Alignment Benchmark Research Articles

Sequence alignment by passing messages

BackgroundSequence alignment has become an indispensable tool in modern molecular biology research, and probabilistic sequence alignment models have been shown to provide an effective framework for building accurate sequence alignment tools. One such example is the pair hidden Markov model (pair-HMM), which has been especially popular in comparative sequence analysis for several reasons, including their effectiveness in modeling and detecting sequence homology, model simplicity, and the existence of efficient algorithms for applying the model to sequence alignment problems. However, despite these advantages, pair-HMMs also have a number of practical limitations that may degrade their alignment performance or render them unsuitable for certain alignment tasks.ResultsIn this work, we propose a novel scheme for comparing and aligning biological sequences that can effectively address the shortcomings of the traditional pair-HMMs. The proposed scheme is based on a simple message-passing approach, where messages are exchanged between neighboring symbol pairs that may be potentially aligned in the optimal sequence alignment. The message-passing process yields probabilistic symbol alignment confidence scores, which may be used for predicting the optimal alignment that maximizes the expected number of correctly aligned symbol pairs.ConclusionsExtensive performance evaluation on protein alignment benchmark datasets shows that the proposed message-passing scheme clearly outperforms the traditional pair-HMM-based approach, in terms of both alignment accuracy and computational efficiency. Furthermore, the proposed scheme is numerically robust and amenable to massive parallelization.

GLProbs: Aligning Multiple Sequences Adaptively

This paper introduces a simple and effective approach to improve the accuracy of multiple sequence alignment. We use a natural measure to estimate the similarity of the input sequences, and based on this measure, we align the input sequences differently. For example, for inputs with high similarity, we consider the whole sequences and align them globally, while for those with moderately low similarity, we may ignore the flank regions and align them locally. To test the effectiveness of this approach, we have implemented a multiple sequence alignment tool called GLProbs and compared its performance with about one dozen leading alignment tools on three benchmark alignment databases, and GLProbs's alignments have the best scores in almost all testings. We have also evaluated the practicability of the alignments of GLProbs by applying the tool to three biological applications, namely phylogenetic trees construction, protein secondary structure prediction and the detection of high risk members for cervical cancer in the HPV-E6 family, and the results are very encouraging.

A Network Synthesis Model for Generating Protein Interaction Network Families

In this work, we introduce a novel network synthesis model that can generate families of evolutionarily related synthetic protein–protein interaction (PPI) networks. Given an ancestral network, the proposed model generates the network family according to a hypothetical phylogenetic tree, where the descendant networks are obtained through duplication and divergence of their ancestors, followed by network growth using network evolution models. We demonstrate that this network synthesis model can effectively create synthetic networks whose internal and cross-network properties closely resemble those of real PPI networks. The proposed model can serve as an effective framework for generating comprehensive benchmark datasets that can be used for reliable performance assessment of comparative network analysis algorithms. Using this model, we constructed a large-scale network alignment benchmark, called NAPAbench, and evaluated the performance of several representative network alignment algorithms. Our analysis clearly shows the relative performance of the leading network algorithms, with their respective advantages and disadvantages. The algorithm and source code of the network synthesis model and the network alignment benchmark NAPAbench are publicly available at http://www.ece.tamu.edu/bjyoon/NAPAbench/.

Protein Sequence Alignment Analysis by Local Covariation: Coevolution Statistics Detect Benchmark Alignment Errors

The use of sequence alignments to understand protein families is ubiquitous in molecular biology. High quality alignments are difficult to build and protein alignment remains one of the largest open problems in computational biology. Misalignments can lead to inferential errors about protein structure, folding, function, phylogeny, and residue importance. Identifying alignment errors is difficult because alignments are built and validated on the same primary criteria: sequence conservation. Local covariation identifies systematic misalignments and is independent of conservation. We demonstrate an alignment curation tool, LoCo, that integrates local covariation scores with the Jalview alignment editor. Using LoCo, we illustrate how local covariation is capable of identifying alignment errors due to the reduction of positional independence in the region of misalignment. We highlight three alignments from the benchmark database, BAliBASE 3, that contain regions of high local covariation, and investigate the causes to illustrate these types of scenarios. Two alignments contain sequential and structural shifts that cause elevated local covariation. Realignment of these misaligned segments reduces local covariation; these alternative alignments are supported with structural evidence. We also show that local covariation identifies active site residues in a validated alignment of paralogous structures. Loco is available at https://sourceforge.net/projects/locoprotein/files/

A novel and well-defined benchmarking method for second generation read mapping

BackgroundSecond generation sequencing technologies yield DNA sequence data at ultra high-throughput. Common to most biological applications is a mapping of the reads to an almost identical or highly similar reference genome. The assessment of the quality of read mapping results is not straightforward and has not been formalized so far. Hence, it has not been easy to compare different read mapping approaches in a unified way and to determine which program is the best for what task.ResultsWe present a new benchmark method, called Rabema (Read Alignment BEnchMArk), for read mappers. It consists of a strict definition of the read mapping problem and of tools to evaluate the result of arbitrary read mappers supporting the SAM output format.ConclusionsWe show the usefulness of the benchmark program by performing a comparison of popular read mappers. The tools supporting the benchmark are licensed under the GPL and available from http://www.seqan.de/projects/rabema.html.

Bridging the gap between single-template and fragment based protein structure modeling using Spanner

Background: As the coverage of experimentally determined protein structures increases, fragment-based structural modeling approaches are expected to play an ever more important role in structural modeling. Here we introduce a structural modeling method by which an initial structural template can be extended by the addition of structural fragments to more closely match an aligned query sequence. A database of pro-tein fragments indexed by their internal coordinates was created and a novel methodology for their retrieval was implemented. After fragment selection and assembly, sidechains are replaced and the all-atom model is refined by restrained energy minimization. We implemented the proposed method in the program Span-ner and benchmarked it using a previously published set of 367 immunoglobulin (Ig) loops, 206 historical query-template pairs and alignments from the Critical Assessment of protein Structure Prediction (CASP) experiment, and 217 structural alignments between remotely homologous query-template pairs. The con-straint-based modeling software MODELLER and previously reported results for RosettaAntibody, were used as references. Results: The error in the modeled structures was assessed by root-mean square deviation (RMSD) from the native structure, as a function of the query-template sequence identity. For the Ig benchmark set, for which a single fragment was used to model each loop, the average RMSD for Spanner (3 +/- 1.5 A) was found to lie midway between that of MODELLER (4 +/- 2 A) and RosettaAntibody (2 +/- 1 A). For the CASP and structural alignment benchmarks, for which gaps represent a small fraction of the modeled residues, the difference between Spanner and MODELLER were much smaller then the standard deviations of either program. The Spanner web server and source code are available at http://sysimm.ifrec.osaka-u.ac.jp/Spanner/. Conclusions: For typical homology modeling, Spanner is at least as good, on average as the template-free constraint-driven approach used by MODELLER. The Ig model results suggest that when gap regions represent a significant fraction of the alignment, Spanner’s efficient use of fragment libraries, along with local sequence and secondary structural information, significantly improve model accuracy without a dra-matic increase in computational cost.

WebPRANK: a phylogeny-aware multiple sequence aligner with interactive alignment browser

BackgroundPhylogeny-aware progressive alignment has been found to perform well in phylogenetic alignment benchmarks and to produce superior alignments for the inference of selection on codon sequences. Its implementation in the PRANK alignment program package also allows modelling of complex evolutionary processes and inference of posterior probabilities for sequence sites evolving under each distinct scenario, either simultaneously with the alignment of sequences or as a post-processing step for an existing alignment. This has led to software with many advanced features, and users may find it difficult to generate optimal alignments, visualise the full information in their alignment results, or post-process these results, e.g. by objectively selecting subsets of alignment sites.ResultsWe have created a web server called webPRANK that provides an easy-to-use interface to the PRANK phylogeny-aware alignment algorithm. The webPRANK server supports the alignment of DNA, protein and codon sequences as well as protein-translated alignment of cDNAs, and includes built-in structure models for the alignment of genomic sequences. The resulting alignments can be exported in various formats widely used in evolutionary sequence analyses. The webPRANK server also includes a powerful web-based alignment browser for the visualisation and post-processing of the results in the context of a cladogram relating the sequences, allowing (e.g.) removal of alignment columns with low posterior reliability. In addition to de novo alignments, webPRANK can be used for the inference of ancestral sequences with phylogenetically realistic gap patterns, and for the annotation and post-processing of existing alignments. The webPRANK server is freely available on the web at http://tinyurl.com/webprank .ConclusionsThe webPRANK server incorporates phylogeny-aware multiple sequence alignment, visualisation and post-processing in an easy-to-use web interface. It widens the user base of phylogeny-aware multiple sequence alignment and allows the performance of all alignment-related activity for small sequence analysis projects using only a standard web browser.

A Probabilistic-Logical Framework for Ontology Matching

Ontology matching is the problem of determining correspondences between concepts, properties, and individuals of different heterogeneous ontologies. With this paper we present a novel probabilistic-logical framework for ontology matching based on Markov logic. We define the syntax and semantics and provide a formalization of the ontology matching problem within the framework. The approach has several advantages over existing methods such as ease of experimentation, incoherence mitigation during the alignment process, and the incorporation of a-priori confidence values. We show empirically that the approach is efficient and more accurate than existing matchers on an established ontology alignment benchmark dataset.

Exhaustive genetic algorithm based on GC-GM for multiple sequence alignment

Multiple sequence alignment is an unsolved NP-complete combinatorial optimization problem.This paper described a new exhaustive genetic algorithm based on Gap Crossover and Gap Mutation(GC-GM) for multiple sequence alignment by genetic operation on gap matrixes reassembled.The approach was examined by using a set of standard instances taken from the benchmark alignment database BAliBASE.Numerical simulation results were compared with those obtained by using the Clustal W algorithm and showed the effectiveness of the new approach.

An Alignment Confidence Score Capturing Robustness to Guide Tree Uncertainty

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.

Aligning Protein Sequences with Predicted Secondary Structure

Accurately aligning distant protein sequences is notoriously difficult. Since the amino acid sequence alone often does not provide enough information to obtain accurate alignments under the standard alignment scoring functions, a recent approach to improving alignment accuracy is to use additional information such as secondary structure. We make several advances in alignment of protein sequences annotated with predicted secondary structure: (1) more accurate models for scoring alignments, (2) efficient algorithms for optimal alignment under these models, and (3) improved learning criteria for setting model parameters through inverse alignment, as well as (4) in-depth experiments evaluating model variants on benchmark alignments. More specifically, the new models use secondary structure predictions and their confidences to modify the scoring of both substitutions and gaps. All models have efficient algorithms for optimal pairwise alignment that run in near-quadratic time. These models have many parameters, which are rigorously learned using inverse alignment under a new criterion that carefully balances score error and recovery error. We then evaluate these models by studying how accurately an optimal alignment under the model recovers benchmark reference alignments that are based on the known three-dimensional structures of the proteins. The experiments show that these new models provide a significant boost in accuracy over the standard model for distant sequences. The improvement for pairwise alignment is as much as 15% for sequences with less than 25% identity, while for multiple alignment the improvement is more than 20% for difficult benchmarks whose accuracy under standard tools is at most 40%.

Quality measures for protein alignment benchmarks

Multiple protein sequence alignment methods are central to many applications in molecular biology. These methods are typically assessed on benchmark datasets including BALIBASE, OXBENCH, PREFAB and SABMARK, which are important to biologists in making informed choices between programs. In this article, annotations of domain homology and secondary structure are used to define new measures of alignment quality and are used to make the first systematic, independent evaluation of these benchmarks. These measures indicate sensitivity and specificity while avoiding the ambiguous residue correspondences and arbitrary distance cutoffs inherent to structural superpositions. Alignments by selected methods that indicate high-confidence columns (ALIGN-M, DIALIGN-T, FSA and MUSCLE) are also assessed. Fold space coverage and effective benchmark database sizes are estimated by reference to domain annotations, and significant redundancy is found in all benchmarks except SABMARK. Questionable alignments are found in all benchmarks, especially in BALIBASE where 87% of sequences have unknown structure, 20% of columns contain different folds according to SUPERFAMILY and 30% of ‘core block’ columns have conflicting secondary structure according to DSSP. A careful analysis of current protein multiple alignment benchmarks calls into question their ability to determine reliable algorithm rankings.

Optimizing substitution matrix choice and gap parameters for sequence alignment

BackgroundWhile substitution matrices can readily be computed from reference alignments, it is challenging to compute optimal or approximately optimal gap penalties. It is also not well understood which substitution matrices are the most effective when alignment accuracy is the goal rather than homolog recognition. Here a new parameter optimization procedure, POP, is described and applied to the problems of optimizing gap penalties and selecting substitution matrices for pair-wise global protein alignments.ResultsPOP is compared to a recent method due to Kim and Kececioglu and found to achieve from 0.2% to 1.3% higher accuracies on pair-wise benchmarks extracted from BALIBASE. The VTML matrix series is shown to be the most accurate on several global pair-wise alignment benchmarks, with VTML200 giving best or close to the best performance in all tests. BLOSUM matrices are found to be slightly inferior, even with the marginal improvements in the bug-fixed RBLOSUM series. The PAM series is significantly worse, giving accuracies typically 2% less than VTML. Integer rounding is found to cause slight degradations in accuracy. No evidence is found that selecting a matrix based on sequence divergence improves accuracy, suggesting that the use of this heuristic in CLUSTALW may be ineffective. Using VTML200 is found to improve the accuracy of CLUSTALW by 8% on BALIBASE and 5% on PREFAB.ConclusionThe hypothesis that more accurate alignments of distantly related sequences may be achieved using low-identity matrices is shown to be false for commonly used matrix types. Source code and test data is freely available from the author's web site at http://www.drive5.com/pop.

Ontology matching for dynamic publication in semantic portals

Abstract Semantic portals are characterized for storing and structuring content according to specific domain ontologies. This content is represented through ontological languages, which enable not only adding semantic value to information treatment, but also inferring new knowledge from it. Publication in a semantic portal is typically done by instantiating its ontology, and this is often performed manually or through the use of specific forms. However, in order to keep portals constantly up-to-date, it is necessary to provide means for a more dynamic publication, integrating the portal content with information retrieved from different ontology-based sites on the same or on complementary domains. Reusing information from different ontologies requires specific and efficient mechanisms to align them, taking into account syntactical and semantical conflicts. This paper proposes an extension of the Crosi Mapping System, a matching mechanism which calculates similarities between ontologies. Some of its original algorithms have been enriched with additional functionality. This extension, namede-CMS, has been evaluated using the OAEI ontology alignment benchmark, and results show an increase of 69% in alignment precision when compared to the CMS original version. In order to illustrate its use, thee-CMS strategy was applied to SiGePoS, a System for Generating Semantic Portals. The semantic module, one of the system components, implements the alignment mechanism between ontologies, which is performed by thee-CMS.

Ontology matching for dynamic publication in semantic portals

Semantic portals are characterized for storing and structuring content according to specific domain ontologies. This content is represented through ontological languages, which enable not only adding semantic value to information treatment, but also inferring new knowledge from it. Publication in a semantic portal is typically done by instantiating its ontology, and this is often performed manually or through the use of specific forms. However, in order to keep portals constantly up-to-date, it is necessary to provide means for a more dynamic publication, integrating the portal content with information retrieved from different ontology-based sites on the same or on complementary domains. Reusing information from different ontologies requires specific and efficient mechanisms to align them, taking into account syntactical and semantical conflicts. This paper proposes an extension of the Crosi Mapping System, a matching mechanism which calculates similarities between ontologies. Some of its original algorithms have been enriched with additional functionality. This extension, named e-CMS, has been evaluated using the OAEI ontology alignment benchmark, and results show an increase of 69% in alignment precision when compared to the CMS original version. In order to illustrate its use, the e-CMS strategy was applied to SiGePoS, a System for Generating Semantic Portals. The semantic module, one of the system components, implements the alignment mechanism between ontologies, which is performed by the e-CMS.

Inexact matching of ontology graphs using expectation-maximization

We present a new method for mapping ontology schemas that address similar domains. The problem of ontology matching is crucial since we are witnessing a decentralized development and publication of ontological data. We formulate the problem of inferring a match between two ontologies as a maximum likelihood problem, and solve it using the technique of expectation-maximization (EM). Specifically, we adopt directed graphs as our model for ontology schemas and use a generalized version of EM to arrive at a map between the nodes of the graphs. We exploit the structural, lexical and instance similarity between the graphs, and differ from the previous approaches in the way we utilize them to arrive at, a possibly inexact, match. Inexact matching is the process of finding a best possible match between the two graphs when exact matching is not possible or is computationally difficult. In order to scale the method to large ontologies, we identify the computational bottlenecks and adapt the generalized EM by using a memory bounded partitioning scheme. We provide comparative experimental results in support of our method on two well-known ontology alignment benchmarks and discuss their implications.

Inexact Matching of Ontology Graphs Using Expectation-Maximization

We present a new method for mapping ontology schemas that address similar domains. The problem of ontology matching is crucial since we are witnessing a decentralized development and publication of ontological data.We formulate the problem of inferring a match between two ontologies as a maximum likelihood problem, and solve it using the technique of expectation-maximization (EM). Specifically, we adopt directed graphs as our model for ontology schemas and use a generalized version of EM to arrive at a map between the nodes of the graphs.We exploit the structural, lexical and instance similarity between the graphs, and differ from the previous approaches in the way we utilize them to arrive at, a possibly inexact, match. Inexact matching is the process of finding a best possible match between the two graphs when exact matching is not possible or is computationally difficult. In order to scale the method to large ontologies, we identify the computational bottlenecks and adapt the generalized EM by using a memory bounded partitioning scheme. We provide comparative experimental results in support of our method on two well-known ontology alignment benchmarks and discuss their implications.

Nomadic genetic algorithm for multiple sequence alignment (MSANGA)

Genetic algorithms (GA) are adaptive search procedures that try to produce a globally optimum solution for problems of huge search space. This paper speaks about a variant of the standard genetic algorithm (SGA) called nomadic genetic algorithm (NGA) which is based on the concept of 'birds of the same feather flock together'. This NGA is found to maintain the diversity of individuals in the population by intelligently adapting to its environment as well as results in faster convergence of the solution. The objective of this paper is to prove the merits of NGA over SGA for problems of large search space like the problem of multiple sequence alignment (MSA) in bioinformatics. NGA was applied to MSA (MSANGA) and the convergence of NGA is compared with that of SGA and the results tabulated. Also, the accuracy of the alignment produced using MSANGA is compared with nine other popular tools for the data sets chosen from the standard BaliBASE benchmark alignment suite, illustrating the superiority of NGA over SGA and all other tools to produce quality alignment at a faster rate.

Improving accuracy of multiple sequence alignment algorithms based on alignment of neighboring residues

While most of the recent improvements in multiple sequence alignment accuracy are due to better use of vertical information, which include the incorporation of consistency-based pairwise alignments and the use of profile alignments, we observe that it is possible to further improve accuracy by taking into account alignment of neighboring residues when aligning two residues, thus making better use of horizontal information. By modifying existing multiple alignment algorithms to make use of horizontal information, we show that this strategy is able to consistently improve over existing algorithms on a few sets of benchmark alignments that are commonly used to measure alignment accuracy, and the average improvements in accuracy can be as much as 1–3% on protein sequence alignment and 5–10% on DNA/RNA sequence alignment. Unlike previous algorithms, consistent average improvements can be obtained across all identity levels.

The meaning of alignment: lessons from structural diversity

BackgroundProtein structural alignment provides a fundamental basis for deriving principles of functional and evolutionary relationships. It is routinely used for structural classification and functional characterization of proteins and for the construction of sequence alignment benchmarks. However, the available techniques do not fully consider the implications of protein structural diversity and typically generate a single alignment between sequences.ResultsWe have taken alternative protein crystal structures and generated simulation snapshots to explicitly investigate the impact of structural changes on the alignments. We show that structural diversity has a significant effect on structural alignment. Moreover, we observe alignment inconsistencies even for modest spatial divergence, implying that the biological interpretation of alignments is less straightforward than commonly assumed. A salient example is the GroES 'mobile loop' where sub-Ångstrom variations give rise to contradictory sequence alignments.ConclusionA comprehensive treatment of ambiguous alignment regions is crucial for further development of structural alignment applications and for the representation of alignments in general. For this purpose we have developed an on-line database containing our data and new ways of visualizing alignment inconsistencies, which can be found at .