LMAP_S: Lightweight Multigene Alignment and Phylogeny eStimation

Emanuel Maldonado,Agostinho Antunes

doi:10.1186/s12859-019-3292-5

Abstract

BackgroundRecent advances in genome sequencing technologies and the cost drop in high-throughput sequencing continue to give rise to a deluge of data available for downstream analyses. Among others, evolutionary biologists often make use of genomic data to uncover phenotypic diversity and adaptive evolution in protein-coding genes. Therefore, multiple sequence alignments (MSA) and phylogenetic trees (PT) need to be estimated with optimal results. However, the preparation of an initial dataset of multiple sequence file(s) (MSF) and the steps involved can be challenging when considering extensive amount of data. Thus, it becomes necessary the development of a tool that removes the potential source of error and automates the time-consuming steps of a typical workflow with high-throughput and optimal MSA and PT estimations.ResultsWe introduce LMAP_S (Lightweight Multigene Alignment and Phylogeny eStimation), a user-friendly command-line and interactive package, designed to handle an improved alignment and phylogeny estimation workflow: MSF preparation, MSA estimation, outlier detection, refinement, consensus, phylogeny estimation, comparison and editing, among which file and directory organization, execution, manipulation of information are automated, with minimal manual user intervention. LMAP_S was developed for the workstation multi-core environment and provides a unique advantage for processing multiple datasets. Our software, proved to be efficient throughout the workflow, including, the (unlimited) handling of more than 20 datasets.ConclusionsWe have developed a simple and versatile LMAP_S package enabling researchers to effectively estimate multiple datasets MSAs and PTs in a high-throughput fashion. LMAP_S integrates more than 25 software providing overall more than 65 algorithm choices distributed in five stages. At minimum, one FASTA file is required within a single input directory. To our knowledge, no other software combines MSA and phylogeny estimation with as many alternatives and provides means to find optimal MSAs and phylogenies. Moreover, we used a case study comparing methodologies that highlighted the usefulness of our software. LMAP_S has been developed as an open-source package, allowing its integration into more complex open-source bioinformatics pipelines. LMAP_S package is released under GPLv3 license and is freely available at https://lmap-s.sourceforge.io/.

Highlights

Recent advances in genome sequencing technologies and the cost drop in high-throughput sequencing continue to give rise to a deluge of data available for downstream analyses
phylogenetic trees (PT) are of great importance for various biological research, for instance, the inference of trait evolution, protein structure and function [32] or in other phylogenomics areas, e.g. gene family evolution [9, 10]. They suffer from identical uncertainty [13, 19, 33] issues. This has taken to the development of several improvements in algorithms and heuristics leading to alternatives, such as PAUP [35], PHYLIP [36], PhyML [37], RaxML [38], FastTree [39], or MrBayes [40]
Lightweight Multigene/Multi-core Alignment and Phylogeny eStimation (LMAP_S) phylogeny comparison and consensus (PCC) method Here we describe the implemented procedures devised to allow the comparison of several PTs both topologically and statistically

Summary

Introduction

Recent advances in genome sequencing technologies and the cost drop in high-throughput sequencing continue to give rise to a deluge of data available for downstream analyses. PTs are of great importance for various biological research, for instance, the inference of trait evolution, protein structure and function [32] or in other phylogenomics areas, e.g. gene family evolution [9, 10] Likewise, they suffer from identical uncertainty [13, 19, 33] issues ( aggravated by the MSA issues [13,14,15,16,17, 19, 20, 23, 34]). This has taken to the development of several improvements in algorithms and heuristics leading to alternatives, such as PAUP [35], PHYLIP [36], PhyML [37], RaxML [38], FastTree [39], or MrBayes [40]

Results

Discussion

Conclusion