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

Inference of Many-Taxon Phylogenies

Phylogenetic trees are tree topologies that represent the evolutionary history of a set of organisms. In this thesis, we address computational challenges related to the analysis of large-scale datasets with Maximum Likelihood based phylogenetic inference. We have approached this using different strategies: reduction of memory requirements, reduction of running time, and reduction of man-hours

Parallelizing Epistasis Detection in GWAS on FPGA and GPU-Accelerated Computing Systems

This is a post-peer-review, pre-copyedit version of an article published in IEEE - ACM Transactions on Computational Biology and Bioinformatics. The final authenticated version is available online at: http://dx.doi.org/10.1109/TCBB.2015.2389958[Abstract] High-throughput genotyping technologies (such as SNP-arrays) allow the rapid collection of up to a few million genetic markers of an individual. Detecting epistasis (based on 2-SNP interactions) in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. Computational methods to detect epistasis therefore suffer from prohibitively long runtimes; e.g., processing a moderately-sized dataset consisting of about 500,000 SNPs and 5,000 samples requires several days using state-of-the-art tools on a standard 3 GHz CPU. In this paper, we demonstrate how this task can be accelerated using a combination of fine-grained and coarse-grained parallelism on two different computing systems. The first architecture is based on reconfigurable hardware (FPGAs) while the second architecture uses multiple GPUs connected to the same host. We show that both systems can achieve speedups of around four orders-of-magnitude compared to the sequential implementation. This significantly reduces the runtimes for detecting epistasis to only a few minutes for moderatelysized datasets and to a few hours for large-scale datasets.London. Wellcome Trust; 076113London. Wellcome Trust; 08547

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

FPGAs in Bioinformatics: Implementation and Evaluation of Common Bioinformatics Algorithms in Reconfigurable Logic

Life. Much effort is taken to grant humanity a little insight in this fascinating and complex but fundamental topic. In order to understand the relations and to derive consequences humans have begun to sequence their genomes, i.e. to determine their DNA sequences to infer information, e.g. related to genetic diseases. The process of DNA sequencing as well as subsequent analysis presents a computational challenge for recent computing systems due to the large amounts of data alone. Runtimes of more than one day for analysis of simple datasets are common, even if the process is already run on a CPU cluster. This thesis shows how this general problem in the area of bioinformatics can be tackled with reconfigurable hardware, especially FPGAs. Three compute intensive problems are highlighted: sequence alignment, SNP interaction analysis and genotype imputation. In the area of sequence alignment the software BLASTp for protein database searches is exemplarily presented, implemented and evaluated.SNP interaction analysis is presented with three applications performing an exhaustive search for interactions including the corresponding statistical tests: BOOST, iLOCi and the mutual information measurement. All applications are implemented in FPGA-hardware and evaluated, resulting in an impressive speedup of more than in three orders of magnitude when compared to standard computers. The last topic of genotype imputation presents a two-step process composed of the phasing step and the actual imputation step. The focus lies on the phasing step which is targeted by the SHAPEIT2 application. SHAPEIT2 is discussed with its underlying mathematical methods in detail, and finally implemented and evaluated. A remarkable speedup of 46 is reached here as well

Evolutionary Genomics

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Parallel algorithms for real-time peptide-spectrum matching

Tandem mass spectrometry is a powerful experimental tool used in molecular biology to determine the composition of protein mixtures. It has become a standard technique for protein identification. Due to the rapid development of mass spectrometry technology, the instrument can now produce a large number of mass spectra which are used for peptide identification. The increasing data size demands efficient software tools to perform peptide identification. In a tandem mass experiment, peptide ion selection algorithms generally select only the most abundant peptide ions for further fragmentation. Because of this, the low-abundance proteins in a sample rarely get identified. To address this problem, researchers develop the notion of a `dynamic exclusion list', which maintains a list of newly selected peptide ions, and it ensures these peptide ions do not get selected again for a certain time. In this way, other peptide ions will get more opportunity to be selected and identified, allowing for identification of peptides of lower abundance. However, a better method is to also include the identification results into the `dynamic exclusion list' approach. In order to do this, a real-time peptide identification algorithm is required. In this thesis, we introduce methods to improve the speed of peptide identification so that the `dynamic exclusion list' approach can use the peptide identification results without affecting the throughput of the instrument. Our work is based on RT-PSM, a real-time program for peptide-spectrum matching with statistical significance. We profile the speed of RT-PSM and find out that the peptide-spectrum scoring module is the most time consuming portion. Given by the profiling results, we introduce methods to parallelize the peptide-spectrum scoring algorithm. In this thesis, we propose two parallel algorithms using different technologies. We introduce parallel peptide-spectrum matching using SIMD instructions. We implemented and tested the parallel algorithm on Intel SSE architecture. The test results show that a 18-fold speedup on the entire process is obtained. The second parallel algorithm is developed using NVIDIA CUDA technology. We describe two CUDA kernels based on different algorithms and compare the performance of the two kernels. The more efficient algorithm is integrated into RT-PSM. The time measurement results show that a 190-fold speedup on the scoring module is achieved and 26-fold speedup on the entire process is obtained. We perform profiling on the CUDA version again to show that the scoring module has been optimized sufficiently to the point where it is no longer the most time-consuming module in the CUDA version of RT-PSM. In addition, we evaluate the feasibility of creating a metric index to reduce the number of candidate peptides. We describe evaluation methods, and show that general indexing methods are not likely feasible for RT-PSM