A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://spiral.imperial.ac.uk:8443/bitstream/10044/1/76743/2/Paper-v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="Oxford University Press">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/op7ztx4fhvairowgqifu7dnvsi" style="color: black;">Briefings in Bioinformatics</a>
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<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bib/bby106">doi:10.1093/bib/bby106</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30462158">pmid:30462158</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jbjd3n6eungbfo4etdtlns4wji">fatcat:jbjd3n6eungbfo4etdtlns4wji</a> </span>
more »... hese 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.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200507200911/https://spiral.imperial.ac.uk:8443/bitstream/10044/1/76743/2/Paper-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7f/68/7f6863099338dbfeba871daa1aad9da5a057bd8e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bib/bby106"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a>