Large-Scale Multiple Sequence Alignment and Tree Estimation Using SATé

Abstract

SATé is a method for estimating multiple sequence alignments and trees that has been shown to produce highly accurate results for datasets with large numbers of sequences. Running SATé using its default settings is very simple, but improved accuracy can be obtained by modifying its algorithmic parameters. We provide a detailed introduction to the algorithmic approach used by SATé, and instructions for running a SATé analysis using the GUI under default settings. We also provide a discussion of how to modify these settings to obtain improved results, and how to use SATé in a phylogenetic analysis pipeline.

The Harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes

“…Multiple genome alignment is a fundamental tool in genomics essential for tracking genome evolution [6][7][8] [26,40], recombination, homoplasy, gene conversion, mobile genetic elements, pseudogenization, and convoluted orthology relationships [25]. In addition, the computational burden of multiple sequence alignment remains very high [41] despite recent progress [42].…”

“…Multiple genome alignment is a fundamental tool in genomics essential for tracking genome evolution [6][7][8] [26,40], recombination, homoplasy, gene conversion, mobile genetic elements, pseudogenization, and convoluted orthology relationships [25]. In addition, the computational burden of multiple sequence alignment remains very high [41] despite recent progress [42].The current influx of microbial sequencing data necessitates methods for large-scale comparative genomics and shifts the focus towards scalability. Current microbial genome alignment methods focus on all-versus-all progressive alignment [31,36] to detect subset relationships (that is, gene gain/loss), but these methods are bounded at various steps by quadratic time complexity.…”

“…While several tools exist (LS-BSR [28], Magic [29], Mavid [30], Mauve [31][32][33], MGA [34], M-GCAT [35], Mugsy [36], TBA [37], multi-LAGAN [38], PECAN [39]), multiple genome alignment remains a challenging task due to the prevalence of horizontal gene transfer [26,40], recombination, homoplasy, gene conversion, mobile genetic elements, pseudogenization, and convoluted orthology relationships [25]. In addition, the computational burden of multiple sequence alignment remains very high [41] despite recent progress [42].…”

Comparing different machine learning and mathematical regression models to evaluate multiple sequence alignments

“…The study of these similarities and differences is usually required by a huge number of applications such as phylogenetic analyses [1], structural modelling [2], functional predictions [3] or sequence database searching [4]. MSAs are also facilitating the analysis of functional, structural and genomic information provided by novel high-throughput and next-generation sequencing (NGS) experiments [5].…”

Creation of a Database Including a Set of Biological Features Related to Protein Sequences and Their Corresponding Alignment

“…These approaches were firstly designed for homology transfer [1] where poorly characterized protein sequences could be compared to wellstudied homologs from typical model organisms. Additionally, MSA strategies have been increasingly used to researches in phylogenetic analyses [2] structural modeling [3], or functional predictions [4].…”