SparkSeq: fast, scalable and cloud-ready tool for the interactive genomic data analysis with nucleotide precision.

Wiewiórka, Marek S; Messina, Antonio; Pacholewska, Alicja Elzbieta; Maffioletti, Sergio; Gawrysiak, Piotr; Okoniewski, Michał J (2014). SparkSeq: fast, scalable and cloud-ready tool for the interactive genomic data analysis with nucleotide precision. Bioinformatics, 30(18), pp. 2652-2653. Oxford University Press 10.1093/bioinformatics/btu343

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Summary: Many time-consuming analyses of next-generation sequencing data can be addressed with modern cloud computing. The Apache Hadoop-based solutions have become popular in genomics because of their scalability in a cloud infrastructure. So far, most of these tools have been used for batch data processing rather than interactive data querying. The SparkSeq software has been created to take advantage of a new MapReduce framework, Apache Spark, for next-generation sequencing data. SparkSeq is a general-purpose, flexible and easily extendable library for genomic cloud computing. It can be used to build genomic analysis pipelines in Scala and run them in an interactive way. SparkSeq opens up the possibility of customized ad hoc secondary analyses and iterative machine learning algorithms. This article demonstrates its scalability and overall fast performance by running the analyses of sequencing datasets. Tests of SparkSeq also prove that the use of cache and HDFS block size can be tuned for the optimal performance on multiple worker nodes.

Item Type:

Journal Article (Original Article)

Division/Institute:

05 Veterinary Medicine > Department of Clinical Veterinary Medicine (DKV) > Equine Clinic
05 Veterinary Medicine > Department of Clinical Research and Veterinary Public Health (DCR-VPH) > Institute of Genetics
05 Veterinary Medicine > Department of Clinical Veterinary Medicine (DKV)
05 Veterinary Medicine > Department of Clinical Research and Veterinary Public Health (DCR-VPH)

UniBE Contributor:

Pacholewska, Alicja Elzbieta

Subjects:

500 Science > 590 Animals (Zoology)
600 Technology > 630 Agriculture
000 Computer science, knowledge & systems > 040 Unassigned
500 Science > 570 Life sciences; biology

ISSN:

1367-4803

Publisher:

Oxford University Press

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Alicja Elzbieta Pacholewska

Date Deposited:

04 Nov 2016 17:48

Last Modified:

28 Apr 2018 02:31

Publisher DOI:

10.1093/bioinformatics/btu343

PubMed ID:

24845651

Web of Science ID:

000342913000015

BORIS DOI:

10.7892/boris.79078

URI:

https://boris.unibe.ch/id/eprint/79078

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