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Related Experiment Video

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Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

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Published on: June 9, 2023

Cloud-Coffee: implementation of a parallel consistency-based multiple alignment algorithm in the T-Coffee package and

Paolo Di Tommaso¹, Miquel Orobitg, Fernando Guirado

¹Centre For Genomic Regulation (Pompeu Fabra University), Carrer del Doctor Aiguader 88, Barcelona, Spain.

Bioinformatics (Oxford, England)

|July 8, 2010

Summary

The T-Coffee multiple sequence alignment tool now has a parallel implementation, showing effective performance on cloud computing platforms. Cloud deployment offers a cost-effective solution for web servers with moderate traffic.

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Published on: November 3, 2011

Area of Science:

Computational Biology
Bioinformatics
High-Performance Computing

Background:

T-Coffee is a widely used, freeware, open-source multiple sequence alignment package.
Existing implementations may face performance limitations with large datasets or high user loads.

Purpose of the Study:

To introduce the first parallel implementation of the T-Coffee consistency-based multiple aligner.
To evaluate the performance and cost-effectiveness of this parallel implementation using cloud computing resources.

Main Methods:

Developed a parallel version of the T-Coffee multiple sequence alignment algorithm.
Benchmarked the parallel implementation on Amazon Elastic Cloud (EC2) infrastructure.
Assessed performance metrics including speedup and resource utilization.

Main Results:

The parallel implementation of T-Coffee demonstrates reasonably effective performance gains.
Benchmarking on EC2 confirmed the viability of the parallelization strategy.
Cloud-based deployment is shown to be a cost-effective option for moderate web server usage.

Conclusions:

The parallel T-Coffee aligner offers improved computational efficiency.
Cloud computing platforms like EC2 provide a scalable and economical solution for bioinformatics tools.
This work paves the way for more efficient large-scale sequence alignment in the cloud.