Comparative Analysis of Computationally Accelerated NGS Alignment

The Smith-Waterman algorithm is the basis of most current sequence alignment technology, which can be used to identify similarities between sequences for cancer detection and treatment because it provides researchers with potential targets for early diagnosis and personalized treatment. The growing number of DNA and RNA sequences available to analyze necessitates faster alignment processes than are possible with current iterations of the Smith-Waterman (S-W) algorithm. This project aimed to identify the most effective and efficient methods for accelerating the S-W algorithm by investigating recent advances in sequence alignment. Out of a total of 22 articles considered in this project, 17 articles had to be excluded from the study due to lack of standardization of data reporting. Only one study by Chen et al. obtained in this project contained enough information to compare accuracy and alignment speed. When accuracy was excluded from the criteria, five studies contained enough information to rank their efficiency. The study conducted by Rucci et al. was the fastest at 268.83 Giga Cell Updates Per Second (GCUPS), and the method by Pérez-Serrano et al. came close at 229.93 GCUPS while testing larger sequences. It was determined that reporting standards in this field are not sufficient, and the study by Chen et al. should set a benchmark for future reporting

IMPROVING BWA-MEM WITH GPU PARALLEL COMPUTING

Due to the many advances made in designing algorithms, especially the ones used in bioinformatics, it is becoming harder and harder to improve their efficiencies. Therefore, hardware acceleration using General-Purpose computing on Graphics Processing Unit has become a popular choice. BWA-MEM is an important part of the BWA software package for sequence mapping. Because of its high speed and accuracy, we choose to parallelize the popular short DNA sequence mapper. BWA has been a prevalent single node tool in genome alignment, and it has been widely studied for acceleration for a long time since the first version of the BWA package came out. This thesis presents the Big Data GPGPU distributed BWA-MEM, a tool that combines GPGPU acceleration and distributed computing. The four hardware parallelization techniques used are CPU multi-threading, GPU paralleled, CPU distributed, and GPU distributed. The GPGPU distributed software typically outperforms other parallelization versions. The alignment is performed on a distributed network, and each node in the network executes a separate GPGPU paralleled version of the software. We parallelize the chain2aln function in three levels. In Level 1, the function ksw\_extend2, an algorithm based on Smith-Waterman, is parallelized to handle extension on one side of the seed. In Level 2, the function chain2aln is parallelized to handle chain extension, where all seeds within the same chain are extended. In Level 3, part of the function mem\_align1\_core is parallelized for extending multiple chains. Due to the program's complexity, the parallelization work was limited at the GPU version of ksw\_extend2 parallelization Level 3. However, we have successfully combined Spark with BWA-MEM and ksw\_extend2 at parallelization Level 1, which has shown that the proposed framework is possible. The paralleled Level 3 GPU version of ksw\_extend2 demonstrated noticeable speed improvement with the test data set

The Parallelism Motifs of Genomic Data Analysis

Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share this data with the research community, but some of these genomic data analysis problems require large scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high end parallel systems today and place different requirements on programming support, software libraries, and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high performance genomics analysis, including alignment, profiling, clustering, and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or motifs that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing

An Improved Smith-Waterman Algorithm Based on Spark Parallelization

This paper proposes the design and the implementation of a Spark parallelization plan for improving the Smith-Waterman (SW) algorithm, named the Spark-OSW algorithm. Then, the Spark-OSW was verified through accuracy, performance and acceleration tests. The results show that the proposed algorithm achieved 100% accuracy, ran much faster than the SW, and performed well in cluster environment. The research findings shed important new light on the database search for gene sequences

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