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

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
Jordi LladósFernando Guirado

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.

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Citations

Aug 8, 2018·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Miguel A Vega-Rodríguez, José M Granado-Criado

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Software Mentioned

PASTA
MAFFT
NoSQL
SparkSeq
PPCAS
COFFEE
HomFam
PASTASpark
Apache Spark ( PPCAS )
Clustal

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