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PubMed2004

Analysis of multiple genomic sequence alignments: a web resource, online tools, and lessons learned from analysis of mammalian SCL loci.

Comparative analysis of genomic sequences is becoming a standard technique for studying gene regulation. However, only a limited number of tools are currently available for the analysis of multiple genomic sequences. An extensive data set for the testing and training of such tools is provided by the SCL gene locus. Here we have expanded the data set to eight vertebrate species by sequencing the dog SCL locus and by annotating the dog and rat SCL loci. To provide a resource for the bioinformatics community, all SCL sequences and functional annotations, comprising a collation of the extensive experimental evidence pertaining to SCL regulation, have been made available via a Web server. A Web interface to new tools specifically designed for the display and analysis of multiple sequence alignments was also implemented. The unique SCL data set and new sequence comparison tools allowed us to perform a rigorous examination of the true benefits of multiple sequence comparisons. We demonstrate that multiple sequence alignments are, overall, superior to pairwise alignments for identification of mammalian regulatory regions. In the search for individual transcription factor binding sites, multiple alignments markedly increase the signal-to-noise ratio compared to pairwise alignments.

Animals↗

PubMed1995

Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments.

Recently Yi & Lander used a neural network and nearest-neighbor method with a scoring system that combined a sequence-similarity matrix with the local structural environment scoring scheme described by Bowie and co-workers for predicting protein secondary structure. We have improved their scoring system by taking into consideration N and C-terminal positions of alpha-helices and beta-strands and also beta-turns as distinctive types of secondary structure. Another improvement, which also decreases the time of computation, is performed by restricting a data base with a smaller subset of proteins that are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision procedure our method reaches a sustained overall three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural-network approach, tested on the same data set of 126 non-homologous protein chains.

Algorithms↗

PubMed2000

Finite-temperature sequence alignment.

We develop a statistical theory of probabilistic sequence alignments derived from a 'thermodynamic' partition function at finite temperature. Such alignments are a generalization of those obtained from information-theoretic approaches. Finite-temperature statistics can be used to characterize the significance of an alignment and the reliability of its single element pairs.

Algorithms↗

PubMed2002

Recent progress in multiple sequence alignment: a survey.

The assembly of a multiple sequence alignment (MSA) has become one of the most common tasks when dealing with sequence analysis. Unfortunately, the wide range of available methods and the differences in the results given by these methods makes it hard for a non-specialist to decide which program is best suited for a given purpose. In this review we briefly describe existing techniques and expose the potential strengths and weaknesses of the most widely used multiple alignment packages.

Algorithms↗

PubMed1997

Generating benchmarks for multiple sequence alignments and phylogenetic reconstructions.

We present a new probabilistic model of evolution of RNA-, DNA-, or protein-like sequences and a tool rose that implements this model. By insertion, deletion and substitution of characters, a family of sequences is created from a common ancestor. During this artificial evolutionary process, the "true" history is logged and the "correct" multiple sequence alignment is created simultaneously. We also allow for varying rates of mutation within the sequences making it possible to establish so-called sequence motifs. The results are suitable for the evaluation of methods in multiple sequence alignment computation and the prediction of phylogenetic relationships.

Amino Acid Sequence↗

PubMed1997

Embedding strategies for effective use of information from multiple sequence alignments.

We describe a new strategy for utilizing multiple sequence alignment information to detect distant relationships in searches of sequence databases. A single sequence representing a protein family is enriched by replacing conserved regions with position-specific scoring matrices (PSSMs) or consensus residues derived from multiple alignments of family members. In comprehensive tests of these and other family representations, PSSM-embedded queries produced the best results overall when used with a special version of the Smith-Waterman searching algorithm. Moreover, embedding consensus residues instead of PSSMs improved performance with readily available single sequence query searching programs, such as BLAST and FASTA. Embedding PSSMs or consensus residues into a representative sequence improves searching performance by extracting multiple alignment information from motif regions while retaining single sequence information where alignment is uncertain.

Algorithms↗

PubMed2002

Scoring pairwise genomic sequence alignments.

The parameters by which alignments are scored can strongly affect sensitivity and specificity of alignment procedures. While appropriate parameter choices are well understood for protein alignments, much less is known for genomic DNA sequences. We describe a straightforward approach to scoring nucleotide substitutions in genomic sequence alignments, especially human-mouse comparisons. Scores are obtained from relative frequencies of aligned nucleotides observed in alignments of non-coding, non-repetitive genomic regions, and can be theoretically motivated through substitution models. Additional accuracy can be attained by down-weighting alignments characterized by low compositional complexity. We also describe an evaluation protocol that is relevant when alignments are intended to identify all and only the orthologous positions. One particular scoring matrix, called HOXD70, has proven to be generally effective for human-mouse comparisons, and has been used by the PipMaker server since July, 2000. We discuss but leave open the problem of effectively scoring regions of strongly biased nucleotide composition, such as low G + C content.

Base Pairing↗

PubMed1996

A general method for fast multiple sequence alignment.

We have developed a fast heuristic algorithm for multiple sequence alignment which provides near-to-optimal results for sufficiently homologous sequences. The algorithm makes use of the standard dynamic programming procedure by applying it to all pairs of sequences. The resulting score matrices for pair-wise alignment give rise to secondary matrices containing the additional charges imposed by forcing the alignment path to run through a particular vertex. Such a constraint corresponds to slicing the sequences at the positions defining that vertex, and aligning the remaining pairs of prefix and suffix sequences separately. From these secondary matrices, one can compute-for any given family of sequences-suitable positions for cutting all of these sequences simultaneously, thus reducing the problem of aligning a family of n sequences of average length l in a Divide and Conquer fashion to aligning two families of n sequences of approximately half that length. In this paper, we explain the method for the case of 3 sequences in detail, and we demonstrate its potential and its limits by discussing its behaviour for several test families. A generalization for aligning more than 3 sequences is lined out, and some actual alignments constructed by our algorithm for various user-defined parameters are presented.

Algorithms↗

PubMed1996

Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments.

The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the three secondary structures alpha-helix, beta-strand, and coil. The networks are designed using a priori knowledge of amino acid properties with respect to the secondary structure and the characteristic periodicity in alpha-helices. Since these single-structure networks all have less than 600 adjustable weights, overfitting is avoided. To obtain a three-state prediction of alpha-helix, beta-strand, or coil, ensembles of single-structure networks are combined with another neural network. This method gives an overall prediction accuracy of 66.3% when using 7-fold cross-validation on a database of 126 nonhomologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins increases the prediction accuracy significantly to 71.3% with corresponding Matthew's correlation coefficients C alpha = 0.59, C beta = 0.52, and Cc = 0.50. More than 72% of the residues in the database are predicted with an accuracy of 80%. It is shown that the network outputs can be interpreted as estimated probabilities of correct prediction, and, therefore, these numbers indicate which residues are predicted with high confidence.

Amino Acid Sequence↗

PubMed1993

Protein sequence alignments: a strategy for the hierarchical analysis of residue conservation.

An algorithm is described for the systematic characterization of the physico-chemical properties seen at each position in a multiple protein sequence alignment. The new algorithm allows questions important in the design of mutagenesis experiments to be quickly answered since positions in the alignment that show unusual or interesting residue substitution patterns may be rapidly identified. The strategy is based on a flexible set-based description of amino acid properties, which is used to define the conservation between any group of amino acids. Sequences in the alignment are gathered into subgroups on the basis of sequence similarity, functional, evolutionary or other criteria. All pairs of subgroups are then compared to highlight positions that confer the unique features of each subgroup. The algorithm is encoded in the computer program AMAS (Analysis of Multiply Aligned Sequences) which provides a textual summary of the analysis and an annotated (boxed, shaded and/or coloured) multiple sequence alignment. The algorithm is illustrated by application to an alignment of 67 SH2 domains where patterns of conserved hydrophobic residues that constitute the protein core are highlighted. The analysis of charge conservation across annexin domains identifies the locations at which conserved charges change sign. The algorithm simplifies the analysis of multiple sequence data by condensing the mass of information present, and thus allows the rapid identification of substitutions of structural and functional importance.

Algorithms↗

PubMed2001

A new approach to sequence comparison: normalized sequence alignment.

The Smith-Waterman algorithm for local sequence alignment is one of the most important techniques in computational molecular biology. This ingenious dynamic programming approach was designed to reveal the highly conserved fragments by discarding poorly conserved initial and terminal segments. However, the existing notion of local similarity has a serious flaw: it does not discard poorly conserved intermediate segments. The Smith-Waterman algorithm finds the local alignment with maximal score but it is unable to find local alignment with maximum degree of similarity (e.g. maximal percent of matches). Moreover, there is still no efficient algorithm that answers the following natural question: do two sequences share a (sufficiently long) fragment with more than 70% of similarity? As a result, the local alignment sometimes produces a mosaic of well-conserved fragments artificially connected by poorly-conserved or even unrelated fragments. This may lead to problems in comparison of long genomic sequences and comparative gene prediction as recently pointed out by Zhang et al. (Bioinformatics, 15, 1012-1019, 1999). In this paper we propose a new sequence comparison algorithm (normalized local alignment ) that reports the regions with maximum degree of similarity. The algorithm is based on fractional programming and its running time is O(n2log n). In practice, normalized local alignment is only 3-5 times slower than the standard Smith-Waterman algorithm.

Algorithms↗

PubMed1993

CEDIT: a C interface and macro facility for protein sequence alignment editing in colour with Microsoft Word 5.0 for PCs.

CEDIT, a C interface and macro facility that provides for the colour editing of protein sequence alignments (up to 2000 sequences, 5000 residues each) using Microsoft Word 5.0 for PCs is presented. CEDIT uses the ability of MS-Word 5.0 to display letters with the desired colour to easily identify conservative homologies across the sequences. A glossary file with useful macros for the sequence editing is provided, along with several utilities programs for error checking, estimating sequence similarities and homology significance. CEDIT has a menu interface and a context sensitive help.

Amino Acid Sequence↗

PubMed1998

Easy method to predict solvent accessibility from multiple protein sequence alignments.

An easy and uncomplicated method to predict the solvent accessibility state of a site in a multiple protein sequence alignment is described. The approach is based on amino acid exchange and compositional preference matrices for each of three accessibility states: buried, exposed, and intermediate. Calculations utilized a modified version of the 3D_ali databank, a collection of multiple sequence alignments anchored through protein tertiary structural superpositions. The technique achieves the same accuracy as much more complex methods and thus provides such advantages as computational affordability, facile updating, and easily understood residue substitution patterns useful to biochemists involved in protein engineering, design, and structural prediction. The program is available from the authors; and, due to its simplicity, the algorithm can be readily implemented on any system. For a given alignment site, a hand calculation can yield a comparative prediction.

Amino Acid Sequence↗

PubMed2003

Hidden Markov models and optimized sequence alignments.

We present a formulation of the Needleman-Wunsch type algorithm for sequence alignment in which the mutation matrix is allowed to vary under the control of a hidden Markov process. The fully trainable model is applied to two problems in bioinformatics: the recognition of related gene/protein names and the alignment and scoring of homologous proteins.

Animals↗

PubMed1999

ESPript: analysis of multiple sequence alignments in PostScript.

MOTIVATION: The program ESPript (Easy Sequencing in PostScript) allows the rapid visualization, via PostScript output, of sequences aligned with popular programs such as CLUSTAL-W or GCG PILEUP. It can read secondary structure files (such as that created by the program DSSP) to produce a synthesis of both sequence and structural information. RESULTS: ESPript can be run via a command file or a friendly html-based user interface. The program calculates an homology score by columns of residues and can sort this calculation by groups of sequences. It offers a palette of markers to highlight important regions in the alignment. ESPript can also paste information on residue conservation into coordinate files, for subsequent visualization with a graphics program. AVAILABILITY: ESPript can be accessed on its Web site at http://www.ipbs.fr/ESPript. Sources and helpfiles can be downloaded via anonymous ftp from ftp.ipbs.fr. A tar file is held in the directory pub/ESPript.

Amino Acid Sequence↗

PubMed2002

Multiple sequence alignment with arbitrary gap costs: computing an optimal solution using polyhedral combinatorics.

Multiple sequence alignment is one of the dominant problems in computational molecular biology. Numerous scoring functions and methods have been proposed, most of which result in NP-hard problems. In this paper we propose for the first time a general formulation for multiple alignment with arbitrary gap-costs based on an integer linear program (ILP). In addition we describe a branch-and-cut algorithm to effectively solve the ILP to optimality. We evaluate the performances of our approach in terms of running time and quality of the alignments using the BAliBase database of reference alignments. The results show that our implementation ranks amongst the best programs developed so far.

Algorithms↗

PubMed2002

Use of covariance analysis for the prediction of structural domain boundaries from multiple protein sequence alignments.

Current methods for identification of domains within protein sequences require either structural information or the identification of homologous domain sequences in different sequence contexts. Knowledge of structural domain boundaries is important for fold recognition experiments and structural determination by X-ray crystallography or nuclear magnetic resonance spectroscopy using the divide-and-conquer approach. Here, a new and conceptually simple method for the identification of structural domain boundaries in multiple protein sequence alignments is presented. Analysis of covariance at positions within the alignment is first used to predict 3D contacts. By the nature of the domain as an independent folding unit, inter-domain predicted contacts are fewer than intra-domain predicted contacts. By analysing all possible domain boundaries and constructing a smoothed profile of predicted contact density (PCD), true structural domain boundaries are predicted as local profile minima associated with low PCD. A training data set is constructed from 52 non-homologous two-domain protein sequences of known 3D structure and used to determine optimal parameters for the profile analysis. The alignments in the training data set contained 48 +/- 17 (mean +/- SD) sequences and lengths of 257 +/- 121 residues. Of the 47 alignments yielding predictions, 35% of true domain boundaries are predicted to within 15 amino acids by the local profile minimum with the lowest profile value. Including predictions from the second- and third-lowest local minima increases the correct domain boundary coverage to 60%, whereas the lowest five local minima cover 79% of correct domain boundaries. Through further profile analysis, criteria are presented which reliably identify subsets of more accurate predictions. Retrospective analysis of CASP3 targets shows predictions of sufficient accuracy to enable dramatically improved fold recognition results. Finally, a prediction is made for geminivirus AL1 protein which is in full agreement with biochemical data, yielding a plausible, novel threading result.

DNA-Binding Proteins↗

PubMed2000

Analysis and prediction of functional sub-types from protein sequence alignments.

The increasing number and diversity of protein sequence families requires new methods to define and predict details regarding function. Here, we present a method for analysis and prediction of functional sub-types from multiple protein sequence alignments. Given an alignment and set of proteins grouped into sub-types according to some definition of function, such as enzymatic specificity, the method identifies positions that are indicative of functional differences by comparison of sub-type specific sequence profiles, and analysis of positional entropy in the alignment. Alignment positions with significantly high positional relative entropy correlate with those known to be involved in defining sub-types for nucleotidyl cyclases, protein kinases, lactate/malate dehydrogenases and trypsin-like serine proteases. We highlight new positions for these proteins that suggest additional experiments to elucidate the basis of specificity. The method is also able to predict sub-type for unclassified sequences. We assess several variations on a prediction method, and compare them to simple sequence comparisons. For assessment, we remove close homologues to the sequence for which a prediction is to be made (by a sequence identity above a threshold). This simulates situations where a protein is known to belong to a protein family, but is not a close relative of another protein of known sub-type. Considering the four families above, and a sequence identity threshold of 30 %, our best method gives an accuracy of 96 % compared to 80 % obtained for sequence similarity and 74 % for BLAST. We describe the derivation of a set of sub-type groupings derived from an automated parsing of alignments from PFAM and the SWISSPROT database, and use this to perform a large-scale assessment. The best method gives an average accuracy of 94 % compared to 68 % for sequence similarity and 79 % for BLAST. We discuss implications for experimental design, genome annotation and the prediction of protein function and protein intra-residue distances.

Adenylyl Cyclases↗