APDB: a web server to evaluate the accuracy of sequence alignments using structural information

The APDB webserver uses structural information to evaluate the alignment of sequences with known structures. It returns a score correlated to the overall alignment accuracy as well as a local evaluation. Any sequence alignment can be analyzed with APDB provided it includes at least two proteins with known structures. Sequences without a known structure are simply ignored and do not contribute to the scoring procedure. AVAILABILITY: APDB is part of the T-Coffee suite of tools for alignment analysis, it is available on www.tcoffee.org. A stand-alone version of the package is also available as a freeware open source from the same address.     

Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments

Background  

While the pairwise alignments produced by sequence similarity searches are a powerful tool for identifying homologous proteins - proteins that share a common ancestor and a similar structure; pairwise sequence alignments often fail to represent accurately the structural alignments inferred from three-dimensional coordinates. Since sequence alignment algorithms produce optimal alignments, the best structural alignments must reflect suboptimal sequence alignment scores. Thus, we have examined a range of suboptimal sequence alignments and a range of scoring parameters to understand better which sequence alignments are likely to be more structurally accurate.     

Comparative modeling without implicit sequence alignments

MOTIVATION: The number of known protein sequences is about thousand times larger than the number of experimentally solved 3D structures. For more than half of the protein sequences a close or distant structural analog could be identified. The key starting point in a classical comparative modeling is to generate the best possible sequence alignment with a template or templates. With decreasing sequence similarity, the number of errors in the alignments increases and these errors are the main causes of the decreasing accuracy of the molecular models generated. Here we propose a new approach to comparative modeling, which does not require the implicit alignment - the model building phase explores geometric, evolutionary and physical properties of a template (or templates). RESULTS: The proposed method requires prior identification of a template, although the initial sequence alignment is ignored. The model is built using a very efficient reduced representation search engine CABS to find the best possible superposition of the query protein onto the template represented as a 3D multi-featured scaffold. The criteria used include: sequence similarity, predicted secondary structure consistency, local geometric features and hydrophobicity profile. For more difficult cases, the new method qualitatively outperforms existing schemes of comparative modeling. The algorithm unifies de novo modeling, 3D threading and sequence-based methods. The main idea is general and could be easily combined with other efficient modeling tools as Rosetta, UNRES and others.     

Issues in bioinformatics benchmarking: the case study of multiple sequence alignment

The post-genomic era presents many new challenges for the field of bioinformatics. Novel computational approaches are now being developed to handle the large, complex and noisy datasets produced by high throughput technologies. Objective evaluation of these methods is essential (i) to assure high quality, (ii) to identify strong and weak points of the algorithms, (iii) to measure the improvements introduced by new methods and (iv) to enable non-specialists to choose an appropriate tool. Here, we discuss the development of formal benchmarks, designed to represent the current problems encountered in the bioinformatics field. We consider several criteria for building good benchmarks and the advantages to be gained when they are used intelligently. To illustrate these principles, we present a more detailed discussion of benchmarks for multiple alignments of protein sequences. As in many other domains, significant progress has been achieved in the multiple alignment field and the datasets have become progressively more challenging as the existing algorithms have evolved. Finally, we propose directions for future developments that will ensure that the bioinformatics benchmarks correspond to the challenges posed by the high throughput data.     

Large-scale comparison of protein sequence alignment algorithms with structure alignments

Sequence alignment programs such as BLAST and PSI-BLAST are used routinely in pairwise, profile-based, or intermediate-sequence-search (ISS) methods to detect remote homologies for the purposes of fold assignment and comparative modeling. Yet, the sequence alignment quality of these methods at low sequence identity is not known. We have used the CE structure alignment program (Shindyalov and Bourne, Prot Eng 1998;11:739) to derive sequence alignments for all superfamily and family-level related proteins in the SCOP domain database. CE aligns structures and their sequences based on distances within each protein, rather than on interprotein distances. We compared BLAST, PSI-BLAST, CLUSTALW, and ISS alignments with the CE structural alignments. We found that global alignments with CLUSTALW were very poor at low sequence identity (<25%), as judged by the CE alignments. We used PSI-BLAST to search the nonredundant sequence database (nr) with every sequence in SCOP using up to four iterations. The resulting matrix was used to search a database of SCOP sequences. PSI-BLAST is only slightly better than BLAST in alignment accuracy on a per-residue basis, but PSI-BLAST matrix alignments are much longer than BLAST's, and so align correctly a larger fraction of the total number of aligned residues in the structure alignments. Any two SCOP sequences in the same superfamily that shared a hit or hits in the nr PSI-BLAST searches were identified as linked by the shared intermediate sequence. We examined the quality of the longest SCOP-query/ SCOP-hit alignment via an intermediate sequence, and found that ISS produced longer alignments than PSI-BLAST searches alone, of nearly comparable per-residue quality. At 10-15% sequence identity, BLAST correctly aligns 28%, PSI-BLAST 40%, and ISS 46% of residues according to the structure alignments. We also compared CE structure alignments with FSSP structure alignments generated by the DALI program. In contrast to the sequence methods, CE and structure alignments from the FSSP database identically align 75% of residue pairs at the 10-15% level of sequence identity, indicating that there is substantial room for improvement in these sequence alignment methods. BLAST produced alignments for 8% of the 10,665 nonimmunoglobulin SCOP superfamily sequence pairs (nearly all <25% sequence identity), PSI-BLAST matched 17% and the double-PSI-BLAST ISS method aligned 38% with E-values <10.0. The results indicate that intermediate sequences may be useful not only in fold assignment but also in achieving more complete sequence alignments for comparative modeling.     

Calling SNPs without a reference sequence

Background  

The most common application for the next-generation sequencing technologies is resequencing, where short reads from the genome of an individual are aligned to a reference genome sequence for the same species. These mappings can then be used to identify genetic differences among individuals in a population, and perhaps ultimately to explain phenotypic variation. Many algorithms capable of aligning short reads to the reference, and determining differences between them have been reported. Much less has been reported on how to use these technologies to determine genetic differences among individuals of a species for which a reference sequence is not available, which drastically limits the number of species that can easily benefit from these new technologies.     

The iRMSD: a local measure of sequence alignment accuracy using structural information

MOTIVATION: We introduce the iRMSD, a new type of RMSD, independent from any structure superposition and suitable for evaluating sequence alignments of proteins with known structures. RESULTS: We demonstrate that the iRMSD is equivalent to the standard RMSD although much simpler to compute and we also show that it is suitable for comparing sequence alignments and benchmarking multiple sequence alignment methods. We tested the iRMSD score on 6 established multiple sequence alignment packages and found the results to be consistent with those obtained using an established reference alignment collection like Prefab. AVAILABILITY: The iRMSD is part of the T-Coffee package and is distributed as an open source freeware (http://www.tcoffee.org/).     

A novel approach to local reliability of sequence alignments

MOTIVATION: The pairwise alignment of biological sequences obtained from an algorithm will in general contain both correct and incorrect parts. Hence, to allow for a valid interpretation of the alignment, the local trustworthiness of the alignment has to be quantified. RESULTS: We present a novel approach that attributes a reliability index to every pair of residues, including gapped regions, in the optimal alignment of two protein sequences. The method is based on a fuzzy recast of the dynamic programming algorithm for sequence alignment in terms of mean field annealing. An extensive evaluation with structural reference alignments not only shows that the probability for a pair of residues to be correctly aligned grows consistently with increasing reliability index, but moreover demonstrates that the value of the reliability index can directly be translated into an estimate of the probability for a correct alignment.     

A novel Markov pairwise protein sequence alignment method for sequence comparison

The Smith-Waterman (SW) algorithm is a typical technique for local sequence alignment in computational biology. However, the SW algorithm does not consider the local behaviours of the amino acids, which may result in loss of some useful information. Inspired by the success of Markov Edit Distance (MED) method, this paper therefore proposes a novel Markov pairwise protein sequence alignment (MPPSA) method that takes the local context dependencies into consideration. The numerical results have shown its superiority to the SW for pairwise protein sequence comparison.     

Building multiple sequence alignments with a flavor of HSSP alignments

Homology-derived secondary structure of proteins (HSSP) is a well-known database of multiple sequence alignments (MSAs) which merges information of protein sequences and their three-dimensional structures. It is available for all proteins whose structure is deposited in the PDB. It is also used by STING and (Java)Protein Dossier to calculate and present relative entropy as a measure of the degree of conservation for each residue of proteins whose structure has been solved and deposited in the PDB. However, if the STING and (Java)Protein Dossier are to provide support for analysis of protein structures modeled in computers or being experimentally solved but not yet deposited in the PDB, then we need a new method for building alignments having a flavor of HSSP alignments (myMSAr). The present study describes a new method and its corresponding databank (SH2QS--database of sequences homologue to the query [structure-having] sequence). Our main interest in making myMSAr was to measure the degree of residue conservation for a given query sequence, regardless of whether it has a corresponding structure deposited in the PDB. In this study, we compare the measurement of residue conservation provided by corresponding alignments produced by HSSP and SH2QS. As a case study, we also present two biologically relevant examples, the first one highlighting the equivalence of analysis of the degree of residue conservation by using HSSP or SH2QS alignments, and the second one presenting the degree of residue conservation for a structure modeled in a computer, which , as a consequence, does not have an alignment reported by HSSP.     

M-Coffee: combining multiple sequence alignment methods with T-Coffee

We introduce M-Coffee, a meta-method for assembling multiple sequence alignments (MSA) by combining the output of several individual methods into one single MSA. M-Coffee is an extension of T-Coffee and uses consistency to estimate a consensus alignment. We show that the procedure is robust to variations in the choice of constituent methods and reasonably tolerant to duplicate MSAs. We also show that performances can be improved by carefully selecting the constituent methods. M-Coffee outperforms all the individual methods on three major reference datasets: HOMSTRAD, Prefab and Balibase. We also show that on a case-by-case basis, M-Coffee is twice as likely to deliver the best alignment than any individual method. Given a collection of pre-computed MSAs, M-Coffee has similar CPU requirements to the original T-Coffee. M-Coffee is a freeware open-source package available from http://www.tcoffee.org/.     

Ribosomal RNA as molecular barcodes: a simple correlation analysis without sequence alignment

MOTIVATION: We explored the feasibility of using unaligned rRNA gene sequences as DNA barcodes, based on correlation analysis of composition vectors (CVs) derived from nucleotide strings. We tested this method with seven rRNA (including 12, 16, 18, 26 and 28S) datasets from a wide variety of organisms (from archaea to tetrapods) at taxonomic levels ranging from class to species. RESULT: Our results indicate that grouping of taxa based on CV analysis is always in good agreement with the phylogenetic trees generated by traditional approaches, although in some cases the relationships among the higher systemic groups may differ. The effectiveness of our analysis might be related to the length and divergence among sequences in a dataset. Nevertheless, the correct grouping of sequences and accurate assignment of unknown taxa make our analysis a reliable and convenient approach in analyzing unaligned sequence datasets of various rRNAs for barcoding purposes. AVAILABILITY: The newly designed software (CVTree 1.0) is publicly available at the Composition Vector Tree (CVTree) web server http://cvtree.cbi.pku.edu.cn.     

MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform

A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.     

Average values of a dissimilarity measure not requiring sequence alignment are twice the averages of conventional mismatch counts requiring sequence alignment for a computer-generated model system

Summary Three measures of sequence dissimilarity have been compared on a computer-generated model system in which substitutions in random sequences were made at randomly selected sites and the replacement character was chosen at random from the set of characters different from the original occupant of the site. The three measures were the conventionalmmismatch count between aligned sequences (AMC=m) and two measures not requiring prior sequence alignment. The latter two measures were the squared Euclidean distance between vectors of counts of t-tuples (t=1–6) of characters in the two sequences (multiplet distribution distances or MDD=d) and counts of characters not covered by word structures of statistically significant length common to the two sequences (common long words or CLW=SIB, SIS, or SAB). Average MDD distances were found to be two times average mismatch counts in the simulated sequences for all values of t from 1 to 6 and all degrees of substitution from one per sequence to so many as to produce, effectively, random sequences. This simple relation held independently of sequence length and of sequence composition. The relation was confirmed by exact results on small model systems and by formal asymptotic results in the limit of so few substitutions that no double hits occur and in the limit of two random sequences. The coefficient of variation for MDD distances was greater than that for mismatch counts for singlets but both measures approached the same low value for sextets. Needleman-Wunsch alignment produced incorrect mismatch counts at higher degrees of substitution. The model satisfied the conditions for the derivation of the Jukes-Cantor asymptotic adjustment, but its application produced increasingly bad results with increasing degrees of substitution in accord with earlier results on model and natural sequences. This fact was a consequence of the increase with increasing degrees of substitution of the sensitivity of the adjustment to error in the observations. Average CLW distances for a variety of common word structures were more or less parallel to MDD distances for appropriately long t-tuples. These results on model systems supported the validity of the two dissimilarity measures not requiring sequence alignment that was found in earlier work on natural sequences (Blaisdell 1989).     

Efficient filtering methods for clustering cDNAs with spliced sequence alignment

MOTIVATION: Clustering sequences of a full-length cDNA library into alternative splice form candidates is a very important problem. RESULTS: We developed a new efficient algorithm to cluster sequences of a full-length cDNA library into alternative splice form candidates. Current clustering algorithms for cDNAs tend to produce too many clusters containing incorrect splice form candidates. Our algorithm is based on a spliced sequence alignment algorithm that considers splice sites. The spliced sequence alignment algorithm is a variant of an ordinary dynamic programming algorithm, which requires O(nm) time for checking a pair of sequences where n and m are the lengths of the two sequences. Since the time bound is too large to perform all-pair comparison for a large set of sequences, we developed new techniques to reduce the computation time without affecting the accuracy of the output clusters. Our algorithm was applied to 21 076 mouse cDNA sequences of the FANTOM 1.10 database to examine its performance and accuracy. In these experiments, we achieved about 2-12-fold speedup against a method using only a traditional hash-based technique. Moreover, without using any information of the mouse genome sequence data or any gene data in public databases, we succeeded in listing 87-89% of all the clusters that biologists have annotated manually. AVAILABILITY: We provide a web service for cDNA clustering located at https://access.obigrid.org/ibm/cluspa/, for which registration for the OBIGrid (http://www.obigrid.org) is required.     

PIR-ALN: a database of protein sequence alignments.

MOTIVATION: The Protein Information Resource (PIR) maintains a database of annotated and curated alignments in order to visually represent interrelationships among sequences in the PIR-International Protein Sequence Database, to spread and standardize protein names, features and keywords among members of a family or superfamily, and to aid us in classifying sequences, in identifying conserved regions, and in defining new homology domains. RESULTS: Release 22.0, (December 1998), of the PIR-ALN database contains a total of 3806 alignments, including 1303 superfamily, 2131 family and 372 homology domain alignments. This is an appropriate dataset to develop and extract patterns, test profiles, train neural networks or build Hidden Markov Models (HMMs). These alignments can be used to standardize and spread annotation to newer members by homology, as well as to understand the modular architecture of multidomain proteins. PIR-ALN includes 529 alignments that can be used to develop patterns not represented in PROSITE, Blocks, PRINTS and Pfam databases. The ATLAS information retrieval system can be used to browse and query the PIR-ALN alignments. AVAILABILITY: PIR-ALN is currently being distributed as a single ASCII text file along with the title, member, species, superfamily and keyword indexes. The quarterly and weekly updates can be accessed via the WWW at pir.georgetown.edu. The quarterly updates can also be obtained by anonymous FTP from the PIR FTP site at NBRF.Georgetown.edu, directory [ANONYMOUS.PIR.ALIGNMENT].     

Efficient methods for multiple sequence alignment with guaranteed error bounds

Multiple string (sequence) alignment is a difficult and important problem in computational biology, where it is central in two related tasks: finding highly conserved subregions or embedded patterns of a set of biological sequences (strings of DNA, RNA or amino acids), and inferring the evolutionary history of a set of taxa from their associated biological sequences. Several precise measures have been proposed for evaluating the goodness of a multiple alignment, but no efficient methods are known which compute the optimal alignment for any of these measures in any but small cases. In this paper, we consider two previously proposed measures, and given two computationaly efficient multiple alignment methods (one for each measure) whose deviation from the optimal value isguaranteed to be less than a factor of two. This is the novel feature of these methods, but the methods have additional virtues as well. For both methods, the guaranteed bounds are much smaller than two when the number of strings is small (1.33 for three strings of any length); for one of the methods we give a related randomized method which is much faster and which gives, with high probability, multiple alignments with fairly small error bounds; and for the other measure, the method given yields a non-obviouslower bound on the value of the optimal alignment.     

Heads or tails: a simple reliability check for multiple sequence alignments

The question of multiple sequence alignment quality has received much attention from developers of alignment methods. Less forthcoming, however, are practical measures for addressing alignment quality issues in real life settings. Here, we present a simple methodology to help identify and quantify the uncertainties in multiple sequence alignments and their effects on subsequent analyses. The proposed methodology is based upon the a priori expectation that sequence alignment results should be independent of the orientation of the input sequences. Thus, for totally unambiguous cases, reversing residue order prior to alignment should yield an exact reversed alignment of that obtained by using the unreversed sequences. Such "ideal" alignments, however, are the exception in real life settings, and the two alignments, which we term the heads and tails alignments, are usually different to a greater or lesser degree. The degree of agreement or discrepancy between these two alignments may be used to assess the reliability of the sequence alignment. Furthermore, any alignment dependent sequence analysis protocol can be carried out separately for each of the two alignments, and the two sets of results may be compared with each other, providing us with valuable information regarding the robustness of the whole analytical process. The heads-or-tails (HoT) methodology can be easily implemented for any choice of alignment method and for any subsequent analytical protocol. We demonstrate the utility of HoT for phylogenetic reconstruction for the case of 130 sequences belonging to the chemoreceptor superfamily in Drosophila melanogaster, and by analysis of the BaliBASE alignment database. Surprisingly, Neighbor-Joining methods of phylogenetic reconstruction turned out to be less affected by alignment errors than maximum likelihood and Bayesian methods.     

SAD--a normalized structural alignment database: improving sequence-structure alignments

MOTIVATION: We present a structural alignment database that is specifically targeted for use in derivation and optimization of sequence-structure alignment algorithms for homology modeling. We have paid attention to ensure that fold-space is properly sampled, that the structures involved in alignments are of significant resolution (better than 2.5 A) and the alignments are accurate and reliable. RESULTS: Alignments have been taken from the HOMSTRAD, BAliBASE and SCOP-based Gerstein databases along with alignments generated by a global structural alignment method described here. In order to discriminate between equivalent alignments from these different sources, we have developed a novel scoring function, Contact Alignment Quality score, which evaluates trial alignments by their statistical significance combined with their ability to reproduce conserved three-dimensional residue contacts. The resulting non-redundant, unbiased database contains 1927 alignments from across fold-space with high-resolution structures and a wide range of sequence identities. AVAILABILITY: The database can be interactively queried either over the web at http://abagyan.scripps.edu/lab/web/sad/show.cgi or by using MySQL, and is also available to download over the web.     

A comparison of four pair-wise sequence alignment methods

Protein sequence alignment has become an essential task in modern molecular biology research. A number of alignment techniques have been documented in literature and their corresponding tools are made available as freeware and commercial software. The choice and use of these tools for sequence alignment through the complete interpretation of alignment results is often considered non-trivial by end-users with limited skill in Bioinformatics algorithm development. Here, we discuss the comparison of sequence alignment techniques based on dynamic programming (N-W, S-W) and heuristics (LFASTA, BL2SEQ) for four sets of sequence data towards an educational purpose. The analysis suggests that heuristics based methods are faster than dynamic programming methods in alignment speed.