PPCAS: Implementation of a Probabilistic Pairwise Model for Consistency-Based Multiple Alignment in Apache Spark
| Autor: | Jordi Lladós, Fernando Guirado, Fernando Cores |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Data processing Multiple sequence alignment business.industry Computer science Probabilistic logic 02 engineering and technology Machine learning computer.software_genre Domain (software engineering) 03 medical and health sciences Consistency (database systems) 030104 developmental biology 020204 information systems Scalability Spark (mathematics) 0202 electrical engineering electronic engineering information engineering Pairwise comparison Data mining Artificial intelligence business computer |
| Zdroj: | Algorithms and Architectures for Parallel Processing ISBN: 9783319654812 ICA3PP |
| DOI: | 10.1007/978-3-319-65482-9_45 |
| Popis: | Large-scale data processing techniques, currently known as Big-Data, are used to manage the huge amount of data that are generated by sequencers. Although these techniques have significant advantages, few biological applications have adopted them. In the Bioinformatic scientific area, Multiple Sequence Alignment (MSA) tools are widely applied for evolution and phylogenetic analysis, homology and domain structure prediction. Highly-rated MSA tools, such as MAFFT, ProbCons and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage in order to improve the final accuracy. In this paper, a novel approach named PPCAS (Probabilistic Pairwise model for Consistency-based multiple alignment in Apache Spark) is presented. PPCAS is based on the MapReduce processing paradigm in order to enable large datasets to be processed with the aim of improving the performance and scalability of the original algorithm. |
| Databáze: | OpenAIRE |
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