Computational Strategies for Scalable Genomics Analysis.

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications

High Performance Computing for DNA Sequence Alignment and Assembly

Recent advances in DNA sequencing technology have dramatically increased the scale and scope of DNA sequencing. These data are used for a wide variety of important biological analyzes, including genome sequencing, comparative genomics, transcriptome analysis, and personalized medicine but are complicated by the volume and complexity of the data involved. Given the massive size of these datasets, computational biology must draw on the advances of high performance computing. Two fundamental computations in computational biology are read alignment and genome assembly. Read alignment maps short DNA sequences to a reference genome to discover conserved and polymorphic regions of the genome. Genome assembly computes the sequence of a genome from many short DNA sequences. Both computations benefit from recent advances in high performance computing to efficiently process the huge datasets involved, including using highly parallel graphics processing units (GPUs) as high performance desktop processors, and using the MapReduce framework coupled with cloud computing to parallelize computation to large compute grids. This dissertation demonstrates how these technologies can be used to accelerate these computations by orders of magnitude, and have the potential to make otherwise infeasible computations practical

Large Genomes Assembly Using MAPREDUCE Framework

Knowing the genome sequence of an organism is the essential step toward understanding its genomic and genetic characteristics. Currently, whole genome shotgun (WGS) sequencing is the most widely used genome sequencing technique to determine the entire DNA sequence of an organism. Recent advances in next-generation sequencing (NGS) techniques have enabled biologists to generate large DNA sequences in a high-throughput and low-cost way. However, the assembly of NGS reads faces significant challenges due to short reads and an enormously high volume of data. Despite recent progress in genome assembly, current NGS assemblers cannot generate high-quality results or efficiently handle large genomes with billions of reads. In this research, we proposed a new Genome Assembler based on MapReduce (GAMR), which tackles both limitations. GAMR is based on a bi-directed de Bruijn graph and implemented using the MapReduce framework. We designed a distributed algorithm for each step in GAMR, making it scalable in assembling large-scale genomes. We also proposed novel gap-filling algorithms to improve assembly results to achieve higher accuracy and more extended continuity. We evaluated the assembly performance of GAMR using benchmark data and compared it against other NGS assemblers. We also demonstrated the scalability of GAMR by using it to assemble loblolly pine (~22Gbp). The results showed that GAMR finished the assembly much faster and with a much lower requirement of computing resources

Performance Improvement of Distributed Computing Framework and Scientific Big Data Analysis

Analysis of Big data to gain better insights has been the focus of researchers in the recent past. Traditional desktop computers or database management systems may not be suitable for efficient and timely analysis, due to the requirement of massive parallel processing. Distributed computing frameworks are being explored as a viable solution. For example, Google proposed MapReduce, which is becoming a de facto computing architecture for Big data solutions. However, scheduling in MapReduce is coarse grained and remains as a challenge for improvement. Related with MapReduce scheduler when configured over distributed clusters, we identify two issues: data locality disruption and random assignment of non-local map tasks. We propose a network aware scheduler to extend the existing rack awareness. The tasks are scheduled in the order of node, rack and any other rack within the same cluster to achieve cluster level data locality. The issue of random assignment non-local map tasks is handled by enhancing the scheduler to consider the network parameters, such as delay, bandwidth and packet loss between remote clusters. As part of Big data analysis at computational biology, we consider two major data intensive applications: indexing genome sequences and de Novo assembly. Both of these applications deal with the massive amount data generated from DNA sequencers. We developed a scalable algorithm to construct sub-trees of a suffix tree in parallel to address huge memory requirements needed for indexing the human genome. For the de Novo assembly, we propose Parallel Giraph based Assembler (PGA) to address the challenges associated with the assembly of large genomes over commodity hardware. PGA uses the de Bruijn graph to represent the data generated from sequencers. Huge memory demands and performance expectations are addressed by developing parallel algorithms based on the distributed graph-processing framework, Apache Giraph

SMusket: Spark-based DNA error correction on distributed-memory systems

©2020 Elsevier B.V. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/bync-nd/4.0/. This version of the article has been accepted for publication in Future Generation Computer Systems. The Version of Record is available online at https://doi.org/10.1016/j.future.2019.10.038This is the accepted version of: R. R. Expósito, J. González-Domínguez, and J. Touriño, "SMusket: Sparkbased DNA error correction on distributed-memory systems", Future Generation Computer Systems, vol. 111, pp. 698-713, 2020, https://doi.org/10.1016/j.future.2019.10.038[Abstract]: Next-Generation Sequencing (NGS) technologies have revolutionized genomics research over the last decade, bringing new opportunities for scientists to perform groundbreaking biological studies. Error correction in NGS datasets is considered an important preprocessing step in many workflows as sequencing errors can severely affect the quality of downstream analysis. Although current error correction approaches provide reasonably high accuracies, their computational cost can be still unacceptable when processing large datasets. In this paper we propose SparkMusket (SMusket), a Big Data tool built upon the open-source Apache Spark cluster computing framework to boost the performance of Musket, one of the most widely adopted and top-performing multithreaded correctors. Our tool efficiently exploits Spark features to implement a scalable error correction algorithm intended for distributed-memory systems built using commodity hardware. The experimental evaluation on a 16-node cluster using four publicly available datasets has shown that SMusket is up to 15.3 times faster than previous state-of-the-art MPI-based tools, also providing a maximum speedup of 29.8 over its multithreaded counterpart. SMusket is publicly available under an open-source license at https://github.com/rreye/smusketThis work was supported by the Ministry of Economy, Industry and Competitiveness of Spain and FEDER, Spain funds of the European Union (project TIN2016-75845-P, AEI/FEDER/EU); and by Xunta de Galicia, Spain (projects ED431G/01 and ED431C 2017/04).Xunta de galicia; ED431G/01Xunta de Galicia; ED431C 2017/0

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

Hadooping the genome: The impact of big data tools on biology

This essay examines the consequences of the so-called ‘big data’ technologies in biomedicine. Analyzing algorithms and data structures used by biologists can provide insight into how biologists perceive and understand their objects of study. As such, I examine some of the most widely used algorithms in genomics: those used for sequence comparison or sequence mapping. These algorithms are derived from the powerful tools for text searching and indexing that have been developed since the 1950s and now play an important role in online search. In biology, sequence comparison algorithms have been used to assemble genomes, process next-generation sequence data, and, most recently, for ‘precision medicine.’ I argue that the predominance of a specific set of text-matching and pattern-finding tools has influenced problem choice in genomics. It allowed genomics to continue to think of genomes as textual objects and to increasingly lock genomics into ‘big data’-driven text-searching methods. Many ‘big data’ methods are designed for finding patterns in human-written texts. However, genomes and other’ omic data are not human-written and are unlikely to be meaningful in the same way

novoBreak: local assembly for breakpoint detection in cancer genomes.

We present novoBreak, a genome-wide local assembly algorithm that discovers somatic and germline structural variation breakpoints in whole-genome sequencing data. novoBreak consistently outperformed existing algorithms on real cancer genome data and on synthetic tumors in the ICGC-TCGA DREAM 8.5 Somatic Mutation Calling Challenge primarily because it more effectively utilized reads spanning breakpoints. novoBreak also demonstrated great sensitivity in identifying short insertions and deletions