Q-score does not directly penalize for aligning positions that are unaligned in the reference, also known as over-alignment. Q-score is defined as the average pairwise fraction of reference homologies that are also found in the test alignment (i.e, the alignment program’s output). The focus on maximizing true homologies has been furthered by a reliance on Q-score for performance comparisons with structural benchmarks. Nevertheless, structural benchmarks have generally been preferred over simulated benchmarks, resulting in an emphasis on the maximization of true homologies in “core blocks” (homologous regions), with less regard for false homologies. Some downstream applications of multiple sequence alignment may be especially sensitive to false homologies in gappy regions, such as tree building and the detection of positive selection. A major downside of structural benchmarks is that “gappy” regions are typically not considered in scoring because they are not superimposable in space. By this definition residues in the same column of an alignment should occupy the same structural position in space. In contrast, many structural benchmarks have been built from related RNA or protein tertiary structures that have been superimposed to provide an empirical alignment that is free of many of the simplifications of simulated alignments. Furthermore, real sequence sets often include spurious (e.g., chimeric ) sequences, sequencing errors, uneven taxon sampling, rearrangements, and uneven lengths that have largely been neglected in studies relying on simulations. In typical simulations, the choice of insertion and deletion rates across sites is specified, a substitution matrix is used, covariation between positions is ignored, and there is no selective pressure on the tertiary structure. Therefore, the complete evolutionary history of the sequences is known and the entire alignment can be used as a reference. Simulated alignments are generated by “evolving” sequences along a predetermined tree under a model of substitution. There is an ongoing debate over whether simulated, structural, or other types of benchmark are preferable. In this way, benchmarks determine the objective to which alignment programs strive to attain. MSA programs are typically optimized and assessed based on their ability to recreate the alignments in benchmark datasets. Since structure is more conserved than primary sequence, it is possible that structure-based alignment will maintain accuracy even as sequence-based alignment loses integrity. A possible third strategy, proposed here, is to shift reliance onto structural information as alignments become larger. The second strategy is to use an iterative divide-and-conquer approach that shows good performance on simulated sequence sets, but performs comparably to other methods on large empirical protein benchmarks. However, this approach performs poorly on simulated sequence alignments, and may not be applicable for phylogenetic analyses. The first strategy is to use a chained guide tree, which is efficient to construct and allows reasonable accuracy to be maintained on large empirical datasets (>1,000 sequences). Two main strategies have been proposed to combat the loss in quality as alignments grow in size. It is generally believed that the poor scalability of alignment can be attributed to the build-up of error or the increasing level of ambiguity as more-and-more sequences are aligned. Thus, the accurate alignment of large numbers of sequences remains an unsolved challenge that is frequently encountered in modern datasets. Furthermore, performance tends to decrease dramatically beyond a certain point as more sequences are added to the input set. A variety of methods have been developed to provide more accurate alignments, yet many of these approaches are not amenable to aligning thousands of sequences in a reasonable amount of time. Many of these applications depend on the correct alignment of thousands of diverse sequences. A multiple sequence alignment may reveal many aspects about a gene: which regions are constrained, which sites undergo positive selection, and potentially the structure of its gene product. Multiple sequence alignment (MSA) is a ubiquitous task in biology, and has a wide variety of applications including homology detection, predicting residue couplings, finding evolutionarily important sites, oligonucleotide design, and phylogenetics.
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