Optimization of hadoop cluster for analyzing large-scale sequence data in bioinformatics

Tóth, Ádám, Karimi, Ramin (2019) Optimization of hadoop cluster for analyzing large-scale sequence data in bioinformatics Annales Mathematicae et Informaticae. 50. pp. 187-202. ISSN 1787-5021 (Print) 1787-6117 (Online)

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Hivatalos webcím (URL): https://doi.org/10.33039/ami.2019.01.002

Absztrakt (kivonat)

Unexpected growth of high-throughput sequencing platforms in recent years impacted virtually all areas of modern biology. However, the ability to produce data continues to outpace the ability to analyze them. Therefore, continuous efforts are also needed to improve bioinformatics applications for a better use of these research opportunities. Due to the complexity and diversity of metagenomics data, it has been a major challenging field of bioinformatics. Sequence-based identification methods such as using DNA signature (unique k-mer) are the most recent popular methods of real-time analysis of raw sequencing data. DNA signature discovery is compute-intensive and time-consuming. Hadoop, the application of parallel and distributed computing is one of the popular applications for the analysis of large scale data in bioinformatics. Optimization of the time-consumption and computational resource usages such as CPU consumption and memory usage are the main goals of this paper, along with the management of the Hadoop cluster nodes.

Mű típusa: Folyóiratcikk
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Kulcsszavak: hadoop, optimization, next-Generation Sequencing, DNA signature, resource management
Nyelv: angol
Kötetszám: 50.
DOI azonosító: 10.33039/ami.2019.01.002
ISSN: 1787-5021 (Print) 1787-6117 (Online)
Felhasználó: Tibor Gál
Dátum: 27 Feb 2019 12:02
Utolsó módosítás: 06 Jan 2020 10:33
URI: http://publikacio.uni-eszterhazy.hu/id/eprint/2954
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