High sensitivity to aligner and high rate of false positives in the estimates of positive selection in the 12 Drosophila genomes

Abstract

We investigate the effect of aligner choice on inferences of positive selection using site-specific models of molecular evolution. We find that independently of the choice of aligner, the rate of false positives is unacceptably high. Our study is a whole-genome analysis of all protein-coding genes in 12 Drosophila genomes annotated in either all 12 species (~6690 genes) or in the six melanogaster group species. We compare six popular aligners: PRANK, T-Coffee, ClustalW, ProbCons, AMAP, and MUSCLE, and find that the aligner choice strongly influences the estimates of positive selection. Differences persist when we use (1) different stringency cutoffs, (2) different selection inference models, (3) alignments with or without gaps, and/or additional masking, (4) per-site versus per-gene statistics, (5) closely related melanogaster group species versus more distant 12 Drosophila genomes. Furthermore, we find that these differences are consequential for downstream analyses such as determination of over/under-represented GO terms associated with positive selection. Visual analysis indicates that most sites inferred as positively selected are, in fact, misaligned at the codon level, resulting in false positive rates of 48%-82%. PRANK, which has been reported to outperform other aligners in simulations, performed best in our empirical study as well. Unfortunately, PRANK still had a high, and unacceptable for most applications, false positives rate of 50%-55%. We identify misannotations and indels, many of which appear to be located in disordered protein regions, as primary culprits for the high misalignment-related error levels and discuss possible workaround approaches to this apparently pervasive problem in genome-wide evolutionary analyses.

Selectome update: quality control and computational improvements to a database of positive selection

“…In total, 8.7% of MSA columns were removed before selection computations for Primates, versus 4.4% in Selectome 5 (GBLOCKS based pipeline); 12% of columns were removed for Glires, and 34% of columns for Euteleostomi, consistent with the expectation that more divergent sequences are more difficult to align reliably. More in detail, in Selectome 5, in Primates we identified 246 678 out of 1 149 639 sites (21%) as under positive selection, including long continuous stretches of ‘positively selected’ sites, which manual examination showed to be alignment or gene model errors [consistent with (10)]. In Selectome 6, filtering reduced the number of sites analyzed to 392 104, of which 61 119 are identified as under positive selection (16%); there are no more long stretches of sites, and manual inspection does not identify any obvious false positives.…”

Divergence of the Yeast Transcription Factor FZF1 Affects Sulfite Resistance

“…The substitution rate upstream of FZF1 is characterized by an accelerated rate along the lineages leading to Saccharomyces cerevisiae and Saccharomyces paradoxus relative to that along the lineages leading to Saccharomyces mikatae and Saccharomyces bayanus (Figure 1). However, previous studies have shown that signals of selection are highly dependent on the alignment [40], [41]. To determine whether the evidence for rate heterogeneity upstream of FZF1 is dependent on the alignment used, we generated additional alignments using alternative alignment parameters and algorithms, and tested each for substitution rate heterogeneity.…”

Brain transcriptome of the violet-eared waxbillUraeginthus granatinaand recent evolution in the songbird genome

“…As these alignments required only two species to overlap, they resulted in a larger number of alignments and longer alignments. The quality and method of sequence alignment have important impacts on inferences regarding rates of evolution [46]. During the course of this study, we tested multiple alignment pipelines, including the use of amino acid sequence-based approaches [47].…”