An Alignment Confidence Score Capturing Robustness to Guide Tree Uncertainty

Abstract

Multiple sequence alignment (MSA) is the basis for a wide range of comparative sequence analyses from molecular phylogenetics to 3D structure prediction. Sophisticated algorithms have been developed for sequence alignment, but in practice, many errors can be expected and extensive portions of the MSA are unreliable. Hence, it is imperative to understand and characterize the various sources of errors in MSAs and to quantify site-specific alignment confidence. In this paper, we show that uncertainties in the guide tree used by progressive alignment methods are a major source of alignment uncertainty. We use this insight to develop a novel method for quantifying the robustness of each alignment column to guide tree uncertainty. We build on the widely used bootstrap method for perturbing the phylogenetic tree. Specifically, we generate a collection of trees and use each as a guide tree in the alignment algorithm, thus producing a set of MSAs. We next test the consistency of every column of the MSA obtained from the unperturbed guide tree with respect to the set of MSAs. We name this measure the "GUIDe tree based AligNment ConfidencE" (GUIDANCE) score. Using the Benchmark Alignment data BASE benchmark as well as simulation studies, we show that GUIDANCE scores accurately identify errors in MSAs. Additionally, we compare our results with the previously published Heads-or-Tails score and show that the GUIDANCE score is a better predictor of unreliably aligned regions.

Comprehensive, structurally-curated alignment and phylogeny of vertebrate biogenic amine receptors

“…In spite of a large body of literature investigating the utility of MSA filtering, the particular circumstances under which filtering is beneficial remain ambiguous (Castresana, 2000;Talavera and Castresana, 2007;Schloss, 2010;Penn et al, 2010;Jordan and Goldman, 2012;Privman et al, 2012;Wu et al, 2012;Spielman et al, 2014). Here, we find that masking residues based on biological information regarding protein structure can greatly improve phylogenetic fit ( Table 2), indicating that residue filtering, at least in this circumstance, successfully increased phylogenetic signal.…”

Legume phylogeny and classification in the 21st century: Progress, prospects and lessons for other species–rich clades

“…Thus, more thorough alignment options may be promising in that algorithms for multiple sequence alignment can be scaled up to keep pace with the taxonomic accumulation of nrDNA ITS or other highly informative non-coding sequences, but it is necessary to look carefully at alignment quality when the taxonomic scope is broad. It may also be possible to harness some recent attempts to quantify alignment uncertainty (e.g., the bootstrap procedure of Penn & al., 2010), but this area is not especially well developed, and there are several distinct methods to identify different sources of alignment uncertainty. An alternative solution is to separate datasets like nrDNA ITS into smaller datasets (i.e., corresponding to less inclusive groups), within which alignment quality is preserved, and analyze them as though they were separate "pseudo-loci" (e.g., McMahon & Sanderson, 2006).…”

Molecular evolution of growth hormone and insulin-like growth factor 1 receptors in long-lived, small-bodied mammals

“…Nucleotide sequences of each gene were aligned with GUIDANCE (Penn et al, 2010) using the PRANK algorithm (Löytynoja and Goldman 2005), with codons enforced and 10 bootstraps. Low-quality sequences, those that obtained a quality score of below 0.6, were removed from the multi-fasta file and the alignment recalculated.…”