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D G Higgins

Publications and source records attributed to D G Higgins.

At least 19 recordsLinked to original sources
PubMed2000

T-Coffee: A novel method for fast and accurate multiple sequence alignment.

We describe a new method (T-Coffee) for multiple sequence alignment that provides a dramatic improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives. The method is broadly based on the popular progressive approach to multiple alignment but avoids the most serious pitfalls caused by the greedy nature of this algorithm. With T-Coffee we pre-process a data set of all pair-wise alignments between the sequences. This provides us with a library of alignment information that can be used to guide the progressive alignment. Intermediate alignments are then based not only on the sequences to be aligned next but also on how all of the sequences align with each other. This alignment information can be derived from heterogeneous sources such as a mixture of alignment programs and/or structure superposition. Here, we illustrate the power of the approach by using a combination of local and global pair-wise alignments to generate the library. The resulting alignments are significantly more reliable, as determined by comparison with a set of 141 test cases, than any of the popular alternatives that we tried. The improvement, especially clear with the more difficult test cases, is always visible, regardless of the phylogenetic spread of the sequences in the tests.

Algorithms↗
PubMed1999

Molecular evolution of immunoglobulin and fibronectin domains in titin and related muscle proteins.

The family of regulatory and structural muscle proteins, which includes the giant kinases titin, twitchin and projectin, has sequences composed predominantly of serially linked immunoglobulin I set (Ig) and fibronectin type III (FN3) domains. This paper explores the evolutionary relationships between 16 members of this family. In titin, groups of Ig and FN3 domains are arranged in a regularly repeating pattern of seven and 11 domains. The 11-domain super-repeat has its origins in the seven-domain super-repeat and a model for the duplications which gave rise to this super-repeat is proposed. A super-repeat composed solely of immunoglobulin domains is found in the skeletal muscle isoform of titin. Twitchin and projectin, which are presumed to be orthologs, have undergone significant insertion/deletion of domains since their divergence. The common ancestry of myomesin, skelemin and M-protein is shown. The relationship between myosin binding proteins (MyBPs) C and H is confirmed, and MyBP-H is proposed to have given rise to MyBP-C by the acquisition of some titin domains.

Amino Acid Sequence↗
PubMed1998

Sequence analysis of DNA randomly amplified from the Saccharomyces cerevisiae genome.

Despite its widespread use, the molecular basis of random amplification is poorly understood. Here the basis of random amplification has been investigated by cloning and sequencing the products of a random amplification of polymorphic DNA (RAPD) amplification from Saccharomyces cerevisiae DNA. The genomic origin of the amplified products was determined by sequence comparison with the S. cerevisiae Genome Database (SGD). This allowed analysis of the degree of identity between the random primer and the primer binding sites on the genome. There was no relationship between RAPD size, GC content and relative abundance. The degree of matching between the primer and the primer binding sites increased towards the 3; end of the primer and decreased towards the 5; end. The maximum number of mismatches observed between primer and primer binding sites was never more than one between positions 1-7 of the primer. Nucleotide compositional biases were also observed upstream and downstream of the primer binding site with a marked preference for AT richness upstream of the primer binding sites and for a GC preference directly following the 3; end of the primer. These findings have important ramifications for primer design for multiplex, low stringency and degenerate polymerase chain reaction (PCR).

Base Sequence↗
PubMed1998

Empirical estimation of the reliability of ribosomal RNA alignments.

MOTIVATION: The automatic alignment of rRNA sequences can reproduce manual expert alignments with high, but not perfect, fidelity. We examine the use of empirical methods for the identification of regions of an alignment of a new sequence with an existing large alignment which can confidently be predicted to be correctly aligned. RESULTS: We show how to use a simple jack-knife procedure to derive an estimate of the reliability that is to be expected at each position of a large alignment of eukaryotic rRNA sequences. These reliabilities are then improved using measures that are specific to the input sequence. Regions where the sequence-specific reliability method performs particularly well are identified and seen to correspond with elements in the structure of the rRNA molecules that vary between species in the alignment. We also compare these reliability measures to an algorithmic alignment stability measure. AVAILABILITY: The software is available free of charge by sending an e-mail message to emmet@chah.ucc.ie. CONTACT: emmet@chah.ucc.ie

Algorithms↗
PubMed1998

Optimization of ribosomal RNA profile alignments.

MOTIVATION: Large alignments of ribosomal RNA sequences are maintained at various sites. New sequences are added to these alignments using a combination of manual and automatic methods. We examine the use of profile alignment methods for rRNA alignment and try to optimize the choice of parameters and sequence weights. RESULTS: Using a large alignment of eukaryotic SSU rRNA sequences as a test case, we empirically compared the performance of various sequence weighting schemes over a range of gap penalties. We developed a new weighting scheme which gives most weight to the sequences in the profile that are most similar to the new sequence. We show that it gives the most accurate alignments when combined with a more traditional sequence weighting scheme. AVAILABILITY: The source code of all software is freely available by anonymous ftp from chah.ucc.ie in the directory /home/ftp/pub/emmet,in the compressed file PRNAA.tar: CONTACT: emmet@chah.ucc.ie, des@chah.ucc.ie

Animals↗
PubMed1998

COFFEE: an objective function for multiple sequence alignments.

MOTIVATION: In order to increase the accuracy of multiple sequence alignments, we designed a new strategy for optimizing multiple sequence alignments by genetic algorithm. We named it COFFEE (Consistency based Objective Function For alignmEnt Evaluation). The COFFEE score reflects the level of consistency between a multiple sequence alignment and a library containing pairwise alignments of the same sequences. RESULTS: We show that multiple sequence alignments can be optimized for their COFFEE score with the genetic algorithm package SAGA. The COFFEE function is tested on 11 test cases made of structural alignments extracted from 3D_ali. These alignments are compared to those produced using five alternative methods. Results indicate that COFFEE outperforms the other methods when the level of identity between the sequences is low. Accuracy is evaluated by comparison with the structural alignments used as references. We also show that the COFFEE score can be used as a reliability index on multiple sequence alignments. Finally, we show that given a library of structure-based pairwise sequence alignments extracted from FSSP, SAGA can produce high-quality multiple sequence alignments. The main advantage of COFFEE is its flexibility. With COFFEE, any method suitable for making pairwise alignments can be extended to making multiple alignments. AVAILABILITY: The package is available along with the test cases through the WWW: http://www. ebi.ac.uk/cedric CONTACT: cedric.notredame@ebi.ac.uk

Algorithms↗
PubMed1997

The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools.

CLUSTAL X is a new windows interface for the widely-used progressive multiple sequence alignment program CLUSTAL W. The new system is easy to use, providing an integrated system for performing multiple sequence and profile alignments and analysing the results. CLUSTAL X displays the sequence alignment in a window on the screen. A versatile sequence colouring scheme allows the user to highlight conserved features in the alignment. Pull-down menus provide all the options required for traditional multiple sequence and profile alignment. New features include: the ability to cut-and-paste sequences to change the order of the alignment, selection of a subset of the sequences to be realigned, and selection of a sub-range of the alignment to be realigned and inserted back into the original alignment. Alignment quality analysis can be performed and low-scoring segments or exceptional residues can be highlighted. Quality analysis and realignment of selected residue ranges provide the user with a powerful tool to improve and refine difficult alignments and to trap errors in input sequences. CLUSTAL X has been compiled on SUN Solaris, IRIX5.3 on Silicon Graphics, Digital UNIX on DECstations, Microsoft Windows (32 bit) for PCs, Linux ELF for x86 PCs, and Macintosh PowerMac.

Algorithms↗
PubMed1997

RAGA: RNA sequence alignment by genetic algorithm.

We describe a new approach for accurately aligning two homologous RNA sequences when the secondary structure of one of them is known. To do so we developed two software packages, called RAGA and PRAGA, which use a genetic algorithm approach to optimize the alignments. RAGA is mainly an extension of SAGA, an earlier package for multiple protein sequence alignment. In PRAGA several genetic algorithms run in parallel and exchange individual solutions. This method allows us to optimize an objective function that describes the quality of a RNA pairwise alignment, taking into account both primary and secondary structure, including pseudoknots. We report results obtained using PRAGA on nine test cases of pairs of eukaryotic small subunit rRNA sequence (nuclear and mitochondrial).

Algorithms↗
PubMed1996

SAGA: sequence alignment by genetic algorithm.

We describe a new approach to multiple sequence alignment using genetic algorithms and an associated software package called SAGA. The method involves evolving a population of alignments in a quasi evolutionary manner and gradually improving the fitness of the population as measured by an objective function which measures multiple alignment quality. SAGA uses an automatic scheduling scheme to control the usage of 22 different operators for combining alignments or mutating them between generations. When used to optimise the well known sums of pairs objective function, SAGA performs better than some of the widely used alternative packages. This is seen with respect to the ability to achieve an optimal solution and with regard to the accuracy of alignment by comparison with reference alignments based on sequences of known tertiary structure. The general attraction of the approach is the ability to optimise any objective function that one can invent.

Algorithms↗
PubMed1996

Using CLUSTAL for multiple sequence alignments.

We have tested CLUSTAL W in a wide variety of situations, and it is capable of handling some very difficult protein alignment problems. If the data set consists of enough closely related sequences so that the first alignments are accurate, then CLUSTAL W will usually find an alignment that is very close to ideal. Problems can still occur if the data set includes sequences of greatly different lengths or if some sequences include long regions that are impossible to align with the rest of the data set. Trying to balance the need for long insertions and deletions in some alignments with the need to avoid them in others is still a problem. The default values for our parameters were tested empirically using test cases of sets of globular proteins where some information as to the correct alignment was available. The parameter values may not be very appropriate with nonglobular proteins. We have argued that using one weight matrix and two gap penalties is too simplistic to be of general use in the most difficult cases. We have replaced these parameters with a large number of new parameters designed primarily to help encourage gaps in loop regions. Although these new parameters are largely heuristic in nature, they perform surprisingly well and are simple to implement. The underlying speed of the progressive alignment approach is not adversely affected. The disadvantage is that the parameter space is now huge; the number of possible combinations of parameters is more than can easily be examined by hand. We justify this by asking the user to treat CLUSTAL W as a data exploration tool rather than as a definitive analysis method. It is not sensible to automatically derive multiple alignments and to trust particular algorithms as being capable of always getting the correct answer. One must examine the alignments closely, especially in conjunction with the underlying phylogenetic tree (or estimate of it) and try varying some of the parameters. Outliers (sequences that have no close relatives) should be aligned carefully, as should fragments of sequences. The program will automatically delay the alignment of any sequences that are less than 40% identical to any others until all other sequences are aligned, but this can be set from a menu by the user. It may be useful to build up an alignment of closely related sequences first and to then add in the more distant relatives one at a time or in batches, using the profile alignments and weighting scheme described earlier and perhaps using a variety of parameter settings. We give one example using SH2 domains. SH2 domains are widespread in eukaryotic signalling proteins where they function in the recognition of phosphotyrosine-containing peptides. In the chapter by Bork and Gibson ([11], this volume), Blast and pattern/profile searches were used to extract the set of known SH2 domains and to search for new members. (Profiles used in database searches are conceptually very similar to the profiles used in CLUSTAL W: see the chapters [11] and [13] for profile search methods.) The profile searches detected SH2 domains in the JAK family of protein tyrosine kinases, which were thought not to contain SH2 domains. Although the JAK family SH2 domains are rather divergent, they have the necessary core structural residues as well as the critical positively charged residue that binds phosphotyrosine, leaving no doubt that they are bona fide SH2 domains. The five new JAK family SH2 domains were added sequentially to the existing alignment of 65 SH2 domains using the CLUSTAL W profile alignment option. Figure 6 shows part of the resulting alignment. Despite their divergent sequences, the new SH2 domains have been aligned nearly perfectly with the old set. No insertions were placed in the original SH2 domains. In this example, the profile alignment procedure has produced better results than a one-step full alignment of all 70 SH2 domains, and in considerably less time. (ABSTRACT TRUNCATED)

Amino Acid Sequence↗
PubMed1995

Finding flexible patterns in unaligned protein sequences.

We present a new method for the identification of conserved patterns in a set of unaligned related protein sequences. It is able to discover patterns of a quite general form, allowing for both ambiguous positions and for variable length wildcard regions. It allows the user to define a class of patterns (e.g., the degree of ambiguity allowed and the length and number of gaps), and the method is then guaranteed to find the conserved patterns in this class scoring highest according to a significance measure defined. Identified patterns may be refined using one of two new algorithms. We present a new (nonstatistical) significance measure for flexible patterns. The method is shown to recover known motifs for PROSITE families and is also applied to some recently described families from the literature.

Algorithms↗
PubMed1994

CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.

Algorithms↗