eCOMPASS: evaluative comparison of multiple protein alignments by statistical score

Motivation

Detecting subtle biologically relevant patterns in protein sequences often requires the construction of a large and accurate multiple sequence alignment (MSA). Methods for constructing MSAs are usually evaluated using benchmark alignments, which, however, typically contain very few sequences and are therefore inappropriate when dealing with large numbers of proteins.

Results

eCOMPASS addresses this problem using a statistical measure of relative alignment quality based on direct coupling analysis (DCA): to maintain protein structural integrity over evolutionary time, substitutions at one residue position typically result in compensating substitutions at other positions. eCOMPASS computes the statistical significance of the congruence between high scoring directly coupled pairs and 3D contacts in corresponding structures, which depends upon properly aligned homologous residues. We illustrate eCOMPASS using both simulated and real MSAs.

Availability and implementation

The eCOMPASS executable, C++ open source code and input data sets are available at https://www.igs.umaryland.edu/labs/neuwald/software/compass

Supplementary information

Supplementary data are available at Bioinformatics online.

Evolution of Sequence-Diverse Disordered Regions in a Protein Family: Order within the Chaos

Approaches for studying the evolution of globular proteins are now well established yet are unsuitable for disordered sequences. Our understanding of the evolution of proteins containing disordered regions therefore lags that of globular proteins, limiting our capacity to estimate their evolutionary history, classify paralogs, and identify potential sequence–function relationships. Here, we overcome these limitations by using new analytical approaches that project representations of sequence space to dissect the evolution of proteins with both ordered and disordered regions, and the correlated changes between these. We use the fasciclin-like arabinogalactan proteins (FLAs) as a model family, since they contain a variable number of globular fasciclin domains as well as several distinct types of disordered regions: proline (Pro)-rich arabinogalactan (AG) regions and longer Pro-depleted regions.

Sequence space projections of fasciclin domains from 2019 FLAs from 78 species identified distinct clusters corresponding to different types of fasciclin domains. Clusters can be similarly identified in the seemingly random Pro-rich AG and Pro-depleted disordered regions. Sequence features of the globular and disordered regions clearly correlate with one another, implying coevolution of these distinct regions, as well as with the N-linked and O-linked glycosylation motifs. We reconstruct the overall evolutionary history of the FLAs, annotated with the changing domain architectures, glycosylation motifs, number and length of AG regions, and disordered region sequence features. Mapping these features onto the functionally characterized FLAs therefore enables their sequence–function relationships to be interrogated. These findings will inform research on the abundant disordered regions in protein families from all kingdoms of life.

Learning Parameter-Advising Sets for Multiple Sequence Alignment

While the multiple sequence alignment output by an aligner strongly depends on the parameter values used for the alignment scoring function (such as the choice of gap penalties and substitution scores), most users rely on the single default parameter setting provided by the aligner. A different parameter setting, however, might yield a much higher-quality alignment for the specific set of input sequences. The problem of picking a good choice of parameter values for specific input sequences is called parameter advising. A parameter advisor has two ingredients: (i) a set of parameter choices to select from, and (ii) an estimator that provides an estimate of the accuracy of the alignment computed by the aligner using a parameter choice. The parameter advisor picks the parameter choice from the set whose resulting alignment has highest estimated accuracy. In this paper, we consider for the first time the problem of learning the optimal set of parameter choices for a parameter advisor that uses a given accuracy estimator. The optimal set is one that maximizes the expected true accuracy of the resulting parameter advisor, averaged over a collection of training data. While we prove that learning an optimal set for an advisor is NP-complete, we show there is a natural approximation algorithm for this problem, and prove a tight bound on its approximation ratio. Experiments with an implementation of this approximation algorithm on biological benchmarks, using various accuracy estimators from the literature, show it finds sets for advisors that are surprisingly close to optimal. Furthermore, the resulting parameter advisors are significantly more accurate in practice than simply aligning with a single default parameter choice.

Complex Evolutionary History of the Mammalian Histone H1.1-H1.5 Gene Family

H1 is involved in chromatin higher-order structure and gene regulation. H1 has a tripartite structure. The central domain is stably folded in solution, while the N- and C-terminal domains are intrinsically disordered. The terminal domains are encoded by DNA of low sequence complexity, and are thus prone to short insertions/deletions (indels). We have examined the evolution of the H1.1-H1.5 gene family from 27 mammalian species. Multiple sequence alignment has revealed a strong preferential conservation of the number and position of basic residues among paralogs, suggesting that overall H1 basicity is under a strong purifying selection. The presence of a conserved pattern of indels, ancestral to the splitting of mammalian orders, in the N- and C-terminal domains of the paralogs, suggests that slippage may have favored the rapid divergence of the subtypes and that purifying selection has maintained this pattern because it is associated with function. Evolutionary analyses have found evidences of positive selection events in H1.1, both before and after the radiation of mammalian orders. Positive selection ancestral to mammalian radiation involved changes at specific sites that may have contributed to the low relative affinity of H1.1 for chromatin. More recent episodes of positive selection were detected at codon positions encoding amino acids of the C-terminal domain of H1.1, which may modulate the folding of the CTD. The detection of putative recombination points in H1.1-H1.5 subtypes suggests that this process may has been involved in the acquisition of the tripartite H1 structure.

Identification of cis-suppression of human disease mutations by comparative genomics

Patterns of amino acid conservation have served as a tool for understanding protein evolution. The same principles have also found broad application in human genomics, driven by the need to interpret the pathogenic potential of variants in patients. Here we performed a systematic comparative genomics analysis of human disease-causing missense variants. We found that an appreciable fraction of disease-causing alleles are fixed in the genomes of other species, suggesting a role for genomic context. We developed a model of genetic interactions that predicts most of these to be simple pairwise compensations. Functional testing of this model on two known human disease genes revealed discrete cis amino acid residues that, although benign on their own, could rescue the human mutations in vivo. This approach was also applied to ab initio gene discovery to support the identification of a de novo disease driver in BTG2 that is subject to protective cis-modification in more than 50 species. Finally, on the basis of our data and models, we developed a computational tool to predict candidate residues subject to compensation. Taken together, our data highlight the importance of cis-genomic context as a contributor to protein evolution; they provide an insight into the complexity of allele effect on phenotype; and they are likely to assist methods for predicting allele pathogenicity.

Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs

BACKGROUND

A standard procedure in many areas of bioinformatics is to use a single multiple sequence alignment (MSA) as the basis for various types of analysis. However, downstream results may be highly sensitive to the alignment used, and neglecting the uncertainty in the alignment can lead to significant bias in the resulting inference. In recent years, a number of approaches have been developed for probabilistic sampling of alignments, rather than simply generating a single optimum. However, this type of probabilistic information is currently not widely used in the context of downstream inference, since most existing algorithms are set up to make use of a single alignment.

RESULTS

In this work we present a framework for representing a set of sampled alignments as a directed acyclic graph (DAG) whose nodes are alignment columns; each path through this DAG then represents a valid alignment. Since the probabilities of individual columns can be estimated from empirical frequencies, this approach enables sample-based estimation of posterior alignment probabilities. Moreover, due to conditional independencies between columns, the graph structure encodes a much larger set of alignments than the original set of sampled MSAs, such that the effective sample size is greatly increased.

CONCLUSIONS

The alignment DAG provides a natural way to represent a distribution in the space of MSAs, and allows for existing algorithms to be efficiently scaled up to operate on large sets of alignments. As an example, we show how this can be used to compute marginal probabilities for tree topologies, averaging over a very large number of MSAs. This framework can also be used to generate a statistically meaningful summary alignment; example applications show that this summary alignment is consistently more accurate than the majority of the alignment samples, leading to improvements in downstream tree inference. Implementations of the methods described in this article are available at http://statalign.github.io/WeaveAlign .

On the necessity of dissecting sequence similarity scores into segment-specific contributions for inferring protein homology, function prediction and annotation

Background

Protein sequence similarities to any types of non-globular segments (coiled coils, low complexity regions, transmembrane regions, long loops, etc. where either positional sequence conservation is the result of a very simple, physically induced pattern or rather integral sequence properties are critical) are pertinent sources for mistaken homologies. Regretfully, these considerations regularly escape attention in large-scale annotation studies since, often, there is no substitute to manual handling of these cases. Quantitative criteria are required to suppress events of function annotation transfer as a result of false homology assignments.

Results

The sequence homology concept is based on the similarity comparison between the structural elements, the basic building blocks for conferring the overall fold of a protein. We propose to dissect the total similarity score into fold-critical and other, remaining contributions and suggest that, for a valid homology statement, the fold-relevant score contribution should at least be significant on its own. As part of the article, we provide the DissectHMMER software program for dissecting HMMER2/3 scores into segment-specific contributions. We show that DissectHMMER reproduces HMMER2/3 scores with sufficient accuracy and that it is useful in automated decisions about homology for instructive sequence examples. To generalize the dissection concept for cases without 3D structural information, we find that a dissection based on alignment quality is an appropriate surrogate. The approach was applied to a large-scale study of SMART and PFAM domains in the space of seed sequences and in the space of UniProt/SwissProt.

Conclusions

Sequence similarity core dissection with regard to fold-critical and other contributions systematically suppresses false hits and, additionally, recovers previously obscured homology relationships such as the one between aquaporins and formate/nitrite transporters that, so far, was only supported by structure comparison.

Accuracy estimation and parameter advising for protein multiple sequence alignment

Abstract We develop a novel and general approach to estimating the accuracy of multiple sequence alignments without knowledge of a reference alignment, and use our approach to address a new task that we call parameter advising: the problem of choosing values for alignment scoring function parameters from a given set of choices to maximize the accuracy of a computed alignment. For protein alignments, we consider twelve independent features that contribute to a quality alignment. An accuracy estimator is learned that is a polynomial function of these features; its coefficients are determined by minimizing its error with respect to true accuracy using mathematical optimization. Compared to prior approaches for estimating accuracy, our new approach (a) introduces novel feature functions that measure nonlocal properties of an alignment yet are fast to evaluate, (b) considers more general classes of estimators beyond linear combinations of features, and (c) develops new regression formulations for learning an estimator from examples; in addition, for parameter advising, we (d) determine the optimal parameter set of a given cardinality, which specifies the best parameter values from which to choose. Our estimator, which we call Facet (for "feature-based accuracy estimator"), yields a parameter advisor that on the hardest benchmarks provides more than a 27% improvement in accuracy over the best default parameter choice, and for parameter advising significantly outperforms the best prior approaches to assessing alignment quality.

Phylogenetic relationships within the Opisthokonta based on phylogenomic analyses of conserved single-copy protein domains

Many of the eukaryotic phylogenomic analyses published to date were based on alignments of hundreds to thousands of genes. Frequently, in such analyses, the most realistic evolutionary models currently available are often used to minimize the impact of systematic error. However, controversy remains over whether or not idiosyncratic gene family dynamics (i.e., gene duplications and losses) and incorrect orthology assignments are always appropriately taken into account. In this paper, we present an innovative strategy for overcoming orthology assignment problems. Rather than identifying and eliminating genes with paralogy problems, we have constructed a data set comprised exclusively of conserved single-copy protein domains that, unlike most of the commonly used phylogenomic data sets, should be less confounded by orthology miss-assignments. To evaluate the power of this approach, we performed maximum likelihood and Bayesian analyses to infer the evolutionary relationships within the opisthokonts (which includes Metazoa, Fungi, and related unicellular lineages). We used this approach to test 1) whether Filasterea and Ichthyosporea form a clade, 2) the interrelationships of early-branching metazoans, and 3) the relationships among early-branching fungi. We also assessed the impact of some methods that are known to minimize systematic error, including reducing the distance between the outgroup and ingroup taxa or using the CAT evolutionary model. Overall, our analyses support the Filozoa hypothesis in which Ichthyosporea are the first holozoan lineage to emerge followed by Filasterea, Choanoflagellata, and Metazoa. Blastocladiomycota appears as a lineage separate from Chytridiomycota, although this result is not strongly supported. These results represent independent tests of previous phylogenetic hypotheses, highlighting the importance of sophisticated approaches for orthology assignment in phylogenomic analyses.

Estimation of bacterial diversity using next generation sequencing of 16S rDNA: a comparison of different workflows

Background

Next generation sequencing (NGS) enables a more comprehensive analysis of bacterial diversity from complex environmental samples. NGS data can be analysed using a variety of workflows. We test several simple and complex workflows, including frequently used as well as recently published tools, and report on their respective accuracy and efficiency under various conditions covering different sequence lengths, number of sequences and real world experimental data from rhizobacterial populations of glyphosate-tolerant maize treated or untreated with two different herbicides representative of differential diversity studies.

Results

Alignment and distance calculations affect OTU estimations, and multiple sequence alignment exerts a major impact on the computational time needed. Generally speaking, most of the analyses produced consistent results that may be used to assess differential diversity changes, however, dataset characteristics dictate which workflow should be preferred in each case.

Conclusions

When estimating bacterial diversity, ESPRIT as well as the web-based workflow, RDP pyrosequencing pipeline, produced good results in all circumstances, however, its computational requirements can make method-combination workflows more attractive, depending on sequence variability, number and length.

GIGA: a simple, efficient algorithm for gene tree inference in the genomic age

Background

Phylogenetic relationships between genes are not only of theoretical interest: they enable us to learn about human genes through the experimental work on their relatives in numerous model organisms from bacteria to fruit flies and mice. Yet the most commonly used computational algorithms for reconstructing gene trees can be inaccurate for numerous reasons, both algorithmic and biological. Additional information beyond gene sequence data has been shown to improve the accuracy of reconstructions, though at great computational cost.

Results

We describe a simple, fast algorithm for inferring gene phylogenies, which makes use of information that was not available prior to the genomic age: namely, a reliable species tree spanning much of the tree of life, and knowledge of the complete complement of genes in a species' genome. The algorithm, called GIGA, constructs trees agglomeratively from a distance matrix representation of sequences, using simple rules to incorporate this genomic age information. GIGA makes use of a novel conceptualization of gene trees as being composed of orthologous subtrees (containing only speciation events), which are joined by other evolutionary events such as gene duplication or horizontal gene transfer. An important innovation in GIGA is that, at every step in the agglomeration process, the tree is interpreted/reinterpreted in terms of the evolutionary events that created it. Remarkably, GIGA performs well even when using a very simple distance metric (pairwise sequence differences) and no distance averaging over clades during the tree construction process.

Conclusions

GIGA is efficient, allowing phylogenetic reconstruction of very large gene families and determination of orthologs on a large scale. It is exceptionally robust to adding more gene sequences, opening up the possibility of creating stable identifiers for referring to not only extant genes, but also their common ancestors. We compared trees produced by GIGA to those in the TreeFam database, and they were very similar in general, with most differences likely due to poor alignment quality. However, some remaining differences are algorithmic, and can be explained by the fact that GIGA tends to put a larger emphasis on minimizing gene duplication and deletion events.

Planning the human variome project: the Spain report

The remarkable progress in characterizing the human genome sequence, exemplified by the Human Genome Project and the HapMap Consortium, has led to the perception that knowledge and the tools (e.g., microarrays) are sufficient for many if not most biomedical research efforts. A large amount of data from diverse studies proves this perception inaccurate at best, and at worst, an impediment for further efforts to characterize the variation in the human genome. Because variation in genotype and environment are the fundamental basis to understand phenotypic variability and heritability at the population level, identifying the range of human genetic variation is crucial to the development of personalized nutrition and medicine. The Human Variome Project (HVP; http://www.humanvariomeproject.org/) was proposed initially to systematically collect mutations that cause human disease and create a cyber infrastructure to link locus specific databases (LSDB). We report here the discussions and recommendations from the 2008 HVP planning meeting held in San Feliu de Guixols, Spain, in May 2008.