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PubMed2002

The semantic metadatabase (SEMEDA): ontology based integration of federated molecular biological data sources.

A system for "intelligent" semantic integration and querying of federated databases is being implemented by using three main components: A component which enables SQL access to integrated databases by database federation (MARGBench), an ontology based semantic metadatabase (SEMEDA) and an ontology based query interface (SEMEDA-query). In this publication we explain and demonstrate the principles, architecture and the use of SEMEDA. Since SEMEDA is implemented as 3 tiered web application database providers can enter all relevant semantic and technical information about their databases by themselves via a web browser. SEMEDA' s collaborative ontology editing feature is not restricted to database integration, and might also be useful for ongoing ontology developments, such as the "Gene Ontology" [2]. SEMEDA can be found at http://www-bm.cs.uni-magdeburg.de/semeda/. We explain how this ontologically structured information can be used for semantic database integration. In addition, requirements to ontologies for molecular biological database integration are discussed and relevant existing ontologies are evaluated. We further discuss how ontologies and structured knowledge sources can be used in SEMEDA and whether they can be merged supplemented or updated to meet the requirements for semantic database integration.

Databases, Genetic↗

PubMed2003

Proteome analysis using isoelectric focusing in immobilized pH gradient gels followed by mass spectrometry.

Over the past several years, a large effort has been focused on improvements of two-dimensional (2-D) gel electrophoresis-based proteomics technology, and on development of novel approaches for proteome analysis. Here, we describe the application of an alternative strategy for the analysis of complex proteomes. The strategy combines isoelectric focusing in immobilized pH gradient strips (in-gel IEF), mass spectrometry (MS), and bioinformatics. A protein mixture is separated by in-gel IEF, and the entire strip is cut into a set of gel sections. Proteins in each gel section are digested with trypsin, and the tryptic peptides are subjected to liquid chromatography-nanoelectrospray-quadrupole ion-trap tandem mass spectrometry (LC-ESI-MS/MS). The LC-ESI-MS/MS data are used to identify the proteins through searches of a protein sequence database. Using this in-gel IEF-LC-MS/MS strategy, we have identified 127 proteins from a human pituitary. This study demonstrates the potential of the in-gel IEF-LC-MS/MS approach for analyses of complex mammalian proteomes.

Computational Biology↗

PubMed2006

Bioinformatics database infrastructure for biotechnology research.

Many databases are available that provide valuable data resources for the biotechnological researcher. According to their core data, they can be divided into different types. Some databases provide primary data, like all published nucleotide sequences, others deal with protein sequences. In addition to these two basic types of databases, a huge number of more specialized resources are available, like databases about protein structures, protein identification, special features of genes and/or proteins, or certain organisms. Furthermore, some resources offer integrated views on different types of data, allowing the user to do easy customized queries over large datasets and to compare different types of data.

Animals↗

PubMed2004

FusionDB: a database for in-depth analysis of prokaryotic gene fusion events.

FusionDB (http://igs-server.cnrs-mrs.fr/FusionDB/) constitutes a resource dedicated to in-depth analysis of bacterial and archaeal gene fusion events. Such events can provide the 'Rosetta stone' in the search for potential protein-protein interactions, as well as metabolic and regulatory networks. However, the false positive rate of this approach may be quite high, prompting a detailed scrutiny of putative gene fusion events. FusionDB readily provides much of the information required for that task. Moreover, FusionDB extends the notion of gene fusion from that of a single gene to that of a family of genes by assembling pairs of genes from different genomes that belong to the same Cluster of Orthogonal Groups (COG). Multiple sequence alignments and phylogenetic tree reconstruction for the N- and C-terminal parts of these 'COG fusion' events are provided to distinguish single and multiple fusion events from cases of gene fission, pseudogenes and other false positives. Finally, gene fusion events with matches to known structures of heterodimers in the Protein Data Bank (PDB) are identified and may be visualized. FusionDB is fully searchable with access to sequence and alignment data at all levels. A number of different scores are provided to easily differentiate 'real' from 'questionable' cases, especially when larger database searches are performed. FusionDB is cross-linked with the 'Phylogenomic Display of Bacterial Genes' (PhydBac) online web server. Together, these servers provide the complete set of information required for in-depth analysis of non-homology-based gene function attribution.

Artificial Gene Fusion↗

PubMed2005

PAT: a protein analysis toolkit for integrated biocomputing on the web.

PAT, for Protein Analysis Toolkit, is an integrated biocomputing server. The main goal of its design was to facilitate the combination of different processing tools for complex protein analyses and to simplify the automation of repetitive tasks. The PAT server provides a standardized web interface to a wide range of protein analysis tools. It is designed as a streamlined analysis environment that implements many features which strongly simplify studies dealing with protein sequences and structures and improve productivity. PAT is able to read and write data in many bioinformatics formats and to create any desired pipeline by seamlessly sending the output of a tool to the input of another tool. PAT can retrieve protein entries from identifier-based queries by using pre-computed database indexes. Users can easily formulate complex queries combining different analysis tools with few mouse clicks, or via a dedicated macro language, and a web session manager provides direct access to any temporary file generated during the user session. PAT is freely accessible on the Internet at http://pat.cbs.cnrs.fr.

Computational Biology↗

PubMed2003

The mouse secretome: functional classification of the proteins secreted into the extracellular environment.

We have developed a computational strategy to identify the set of soluble proteins secreted into the extracellular environment of a cell. Within the protein sequences predominantly derived from the RIKEN representative transcript and protein set, we identified 2033 unique soluble proteins that are potentially secreted from the cell. These proteins contain a signal peptide required for entry into the secretory pathway and lack any transmembrane domains or intracellular localization signals. This class of proteins, which we have termed the mouse secretome, included >500 novel proteins and 92 proteins <100 amino acids in length. Functional analysis of the secretome included identification of human orthologs, functional units based on InterPro and SCOP Superfamily predictions, and expression of the proteins within the RIKEN READ microarray database. To highlight the utility of this information, we discuss the CUB domain-containing protein family.

Animals↗

PubMed2004

Target SNP selection in complex disease association studies.

BACKGROUND: The massive amount of SNP data stored at public internet sites provides unprecedented access to human genetic variation. Selecting target SNP for disease-gene association studies is currently done more or less randomly as decision rules for the selection of functional relevant SNPs are not available. RESULTS: We implemented a computational pipeline that retrieves the genomic sequence of target genes, collects information about sequence variation and selects functional motifs containing SNPs. Motifs being considered are gene promoter, exon-intron structure, AU-rich mRNA elements, transcription factor binding motifs, cryptic and enhancer splice sites together with expression in target tissue. As a case study, 396 genes on chromosome 6p21 in the extended HLA region were selected that contributed nearly 20,000 SNPs. By computer annotation ~2,500 SNPs in functional motifs could be identified. Most of these SNPs are disrupting transcription factor binding sites but only those introducing new sites had a significant depressing effect on SNP allele frequency. Other decision rules concern position within motifs, the validity of SNP database entries, the unique occurrence in the genome and conserved sequence context in other mammalian genomes. CONCLUSION: Only 10% of all gene-based SNPs have sequence-predicted functional relevance making them a primary target for genotyping in association studies.

Amino Acid Substitution↗

PubMed2006

PeanutMap: an online genome database for comparative molecular maps of peanut.

BACKGROUND: Molecular maps have been developed for many species, and are of particular importance for varietal development and comparative genomics. However, despite the existence of multiple sets of linkage maps, databases of these data are lacking for many species, including peanut. DESCRIPTION: PeanutMap http://peanutgenetics.tamu.edu/cmap provides a web-based interface for viewing specific linkage groups of a map set. PeanutMap can display and compare multiple maps of a set based upon marker or trait correspondences, which is particularly important as cultivated peanut is a disomic tetraploid. The database can also compare linkage groups among multiple map sets, allowing identification of corresponding linkage groups from results of different research projects. Data from the two published peanut genome map sets, and also from three maps sets of phenotypic traits are present in the database. Data from PeanutMap have been incorporated into the Legume Information System website http://www.comparative-legumes.org to allow peanut map data to be used for cross-species comparisons. CONCLUSION: The utility of the database is expected to increase as several SSR-based maps are being developed currently, and expanded efforts for comparative mapping of legumes are underway. Optimal use of these data will benefit from the development of tools to facilitate comparative analysis.

Arachis↗

PubMed2002

Mining genome databases to identify and understand new gene regulatory systems.

The availability of a large number of sequenced microbial genomes allows us to conduct systematic studies on microbial gene regulatory systems. Computational methods, using comparative genomics approaches, are powerful tools to understand their mechanisms and evolutionary history. Recent advances in computational methodology for uncovering transcriptional regulatory components and their interactions are discussed.

Computational Biology↗

PubMed2001

The human histone deacetylase family.

Since the identification of the first histone deacetylase (Taunton et al., Science 272, 408-411), several new members have been isolated. They can loosely be separated into entities on the basis of their similarity to various yeast histone deacetylases. The first class is represented by its closeness to the yeast Rpd3-like proteins, and the second most recently discovered class has similarities to yeast Hda1-like proteins. However, due to the fact that several different research groups isolated the Hda1-like histone deacetylases independently, there have been various different nomenclatures used to describe the various members, which can lead to confusion in the interpretation of this family's functions and interactions. With the discovery of another novel murine histone deacetylase, homologous to yeast Sir2, the number of members of this family is set to increase, as 7 human homologues of this gene have been isolated. In the light of these recent discoveries, we have examined the literature data and conducted a database analysis of the isolated histone deacetylases and potential candidates. The results obtained suggest that the number of histone deacetylases within the human genome may be as high as 17 and are discussed in relation to their homology to the yeast histone deacetylases.

Amino Acid Sequence↗

PubMed2005

Informatics for protein identification by mass spectrometry.

High throughput protein analysis (i.e., proteomics) first became possible when sensitive peptide mass mapping techniques were developed, thereby allowing for the possibility of identifying and cataloging most 2D gel electrophoresis spots. Shortly thereafter a few groups pioneered the idea of identifying proteins by using peptide tandem mass spectra to search protein sequence databases. Hence, it became possible to identify proteins from very complex mixtures. One drawback to these latter techniques is that it is not entirely straightforward to make matches using tandem mass spectra of peptides that are modified or have sequences that differ slightly from what is present in the sequence database that is being searched. This has been part of the motivation behind automated de novo sequencing programs that attempt to derive a peptide sequence regardless of its presence in a sequence database. The sequence candidates thus generated are then subjected to homology-based database search programs (e.g., BLAST or FASTA). These homology search programs, however, were not developed with mass spectrometry in mind, and it became necessary to make minor modifications such that mass spectrometric ambiguities can be taken into account when comparing query and database sequences. Finally, this review will discuss the important issue of validating protein identifications. All of the search programs will produce a top ranked answer; however, only the credulous are willing to accept them carte blanche.

Amino Acid Sequence↗

PubMed2003

The challenge of constructing large phylogenetic trees.

The amount of sequence data available to reconstruct the evolutionary history of genes and species has increased 20-fold in the past decade. Consequently the size of phylogenetic analyses has grown as well, and phylogenetic methods, algorithms and their implementations have struggled to keep pace. Computational and other challenges raised by this burgeoning database emerge at several stages of analysis, from the optimal assembly of large data matrices from sequence databases, to the efficient construction of trees from these large matrices and the piece-wise assembly of 'supertrees' from those trees in turn. A final challenge is posed by the difficulty of visualizing and making inferences from trees that might soon routinely contain thousands of species.

Algorithms↗

PubMed2002

The PRINTS database: a resource for identification of protein families.

The PRINTS database houses a collection of protein fingerprints, which may be used to assign family and functional attributes to uncharacterised sequences, such as those currently emanating from the various genome-sequencing projects. The April 2002 release includes 1,700 family fingerprints, encoding approximately 10,500 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. Fingerprints are groups of conserved motifs that, taken together, provide diagnostic protein family signatures. They derive much of their potency from the biological context afforded by matching motif neighbours; this makes them at once more flexible and powerful than single-motif approaches. The technique further departs from other pattern-matching methods by readily allowing the creation of fingerprints at superfamily-, family- and subfamily-specific levels, thereby allowing more fine-grained diagnoses. Here, we provide an overview of the method of protein fingerprinting and how the results of fingerprint analyses are used to build PRINTS and its relational cousin, PRINTS-S.

Amino Acid Motifs↗

PubMed2016

KERIS: kaleidoscope of gene responses to inflammation between species.

A cornerstone of modern biomedical research is the use of animal models to study disease mechanisms and to develop new therapeutic approaches. In order to help the research community to better explore the similarities and differences of genomic response between human inflammatory diseases and murine models, we developed KERIS: kaleidoscope of gene responses to inflammation between species (available at http://www.igenomed.org/keris/). As of June 2016, KERIS includes comparisons of the genomic response of six human inflammatory diseases (burns, trauma, infection, sepsis, endotoxin and acute respiratory distress syndrome) and matched mouse models, using 2257 curated samples from the Inflammation and the Host Response to Injury Glue Grant studies and other representative studies in Gene Expression Omnibus. A researcher can browse, query, visualize and compare the response patterns of genes, pathways and functional modules across different diseases and corresponding murine models. The database is expected to help biologists choosing models when studying the mechanisms of particular genes and pathways in a disease and prioritizing the translation of findings from disease models into clinical studies.

Animals↗

PubMed2005

The predictive power of the CluSTr database.

SUMMARY: The CluSTr database employs a fully automatic single-linkage hierarchical clustering method based on a similarity matrix. In order to compute the matrix, first all-against-all pair-wise comparisons between protein sequences are computed using the Smith-Waterman algorithm. The statistical significance of the similarity scores is then assessed using a Monte Carlo analysis, yielding Z-values, which are used to populate the matrix. This paper describes automated annotation experiments that quantify the predictive power and hence the biological relevance of the CluSTr data. The experiments utilized the UniProt data-mining framework to derive annotation predictions using combinations of InterPro and CluSTr. We show that this combination of data sources greatly increases the precision of predictions made by the data-mining framework, compared with the use of InterPro data alone. We conclude that the CluSTr approach to clustering proteins makes a valuable contribution to traditional protein classifications. AVAILABILITY: http://www.ebi.ac.uk/clustr/.

Algorithms↗

PubMed2003

Evolving from bioinformatics in-the-small to bioinformatics in-the-large.

We argue the significance of a fundamental shift in bioinformatics, from in-the-small to in-the-large. Adopting a large-scale perspective is a way to manage the problems endemic to the world of the small-constellations of incompatible tools for which the effort required to assemble an integrated system exceeds the perceived benefit of the integration. Where bioinformatics in-the-small is about data and tools, bioinformatics in-the-large is about metadata and dependencies. Dependencies represent the complexities of large-scale integration, including the requirements and assumptions governing the composition of tools. The popular make utility is a very effective system for defining and maintaining simple dependencies, and it offers a number of insights about the essence of bioinformatics in-the-large. Keeping an in-the-large perspective has been very useful to us in large bioinformatics projects. We give two fairly different examples, and extract lessons from them showing how it has helped. These examples both suggest the benefit of explicitly defining and managing knowledge flows and knowledge maps (which represent metadata regarding types, flows, and dependencies), and also suggest approaches for developing bioinformatics database systems. Generally, we argue that large-scale engineering principles can be successfully adapted from disciplines such as software engineering and data management, and that having an in-the-large perspective will be a key advantage in the next phase of bioinformatics development.

Computational Biology↗

PubMed2004

Xpro: database of eukaryotic protein-encoding genes.

Xpro is a relational database that contains all the eukaryotic protein-encoding DNA sequences contained in GenBank with associated data required for the analysis of eukaryotic gene architecture. In addition to the information found in the GenBank records, which includes properties such as sequence, position, length and description about introns, exons and protein-coding regions, Xpro provides annotations on the splice sites and intron phases. Furthermore, Xpro validates intron positions using alignment information between the record's sequence and EST sequences found in dbEST. In the process of validation, alternative splicing information is also obtained and can be found in the database. The intron-containing genes in the Xpro are also classified as experimental or predicted based on the intron position validation and specific keywords in the GenBank records that are present in predicted genes. An Entrez-like query system, which is familiar to most biologists, is provided for accessing the information present in the database system. A non-redundant set of Xpro database contents is also obtained by cross-referencing to the Swiss-Prot/TrEMBL and Pfam databases. The database currently contains information for 493,983 genes--351,918 intron- containing genes and 142,065 intron-less genes. Xpro is updated for each new GenBank release and is freely available via the internet at http://origin.bic. nus.edu.sg/xpro.

Alternative Splicing↗

PubMed2005

Protein database searches using compositionally adjusted substitution matrices.

Almost all protein database search methods use amino acid substitution matrices for scoring, optimizing, and assessing the statistical significance of sequence alignments. Much care and effort has therefore gone into constructing substitution matrices, and the quality of search results can depend strongly upon the choice of the proper matrix. A long-standing problem has been the comparison of sequences with biased amino acid compositions, for which standard substitution matrices are not optimal. To address this problem, we have recently developed a general procedure for transforming a standard matrix into one appropriate for the comparison of two sequences with arbitrary, and possibly differing compositions. Such adjusted matrices yield, on average, improved alignments and alignment scores when applied to the comparison of proteins with markedly biased compositions. Here we review the application of compositionally adjusted matrices and consider whether they may also be applied fruitfully to general purpose protein sequence database searches, in which related sequence pairs do not necessarily have strong compositional biases. Although it is not advisable to apply compositional adjustment indiscriminately, we describe several simple criteria under which invoking such adjustment is on average beneficial. In a typical database search, at least one of these criteria is satisfied by over half the related sequence pairs. Compositional substitution matrix adjustment is now available in NCBI's protein-protein version of blast.

Algorithms↗