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PubMed2005

Improvement of TRANSFAC matrices using multiple local alignment of transcription factor binding site sequences.

This paper describes a novel approach to constructing Position-Specific Weight Matrices (PWMs) based on the transcription factor binding site (TFBS) data provide by the TRANSFAC database and comparison of the newly generated PWMs with the original TRANSFAC matrices. Multiple local sequence alignment was performed on the TFBSs of each transcription factor. Several different alignment programs were tested and their matrices were compared to the original TRANSFAC matrices. One of the alignment programs, GLAM, produced comparable matrices in terms of the average ranking of true positive sites across the whole test set of sequences.

Algorithms↗
PubMed2006

Dynamic use of multiple parameter sets in sequence alignment.

The level of conservation between two homologous sequences often varies among sequence regions; functionally important domains are more conserved than the remaining regions. Thus, multiple parameter sets should be used in alignment of homologous sequences with a stringent parameter set for highly conserved regions and a moderate parameter set for weakly conserved regions. We describe an alignment algorithm to allow dynamic use of multiple parameter sets with different levels of stringency in computation of an optimal alignment of two sequences. The algorithm dynamically considers various candidate alignments, partitions each candidate alignment into sections, and determines the most appropriate set of parameter values for each section of the alignment. The algorithm and its local alignment version are implemented in a computer program named GAP4. The local alignment algorithm in GAP4, that in its predecessor GAP3, and an ordinary local alignment program SIM were evaluated on 257,716 pairs of homologous sequences from 100 protein families. On 168,475 of the 257,716 pairs (a rate of 65.4%), alignments from GAP4 were more statistically significant than alignments from GAP3 and SIM.

Algorithms↗
PubMed2006

ARCS: an aggregated related column scoring scheme for aligned sequences.

MOTIVATION: Biologists frequently align multiple biological sequences to determine consensus sequences and/or search for predominant residues and conserved regions. Particularly, determining conserved regions in an alignment is one of the most important activities. Since protein sequences are often several-hundred residues or longer, it is difficult to distinguish biologically important conserved regions (motifs or domains) from others. The widely used tools, Logos, Al2co, Confind, and the entropy-based method, often fail to highlight such regions. Thus a computational tool that can highlight biologically important regions accurately will be highly desired. RESULTS: This paper presents a new scoring scheme ARCS (Aggregated Related Column Score) for aligned biological sequences. ARCS method considers not only the traditional character similarity measure but also column correlation. In an extensive experimental evaluation using 533 PROSITE patterns, ARCS is able to highlight the motif regions with up to 77.7% accuracy corresponding to the top three peaks. AVAILABILITY: The source code is available on http://bio.informatics.indiana.edu/projects/arcs and http://goldengate.case.edu/projects/arcs

Algorithms↗
PubMed2005

PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny.

A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This analysis is complicated by various factors. First, one needs to take the phylogenetic relationship between the species into account in order to distinguish conservation that is due to the occurrence of functional sites from spurious conservation that is due to evolutionary proximity. Second, one has to deal with the complexities of multiple alignments of orthologous intergenic regions, and one has to consider the possibility that functional sites may occur outside of conserved segments. Here we present a new motif sampling algorithm, PhyloGibbs, that runs on arbitrary collections of multiple local sequence alignments of orthologous sequences. The algorithm searches over all ways in which an arbitrary number of binding sites for an arbitrary number of transcription factors (TFs) can be assigned to the multiple sequence alignments. These binding site configurations are scored by a Bayesian probabilistic model that treats aligned sequences by a model for the evolution of binding sites and "background" intergenic DNA. This model takes the phylogenetic relationship between the species in the alignment explicitly into account. The algorithm uses simulated annealing and Monte Carlo Markov-chain sampling to rigorously assign posterior probabilities to all the binding sites that it reports. In tests on synthetic data and real data from five Saccharomyces species our algorithm performs significantly better than four other motif-finding algorithms, including algorithms that also take phylogeny into account. Our results also show that, in contrast to the other algorithms, PhyloGibbs can make realistic estimates of the reliability of its predictions. Our tests suggest that, running on the five-species multiple alignment of a single gene's upstream region, PhyloGibbs on average recovers over 50% of all binding sites in S. cerevisiae at a specificity of about 50%, and 33% of all binding sites at a specificity of about 85%. We also tested PhyloGibbs on collections of multiple alignments of intergenic regions that were recently annotated, based on ChIP-on-chip data, to contain binding sites for the same TF. We compared PhyloGibbs's results with the previous analysis of these data using six other motif-finding algorithms. For 16 of 21 TFs for which all other motif-finding methods failed to find a significant motif, PhyloGibbs did recover a motif that matches the literature consensus. In 11 cases where there was disagreement in the results we compiled lists of known target genes from the literature, and found that running PhyloGibbs on their regulatory regions yielded a binding motif matching the literature consensus in all but one of the cases. Interestingly, these literature gene lists had little overlap with the targets annotated based on the ChIP-on-chip data. The PhyloGibbs code can be downloaded from http://www.biozentrum.unibas.ch/~nimwegen/cgi-bin/phylogibbs.cgi or http://www.imsc.res.in/~rsidd/phylogibbs. The full set of predicted sites from our tests on yeast are available at http://www.swissregulon.unibas.ch.

Algorithms↗
PubMed2005

PriFi: using a multiple alignment of related sequences to find primers for amplification of homologs.

Using a comparative approach, the web program PriFi (http://cgi-www.daimi.au.dk/cgi-chili/PriFi/main) designs pairs of primers useful for PCR amplification of genomic DNA in species where prior sequence information is not available. The program works with an alignment of DNA sequences from phylogenetically related species and outputs a list of possibly degenerate primer pairs fulfilling a number of criteria, such that the primers have a maximal probability of amplifying orthologous sequences in other phylogenetically related species. Operating on a genome-wide scale, PriFi automates the first steps of a procedure for developing general markers serving as common anchor loci across species. To accommodate users with special preferences, configuration settings and criteria can be customized.

DNA Primers↗
PubMed2005

MARNA: multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons.

MOTIVATION: Due to the importance of considering secondary structures in aligning functional RNAs, several pairwise sequence-structure alignment methods have been developed. They use extended alignment scores that evaluate secondary structure information in addition to sequence information. However, two problems for the multiple alignment step remain. First, how to combine pairwise sequence-structure alignments into a multiple alignment and second, how to generate secondary structure information for sequences whose explicit structural information is missing. RESULTS: We describe a novel approach for multiple alignment of RNAs (MARNA) taking into consideration both the primary and the secondary structures. It is based on pairwise sequence-structure comparisons of RNAs. From these sequence-structure alignments, libraries of weighted alignment edges are generated. The weights reflect the sequential and structural conservation. For sequences whose secondary structures are missing, the libraries are generated by sampling low energy conformations. The libraries are then processed by the T-Coffee system, which is a consistency based multiple alignment method. Furthermore, we are able to extract a consensus-sequence and -structure from a multiple alignment. We have successfully tested MARNA on several datasets taken from the Rfam database.

Algorithms↗
PubMed2006

SNAP: Combine and Map modules for multilocus population genetic analysis.

We have added two software tools to our Suite of Nucleotide Analysis Programs (SNAP) for working with DNA sequences sampled from populations. SNAP Map collapses DNA sequence data into unique haplotypes, extracts variable sites and manipulates output into multiple formats for input into existing software packages for evolutionary analyses. Map collapses DNA sequence data into unique haplotypes, extracts variable sites and manipulates output into multiple formats for input into existing software packages for evolutionary analyses. Map includes novel features such as recoding insertions or deletions, including or excluding variable sites that violate an infinite-sites model and the option of collapsing sequences with corresponding phenotypic information, important in testing for significant haplotype-phenotype associations. SNAP Combine merges multiple DNA sequence alignments into a single multiple alignment file. The resulting file can be the union or intersection of the input files. SNAP Combine currently reads from and writes to several sequence alignment file formats including both sequential and interleaved formats. Combine also keeps track of the start and end positions of each separate alignment file allowing the user to exclude variable sites or taxa, important in creating input files for multilocus analyses.

Algorithms↗
PubMed1995

BEAUTY: an enhanced BLAST-based search tool that integrates multiple biological information resources into sequence similarity search results.

BEAUTY (BLAST enhanced alignment utility) is an enhanced version of the NCBI's BLAST data base search tool that facilitates identification of the functions of matched sequences. We have created new data bases of conserved regions and functional domains for protein sequences in NCBI's Entrez data base, and BEAUTY allows this information to be incorporated directly into BLAST search results. A Conserved Regions Data Base, containing the locations of conserved regions within Entrez protein sequences, was constructed by (1) clustering the entire data base into families, (2) aligning each family using our PIMA multiple sequence alignment program, and (3) scanning the multiple alignments to locate the conserved regions within each aligned sequence. A separate Annotated Domains Data Base was constructed by extracting the locations of all annotated domains and sites from sequences represented in the Entrez, PROSITE, BLOCKS, and PRINTS data bases. BEAUTY performs a BLAST search of those Entrez sequences with conserved regions and/or annotated domains. BEAUTY then uses the information from the Conserved Regions and Annotated Domains data bases to generate, for each matched sequence, a schematic display that allows one to directly compare the relative locations of (1) the conserved regions, (2) annotated domains and sites, and (3) the locally aligned regions matched in the BLAST search. In addition, BEAUTY search results include World-Wide Web hypertext links to a number of external data bases that provide a variety of additional types of information on the function of matched sequences. This convenient integration of protein families, conserved regions, annotated domains, alignment displays, and World-Wide Web resources greatly enhances the biological informativeness of sequence similarity searches. BEAUTY searches can be performed remotely on our system using the "BCM Search Launcher" World-Wide Web pages (URL is < http:/ /gc.bcm.tmc.edu:8088/ search-launcher/launcher.html > ).

Amino Acid Sequence↗
PubMed2001

Picasso: generating a covering set of protein family profiles.

MOTIVATION: Evolutionary classification leads to an economical description of protein sequence data because attributes of function and structure are inherited in protein families. This paper presents Picasso, a procedure for deriving a minimal set of protein family profiles that cover all known protein sequences. RESULTS: Picasso starts from highly overlapping sequence neighbourhoods revealed by all-on-all pairwise Blast alignment. Overlaps are reduced by merging sequences or parts of sequences into multiple alignments. For maximum unification, the multiple alignments must reach into the twilight zone of sequence similarity. Sensitive and selective profile-profile comparison allows unification down to about 15% pairwise sequence identity. Families unified through a short conserved sequence motif are associated with multiple full-length alignments describing different subfamilies. Domains that are mobile modules are identified based on their association with different sets of neighbours. The result is 10000 unified domain families (excluding singletons) representing functionally related proteins and recovering classical prolific domain types in high numbers. The classification is useful, for example, in developing strategies for efficient database searching and for selecting targets to complete the map of all 3-D structures.

Algorithms↗
PubMed2004

Finding functional sequence elements by multiple local alignment.

Algorithms that detect and align locally similar regions of biological sequences have the potential to discover a wide variety of functional motifs. Two theoretical contributions to this classic but unsolved problem are presented here: a method to determine the width of the aligned motif automatically; and a technique for calculating the statistical significance of alignments, i.e. an assessment of whether the alignments are stronger than those that would be expected to occur by chance among random, unrelated sequences. Upon exploring variants of the standard Gibbs sampling technique to optimize the alignment, we discovered that simulated annealing approaches perform more efficiently. Finally, we conduct failure tests by applying the algorithm to increasingly difficult test cases, and analyze the manner of and reasons for eventual failure. Detection of transcription factor-binding motifs is limited by the motifs' intrinsic subtlety rather than by inadequacy of the alignment optimization procedure.

Algorithms↗
PubMed2003

RevTrans: Multiple alignment of coding DNA from aligned amino acid sequences.

The simple fact that proteins are built from 20 amino acids while DNA only contains four different bases, means that the 'signal-to-noise ratio' in protein sequence alignments is much better than in alignments of DNA. Besides this information-theoretical advantage, protein alignments also benefit from the information that is implicit in empirical substitution matrices such as BLOSUM-62. Taken together with the generally higher rate of synonymous mutations over non-synonymous ones, this means that the phylogenetic signal disappears much more rapidly from DNA sequences than from the encoded proteins. It is therefore preferable to align coding DNA at the amino acid level and it is for this purpose we have constructed the program RevTrans. RevTrans constructs a multiple DNA alignment by: (i) translating the DNA; (ii) aligning the resulting peptide sequences; and (iii) building a multiple DNA alignment by 'reverse translation' of the aligned protein sequences. In the resulting DNA alignment, gaps occur in groups of three corresponding to entire codons, and analogous codon positions are therefore always lined up. These features are useful when constructing multiple DNA alignments for phylogenetic analysis. RevTrans also accepts user-provided protein alignments for greater control of the alignment process. The RevTrans web server is freely available at http://www.cbs.dtu.dk/services/RevTrans/.

Amino Acid Substitution↗
PubMed2005

Bayesian models and Markov chain Monte Carlo methods for protein motifs with the secondary characteristics.

Statistical methods have been developed for finding local patterns, also called motifs, in multiple protein sequences. The aligned segments may imply functional or structural core regions. However, the existing methods often have difficulties in aligning multiple proteins when sequence residue identities are low (e.g., less than 25%). In this article, we develop a Bayesian model and Markov chain Monte Carlo (MCMC) methods for identifying subtle motifs in protein sequences. Specifically, a motif is defined not only in terms of specific sites characterized by amino acid frequency vectors, but also as a combination of secondary characteristics such as hydrophobicity, polarity, etc. Markov chain Monte Carlo methods are proposed to search for a motif pattern with high posterior probability under the new model. A special MCMC algorithm is developed, involving transitions between state spaces of different dimensions. The proposed methods were supported by a simulated study. It was then tested by two real datasets, including a group of helix-turn-helix proteins, and one set from the CATH Protein Structure Classification Database. Statistical comparisons showed that the new approach worked better than a typical Gibbs sampling approach which is based only on an amino acid model.

Amino Acid Motifs↗
PubMed2001

CHROMA: consensus-based colouring of multiple alignments for publication.

CHROMA annotates multiple protein sequence alignments by consensus to produce formatted and coloured text suitable for incorporation into other documents for publication. The package is designed to be flexible and reliable, and has a simple-to-use graphical user interface running under Microsoft Windows. Both the executables and source code for CHROMA running under Windows and Linux (portable command-line only) are freely available at http://www.lg.ndirect.co.uk/chroma. Software enquiries should be directed to CHROMA@lg.ndirect.co.uk.

Amino Acid Sequence↗
PubMed2006

STRAL: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time.

MOTIVATION: Alignment of RNA has a wide range of applications, for example in phylogeny inference, consensus structure prediction and homology searches. Yet aligning structural or non-coding RNAs (ncRNAs) correctly is notoriously difficult as these RNA sequences may evolve by compensatory mutations, which maintain base pairing but destroy sequence homology. Ideally, alignment programs would take RNA structure into account. The Sankoff algorithm for the simultaneous solution of RNA structure prediction and RNA sequence alignment was proposed 20 years ago but suffers from its exponential complexity. A number of programs implement lightweight versions of the Sankoff algorithm by restricting its application to a limited type of structure and/or only pairwise alignment. Thus, despite recent advances, the proper alignment of multiple structural RNA sequences remains a problem. RESULTS: Here we present StrAl, a heuristic method for alignment of ncRNA that reduces sequence-structure alignment to a two-dimensional problem similar to standard multiple sequence alignment. The scoring function takes into account sequence similarity as well as up- and downstream pairing probability. To test the robustness of the algorithm and the performance of the program, we scored alignments produced by StrAl against a large set of published reference alignments. The quality of alignments predicted by StrAl is far better than that obtained by standard sequence alignment programs, especially when sequence homologies drop below approximately 65%; nevertheless StrAl's runtime is comparable to that of ClustalW.

Algorithms↗
PubMed1997

Multiple sequence threading: an analysis of alignment quality and stability.

Methods that compare a protein sequence directly to a structure can be divided into those that construct a molecular model (threading methods) and those that perform a sequence alignment with the structure encoded as a sequence of structural states (one-dimensional/three-dimensional (1D/3D) matching). The former take into account the internal packing of the molecule but the latter do not. On the other hand, it is simple to include multiple sequence data in a 1D/3D comparison but difficult in a threading method. Here, a protein sequence/structure alignment method is described that uses a combination of matching predicted and observed residue exposure, predicted and observed secondary structure (1D/3D) together with pairwise packing interactions in the core (threading). Using a variety of distantly related and analogous protein structures, the multiple sequence threading (MST) method was compared to a single sequence threading (SST) method (that uses complex potentials of mean-force) and also to a multiple sequence alignment (MSA) program. It was found that the MST method produced alignments that were better than the best that could be obtained with either the SST or MSA method. The method was found to be stable to error in both secondary structure prediction and predicted exposure and also under variation of the key parameters (fully described in an Appendix). The contribution of the pairwise term was found to be small but without it, the correct alignments were less stable and structurally unreasonable deletions were observed when matching against larger structures. Using the parameters derived for alignment, the method was able to recognise related folds in the structure databank with a specificity comparable to other methods.

Algorithms↗