G2D (genes to diseases) is a web resource for prioritizing genes as candidates for inherited diseases. It uses three algorithms based on different prioritization strategies. The input to the server is the genomic region where the user is looking for the disease-causing mutation, plus an additional piece of information depending on the algorithm used. This information can either be the disease phenotype (described as an online Mendelian inheritance in man (OMIM) identifier), one or several genes known or suspected to be associated with the disease (defined by their Entrez Gene identifiers), or a second genomic region that has been linked as well to the disease. In the latter case, the tool uses known or predicted interactions between genes in the two regions extracted from the STRING database. The output in every case is an ordered list of candidate genes in the region of interest. For the first two of the three methods, the candidate genes are first retrieved through sequence homology search, then scored accordingly to the corresponding method. This means that some of them will correspond to well-known characterized genes, and others will overlap with predicted genes, thus providing a wider analysis. G2D is publicly available at http://www.ogic.ca/projects/g2d_2/

The first step in understanding the molecular biology of an inherited disease is to identify which gene or genes are carrying variants. This process starts with locating the mutations in a chromosomal band, as narrow as possible, and follows with the manual analysis of all the genes mapping in this region. Usually this is not an easy task, but it can be facilitated by complementary computational approaches that evaluate all genes in a region of interest. We present here a method that combines literature mining, gene annotations, and sequence homology searches to prioritize candidate genes involved in a given genetic disorder. The method progresses in two steps. Firstly, we compute associations of molecular and phenotypic features as taken from MEDLINE. Secondly, for a disease with a given phenotype and linked to a chromosomal region, sequence homology based searches are carried on the chromosomal region to identify potential candidates that are scored using the precomputed associations. The scoring of associations between biological concepts using links across databases can be extended to other databases in Molecular Biology and to nondisease phenotypes.

One of the major challenges of functional genomics is to unravel the connection between genotype and phenotype. So far no global analysis has attempted to explore those connections in the light of the large phenotypic variability seen in nature. Here, we use an unsupervised, systematic approach for associating genes and phenotypic characteristics that combines literature mining with comparative genome analysis. We first mine the MEDLINE literature database for terms that reflect phenotypic similarities of species. Subsequently we predict the likely genomic determinants: genes specifically present in the respective genomes. In a global analysis involving 92 prokaryotic genomes we retrieve 323 clusters containing a total of 2,700 significant gene-phenotype associations. Some clusters contain mostly known relationships, such as genes involved in motility or plant degradation, often with additional hypothetical proteins associated with those phenotypes. Other clusters comprise unexpected associations; for example, a group of terms related to food and spoilage is linked to genes predicted to be involved in bacterial food poisoning. Among the clusters, we observe an enrichment of pathogenicity-related associations, suggesting that the approach reveals many novel genes likely to play a role in infectious diseases.

BACKGROUND: Human inherited diseases can be associated by genetic linkage with one or more genomic regions. The availability of the complete sequence of the human genome allows examining those locations for an associated gene. We previously developed an algorithm to prioritize genes on a chromosomal region according to their possible relation to an inherited disease using a combination of data mining on biomedical databases and gene sequence analysis. RESULTS: We have implemented this method as a web application in our site G2D (Genes to Diseases). It allows users to inspect any region of the human genome to find candidates genes related to a genetic disease of their interest. In addition, the G2D server includes pre-computed analyses of candidate genes for 552 linked monogenic diseases without an associated gene, and the analysis of 18 asthma loci. CONCLUSIONS: G2D can be publicly accessed at http://www.ogic.ca/projects/g2d_2/.

MOTIVATION: The description of genes in databases by keywords helps the non-specialist to quickly grasp the properties of a gene and increases the efficiency of computational tools that are applied to gene data (e.g. searching a gene database for sequences related to a particular biological process). However, the association of keywords to genes or protein sequences is a difficult process that ultimately implies examination of the literature related to a gene. RESULTS: To support this task, we present a procedure to derive keywords from the set of scientific abstracts related to a gene. Our system is based on the automated extraction of mappings between related terms from different databases using a model of fuzzy associations that can be applied with all generality to any pair of linked databases. We tested the system by annotating genes of the SWISS-PROT database with keywords derived from the abstracts linked to their entries (stored in the MEDLINE database of scientific references). The performance of the annotation procedure was much better for SWISS-PROT keywords (recall of 47%, precision of 68%) than for Gene Ontology terms (recall of 8%, precision of 67%). AVAILABILITY: The algorithm can be publicly accessed and used for the annotation of sequences through a web server at http://www.bork.embl.de/kat

Entries in biological databases are usually linked to scientific references. To generate those links and to keep them up-to-date, database maintainers have to continuously scan the scientific literature to select references that are relevant for each single database entry. The continuous growth of both the corpus of scientific literature and the size of biological databases makes this task very hard. We present a protocol intended to assist the updating of an existing set of literature (abstract) links from a single database entry with new references. It consists of taking the set of MEDLINE neighbour references of the existing linked abstracts and evaluating their relevance according to the existing set of abstracts. To test the applicability of the algorithm, we did a simple benchmark of the system using the references associated with the entries of a protein domain database. Human experts found the references that the algorithm scored highly were more relevant to the database entry than those scored lowly, suggesting that the algorithm was useful.

As scientific literature databases like MEDLINE increase in size, so does the time required to search them. Scientists must frequently inspect long lists of references manually, often just reading the titles. XplorMed is a web tool that aids MEDLINE searching by summarizing the subjects contained in the results, thus allowing users to focus on subjects of interest. Here we describe new features added to XplorMed during the last 2 years (http://www.bork.embl-heidelberg.de/xplormed/).

BACKGROUND: To date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant. RESULTS: In this work we addressed those questions showing that the keyword content of the different sections of a standard scientific article (abstract, introduction, methods, results, and discussion) is very heterogeneous. CONCLUSIONS: Although the abstract contains the best ratio of keywords per total of words, other sections of the article may be a better source of biologically relevant data.

BACKGROUND: Iron uptake from the host is essential for bacteria that infect animals. To find potential targets for drugs active against pathogenic bacteria, we have searched all completely sequenced genomes of pathogenic bacteria for genes relevant for iron transport. RESULTS: We identified a protein domain that appears in variable copy number in bacterial genes that are usually in the vicinity of a putative Fe3+ siderophore transporter. Accordingly, we have denoted this domain NEAT for 'near transporter'. Most of the bacterial species containing this domain are pathogenic. Sequence features indicate that the domain is anchored to the extracellular side of the membrane. The domain seems to be under high selective pressure for rapid independent duplications that are typical of sequences involved in signaling and binding. CONCLUSIONS: The NEAT domain might be functionally related to iron transport. The taxonomic specificity of this domain and its predicted extracellular position could make it an interesting target for designing new drugs against some highly pathogenic bacteria.

Although approximately one-quarter of the roughly 4,000 genetically inherited diseases currently recorded in respective databases (LocusLink, OMIM) are already linked to a region of the human genome, about 450 have no known associated gene. Finding disease-related genes requires laborious examination of hundreds of possible candidate genes (sometimes, these are not even annotated; see, for example, refs 3,4). The public availability of the human genome draft sequence has fostered new strategies to map molecular functional features of gene products to complex phenotypic descriptions, such as those of genetically inherited diseases. Owing to recent progress in the systematic annotation of genes using controlled vocabularies, we have developed a scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene. In a benchmark of the system with 100 known disease-associated genes, the disease-associated gene was among the 8 best-scoring genes with a 25% chance, and among the best 30 genes with a 50% chance, showing that there is a relationship between the score of a gene and its likelihood of being associated with a particular disease. The scoring also indicates that for some diseases, the chance of identifying the underlying gene is higher.

XplorMed is a publicly available web tool conceived to make life easier for MEDLINE(c) users looking for scientific information. Searching scientific literature is an information retrieval problem. Abstracts that are of possible interest to the user are usually selected by a keyword search followed by manual screening, which often results in the retrieval of a large number of abstracts. Interesting references can be buried among irrelevant ones because of nonspecific queries. XplorMed is intended to extract dependency relations between the words of the abstracts. These relations can be filtered and arranged to deduce different subjects in the query and offer a condensed view of the abstract, allowing users to select texts of interest without having to read them all. XplorMed is available http://www.bork. embl-heidelberg.de/xplormed.

The increase of information in biology makes it difficult for researchers in any field to keep current with the literature. The MEDLINE database of scientific abstracts can be quickly scanned using electronic mechanisms. Potentially interesting abstracts can be selected by matching words joined by Boolean operators. However this means of selecting documents is not optimal. Nonspecific queries have to be effected, resulting in large numbers of irrelevant abstracts that have to be manually scanned To facilitate this analysis, we have developed a system that compiles a summary of subjects and related documents on the results of a MEDLINE query. For this, we have applied a fuzzy binary relation formalism that deduces relations between words present in a set of abstracts preprocessed with a standard grammatical tagger. Those relations are used to derive ensembles of related words and their associated subsets of abstracts. The algorithm can be used publicly at http:// www.bork.embl-heidelberg.de/xplormed/.

Internal repetition within proteins has been a successful strategem on multiple separate occasions throughout evolution. Such protein repeats possess regular secondary structures and form multirepeat assemblies in three dimensions of diverse sizes and functions. In general, however, internal repetition affords a protein enhanced evolutionary prospects due to an enlargement of its available binding surface area. Constraints on sequence conservation appear to be relatively lax, due to binding functions ensuing from multiple, rather than, single repeats. Considerable sequence divergence as well as the short lengths of sequence repeats mean that repeat detection can be a particularly arduous task. We also consider the conundrum of how multiple repeats, which show strong structural and functional interdependencies, ever evolved from a single repeat ancestor. In this review, we illustrate each of these points by referring to six prolific repeat types (repeats in beta-propellers and beta-trefoils and tetratricopeptide, ankyrin, armadillo/HEAT, and leucine-rich repeats) and in other less-prolific but nonetheless interesting repeats.

The most frequent access to the MEDLINE database of scientific abstracts is by keyword search. However, this is often not sufficient because although the user might find all the useful abstracts, these are buried in hundreds that are irrelevant. The exploratory tool XplorMed has been developed to analyse the result of any MEDLINE query. It suggests main groups of related topics and documents, sparing the user the need of reading all abstracts.