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

Searching for patterns in Conway's Game of Life

Conway’s Game of Life (Life) is a simple cellular automaton, discovered by John Conway in 1970, that exhibits complex emergent behavior. Life-enthusiasts have been looking for building blocks with specific properties (patterns) to answer unsolved problems in Life for the past five decades. Finding patterns in Life is difficult due to the large search space. Current search algorithms use an explorative approach based on the rules of the game, but this can only sample a small fraction of the search space. More recently, people have used Sat solvers to search for patterns. These solvers are not specifically tuned to this problem and thus waste a lot of time processing Life’s rules in an engine that does not understand them. We propose a novel Sat-based approach that replaces the binary tree used by traditional Sat solvers with a grid-based approach, complemented by an injection of Game of Life specific knowledge. This leads to a significant speedup in searching. As a fortunate side effect, our solver can be generalized to solve general Sat problems. Because it is grid-based, all manipulations are embarrassingly parallel, allowing implementation on massively parallel hardware

Computation and programmability at the nano-bio interface

PhD ThesisThe manipulation of physical reality on the molecular level and construction of devices operating on the nanoscale has been the focal point of nanotechnology. In particular, nanotechnology based on DNA and RNA has a potential to nd applications in the eld of Synthetic Biology thanks to the inherent compatibility of nucleic acids with biological systems. Sca olded DNA origami, proposed by P. Rothemund, is one of the leading and most successful methods in which nanostructures are realised through rational programming of short 'staple' oligomers which fold a long single-stranded DNA called the 'sca old' strand into a variety of desired shapes. DNA origami already has many applications; including intelligent drug delivery, miniaturisation of logic circuits and computation in vivo. However, one of the factors that are limiting the complexity, applicability and scalability of this approach is the source of the sca old which commonly originates from viruses or phages. Furthermore, developing a robust and orthogonal interface between DNA nanotechnology and biological parts remains a signi cant challenge. The rst part of this thesis tackles these issues by challenging the fundamental as- sumption in the eld, namely that a viral sequence is to be used as the DNA origami sca old. A method is introduced for de novo generation of long synthetic sequences based on De Bruijn sequence, which has been previously proposed in combinatorics. The thesis presents a collection of algorithms which allow the construction of custom- made sequences that are uniquely addressable and biologically orthogonal (i.e. they do not code for any known biological function). Synthetic sca olds generated by these algorithms are computationally analysed and compared with their natural counter- parts with respect to: repetition in sequence, secondary structure and thermodynamic addressability. This also aids the design of wet lab experiments pursuing justi cation and veri cation of this novel approach by empirical evidence. The second part of this thesis discusses the possibility of applying evolutionary op- timisation to synthetic DNA sequences under constraints dictated by the biological interface. A multi-strand system is introduced based on an alternative approach to DNA self-assembly, which relies on strand-displacement cascades, for molecular data storage. The thesis demonstrates how a genetic algorithm can be used to generate viable solutions to this sequence optimisation problem which favours the target self- assembly con guration. Additionally, the kinetics of strand-displacement reactions are analysed with existing coarse-grained DNA models (oxDNA). This thesis is motivated by the application of scienti c computing to problems which lie on the boundary of Computer Science and the elds of DNA Nanotechnology, DNA Computing and Synthetic Biology, and thus I endeavour to the best of my ability to establish this work within the context of these disciplines

Parallel and scalable combinatorial string algorithms on distributed memory systems

Methods for processing and analyzing DNA and genomic data are built upon combinatorial graph and string algorithms. The advent of high-throughput DNA sequencing is enabling the generation of billions of reads per experiment. Classical and sequential algorithms can no longer deal with these growing data sizes - which for the last 10 years have greatly out-paced advances in processor speeds. Processing and analyzing state-of-the-art genomic data sets require the design of scalable and efficient parallel algorithms and the use of large computing clusters. Suffix arrays and trees are fundamental string data structures, which lie at the foundation of many string algorithms, with important applications in text processing, information retrieval, and computational biology. Conversely, the parallel construction of these indices is an actively studied problem. However, prior approaches lacked good worst-case run-time guarantees and exhibit poor scaling and overall performance. In this work, we present our distributed-memory parallel algorithms for indexing large datasets, including algorithms for the distributed construction of suffix arrays, LCP arrays, and suffix trees. We formulate a generalized version of the All-Nearest-Smaller-Values problem, provide an optimal distributed solution, and apply it to the distributed construction of suffix trees - yielding a work-optimal parallel algorithm. Our algorithms for distributed suffix array and suffix tree construction improve the state-of-the-art by simultaneously improving worst-case run-time bounds and achieving superior practical performance. Next, we introduce a novel distributed string index, the Distributed Enhanced Suffix Array (DESA) - based on the suffix and LCP arrays, the DESA consists of these and additional distributed data structures. The DESA is designed to allow efficient pattern search queries in distributed memory while requiring at most O(n/p) memory per process. We present efficient distributed-memory parallel algorithms for querying, as well as for the efficient construction of this distributed index. Finally, we present our work on distributed-memory algorithms for clustering de Bruijn graphs and its application to solving a grand challenge metagenomic dataset.Ph.D

Genomic variation detection using dynamic programming methods

Thesis advisor: Gabor T. MarthBackground: Due to the rapid development and application of next generation sequencing (NGS) techniques, large amounts of NGS data have become available for genome-related biological research, such as population genetics, evolutionary research, and genome wide association studies. A crucial step of these genome-related studies is the detection of genomic variation between different species and individuals. Current approaches for the detection of genomic variation can be classified into alignment-based variation detection and assembly-based variation detection. Due to the limitation of current NGS read length, alignment-based variation detection remains the mainstream approach. The Smith-Waterman algorithm, which produces the optimal pairwise alignment between two sequences, is frequently used as a key component of fast heuristic read mapping and variation detection tools for next-generation sequencing data. Though various fast Smith-Waterman implementations are developed, they are either designed as monolithic protein database searching tools, which do not return detailed alignment, or they are embedded into other tools. These issues make reusing these efficient Smith-Waterman implementations impractical. After the alignment step in the traditional variation detection pipeline, the afterward variation detection using pileup data and the Bayesian model is also facing great challenges especially from low-complexity genomic regions. Sequencing errors and misalignment problems still influence variation detection (especially INDEL detection) a lot. The accuracy of genomic variation detection still needs to be improved, especially when we work on low- complexity genomic regions and low-quality sequencing data. Results: To facilitate easy integration of the fast Single-Instruction-Multiple-Data Smith-Waterman algorithm into third-party software, we wrote a C/C++ library, which extends Farrar's Striped Smith-Waterman (SSW) to return alignment information in addition to the optimal Smith-Waterman score. In this library we developed a new method to generate the full optimal alignment results and a suboptimal score in linear space at little cost of efficiency. This improvement makes the fast Single-Instruction-Multiple-Data Smith-Waterman become really useful in genomic applications. SSW is available both as a C/C++ software library, as well as a stand-alone alignment tool at: https://github.com/mengyao/Complete- Striped-Smith-Waterman-Library. The SSW library has been used in the primary read mapping tool MOSAIK, the split-read mapping program SCISSORS, the MEI detector TAN- GRAM, and the read-overlap graph generation program RZMBLR. The speeds of the mentioned software are improved significantly by replacing their ordinary Smith-Waterman or banded Smith-Waterman module with the SSW Library. To improve the accuracy of genomic variation detection, especially in low-complexity genomic regions and on low-quality sequencing data, we developed PHV, a genomic variation detection tool based on the profile hidden Markov model. PHV also demonstrates a novel PHMM application in the genomic research field. The banded PHMM algorithms used in PHV make it a very fast whole-genome variation detection tool based on the HMM method. The comparison of PHV to GATK, Samtools and Freebayes for detecting variation from both simulated data and real data shows PHV has good potential for dealing with sequencing errors and misalignments. PHV also successfully detects a 49 bp long deletion that is totally misaligned by the mapping tool, and neglected by GATK and Samtools. Conclusion: The efforts made in this thesis are very meaningful for methodology development in studies of genomic variation detection. The two novel algorithms stated here will also inspire future work in NGS data analysis.Thesis (PhD) — Boston College, 2014.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Biology