FPGA acceleration of sequence analysis tools in bioinformatics

Thesis (Ph.D.)--Boston UniversityWith advances in biotechnology and computing power, biological data are being produced at an exceptional rate. The purpose of this study is to analyze the application of FPGAs to accelerate high impact production biosequence analysis tools. Compared with other alternatives, FPGAs offer huge compute power, lower power consumption, and reasonable flexibility. BLAST has become the de facto standard in bioinformatic approximate string matching and so its acceleration is of fundamental importance. It is a complex highly-optimized system, consisting of tens of thousands of lines of code and a large number of heuristics. Our idea is to emulate the main phases of its algorithm on FPGA. Utilizing our FPGA engine, we quickly reduce the size of the database to a small fraction, and then use the original code to process the query. Using a standard FPGA-based system, we achieved 12x speedup over a highly optimized multithread reference code. Multiple Sequence Alignment (MSA)--the extension of pairwise Sequence Alignment to multiple Sequences--is critical to solve many biological problems. Previous attempts to accelerate Clustal-W, the most commonly used MSA code, have directly mapped a portion of the code to the FPGA. We use a new approach: we apply prefiltering of the kind commonly used in BLAST to perform the initial all-pairs alignments. This results in a speedup of from 8Ox to 190x over the CPU code (8 cores). The quality is comparable to the original according to a commonly used benchmark suite evaluated with respect to multiple distance metrics. The challenge in FPGA-based acceleration is finding a suitable application mapping. Unfortunately many software heuristics do not fall into this category and so other methods must be applied. One is restructuring: an entirely new algorithm is applied. Another is to analyze application utilization and develop accuracy/performance tradeoffs. Using our prefiltering approach and novel FPGA programming models we have achieved significant speedup over reference programs. We have applied approximation, seeding, and filtering to this end. The bulk of this study is to introduce the pros and cons of these acceleration models for biosequence analysis tools

Compressão eficiente de sequências biológicas usando uma rede neuronal

Background: The increasing production of genomic data has led to an intensified need for models that can cope efficiently with the lossless compression of biosequences. Important applications include long-term storage and compression-based data analysis. In the literature, only a few recent articles propose the use of neural networks for biosequence compression. However, they fall short when compared with specific DNA compression tools, such as GeCo2. This limitation is due to the absence of models specifically designed for DNA sequences. In this work, we combine the power of neural networks with specific DNA and amino acids models. For this purpose, we created GeCo3 and AC2, two new biosequence compressors. Both use a neural network for mixing the opinions of multiple specific models. Findings: We benchmark GeCo3 as a reference-free DNA compressor in five datasets, including a balanced and comprehensive dataset of DNA sequences, the Y-chromosome and human mitogenome, two compilations of archaeal and virus genomes, four whole genomes, and two collections of FASTQ data of a human virome and ancient DNA. GeCo3 achieves a solid improvement in compression over the previous version (GeCo2) of 2:4%, 7:1%, 6:1%, 5:8%, and 6:0%, respectively. As a reference-based DNA compressor, we benchmark GeCo3 in four datasets constituted by the pairwise compression of the chromosomes of the genomes of several primates. GeCo3 improves the compression in 12:4%, 11:7%, 10:8% and 10:1% over the state-of-the-art. The cost of this compression improvement is some additional computational time (1:7_ to 3:0_ slower than GeCo2). The RAM is constant, and the tool scales efficiently, independently from the sequence size. Overall, these values outperform the state-of-the-art. For AC2 the improvements and costs over AC are similar, which allows the tool to also outperform the state-of-the-art. Conclusions: The GeCo3 and AC2 are biosequence compressors with a neural network mixing approach, that provides additional gains over top specific biocompressors. The proposed mixing method is portable, requiring only the probabilities of the models as inputs, providing easy adaptation to other data compressors or compression-based data analysis tools. GeCo3 and AC2 are released under GPLv3 and are available for free download at https://github.com/cobilab/geco3 and https://github.com/cobilab/ac2.Contexto: O aumento da produção de dados genómicos levou a uma maior necessidade de modelos que possam lidar de forma eficiente com a compressão sem perdas de biosequências. Aplicações importantes incluem armazenamento de longo prazo e análise de dados baseada em compressão. Na literatura, apenas alguns artigos recentes propõem o uso de uma rede neuronal para compressão de biosequências. No entanto, os resultados ficam aquém quando comparados com ferramentas de compressão de ADN específicas, como o GeCo2. Essa limitação deve-se à ausência de modelos específicos para sequências de ADN. Neste trabalho, combinamos o poder de uma rede neuronal com modelos específicos de ADN e aminoácidos. Para isso, criámos o GeCo3 e o AC2, dois novos compressores de biosequências. Ambos usam uma rede neuronal para combinar as opiniões de vários modelos específicos. Resultados: Comparamos o GeCo3 como um compressor de ADN sem referência em cinco conjuntos de dados, incluindo um conjunto de dados balanceado de sequências de ADN, o cromossoma Y e o mitogenoma humano, duas compilações de genomas de arqueas e vírus, quatro genomas inteiros e duas coleções de dados FASTQ de um viroma humano e ADN antigo. O GeCo3 atinge uma melhoria sólida na compressão em relação à versão anterior (GeCo2) de 2,4%, 7,1%, 6,1%, 5,8% e 6,0%, respectivamente. Como um compressor de ADN baseado em referência, comparamos o GeCo3 em quatro conjuntos de dados constituídos pela compressão aos pares dos cromossomas dos genomas de vários primatas. O GeCo3 melhora a compressão em 12,4%, 11,7%, 10,8% e 10,1% em relação ao estado da arte. O custo desta melhoria de compressão é algum tempo computacional adicional (1,7 _ a 3,0 _ mais lento do que GeCo2). A RAM é constante e a ferramenta escala de forma eficiente, independentemente do tamanho da sequência. De forma geral, os rácios de compressão superam o estado da arte. Para o AC2, as melhorias e custos em relação ao AC são semelhantes, o que permite que a ferramenta também supere o estado da arte. Conclusões: O GeCo3 e o AC2 são compressores de sequências biológicas com uma abordagem de mistura baseada numa rede neuronal, que fornece ganhos adicionais em relação aos biocompressores específicos de topo. O método de mistura proposto é portátil, exigindo apenas as probabilidades dos modelos como entradas, proporcionando uma fácil adaptação a outros compressores de dados ou ferramentas de análise baseadas em compressão. O GeCo3 e o AC2 são distribuídos sob GPLv3 e estão disponíveis para download gratuito em https://github.com/ cobilab/geco3 e https://github.com/cobilab/ac2.Mestrado em Engenharia de Computadores e Telemátic

Interleaving in Systolic-Arrays: a Throughput Breakthrough

In past years the most common way to improve computers performance was to increase the clock frequency. In recent years this approach suffered the limits of technology scaling, therefore computers architectures are shifting toward the direction of parallel computing to further improve circuits performance. Not only GPU based architectures are spreading in consideration, but also Systolic Arrays are particularly suited for certain classes of algorithms. An important point in favor of Systolic Arrays is that, due to the regularity of their circuit layout, they are appealing when applied to many emerging and very promising technologies, like Quantum-dot Cellular Automata and nanoarrays based on Silicon NanoWire or on Carbon nanotube Field Effect Transistors. In this work we present a systematic method to improve Systolic Arrays performance exploiting Pipelining and Input Data Interleaving. We tackle the problem from a theoretical point of view first, and then we apply it to both CMOS technology and emerging technologies. On CMOS we demonstrate that it is possible to vastly improve the overall throughput of the circuit. By applying this technique to emerging technologies we show that it is possible to overcome some of their limitations greatly improving the throughput, making a considerable step forward toward the post-CMOS era

Many Roads to Synchrony: Natural Time Scales and Their Algorithms

We consider two important time scales---the Markov and cryptic orders---that monitor how an observer synchronizes to a finitary stochastic process. We show how to compute these orders exactly and that they are most efficiently calculated from the epsilon-machine, a process's minimal unifilar model. Surprisingly, though the Markov order is a basic concept from stochastic process theory, it is not a probabilistic property of a process. Rather, it is a topological property and, moreover, it is not computable from any finite-state model other than the epsilon-machine. Via an exhaustive survey, we close by demonstrating that infinite Markov and infinite cryptic orders are a dominant feature in the space of finite-memory processes. We draw out the roles played in statistical mechanical spin systems by these two complementary length scales.Comment: 17 pages, 16 figures: http://cse.ucdavis.edu/~cmg/compmech/pubs/kro.htm. Santa Fe Institute Working Paper 10-11-02

Parallelization of dynamic programming recurrences in computational biology

The rapid growth of biosequence databases over the last decade has led to a performance bottleneck in the applications analyzing them. In particular, over the last five years DNA sequencing capacity of next-generation sequencers has been doubling every six months as costs have plummeted. The data produced by these sequencers is overwhelming traditional compute systems. We believe that in the future compute performance, not sequencing, will become the bottleneck in advancing genome science. In this work, we investigate novel computing platforms to accelerate dynamic programming algorithms, which are popular in bioinformatics workloads. We study algorithm-specific hardware architectures that exploit fine-grained parallelism in dynamic programming kernels using field-programmable gate arrays: FPGAs). We advocate a high-level synthesis approach, using the recurrence equation abstraction to represent dynamic programming and polyhedral analysis to exploit parallelism. We suggest a novel technique within the polyhedral model to optimize for throughput by pipelining independent computations on an array. This design technique improves on the state of the art, which builds latency-optimal arrays. We also suggest a method to dynamically switch between a family of designs using FPGA reconfiguration to achieve a significant performance boost. We have used polyhedral methods to parallelize the Nussinov RNA folding algorithm to build a family of accelerators that can trade resources for parallelism and are between 15-130x faster than a modern dual core CPU implementation. A Zuker RNA folding accelerator we built on a single workstation with four Xilinx Virtex 4 FPGAs outperforms 198 3 GHz Intel Core 2 Duo processors. Furthermore, our design running on a single FPGA is an order of magnitude faster than competing implementations on similar-generation FPGAs and graphics processors. Our work is a step toward the goal of automated synthesis of hardware accelerators for dynamic programming algorithms