Simple chained guide trees give high-quality protein multiple sequence alignments

Abstract

Guide trees are used to decide the order of sequence alignment in the progressive multiple sequence alignment heuristic. These guide trees are often the limiting factor in making large alignments, and considerable effort has been expended over the years in making these quickly or accurately. In this article we show that, at least for protein families with large numbers of sequences that can be benchmarked with known structures, simple chained guide trees give the most accurate alignments. These also happen to be the fastest and simplest guide trees to construct, computationally. Such guide trees have a striking effect on the accuracy of alignments produced by some of the most widely used alignment packages. There is a marked increase in accuracy and a marked decrease in computational time, once the number of sequences goes much above a few hundred. This is true, even if the order of sequences in the guide tree is random.he generation of a multiple sequence alignment (MSA) is standard practice during most comparative analyses of homologous genes or proteins. Since the mid-1980s, most automated MSAs have been made using a heuristic approach that Feng and Doolittle (1) called "progressive alignment." This involves clustering the sequences into a tree or dendrogram-like structure, called a "guide tree" in Higgins et al. (2). This guide tree is then used to align the sequences into progressively larger and larger alignments, following the branching order in the tree. Variations on the method were described by various groups in the 1980s [e.g., Taylor (3) and Barton and Sternberg (4)], but the earliest clear description of the approach is from Hogeweg and Hesper (5). Progressive alignment is a heuristic approach and is not guaranteed to find the best possible alignment for any given scoring scheme. It does, however, allow alignments of many sequences to be made quickly, even on personal computers (6). The quality of the alignments is good enough for the alignments to be used automatically in many analysis pipelines.The computational complexity of the alignment process, once a guide tree is created, is approximately OðNÞ for N sequences of the same length. The creation of the guide tree involves comparing all N sequences to each other to generate a distance matrix, which is clearly going to require OðN 2 Þ time and computer memory. Once the distance matrix is made, it will require a further clustering step that is usually OðN 2 Þ but can be more expensive. For large N, the construction of the guide tree becomes limiting and prevents the routine alignment of more than a few thousand sequences. Over the years, various attempts have been made to get around this problem. One solution is to quickly make a crude guide tree initially and to iterate that from an initial MSA. This approach is adopted in the widely used Muscle (7) and Mafft (8) packages. Barton and Sternberg were the first authors to use iteration, but they used a simple "chained" guide tree topology, effectively aligning the sequences one at a time to a growing...

Application of the MAFFT sequence alignment program to large data—reexamination of the usefulness of chained guide trees

“…For their overall difference, the P -values were estimated to be 0.016 for HomFam-TC and <0.01 for HomFam-SP, OXFam-SP and OXFam-TC by the Wilcoxon signed-rank test. Because this result is sharply inconsistent with the previous report (Boyce et al , 2014), we carefully inspected the cause of the difference. When MAFFT version 7.029 was used, as in their report, an improvement upon use of random chained guide trees was indeed observed.…”

“…The comparison between methods 1 and 2 was already reported in Boyce et al (2014) and Fox et al (2016), which consistently concluded that random chained guide trees (method 2) outperformed normal guide trees (method 1) for large alignments using various programs including MAFFT. The authors used MAFFT version 7.029, but the accuracy for large MSAs has been improved after this version.…”

“…In particular, we closely examined a surprising and controversial finding (Boyce et al , 2014; Fox et al , 2016; Tan et al , 2015) on the effect of guide trees in MSA calculation; do random chained guide trees perform better than conventional guide trees? This issue was first raised by Boyce et al (2014), reporting that chained guide trees give better MSAs than the default ones, especially in MAFFT (Katoh et al , 2002) and MUSCLE (Edgar, 2004a, b).…”

“…This issue was first raised by Boyce et al (2014), reporting that chained guide trees give better MSAs than the default ones, especially in MAFFT (Katoh et al , 2002) and MUSCLE (Edgar, 2004a, b). Tan et al (2015) reported opposite observations that chained guide trees give poorer MSAs, using simulation and phylogeny-based evaluations.…”

“…It is known that the accuracy of MSA can depend on the evaluation criteria or the purpose of downstream analyses, as mentioned in Tan et al (2015) and Fox et al (2016). Therefore, it is important for our reexamination to use the evaluation criteria based on structural conservation, as in Boyce et al (2014) and Fox et al (2016).…”