The application of Hadoop in structural bioinformatics.
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ICR Authors
Authors
Alnasir, JJ
Shanahan, HP
Shanahan, HP
Document Type
Journal Article
Date
2018-11-20
Date Accepted
2018-10-05
Abstract
The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up.
Citation
Briefings in bioinformatics, 2018
Source Title
Publisher
Oxford University Press (OUP)
ISSN
1467-5463
eISSN
1477-4054