HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing

Shixiang Wan,Quan Zou

doi:10.1186/s13015-017-0116-x

Abstract

BackgroundMultiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types.MethodsDistributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction.ResultsThe experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource.ConclusionsTHAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at http://lab.malab.cn/soft/halign.

Highlights

Multiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction
Distributed computing systems based on MapReduce framework present more abstract interfaces and more elastic computing resources than those based on message passing interface (MPI) [14]
In order to focus on the performance with increasing sequences scale, we employ the newest and largest R10 reference set ΦProtein as our protein sequence datasets

Summary

Introduction

Multiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types. More different parallelization strategies are implemented for reducing time and space complexity of MSA. These strategies can be mainly categorized into. Wan and Zou Algorithms Mol Biol (2017) 12:25 cost, most naive algorithms attempted to reduce time and space complexity to cope with ultra-large analysis tasks. Ultra-large biological sequence analysis can be efficiently addressed by assembling distributed and parallel computing systems with numerous cheap devices [15,16,17]

Methods

Results

Discussion

Conclusion