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

Jordi Lladós¹, Fernando Cores¹, Fernando Guirado¹

¹INSPIRES Research Center, Universitat de Lleida , Lleida, Spain .

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|July 14, 2018

Summary

Big data tree-based consistency objective function for alignment evaluation (BDT-Coffee) improves multiple sequence alignment accuracy. This novel approach integrates consistency information using Cassandra and MapReduce for enhanced performance and scalability in bioinformatics.

Keywords:

Cassandra Hadoop MSA Spark T-Coffee large-scale alignments

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing generates vast amounts of genetic data.
Multiple sequence alignment tools like T-Coffee (TC) use probabilistic consistency for accuracy but require significant memory.
Existing big data techniques are underutilized in biological applications.

Purpose of the Study:

To present a novel approach, big data tree-based consistency objective function for alignment evaluation (BDT-Coffee), for large-scale multiple sequence alignment.
To enhance the performance and scalability of existing alignment algorithms.

Main Methods:

Integration of consistency information into T-Coffee using a Cassandra database.
Utilizing the MapReduce processing paradigm to generate consistency information.
Developing a big data tree-based consistency objective function for alignment evaluation.

Main Results:

BDT-Coffee enables processing of large datasets, overcoming memory limitations of traditional methods.
The approach improves the performance and scalability of multiple sequence alignment.
Demonstrates the successful application of big data techniques in bioinformatics.

Conclusions:

BDT-Coffee offers a scalable and efficient solution for multiple sequence alignment of large genetic datasets.
The integration of big data processing and persistence techniques enhances bioinformatics tool performance.
This work paves the way for wider adoption of big data strategies in biological data analysis.