Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions. In this work, we describe the use of a recent knowledge discovery from database (KDD) approach using inductive logic programming (ILP) to automatically extract knowledge about human monogenic diseases. We extracted background knowledge from MSV3d, a database of all human missense variants mapped to 3D protein structure. In this study, we identified 8,117 mutations in 805 proteins with known three-dimensional structures that were known to be involved in human monogenic disease. Our results help to improve our understanding of the relationships between structural, functional or evolutionary features and deleterious mutations. Our inferred rules can also be applied to predict the impact of any single amino acid replacement on the function of a protein. The interpretable rules are available at http://decrypthon.igbmc.fr/kd4v/.
SNP prediction; inductive logic programming; human monogenic disease; genotype-phenotype relation
The chordate proteome history database (http://ioda.univ-provence.fr) comprises some 20,000 evolutionary analyses of proteins from chordate species. Our main objective was to characterize and study the evolutionary histories of the chordate proteome, and in particular to detect genomic events and automatic functional searches. Firstly, phylogenetic analyses based on high quality multiple sequence alignments and a robust phylogenetic pipeline were performed for the whole protein and for each individual domain. Novel approaches were developed to identify orthologs/paralogs, and predict gene duplication/gain/loss events and the occurrence of new protein architectures (domain gains, losses and shuffling). These important genetic events were localized on the phylogenetic trees and on the genomic sequence. Secondly, the phylogenetic trees were enhanced by the creation of phylogroups, whereby groups of orthologous sequences created using OrthoMCL were corrected based on the phylogenetic trees; gene family size and gene gain/loss in a given lineage could be deduced from the phylogroups. For each ortholog group obtained from the phylogenetic or the phylogroup analysis, functional information and expression data can be retrieved. Database searches can be performed easily using biological objects: protein identifier, keyword or domain, but can also be based on events, eg, domain exchange events can be retrieved. To our knowledge, this is the first database that links group clustering, phylogeny and automatic functional searches along with the detection of important events occurring during genome evolution, such as the appearance of a new domain architecture.
phylogenetic reconstruction; ortholog groups; protein architecture; functional inference; family size; genome evolution
Membrane trafficking involves the complex regulation of proteins and lipids intracellular localization and is required for metabolic uptake, cell growth and development. Different trafficking pathways passing through the endosomes are coordinated by the ENTH/ANTH/VHS adaptor protein superfamily. The endosomes are crucial for eukaryotes since the acquisition of the endomembrane system was a central process in eukaryogenesis.
Our in silico analysis of this ENTH/ANTH/VHS superfamily, consisting of proteins gathered from 84 complete genomes representative of the different eukaryotic taxa, revealed that genomic distribution of this superfamily allows to discriminate Fungi and Metazoa from Plantae and Protists. Next, in a four way genome wide comparison, we showed that this discriminative feature is observed not only for other membrane trafficking effectors, but also for proteins involved in metabolism and in cytokinesis, suggesting that metabolism, cytokinesis and intracellular trafficking pathways co-evolved. Moreover, some of the proteins identified were implicated in multiple functions, in either trafficking and metabolism or trafficking and cytokinesis, suggesting that membrane trafficking is central to this co-evolution process.
Our study suggests that membrane trafficking and compartmentalization were not only key features for the emergence of eukaryotic cells but also drove the separation of the eukaryotes in the different taxa.
Membrane trafficking; Cytokinesis; Metabolism; Comparative genomic; Eukaryotic evolution; Phylogeny
A major challenge in the post-genomic era is a better understanding of how human genetic alterations involved in disease affect the gene products. The KD4v (Comprehensible Knowledge Discovery System for Missense Variant) server allows to characterize and predict the phenotypic effects (deleterious/neutral) of missense variants. The server provides a set of rules learned by Induction Logic Programming (ILP) on a set of missense variants described by conservation, physico-chemical, functional and 3D structure predicates. These rules are interpretable by non-expert humans and are used to accurately predict the deleterious/neutral status of an unknown mutation. The web server is available at http://decrypthon.igbmc.fr/kd4v.
The elucidation of the complex relationships linking genotypic and phenotypic variations to protein structure is a major challenge in the post-genomic era. We present MSV3d (Database of human MisSense Variants mapped to 3D protein structure), a new database that contains detailed annotation of missense variants of all human proteins (20 199 proteins). The multi-level characterization includes details of the physico-chemical changes induced by amino acid modification, as well as information related to the conservation of the mutated residue and its position relative to functional features in the available or predicted 3D model. Major releases of the database are automatically generated and updated regularly in line with the dbSNP (database of Single Nucleotide Polymorphism) and SwissVar releases, by exploiting the extensive Décrypthon computational grid resources. The database (http://decrypthon.igbmc.fr/msv3d) is easily accessible through a simple web interface coupled to a powerful query engine and a standard web service. The content is completely or partially downloadable in XML or flat file formats.
The data from high throughput genomics technologies provide unique opportunities for studies of complex biological systems, but also pose many new challenges. The shift to the genome scale in evolutionary biology, for example, has led to many interesting, but often controversial studies. It has been suggested that part of the conflict may be due to errors in the initial sequences. Most gene sequences are predicted by bioinformatics programs and a number of quality issues have been raised, concerning DNA sequencing errors or badly predicted coding regions, particularly in eukaryotes.
We investigated the impact of these errors on evolutionary studies and specifically on the identification of important genetic events. We focused on the detection of asymmetric evolution after duplication, which has been the subject of controversy recently. Using the human genome as a reference, we established a reliable set of 688 duplicated genes in 13 complete vertebrate genomes, where significantly different evolutionary rates are observed. We estimated the rates at which protein sequence errors occur and are accumulated in the higher-level analyses. We showed that the majority of the detected events (57%) are in fact artifacts due to the putative erroneous sequences and that these artifacts are sufficient to mask the true functional significance of the events.
Initial errors are accumulated throughout the evolutionary analysis, generating artificially high rates of event predictions and leading to substantial uncertainty in the conclusions. This study emphasizes the urgent need for error detection and quality control strategies in order to efficiently extract knowledge from the new genome data.
gene duplication; asymmetric evolution; gene prediction; error detection; quality control
Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological networks. An essential part of this process is the reconstruction and analysis of the evolutionary histories of these complex, dynamic networks. Unfortunately, the methodologies for representing and exploiting such complex evolutionary histories in large scale studies are currently limited. Here, we propose a new formalism, called EvoluCode (Evolutionary barCode), which allows the integration of different evolutionary parameters (eg, sequence conservation, orthology, synteny …) in a unifying format and facilitates the multilevel analysis and visualization of complex evolutionary histories at the genome scale. The advantages of the approach are demonstrated by constructing barcodes representing the evolution of the complete human proteome. Two large-scale studies are then described: (i) the mapping and visualization of the barcodes on the human chromosomes and (ii) automatic clustering of the barcodes to highlight protein subsets sharing similar evolutionary histories and their functional analysis. The methodologies developed here open the way to the efficient application of other data mining and knowledge extraction techniques in evolutionary systems biology studies. A database containing all EvoluCode data is available at: http://lbgi.igbmc.fr/barcodes.
systems biology; evolutionary history; multilevel data analysis; data representation; data visualization; data mining
The recent availability of the complete genome sequences of a large number of model organisms, together with the immense amount of data being produced by the new high-throughput technologies, means that we can now begin comparative analyses to understand the mechanisms involved in the evolution of the genome and their consequences in the study of biological systems. Phylogenetic approaches provide a unique conceptual framework for performing comparative analyses of all this data, for propagating information between different systems and for predicting or inferring new knowledge. As a result, phylogeny-based inference systems are now playing an increasingly important role in most areas of high throughput genomics, including studies of promoters (phylogenetic footprinting), interactomes (based on the presence and degree of conservation of interacting proteins), and in comparisons of transcriptomes or proteomes (phylogenetic proximity and co-regulation/co-expression). Here we review the recent developments aimed at making automatic, reliable phylogeny-based inference feasible in large-scale projects. We also discuss how evolutionary concepts and phylogeny-based inference strategies are now being exploited in order to understand the evolution and function of biological systems. Such advances will be fundamental for the success of the emerging disciplines of systems biology and synthetic biology, and will have wide-reaching effects in applied fields such as biotechnology, medicine and pharmacology.
phylogenetic inference; systems biology; evolutionary informatics; information network; functional annotation
The identification of single copy (1-to-1) orthologs in any group of organisms is important for functional classification and phylogenetic studies. The Metazoa are no exception, but only recently has there been a wide-enough distribution of taxa with sufficiently high quality sequenced genomes to gain confidence in the wide-spread single copy status of a gene.
Here, we present a phylogenetic approach for identifying overlooked single copy orthologs from multigene families and apply it to the Metazoa. Using 18 sequenced metazoan genomes of high quality we identified a robust set of 1,126 orthologous groups that have been retained in single copy since the last common ancestor of Metazoa. We found that the use of the phylogenetic procedure increased the number of single copy orthologs found by over a third more than standard taxon-count approaches. The orthologs represented a wide range of functional categories, expression profiles and levels of divergence.
To demonstrate the value of our set of single copy orthologs, we used them to assess the completeness of 24 currently published metazoan genomes and 62 EST datasets. We found that the annotated genes in published genomes vary in coverage from 79% (Ciona intestinalis) to 99.8% (human) with an average of 92%, suggesting a value for the underlying error rate in genome annotation, and a strategy for identifying single copy orthologs in larger datasets. In contrast, the vast majority of EST datasets with no corresponding genome sequence available are largely under-sampled and probably do not accurately represent the actual genomic complement of the organisms from which they are derived.
The correct identification of single copy (1-to-1) orthologs is crucial for functional classification of genes and for phylogenetic studies of groups of organisms, including the Metazoa. Nevertheless, despite the recent increase in the number of genomes and short sequence read datasets (e.g. ESTs) from the Metazoa, we know little about their completeness and how useful they may be for phylogenetic studies. Here we describe a novel approach for the identification of single copy gene families at any hierarchical level and demonstrate its effectiveness by identifying a set of over one thousand gene families that have been in single copy since the last common ancestor of the Metazoa. By comparing our orthologs to those predicted by other datasets we show that our procedure identifies a significantly larger set of single copy orthologs in the Metazoa. We then use this dataset to assess 24 metazoan genomes and 61 metazoan EST datasets for their completeness. We thus identify the underlying error rate in genome annotation and suggest a mechanism for assessing the quality of genomes and EST datasets in terms of their suitability for phylogenetic studies.
Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega
Multiple sequence alignments are fundamental to many sequence analysis methods. The new program Clustal Omega can align virtually any number of protein sequences quickly and has powerful features for adding sequences to existing precomputed alignments.
Multiple sequence alignments are fundamental to many sequence analysis methods. Most alignments are computed using the progressive alignment heuristic. These methods are starting to become a bottleneck in some analysis pipelines when faced with data sets of the size of many thousands of sequences. Some methods allow computation of larger data sets while sacrificing quality, and others produce high-quality alignments, but scale badly with the number of sequences. In this paper, we describe a new program called Clustal Omega, which can align virtually any number of protein sequences quickly and that delivers accurate alignments. The accuracy of the package on smaller test cases is similar to that of the high-quality aligners. On larger data sets, Clustal Omega outperforms other packages in terms of execution time and quality. Clustal Omega also has powerful features for adding sequences to and exploiting information in existing alignments, making use of the vast amount of precomputed information in public databases like Pfam.
bioinformatics; hidden Markov models; multiple sequence alignment
Multiple comparison or alignmentof protein sequences has become a fundamental tool in many different domains in modern molecular biology, from evolutionary studies to prediction of 2D/3D structure, molecular function and inter-molecular interactions etc. By placing the sequence in the framework of the overall family, multiple alignments can be used to identify conserved features and to highlight differences or specificities. In this paper, we describe a comprehensive evaluation of many of the most popular methods for multiple sequence alignment (MSA), based on a new benchmark test set. The benchmark is designed to represent typical problems encountered when aligning the large protein sequence sets that result from today's high throughput biotechnologies. We show that alignmentmethods have significantly progressed and can now identify most of the shared sequence features that determine the broad molecular function(s) of a protein family, even for divergent sequences. However,we have identified a number of important challenges. First, the locally conserved regions, that reflect functional specificities or that modulate a protein's function in a given cellular context,are less well aligned. Second, motifs in natively disordered regions are often misaligned. Third, the badly predicted or fragmentary protein sequences, which make up a large proportion of today's databases, lead to a significant number of alignment errors. Based on this study, we demonstrate that the existing MSA methods can be exploited in combination to improve alignment accuracy, although novel approaches will still be needed to fully explore the most difficult regions. We then propose knowledge-enabled, dynamic solutions that will hopefully pave the way to enhanced alignment construction and exploitation in future evolutionary systems biology studies.
The accurate determination of orthology and inparalogy relationships is essential for comparative sequence analysis, functional gene annotation and evolutionary studies. Various methods have been developed based on either simple blast all-versus-all pairwise comparisons and/or time-consuming phylogenetic tree analyses.
We have developed OrthoInspector, a new software system incorporating an original algorithm for the rapid detection of orthology and inparalogy relations between different species. In comparisons with existing methods, OrthoInspector improves detection sensitivity, with a minimal loss of specificity. In addition, several visualization tools have been developed to facilitate in-depth studies based on these predictions. The software has been used to study the orthology/in-paralogy relationships for a large set of 940,855 protein sequences from 59 different eukaryotic species.
OrthoInspector is a new software system for orthology/paralogy analysis. It is made available as an independent software suite that can be downloaded and installed for local use. Command line querying facilitates the integration of the software in high throughput processing pipelines and a graphical interface provides easy, intuitive access to results for the non-expert.
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.
Phylogenetic profiling encompasses an important set of methodologies for in silico high throughput inference of functional relationships between genes. The simplest profiles represent the distribution of gene presence-absence in a set of species as a sequence of 0's and 1's, and it is assumed that functionally related genes will have more similar profiles. The methodology has been successfully used in numerous studies of prokaryotic genomes, although its application in eukaryotes appears problematic, with reported low accuracy due to the complex genomic organization within this domain of life. Recently some groups have proposed an alternative approach based on the correlation of homologous gene group sizes, taking into account all potentially informative genetic events leading to a change in group size, regardless of whether they result in a de novo group gain or total gene group loss.
We have compared the performance of classical presence-absence and group size based approaches using a large, diverse set of eukaryotic species. In contrast to most previous comparisons in Eukarya, we take into account the species phylogeny. We also compare the approaches using two different group categories, based on orthology and on domain-sharing. Our results confirm a limited overall performance of phylogenetic profiling in eukaryotes. Although group size based approaches initially showed an increase in performance for the domain-sharing based groups, this seems to be an overestimation due to a simplistic negative control dataset and the choice of null hypothesis rejection criteria.
Presence-absence profiling represents a more accurate classifier of related versus non-related profile pairs, when the profiles under consideration have enough information content. Group size based approaches provide a complementary means of detecting domain or family level co-evolution between groups that may be elusive to presence-absence profiling. Moreover positive correlation between co-evolution scores and functional links imply that these methods could be used to estimate functional distances between gene groups and to cluster them based on their functional relatedness. This study should have important implications for the future development and application of phylogenetic profiling methods, not only in eukaryotic, but also in prokaryotic datasets.
To effectively apply evolutionary concepts in genome-scale studies, large numbers of phylogenetic trees have to be automatically analysed, at a level approaching human expertise. Complex architectures must be recognized within the trees, so that associated information can be extracted.
Here, we present a new software library, PhyloPattern, for automating tree manipulations and analysis. PhyloPattern includes three main modules, which address essential tasks in high-throughput phylogenetic tree analysis: node annotation, pattern matching, and tree comparison. PhyloPattern thus allows the programmer to focus on: i) the use of predefined or user defined annotation functions to perform immediate or deferred evaluation of node properties, ii) the search for user-defined patterns in large phylogenetic trees, iii) the pairwise comparison of trees by dynamically generating patterns from one tree and applying them to the other.
PhyloPattern greatly simplifies and accelerates the work of the computer scientist in the evolutionary biology field. The library has been used to automatically identify phylogenetic evidence for domain shuffling or gene loss events in the evolutionary histories of protein sequences. However any workflow that relies on phylogenetic tree analysis, could be automated with PhyloPattern.
Comparative analysis is used throughout biology. When entities under comparison (e.g. proteins, genomes, species) are related by descent, evolutionary theory provides a framework that, in principle, allows N-ary comparisons of entities, while controlling for non-independence due to relatedness. Powerful software tools exist for specialized applications of this approach, yet it remains under-utilized in the absence of a unifying informatics infrastructure. A key step in developing such an infrastructure is the definition of a formal ontology. The analysis of use cases and existing formalisms suggests that a significant component of evolutionary analysis involves a core problem of inferring a character history, relying on key concepts: “Operational Taxonomic Units” (OTUs), representing the entities to be compared; “character-state data” representing the observations compared among OTUs; “phylogenetic tree”, representing the historical path of evolution among the entities; and “transitions”, the inferred evolutionary changes in states of characters that account for observations. Using the Web Ontology Language (OWL), we have defined these and other fundamental concepts in a Comparative Data Analysis Ontology (CDAO). CDAO has been evaluated for its ability to represent token data sets and to support simple forms of reasoning. With further development, CDAO will provide a basis for tools (for semantic transformation, data retrieval, validation, integration, etc.) that make it easier for software developers and biomedical researchers to apply evolutionary methods of inference to diverse types of data, so as to integrate this powerful framework for reasoning into their research.
evolution; phylogeny; ontology; character data; comparative method
Linear motifs (LMs) are abundant short regulatory sites used for modulating the functions of many eukaryotic proteins. They play important roles in post-translational modification, cell compartment targeting, docking sites for regulatory complex assembly and protein processing and cleavage. Methods for LM detection are now being developed that are strongly dependent on scores for motif conservation in homologous proteins. However, most LMs are found in natively disordered polypeptide segments that evolve rapidly, unhindered by structural constraints on the sequence. These regions of modular proteins are difficult to align using classical multiple sequence alignment programs that are specifically optimised to align the globular domains. As a consequence, poor motif alignment quality is hindering efforts to detect new LMs.
We have developed a new benchmark, as part of the BAliBASE suite, designed to assess the ability of standard multiple alignment methods to detect and align LMs. The reference alignments are organised into different test sets representing real alignment problems and contain examples of experimentally verified functional motifs, extracted from the Eukaryotic Linear Motif (ELM) database. The benchmark has been used to evaluate and compare a number of multiple alignment programs. With distantly related proteins, the worst alignment program correctly aligns 48% of LMs compared to 73% for the best program. However, the performance of all the programs is adversely affected by the introduction of other sequences containing false positive motifs. The ranking of the alignment programs based on LM alignment quality is similar to that observed when considering full-length protein alignments, however little correlation was observed between LM and overall alignment quality for individual alignment test cases.
We have shown that none of the programs currently available is capable of reliably aligning LMs in distantly related sequences and we have highlighted a number of specific problems. The results of the tests suggest possible ways to improve program accuracy for difficult, divergent sequences.
In the post-genomic era, systems-level studies are being performed that seek to explain complex biological systems by integrating diverse resources from fields such as genomics, proteomics or transcriptomics. New information management systems are now needed for the collection, validation and analysis of the vast amount of heterogeneous data available. Multiple alignments of complete sequences provide an ideal environment for the integration of this information in the context of the protein family.
MACSIMS is a multiple alignment-based information management program that combines the advantages of both knowledge-based and ab initio sequence analysis methods. Structural and functional information is retrieved automatically from the public databases. In the multiple alignment, homologous regions are identified and the retrieved data is evaluated and propagated from known to unknown sequences with these reliable regions. In a large-scale evaluation, the specificity of the propagated sequence features is estimated to be >99%, i.e. very few false positive predictions are made. MACSIMS is then used to characterise mutations in a test set of 100 proteins that are known to be involved in human genetic diseases. The number of sequence features associated with these proteins was increased by 60%, compared to the features available in the public databases. An XML format output file allows automatic parsing of the MACSIM results, while a graphical display using the JalView program allows manual analysis.
MACSIMS is a new information management system that incorporates detailed analyses of protein families at the structural, functional and evolutionary levels. MACSIMS thus provides a unique environment that facilitates knowledge extraction and the presentation of the most pertinent information to the biologist. A web server and the source code are available at .
The application of high-throughput techniques such as genomics, proteomics or transcriptomics means that vast amounts of heterogeneous data are now available in the public databases. Bioinformatics is responding to the challenge with new integrated management systems for data collection, validation and analysis. Multiple alignments of genomic and protein sequences provide an ideal environment for the integration of this mass of information. In the context of the sequence family, structural and functional data can be evaluated and propagated from known to unknown sequences. However, effective integration is being hindered by syntactic and semantic differences between the different data resources and the alignment techniques employed. One solution to this problem is the development of an ontology that systematically defines the terms used in a specific domain. Ontologies are used to share data from different resources, to automatically analyse information and to represent domain knowledge for non-experts. Here, we present MAO, a new ontology for multiple alignments of nucleic and protein sequences. MAO is designed to improve interoperation and data sharing between different alignment protocols for the construction of a high quality, reliable multiple alignment in order to facilitate knowledge extraction and the presentation of the most pertinent information to the biologist.
Sequence alignments are fundamental to a wide range of applications, including database searching, functional residue identification and structure prediction techniques. These applications predict or propagate structural/functional/evolutionary information based on a presumed homology between the aligned sequences. If the initial hypothesis of homology is wrong, no subsequent application, however sophisticated, can be expected to yield accurate results. Here we present a novel method, LEON, to predict homology between proteins based on a multiple alignment of complete sequences (MACS). In MACS, weak signals from distantly related proteins can be considered in the overall context of the family. Intermediate sequences and the combination of individual weak matches are used to increase the significance of low-scoring regions. Residue composition is also taken into account by incorporation of several existing methods for the detection of compositionally biased sequence segments. The accuracy and reliability of the predictions is demonstrated in large-scale comparisons with structural and sequence family databases, where the specificity was shown to be >99% and the sensitivity was estimated to be ∼76%. LEON can thus be used to reliably identify the complex relationships between large multidomain proteins and should be useful for automatic high-throughput genome annotations, 2D/3D structure predictions, protein–protein interaction predictions etc.
The Clustal series of programs are widely used in molecular biology for the multiple alignment of both nucleic acid and protein sequences and for preparing phylogenetic trees. The popularity of the programs depends on a number of factors, including not only the accuracy of the results, but also the robustness, portability and user-friendliness of the programs. New features include NEXUS and FASTA format output, printing range numbers and faster tree calculation. Although, Clustal was originally developed to run on a local computer, numerous Web servers have been set up, notably at the EBI (European Bioinformatics Institute) (http://www.ebi.ac.uk/clustalw/).
PipeAlign is a protein family analysis tool integrating a five step process ranging from the search for sequence homologues in protein and 3D structure databases to the definition of the hierarchical relationships within and between subfamilies. The complete, automatic pipeline takes a single sequence or a set of sequences as input and constructs a high-quality, validated MACS (multiple alignment of complete sequences) in which sequences are clustered into potential functional subgroups. For the more experienced user, the PipeAlign server also provides numerous options to run only a part of the analysis, with the possibility to modify the default parameters of each software module. For example, the user can choose to enter an existing multiple sequence alignment for refinement, validation and subsequent clustering of the sequences. The aim is to provide an interactive workbench for the validation, integration and presentation of a protein family, not only at the sequence level, but also at the structural and functional levels. PipeAlign is available at http://igbmc.u-strasbg.fr/PipeAlign/.
BAliBASE is specifically designed to serve as an evaluation resource
to address all the problems encountered when aligning complete sequences. The
database contains high quality, manually constructed multiple sequence
alignments together with detailed annotations. The alignments are
all based on three-dimensional structural superpositions, with the
exception of the transmembrane sequences. The first release provided
sets of reference alignments dealing with the problems of high variability,
unequal repartition and large N/C-terminal extensions and internal
insertions. Here we describe version 2.0 of the database, which
incorporates three new reference sets of alignments containing structural
repeats, transmembrane sequences and circular permutations to evaluate
the accuracy of detection/prediction and alignment of these
complex sequences. BAliBASE can be viewed at the web site http://www-igbmc.u-strasbg.fr/BioInfo/BAliBASE2/index.html or
can be downloaded from ftp://ftp-igbmc.u-strasbg.fr/pub/BAliBASE2/.