Alignment-Free Sequence Comparison over Hadoop for Computational Biology

Abstract

Sequence comparison i.e., The assessment of how similar two biological sequences are to each other, is a fundamental and routine task in Computational Biology and Bioinformatics. Classically, alignment methods are the de facto standard for such an assessment. In fact, considerable research efforts for the development of efficient algorithms, both on classic and parallel architectures, has been carried out in the past 50 years. Due to the growing amount of sequence data being produced, a new class of methods has emerged: Alignment-free methods. Research in this ares has become very intense in the past few years, stimulated by the advent of Next Generation Sequencing technologies, since those new methods are very appealing in terms of computational resources needed and biological relevance. Despite such an effort and in contrast with sequence alignment methods, no systematic investigation of how to take advantage of distributed architectures to speed up alignment-free methods, has taken place. We provide a contribution of that kind, by evaluating the possibility of using the Hadoop distributed framework to speed up the running times of these methods, compared to their original sequential formulation

Graph Theory-Based Sequence Descriptors as Remote Homology Predictors

“…Since the beginning of big data models, the need of their application in both AB and AF sequence comparisons has arisen as stated in [175]. Indeed, solutions have arisen including CloudBLAST [176] based on MapReduce to support AB features and, similarly, alignment-free implementations, specifically one based on k-mers over Hadoop [174], to overcome not only time but also space requirements.…”

Efficient Multiple Sequences Alignment Algorithm Generation via Components Assembly Under PAR Framework

“…At present, most of the research on alignment algorithms focus on specific problems ( Isa et al, 2014 ; Cattaneo et al, 2015 ; Chattopadhyay et al, 2015 ; Huo et al, 2016 ) or specific algorithm optimization ( Farrar, 2007 ; Houtgast et al, 2017 ; Junid et al, 2017 ) in the field of sequence similarity analysis, but less on the whole problem domain, so it is difficult to get an algorithm component library with a higher level of abstraction and suitable for the whole field of sequence similarity analysis. To some extent, this leads to the redundancy of the sequence alignment algorithm and the errors that may be caused by the artificial selection algorithm.…”