Scalable Consistency in T-Coffee Through Apache Spark and Cassandra Database

Lladós Segura, Jordi; Cores Prado, Fernando; Guirado Fernández, Fernando

doi:https://doi.org/10.1089/cmb.2018.0084

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Issue date

2018

Author

Lladós Segura, Jordi

Cores Prado, Fernando

Guirado Fernández, Fernando

Suggested citation

Lladós Segura, Jordi; Cores Prado, Fernando; Guirado Fernández, Fernando; . (2018) . Scalable Consistency in T-Coffee Through Apache Spark and Cassandra Database. Journal of Computational Biology, 2018, vol, 25, nun. 8, p. 894-906. https://doi.org/10.1089/cmb.2018.0084.

Abstract

Next-generation sequencing, also known as high-throughput sequencing, has increased the volume of genetic data processed by sequencers. In the bioinformatic scientific area, highly rated multiple sequence alignment tools, such as MAFFT, ProbCons, and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage to improve the final accuracy. However, such methods are severely limited by the memory required to store the consistency information. Big data processing and persistence techniques are used to manage and store the huge amount of information that is generated. Although these techniques have significant advantages, few biological applications have adopted them. In this article, a novel approach named big data tree-based consistency objective function for alignment evaluation (BDT-Coffee) is presented. BDT-Coffee is based on the integration of consistency information through Cassandra database in TC, previously generated by the MapReduce processing paradigm, to enable large data sets to be processed with the aim of improving the performance and scalability of the original algorithm.

URI

http://hdl.handle.net/10459.1/69308

DOI

https://doi.org/10.1089/cmb.2018.0084

Is part of

Journal of Computational Biology, 2018, vol, 25, nun. 8, p. 894-906