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

FPGA acceleration of DNA sequence alignment: design analysis and optimization

Existing FPGA accelerators for short read mapping often fail to utilize the complete biological information in sequencing data for simple hardware design, leading to missed or incorrect alignment. In this work, we propose a runtime reconfigurable alignment pipeline that considers all information in sequencing data for the biologically accurate acceleration of short read mapping. We focus our efforts on accelerating two string matching techniques: FM-index and the Smith-Waterman algorithm with the affine-gap model which are commonly used in short read mapping. We further optimize the FPGA hardware using a design analyzer and merger to improve alignment performance. The contributions of this work are as follows. 1. We accelerate the exact-match and mismatch alignment by leveraging the FM-index technique. We optimize memory access by compressing the data structure and interleaving the access with multiple short reads. The FM-index hardware also considers complete information in the read data to maximize accuracy. 2. We propose a seed-and-extend model to accelerate alignment with indels. The FM-index hardware is extended to support the seeding stage while a Smith-Waterman implementation with the affine-gap model is developed on FPGA for the extension stage. This model can improve the efficiency of indel alignment with comparable accuracy versus state-of-the-art software. 3. We present an approach for merging multiple FPGA designs into a single hardware design, so that multiple place-and-route tasks can be replaced by a single task to speed up functional evaluation of designs. We first experiment with this approach to demonstrate its feasibility for different designs. Then we apply this approach to optimize one of the proposed FPGA aligners for better alignment performance.Open Acces

Exploring the loblolly pine (Pinus taeda L.) genome by BAC sequencing and Cot analysis.

Loblolly pine (LP; Pinus taeda L.) is an economically and ecologically important tree in the southeastern U.S. To advance understanding of the loblolly pine (LP; Pinus taeda L.) genome, we sequenced and analyzed 100 BAC clones and performed a Cot analysis. The Cot analysis indicates that the genome is composed of 57, 24, and 10% highly-repetitive, moderately-repetitive, and single/low-copy sequences, respectively (the remaining 9% of the genome is a combination of fold back and damaged DNA). Although single/low-copy DNA only accounts for 10% of the LP genome, the amount of single/low-copy DNA in LP is still 14 times the size of the Arabidopsis genome. Since gene numbers in LP are similar to those in Arabidopsis, much of the single/low-copy DNA of LP would appear to be composed of DNA that is both gene- and repeat-poor. Macroarrays prepared from a LP bacterial artificial chromosome (BAC) library were hybridized with probes designed from cell wall synthesis/wood development cDNAs, and 50 of the "targeted" clones were selected for further analysis. An additional 25 clones were selected because they contained few repeats, while 25 more clones were selected at random. The 100 BAC clones were Sanger sequenced and assembled. Of the targeted BACs, 80% contained all or part of the cDNA used to target them. One targeted BAC was found to contain fungal DNA and was eliminated from further analysis. Combinations of similarity-based and ab initio gene prediction approaches were utilized to identify and characterize potential coding regions in the 99 BACs containing LP DNA. From this analysis, we identified 154 gene models (GMs) representing both putative protein-coding genes and likely pseudogenes. Ten of the GMs (all of which were specifically targeted) had enough support to be classified as intact genes. Interestingly, the 154 GMs had statistically indistinguishable (α = 0.05) distributions in the targeted and random BAC clones (15.18 and 12.61 GM/Mb, respectively), whereas the low-repeat BACs contained significantly fewer GMs (7.08 GM/Mb). However, when GM length was considered, the targeted BACs had a significantly greater percentage of their length in GMs (3.26%) when compared to random (1.63%) and low-repeat (0.62%) BACs. The results of our study provide insight into LP evolution and inform ongoing efforts to produce a reference genome sequence for LP, while characterization of genes involved in cell wall production highlights carbon metabolism pathways that can be leveraged for increasing wood production

No wisdom in the crowd: genome annotation at the time of big data - current status and future prospects

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader

FPGA acceleration of DNA sequencing analysis and storage

In this work we explore how Field-Programmable Gate Arrays (FPGAs) can be used to alleviate the data processing bottlenecks in DNA sequencing. We focus our efforts on accelerating the FM-index, a data structure used to solve the computationally intensive string matching problems found in DNA sequencing analysis such as short read alignment. The main contributions of this work are: 1) We accelerate the FM-index using FPGAs and develop several novel methods for reducing the memory bottleneck of the search algorithm. These methods include customising the FM-index structure according to the memory architecture of the FPGA platform and minimising the number of memory accesses through both architectural and algorithmic optimisations. 2) We present a new approach for accelerating approximate string matching using the backtracking FM-index. This approach makes use of specialised approximate string matching modules and a run-time reconfigurable architecture in order to achieve both high sensitivity and high performance. 3) We extend the FM-index search algorithm for reference-based compression and accelerate it using FPGAs. This accelerated design is integrated into fastqZip and fastaZip, two new tools that we have developed for the fast and effective compression of sequence data stored in the FASTQ and FASTA formats respectively. We implement our designs on the Maxeler Max4 Platform and show that they are able to outperform state-of-the-art DNA sequencing analysis software. For instance, our hardware-accelerated compression tool for FASTQ data is able to achieve a higher compression ratio than the best performing tool, fastqz, whilst the average compression and decompression speeds are 25 and 43 times faster respectively.Open Acces

Distributed gene clinical decision support system based on cloud computing

Background: The clinical decision support system can effectively break the limitations of doctors’ knowledge and reduce the possibility of misdiagnosis to enhance health care. The traditional genetic data storage and analysis methods based on stand-alone environment are hard to meet the computational requirements with the rapid genetic data growth for the limited scalability. Methods: In this paper, we propose a distributed gene clinical decision support system, which is named GCDSS. And a prototype is implemented based on cloud computing technology. At the same time, we present CloudBWA which is a novel distributed read mapping algorithm leveraging batch processing strategy to map reads on Apache Spark. Results: Experiments show that the distributed gene clinical decision support system GCDSS and the distributed read mapping algorithm CloudBWA have outstanding performance and excellent scalability. Compared with state-of-the-art distributed algorithms, CloudBWA achieves up to 2.63 times speedup over SparkBWA. Compared with stand-alone algorithms, CloudBWA with 16 cores achieves up to 11.59 times speedup over BWA-MEM with 1 core. Conclusions: GCDSS is a distributed gene clinical decision support system based on cloud computing techniques. In particular, we incorporated a distributed genetic data analysis pipeline framework in the proposed GCDSS system. To boost the data processing of GCDSS, we propose CloudBWA, which is a novel distributed read mapping algorithm to leverage batch processing technique in mapping stage using Apache Spark platform. Keywords: Clinical decision support system, Cloud computing, Spark, Alluxio, Genetic data analysis, Read mappin

RNA CoMPASS: RNA Comprehensive Multi-Processor Analysis System for Sequencing

The main theme of this dissertation is to develop a distributed computational pipeline for processing next-generation RNA sequencing (RNA-seq) data. RNA-seq experiments generate hundreds of millions of short reads for each DNA/RNA sample. There are many existing bioinformatics tools developed for the analysis and visualization of this data, but very large studies present computational and organizational challenges that are difficult to overcome manually. We designed a comprehensive pipeline for the analysis of RNA sequencing which leverages many existing tools and parallel computing technology to facilitate the analysis of extremely large studies. RNA CoMPASS provides a web-based graphical user interface and distributed computational pipeline including endogenous transcriptome quantification and additionally the investigation of exogenous sequences