Sequence Alignment Errors Research Articles

On the importance of homology in the age of phylogenomics

Homology is perhaps the most central concept of phylogenetic biology. Molecular systematists have traditionally paid due attention to the homology statements that are implied by their alignments of orthologous sequences, but some authors have suggested that manual gene-by-gene curation is not sustainable in the phylogenomics era. Here, we show that there are multiple ways to efficiently screen for and detect homology errors in phylogenomic data sets. Application of these screening approaches to two phylogenomic data sets, one for birds and another for mammals, shows that these data are replete with homology errors including alignments of different exons to each other, alignments of exons to introns, and alignments of paralogues to each other. The extent of these homology errors weakens the conclusions of studies based on these data sets. Despite advances in automated phylogenomic pipelines, we contend that much of the long, difficult, and sometimes tedious work of systematics is still required to guard against pervasive homology errors. This conclusion is underscored by recent studies that show that just a few outlier genes can impact phylogenetic results at short, tightly spaced internodes that are deep in the Tree of Life. The view that widespread DNA sequence alignment errors are not a major concern for rigorous systematic research is not tenable. If a primary goal of phylogenomics is to resolve the most challenging phylogenetic problems with the abundant data that are now available, researchers must employ effective procedures to screen for and correct homology errors prior to performing downstream phylogenetic analyses.

Characterization of multiple sequence alignment errors using complete-likelihood score and position-shift map.

BackgroundReconstruction of multiple sequence alignments (MSAs) is a crucial step in most homology-based sequence analyses, which constitute an integral part of computational biology. To improve the accuracy of this crucial step, it is essential to better characterize errors that state-of-the-art aligners typically make. For this purpose, we here introduce two tools: the complete-likelihood score and the position-shift map.ResultsThe logarithm of the total probability of a MSA under a stochastic model of sequence evolution along a time axis via substitutions, insertions and deletions (called the “complete-likelihood score” here) can serve as an ideal score of the MSA. A position-shift map, which maps the difference in each residue’s position between two MSAs onto one of them, can clearly visualize where and how MSA errors occurred and help disentangle composite errors. To characterize MSA errors using these tools, we constructed three sets of simulated MSAs of selectively neutral mammalian DNA sequences, with small, moderate and large divergences, under a stochastic evolutionary model with an empirically common power-law insertion/deletion length distribution. Then, we reconstructed MSAs using MAFFT and Prank as representative state-of-the-art single-optimum-search aligners. About 40–99% of the hundreds of thousands of gapped segments were involved in alignment errors. In a substantial fraction, from about 1/4 to over 3/4, of erroneously reconstructed segments, reconstructed MSAs by each aligner showed complete-likelihood scores not lower than those of the true MSAs. Out of the remaining errors, a majority by an iterative option of MAFFT showed discrepancies between the aligner-specific score and the complete-likelihood score, and a majority by Prank seemed due to inadequate exploration of the MSA space. Analyses by position-shift maps indicated that true MSAs are in considerable neighborhoods of reconstructed MSAs in about 80–99% of the erroneous segments for small and moderate divergences, but in only a minority for large divergences.ConclusionsThe results of this study suggest that measures to further improve the accuracy of reconstructed MSAs would substantially differ depending on the types of aligners. They also re-emphasize the importance of obtaining a probability distribution of fairly likely MSAs, instead of just searching for a single optimum MSA.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0945-5) contains supplementary material, which is available to authorized users.

Identifying and Seeing beyond Multiple Sequence Alignment Errors Using Intra-Molecular Protein Covariation

BackgroundThere is currently no way to verify the quality of a multiple sequence alignment that is independent of the assumptions used to build it. Sequence alignments are typically evaluated by a number of established criteria: sequence conservation, the number of aligned residues, the frequency of gaps, and the probable correct gap placement. Covariation analysis is used to find putatively important residue pairs in a sequence alignment. Different alignments of the same protein family give different results demonstrating that covariation depends on the quality of the sequence alignment. We thus hypothesized that current criteria are insufficient to build alignments for use with covariation analyses.Methodology/Principal FindingsWe show that current criteria are insufficient to build alignments for use with covariation analyses as systematic sequence alignment errors are present even in hand-curated structure-based alignment datasets like those from the Conserved Domain Database. We show that current non-parametric covariation statistics are sensitive to sequence misalignments and that this sensitivity can be used to identify systematic alignment errors. We demonstrate that removing alignment errors due to 1) improper structure alignment, 2) the presence of paralogous sequences, and 3) partial or otherwise erroneous sequences, improves contact prediction by covariation analysis. Finally we describe two non-parametric covariation statistics that are less sensitive to sequence alignment errors than those described previously in the literature.Conclusions/SignificanceProtein alignments with errors lead to false positive and false negative conclusions (incorrect assignment of covariation and conservation, respectively). Covariation analysis can provide a verification step, independent of traditional criteria, to identify systematic misalignments in protein alignments. Two non-parametric statistics are shown to be somewhat insensitive to misalignment errors, providing increased confidence in contact prediction when analyzing alignments with erroneous regions because of an emphasis on they emphasize pairwise covariation over group covariation.

Phylogenomic evidence for multiple losses of flight in ratite birds.

Ratites (ostriches, emus, rheas, cassowaries, and kiwis) are large, flightless birds that have long fascinated biologists. Their current distribution on isolated southern land masses is believed to reflect the breakup of the paleocontinent of Gondwana. The prevailing view is that ratites are monophyletic, with the flighted tinamous as their sister group, suggesting a single loss of flight in the common ancestry of ratites. However, phylogenetic analyses of 20 unlinked nuclear genes reveal a genome-wide signal that unequivocally places tinamous within ratites, making ratites polyphyletic and suggesting multiple losses of flight. Phenomena that can mislead phylogenetic analyses, including long branch attraction, base compositional bias, discordance between gene trees and species trees, and sequence alignment errors, have been eliminated as explanations for this result. The most plausible hypothesis requires at least three losses of flight and explains the many morphological and behavioral similarities among ratites by parallel or convergent evolution. Finally, this phylogeny demands fundamental reconsideration of proposals that relate ratite evolution to continental drift.

Characterization of pairwise and multiple sequence alignment errors

We characterize pairwise and multiple sequence alignment (MSA) errors by comparing true alignments from simulations of sequence evolution with reconstructed alignments. The vast majority of reconstructed alignments contain many errors. Error rates rapidly increase with sequence divergence, thus, for even intermediate degrees of sequence divergence, more than half of the columns of a reconstructed alignment may be expected to be erroneous. In closely related sequences, most errors consist of the erroneous positioning of a single indel event and their effect is local. As sequences diverge, errors become more complex as a result of the simultaneous mis-reconstruction of many indel events, and the lengths of the affected MSA segments increase dramatically. We found a systematic bias towards underestimation of the number of gaps, which leads to the reconstructed MSA being on average shorter than the true one. Alignment errors are unavoidable even when the evolutionary parameters are known in advance. Correct reconstruction can only be guaranteed when the likelihood of true alignment is uniquely optimal. However, true alignment features are very frequently sub-optimal or co-optimal, with the result that optimal albeit erroneous features are incorporated into the reconstructed MSA. Progressive MSA utilizes a guide-tree in the reconstruction of MSAs. The quality of the guide-tree was found to affect MSA error levels only marginally.

Method To Assess Packing Quality of Transmembrane α-Helices in Proteins. 2. Validation by “Correct vs Misleading” Test

We describe a set of tests designed to check the ability of the new "membrane score" method (see the first paper of this series) to assess the packing quality of transmembrane (TM) alpha-helical domains in proteins. The following issues were addressed: (1) Whether there is a relation between the score (S(mem)) of a model and its closeness to the "nativelike" conformation? (2) Is it possible to recognize a correct model among misfolded and erroneous ones? (3) To what extent the score of a homology-built model is sensitive to errors in sequence alignment? To answer the first question, two test cases were considered: (i) Several models of bovine aquaporin-1 (target protein) were built on the structural templates provided by its homologs with known X-ray structure. (ii) Side chains in the spatial models of visual rhodopsin and cytochrome c oxidase were rebuilt based on the backbone scaffolds taken from their crystal structures, and the resulting models were iteratively fitted into the full-atom X-ray conformations. It was shown that the higher the S(mem) value of a model is, the lower its root-mean-square deviation is from the "correct" (crystal) structure of a target. Furthermore, the "membrane score" method successfully identifies the rhodopsin crystal structure in an ensemble of "rotamer-type" decoys, thus providing the way to optimize mutual orientations of alpha-helices in models of TM domains. Finally, being applied to a set of homology models of rhodopsin built on its crystal structure with systematically shifted alignment, the approach demonstrates a prominent ability to detect alignment errors. We therefore assume that the "membrane score" method will be helpful in optimization of in silico models of TM domains in proteins, especially those in GPCRs.

Multiple sequence alignment: In pursuit of homologous DNA positions

DNA sequence alignment is a prerequisite to virtually all comparative genomic analyses, including the identification of conserved sequence motifs, estimation of evolutionary divergence between sequences, and inference of historical relationships among genes and species. While it is mere common sense that inaccuracies in multiple sequence alignments can have detrimental effects on downstream analyses, it is important to know the extent to which the inferences drawn from these alignments are robust to errors and biases inherent in all sequence alignments. A survey of investigations into strengths and weaknesses of sequence alignments reveals, as expected, that alignment quality is generally poor for two distantly related sequences and can often be improved by adding additional sequences as stepping stones between distantly related species. Errors in sequence alignment are also found to have a significant negative effect on subsequent inference of sequence divergence, phylogenetic trees, and conserved motifs. However, our understanding of alignment biases remains rudimentary, and sequence alignment procedures continue to be used somewhat like benign formatting operations to make sequences equal in length. Because of the central role these alignments now play in our endeavors to establish the tree of life and to identify important parts of genomes through evolutionary functional genomics, we see a need for increased community effort to investigate influences of alignment bias on the accuracy of large-scale comparative genomics.

Analysis of comparative modeling predictions for CASP2 targets 1, 3, 9, and 17

Comparative modeling targets 1, 3, 9 and 17 were predicted by alignment of multiple sequences and structures, when available, followed by minimization using the program AMMP. The minimization used improved potentials, and distance restraints for regions of common structure. New prediction procedures were evaluated. Three tested solvent corrections did not significantly improve the predictions. Target 17 had 85.3% sequence identity with the parent and no insertions or deletions. The prediction had a root-mean-square deviation from target 17 of 0.56 A on C alpha atoms, and 0.59 A for the ligand atoms, which verified the accuracy of the minimization. Targets 1, 3, and 9 had 36.4%, 46.7%, and 33.3% identity with the parent sequences, and predictions resulted in root-mean-square deviations for 79-85% of C alpha atoms of 1.49, 1.11, and 1.24 A, respectively. Conformational differences between parent and target crystal structures were difficult to predict. The use of distance restraints and multiple structures improved the positioning of gaps in sequence alignment. Distance restraints did not overcome errors in sequence alignment or ambiguities due to conformational variation in proteins. Predictions for targets 3 and 9 successfully reduced large deviations between parent and target structures.

Confronting the problem of interconnected structural changes in the comparative modeling of proteins.

Comparative models of three proteins have been built using a variety of computational methods, heavily supplemented by visual inspection. We consider the accuracy obtained to be worse than expected. A careful analysis of the models shows that a major reason for the poor results is the interconnectedness of the structural differences between the target proteins and the template structures they were modeled from. Side chain conformations are often determined by details of the structure remote in the sequence, and can be influenced by relatively small main chain changes. Almost all of the regions of substantial main chain conformational change interact with at least one other such region, so that they often cannot be modeled independently. Visual inspection is sometimes effective in correcting errors in sequence alignment and in spotting when an alternative template structure is more appropriate. We expect some improvements in the near future through the development of structure-based sequence alignment tools, side chain interconnectedness rotamer choice algorithms, and a better understanding of the context sensitivity of conformational features.