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High Performance computing improvements on bioinformatics consistency-based multiple sequence alignment tools

机译：基于生物信息学一致性的多序列比对工具的高性能计算改进

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摘要

Multiple Sequence Alignment (MSA) is essential for a wide range of applications in Bioinformatics. Traditionally, the alignment accuracy was the main metric used to evaluate the goodness of MSA tools. However, with the growth of sequencing data, other features, such as performance and the capacity to align larger datasets, are gaining strength. To achieve these new requirements, without affecting accuracy, the use of high-performance computing (HPC) resources and techniques is crucial. In this paper, we apply HPC techniques in T-Coffee, one of the more accurate but less scalable MSA tools. We integrate three innovative solutions into T-Coffee: the Balanced Guide Tree to increase the parallelism/performance, the Optimized Library Method with the aim of enhancing the scalability and the Multiple Tree Alignment, which explores different alignments in parallel to improve the accuracy. The results obtained show that the resulting tool, MTA-TCoffee, is able to improve the scalability in both the execution time and also the number of sequences to be aligned. Furthermore, not only is the alignment accuracy not affected by these improvements, as would be expected, but it improves significantly. Finally, we emphasize that the presented methods are not just restricted to T-Coffee, but may be implemented in any other alignment tools that use similar algorithms (progressive alignment, consistency or guide trees).
机译：多序列比对（MSA）对于生物信息学的广泛应用至关重要。传统上，对准精度是用于评估MSA工具的优劣的主要指标。但是，随着测序数据的增长，其他功能（例如性能和对齐较大数据集的能力）正在获得优势。为了在不影响准确性的前提下达到这些新要求，使用高性能计算（HPC）资源和技术至关重要。在本文中，我们将HPC技术应用于T-Coffee，T-Coffee是一种更准确但可扩展性较低的MSA工具。我们将三种创新解决方案集成到T型咖啡中：平衡的引导树（用于提高并行度/性能），优化的库方法（用于增强可伸缩性）和多树对齐（Multiple Tree Alignment），其并行探索不同的对齐方式以提高准确性。获得的结果表明，所得的工具MTA-TCoffee能够提高执行时间和要对齐的序列数的可伸缩性。此外，如预期的那样，不仅对准精度不受这些改进的影响，而且显着提高。最后，我们强调提出的方法不仅限于T-Coffee，而且可以在使用类似算法（渐进式对齐，一致性或指南树）的任何其他对齐工具中实现。

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