be downloaded from https://ngless.embl.de or installed through bioconda

MR-CUDASW - GPU accelerated Smith-Waterman algorithm for medium-length (meta)genomic data

The idea of using a graphics processing unit (GPU) for more than simply graphic output purposes has been around for quite some time in scientific communities. However, it is only recently that its benefits for a range of bioinformatics and life sciences compute-intensive tasks has been recognized. This thesis investigates the possibility of improving the performance of the overlap determination stage of an Overlap Layout Consensus (OLC)-based assembler by using a GPU-based implementation of the Smith-Waterman algorithm. In this thesis an existing GPU-accelerated sequence alignment algorithm is adapted and expanded to reduce its completion time. A number of improvements and changes are made to the original software. Workload distribution, query profile construction, and thread scheduling techniques implemented by the original program are replaced by custom methods specifically designed to handle medium-length reads. Accordingly, this algorithm is the first highly parallel solution that has been specifically optimized to process medium-length nucleotide reads (DNA/RNA) from modern sequencing machines (i.e. Ion Torrent). Results show that the software reaches up to 82 GCUPS (Giga Cell Updates Per Second) on a single-GPU graphic card running on a commodity desktop hardware. As a result it is the fastest GPU-based implemen- tation of the Smith-Waterman algorithm tailored for processing medium-length nucleotide reads. Despite being designed for performing the Smith-Waterman algorithm on medium-length nucleotide sequences, this program also presents great potential for improving heterogeneous computing with CUDA-enabled GPUs in general and is expected to make contributions to other research problems that require sensitive pairwise alignment to be applied to a large number of reads. Our results show that it is possible to improve the performance of bioinformatics algorithms by taking full advantage of the compute resources of the underlying commodity hardware and further, these results are especially encouraging since GPU performance grows faster than multi-core CPUs

Homology sequence analysis using GPU acceleration

A number of problems in bioinformatics, systems biology and computational biology field require abstracting physical entities to mathematical or computational models. In such studies, the computational paradigms often involve algorithms that can be solved by the Central Processing Unit (CPU). Historically, those algorithms benefit from the advancements of computing power in the serial processing capabilities of individual CPU cores. However, the growth has slowed down over recent years, as scaling out CPU has been shown to be both cost-prohibitive and insecure. To overcome this problem, parallel computing approaches that employ the Graphics Processing Unit (GPU) have gained attention as complementing or replacing traditional CPU approaches. The premise of this research is to investigate the applicability of various parallel computing platforms to several problems in the detection and analysis of homology in biological sequence. I hypothesize that by exploiting the sheer amount of computation power and sequencing data, it is possible to deduce information from raw sequences without supplying the underlying prior knowledge to come up with an answer. I have developed such tools to perform analysis at scales that are traditionally unattainable with general-purpose CPU platforms. I have developed a method to accelerate sequence alignment on the GPU, and I used the method to investigate whether the Operational Taxonomic Unit (OTU) classification problem can be improved with such sheer amount of computational power. I have developed a method to accelerate pairwise k-mer comparison on the GPU, and I used the method to further develop PolyHomology, a framework to scaffold shared sequence motifs across large numbers of genomes to illuminate the structure of the regulatory network in yeasts. The results suggest that such approach to heterogeneous computing could help to answer questions in biology and is a viable path to new discoveries in the present and the future.Includes bibliographical reference

High Performance Computing for DNA Sequence Alignment and Assembly

Recent advances in DNA sequencing technology have dramatically increased the scale and scope of DNA sequencing. These data are used for a wide variety of important biological analyzes, including genome sequencing, comparative genomics, transcriptome analysis, and personalized medicine but are complicated by the volume and complexity of the data involved. Given the massive size of these datasets, computational biology must draw on the advances of high performance computing. Two fundamental computations in computational biology are read alignment and genome assembly. Read alignment maps short DNA sequences to a reference genome to discover conserved and polymorphic regions of the genome. Genome assembly computes the sequence of a genome from many short DNA sequences. Both computations benefit from recent advances in high performance computing to efficiently process the huge datasets involved, including using highly parallel graphics processing units (GPUs) as high performance desktop processors, and using the MapReduce framework coupled with cloud computing to parallelize computation to large compute grids. This dissertation demonstrates how these technologies can be used to accelerate these computations by orders of magnitude, and have the potential to make otherwise infeasible computations practical

Metagenomics : tools and insights for analyzing next-generation sequencing data derived from biodiversity studies

Advances in next-generation sequencing (NGS) have allowed significant breakthroughs in microbial ecology studies. This has led to the rapid expansion of research in the field and the establishment of “metagenomics”, often defined as the analysis of DNA from microbial communities in environmental samples without prior need for culturing. Many metagenomics statistical/computational tools and databases have been developed in order to allow the exploitation of the huge influx of data. In this review article, we provide an overview of the sequencing technologies and how they are uniquely suited to various types of metagenomic studies. We focus on the currently available bioinformatics techniques, tools, and methodologies for performing each individual step of a typical metagenomic dataset analysis. We also provide future trends in the field with respect to tools and technologies currently under development. Moreover, we discuss data management, distribution, and integration tools that are capable of performing comparative metagenomic analyses of multiple datasets using well-established databases, as well as commonly used annotation standards

HumanMycobiomeScan: A new bioinformatics tool for the characterization of the fungal fraction in metagenomic samples

Background: Modern metagenomic analysis of complex microbial communities produces large amounts of sequence data containing information on the microbiome in terms of bacterial, archaeal, viral and eukaryotic composition. The bioinformatics tools available are mainly devoted to profiling the bacterial and viral fractions and only a few software packages consider fungi. As the human fungal microbiome (human mycobiome) can play an important role in the onset and progression of diseases, a comprehensive description of host-microbiota interactions cannot ignore this component. Results: HumanMycobiomeScan is a bioinformatics tool for the taxonomic profiling of the mycobiome directly from raw data of next-generation sequencing. The tool uses hierarchical databases of fungi in order to unambiguously assign reads to fungal species more accurately and > 10,000 times faster than other comparable approaches. HumanMycobiomeScan was validated using in silico generated synthetic communities and then applied to metagenomic data, to characterize the intestinal fungal components in subjects adhering to different subsistence strategies. Conclusions: Although blind to unknown species, HumanMycobiomeScan allows the characterization of the fungal fraction of complex microbial ecosystems with good performance in terms of sample denoising from reads belonging to other microorganisms. HumanMycobiomeScan is most appropriate for well-studied microbiomes, for which most of the fungal species have been fully sequenced. This released version is functionally implemented to work with human-associated microbiota samples. In combination with other microbial profiling tools, HumanMycobiomeScan is a frugal and efficient tool for comprehensive characterization of microbial ecosystems through shotgun metagenomics sequencing

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