Spark-based data analytics of sequence motifs in large omics data
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Date
2018
Authors
Sarumi, Oluwafemi
Leung, Carson
Adetunmbi, Adebayo
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Data explosion in bioinformatics in recent years has led to new challenges for researchers to develop novel techniques to discover new knowledge from the avalanche of omics data (e.g., genomics, proteomics, transcriptomics). These data are embedded with a wealth of information including frequently repeated patterns (i.e., sequence motifs). In genomics, deoxyribonucleic acid (DNA) sequence motifs are short repeated contiguous frequent subsequences located in the prompter region. Due to the high volume and various degrees of veracity of these DNA datasets generated by the next-generation sequencing techniques, sequence motif mining from DNA sequences poised a major challenge in bioinformatics. In this article, we present a distributed sequential algorithm—which uses the MapReduce programming model on a cluster of homogeneous distributed-memory system running on an Apache Spark computing framework—for DNA sequence motif mining. Experimental results show the effectiveness of our algorithm in Spark-based data analytics of sequence motifs in large omics data.
Description
Keywords
bioinformatics, Spark, MapReduce, deoxyribonucleic acid (DNA), genomics, sequence motifs
Citation
O.A. Sarumi, C.K. Leung, A.O. Adetunmbi. Spark-based data analytics of sequence motifs in large omics data. Procedia Computer Science, 126 (2018), pp. 596-605