ALGORITHMS AND HIGH PERFORMANCE COMPUTING APPROACHES FOR SEQUENCING-BASED COMPARATIVE GENOMICS

As cost and throughput of second-generation sequencers continue to improve, even modestly resourced research laboratories can now perform DNA sequencing experiments that generate hundreds of billions of nucleotides of data, enough to cover the human genome dozens of times over, in about a week for a few thousand dollars. Such data are now being generated rapidly by research groups across the world, and large-scale analyses of these data appear often in high-profile publications such as Nature, Science, and The New England Journal of Medicine. But with these advances comes a serious problem: growth in per-sequencer throughput (currently about 4x per year) is drastically outpacing growth in computer speed (about 2x every 2 years). As the throughput gap widens over time, sequence analysis software is becoming a performance bottleneck, and the costs associated with building and maintaining the needed computing resources is burdensome for research laboratories. This thesis proposes two methods and describes four open source software tools that help to address these issues using novel algorithms and high-performance computing techniques. The proposed approaches build primarily on two insights. First, that the Burrows-Wheeler Transform and the FM Index, previously used for data compression and exact string matching, can be extended to facilitate fast and memory-efficient alignment of DNA sequences to long reference genomes such as the human genome. Second, that these algorithmic advances can be combined with MapReduce and cloud computing to solve comparative genomics problems in a manner that is scalable, fault tolerant, and usable even by small research groups

Reconfigurable acceleration of genetic sequence alignment: A survey of two decades of efforts

Genetic sequence alignment has always been a computational challenge in bioinformatics. Depending on the problem size, software-based aligners can take multiple CPU-days to process the sequence data, creating a bottleneck point in bioinformatic analysis flow. Reconfigurable accelerator can achieve high performance for such computation by providing massive parallelism, but at the expense of programming flexibility and thus has not been commensurately used by practitioners. Therefore, this paper aims to provide a thorough survey of the proposed accelerators by giving a qualitative categorization based on their algorithms and speedup. A comprehensive comparison between work is also presented so as to guide selection for biologist, and to provide insight on future research direction for FPGA scientists

Genome sequence alignment in processing-In-memory architectures

Finalmente, también realizamos un estudio experimental de varias arquitecturas con diferentes tecnologías de memoria (DDR y HBM) y núcleos de procesamiento de distintos tipos, explotando, en algunos casos, procesamiento en la memoria (PIM). La aplicación de referencia es Bowtie2, una aplicación completa para el alineamiento de secuencias en el genoma. La implementación y evaluación de estas arquitecturas se realiza utilizando un simulador arquitectural basado en gem5.La combinación de la aparición de un cuello de botella en el acceso a los datos y la creciente importancia de las aplicaciones de procesamiento intensivo de datos, muy limitadas por el sistema de memoria, crea un importante problema que debe ser abordado. Por ello, en esta tesis nos proponemos afrontar este problema e intentar reducir su efecto en la medida de lo posible. El principal objetivo de esta tesis es el diseño de nuevas soluciones arquitecturales y algorítmicas para superar el problema del cuello de botella conocido como memory-wall y mejorar el rendimiento de aplicaciones con gran uso de memoria que no son capaces de beneficiarse lo suficiente de las jerarquías de memoria actuales. Además, creemos que es esencial centrarse en la eficiencia energética, un factor cuya importancia crece cada día y uno de los factores más limitantes en la computación de alto rendimiento. Las principales contribuciones de esta tesis son: Primero, analizamos el comportamiento de aplicaciones con accesos de memoria aleatorios, que no aprovechan correctamente las nuevas arquitecturas de memoria con jerarquías cache profundas. Específicamente, analizamos la estructura de datos FM-index y un algoritmo de búsqueda de secuencias basado en esa estructura, ampliamente usado en el alineamiento de secuencias en el genoma. Después de este análisis y de obtener un conocimiento más detallado del cuello de botella de la memoria, proponemos una nueva versión de FM-index que permite reducir el consumo de ancho de banda de memoria, de forma que mejora significativamente el rendimiento computacional. Posteriormente, proponemos una nueva arquitectura energéticamente eficiente, basada en un cubo de memoria en 3D (3D-Stacked) al que añadimos unos núcleos sencillos de bajo consumo en su capa lógica. Esta arquitectura permite la ejecución cerca de los datos (near-data-processing

Inexact Mapping of Short Biological Sequences in High Performance Computational Environments

La bioinformática es la aplicación de las ciencias computacionales a la gestión y análisis de datos biológicos. A partir de 2005, con la aparición de los secuenciadores de ADN de nueva generación surge lo que se conoce como Next Generation Sequencing o NGS. Un único experimento biológico puesto en marcha en una máquina de secuenciación NGS puede producir fácilmente cientos de gigabytes o incluso terabytes de datos. Dependiendo de la técnica elegida este proceso puede realizarse en unas pocas horas o días. La disponibilidad de recursos locales asequibles, tales como los procesadores multinúcleo o las nuevas tarjetas gráfi cas preparadas para el cálculo de propósito general GPGPU (General Purpose Graphic Processing Unit ), constituye una gran oportunidad para hacer frente a estos problemas. En la actualidad, un tema abordado con frecuencia es el alineamiento de secuencias de ADN. En bioinformática, el alineamiento permite comparar dos o más secuencias de ADN, ARN, o estructuras primarias proteicas, resaltando sus zonas de similitud. Dichas similitudes podrían indicar relaciones funcionales o evolutivas entre los genes o proteínas consultados. Además, la existencia de similitudes entre las secuencias de un individuo paciente y de otro individuo con una enfermedad genética detectada podría utilizarse de manera efectiva en el campo de la medicina diagnóstica. El problema en torno al que gira el desarrollo de la tesis doctoral consiste en la localización de fragmentos de secuencia cortos dentro del ADN. Esto se conoce bajo el sobrenombre de mapeo de secuencia o sequence mapping. Dicho mapeo debe permitir errores, pudiendo mapear secuencias incluso existiendo variabilidad genética o errores de lectura en el mapeo. Existen diversas técnicas para abordar el mapeo, pero desde la aparición de la NGS destaca la búsqueda por pre jos indexados y agrupados mediante la transformada de Burrows-Wheeler [28] (o BWT en lo sucesivo). Dicha transformada se empleó originalmente en técnicas de compresión de datos, como es el caso del algoritmo bzip2. Su utilización como herramienta para la indización y búsqueda posterior de información es más reciente [22]. La ventaja es que su complejidad computacional depende únicamente de la longitud de la secuencia a mapear. Por otra parte, una gran cantidad de técnicas de alineamiento se basan en algoritmos de programación dinámica, ya sea Smith-Watterman o modelos ocultos de Markov. Estos proporcionan mayor sensibilidad, permitiendo mayor cantidad de errores, pero su coste computacional es mayor y depende del tamaño de la secuencia multiplicado por el de la cadena de referencia. Muchas herramientas combinan una primera fase de búsqueda con la BWT de regiones candidatas al alineamiento y una segunda fase de alineamiento local en la que se mapean cadenas con Smith-Watterman o HMM. Cuando estamos mapeando permitiendo pocos errores, una segunda fase con un algoritmo de programación dinámica resulta demasiado costosa, por lo que una búsqueda inexacta basada en BWT puede resultar más e ficiente. La principal motivación de la tesis doctoral es la implementación de un algoritmo de búsqueda inexacta basado únicamente en la BWT, adaptándolo a las arquitecturas paralelas modernas, tanto en CPU como en GPGPU. El algoritmo constituirá un método nuevo de rami cación y poda adaptado a la información genómica. Durante el periodo de estancia se estudiarán los Modelos ocultos de Markov y se realizará una implementación sobre modelos de computación funcional GTA (Aggregate o Test o Generate), así como la paralelización en memoria compartida y distribuida de dicha plataforma de programación funcional.Salavert Torres, J. (2014). Inexact Mapping of Short Biological Sequences in High Performance Computational Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/43721TESI

Hardware / Software System for Portable and Low-Cost Genome Assembly

“The enjoyment of the highest attainable standard of health is one of the fundamental rights of every human being without distinction of race, religion, political belief, economic or social condition” [56]. Genomics (the study of the entire DNA) provides such a standard of health for people with rare diseases and helps control the spread of pandemics. Still, millions of human beings are unable to access genomics due to its cost, and portability. In genomics, DNA sequencers digitise DNA information, and computers analyse the digitised information. We have desktop and thumb-sized DNA sequencers, that digitise the DNA data rapidly. But computations necessary for the analysis of this data are inevitably performed on high-performance computers (HPCs) and cloud computers. These computations not only require powerful computers but also necessitate high-speed networks since the data generated are in the hundreds of gigabytes. Relying on HPCs and high-speed networks, deny the benefits that can be reaped by genomics for the masses who live in remote areas and in poorer nations. Developing a low-cost and portable genomics computation platform would provide personalised treatment based on an individual’s DNA and identify the source of the fast-spreading epidemics in remote areas and areas without HPC or network infrastructure. But developing a low-cost and portable genome analysing computing platform is a challenging task. This thesis develops novel computer architecture solutions to assemble the whole human DNA and COVID-19 virus RNA on a low-cost and portable platform. The first phase of the solution describes a ring-pipelined processor architecture for a key genome assembly algorithm. The human genome is partitioned to fit into the small memory footprint of embedded processors. These techniques allow an entire human genome to be assembled using highly portable and low-cost embedded processor cores. These processor cores can be housed within a single chip. Each processor was only 0.08 mm 2 and consumed just 37.5 mW. It has only 2 GB memory, 32-bit instruction width, and a clock with a 1 GHz frequency. The second phase of the solution describes how application-specific instruction-set processors can be sped up to execute a key genome assembly algorithm. A fully automated design system is presented, which improves the performance of large applications (such as genome assembly algorithm) and generates application-specific instructions for a commercial processor design tool (Xtensa). The tool enhances the base processor, which was used in the ring pipeline processor architecture. Thus, the alignment algorithms execute 2.1 times faster with only 11% additional hardware. The energy-delay product was reduced by 7.3× compared to the base processor. This tool is the only one of its type which can handle applications which are large. The third phase of the solution designs a portable low-cost genome assembly computer (PGA). PGA enhances the ring pipeline architecture with the customised processor found in phase two and with improved inter-processor communication. The results show that the COVID-19 virus RNA can be assembled in under 10 minutes and the whole human genome can be assembled in 11 days on a portable platform (HPC take around two days) for 30× coverage. PGA has an area footprint of just 5.68 mm 2 in a 28 nm technology node and is far smaller than a high-performance computer processor chip. The PGA consumes only 4W of power, which is lower than the power requirement of a high-performance processor chip. The manufacturing cost of the PGA also would be much cheaper than the high-performance system cost, when produced in volume. The developed solution can be powered by a USB port of a laptop. This thesis is the first of its type to show the design of a single-chip solution to be able to process a complex genomic problem. This thesis contributes to attaining one of the fundamental rights of every human being wherever they may live

Efficient Storage of Genomic Sequences in High Performance Computing Systems

ABSTRACT: In this dissertation, we address the challenges of genomic data storage in high performance computing systems. In particular, we focus on developing a referential compression approach for Next Generation Sequence data stored in FASTQ format files. The amount of genomic data available for researchers to process has increased exponentially, bringing enormous challenges for its efficient storage and transmission. General-purpose compressors can only offer limited performance for genomic data, thus the need for specialized compression solutions. Two trends have emerged as alternatives to harness the particular properties of genomic data: non-referential and referential compression. Non-referential compressors offer higher compression rations than general purpose compressors, but still below of what a referential compressor could theoretically achieve. However, the effectiveness of referential compression depends on selecting a good reference and on having enough computing resources available. This thesis presents one of the first referential compressors for FASTQ files. We first present a comprehensive analytical and experimental evaluation of the most relevant tools for genomic raw data compression, which led us to identify the main needs and opportunities in this field. As a consequence, we propose a novel compression workflow that aims at improving the usability of referential compressors. Subsequently, we discuss the implementation and performance evaluation for the core of the proposed workflow: a referential compressor for reads in FASTQ format that combines local read-to-reference alignments with a specialized binary-encoding strategy. The compression algorithm, named UdeACompress, achieved very competitive compression ratios when compared to the best compressors in the current state of the art, while showing reasonable execution times and memory use. In particular, UdeACompress outperformed all competitors when compressing long reads, typical of the newest sequencing technologies. Finally, we study the main aspects of the data-level parallelism in the Intel AVX-512 architecture, in order to develop a parallel version of the UdeACompress algorithms to reduce the runtime. Through the use of SIMD programming, we managed to significantly accelerate the main bottleneck found in UdeACompress, the Suffix Array Construction