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PubMed2005

The GATO gene annotation tool for research laboratories.

Large-scale genome projects have generated a rapidly increasing number of DNA sequences. Therefore, development of computational methods to rapidly analyze these sequences is essential for progress in genomic research. Here we present an automatic annotation system for preliminary analysis of DNA sequences. The gene annotation tool (GATO) is a Bioinformatics pipeline designed to facilitate routine functional annotation and easy access to annotated genes. It was designed in view of the frequent need of genomic researchers to access data pertaining to a common set of genes. In the GATO system, annotation is generated by querying some of the Web-accessible resources and the information is stored in a local database, which keeps a record of all previous annotation results. GATO may be accessed from everywhere through the internet or may be run locally if a large number of sequences are going to be annotated. It is implemented in PHP and Perl and may be run on any suitable Web server. Usually, installation and application of annotation systems require experience and are time consuming, but GATO is simple and practical, allowing anyone with basic skills in informatics to access it without any special training. GATO can be downloaded at [http://mariwork.iq.usp.br/gato/]. Minimum computer free space required is 2 MB.

Biomedical Research↗

PubMed2006

Application and utilization of chemoinformatics tools in lead generation and optimization.

The process of Drug Discovery is a complex and high risk endeavor that requires focused attention on experimental hypotheses, the application of diverse sets of technologies and data to facilitate high quality decision-making. All is aimed at enhancing the quality of the chemical development candidate(s) through clinical evaluation and into the market. In support of the lead generation and optimization phases of this endeavor, high throughput technologies such as combinatorial/high throughput synthesis and high throughput and ultra-high throughput screening, have allowed the rapid analysis and generation of large number of compounds and data. Today, for every analog synthesized 100 or more data points can be collected and captured in various centralized databases. The analysis of thousands of compounds can very quickly become a daunting task. In this article we present the process we have developed for both analyzing and prioritizing large sets of data starting from diversity and focused uHTS in support of lead generation and secondary screens supporting lead optimization. We will describe how we use informatics and computational chemistry to focus our efforts on asking relevant questions about the desired attributes of a specific library, and subsequently in guiding the generation of more information-rich sets of analogs in support of both processes.

Chemistry, Pharmaceutical↗

PubMed2005

NdPASA: a novel pairwise protein sequence alignment algorithm that incorporates neighbor-dependent amino acid propensities.

Sequence alignment has become one of the essential bioinformatics tools in biomedical research. Existing sequence alignment methods can produce reliable alignments for homologous proteins sharing a high percentage of sequence identity. The performance of these methods deteriorates sharply for the sequence pairs sharing less than 25% sequence identity. We report here a new method, NdPASA, for pairwise sequence alignment. This method employs neighbor-dependent propensities of amino acids as a unique parameter for alignment. The values of neighbor-dependent propensity measure the preference of an amino acid pair adopting a particular secondary structure conformation. NdPASA optimizes alignment by evaluating the likelihood of a residue pair in the query sequence matching against a corresponding residue pair adopting a particular secondary structure in the template sequence. Using superpositions of homologous proteins derived from the PSI-BLAST analysis and the Structural Classification of Proteins (SCOP) classification of a nonredundant Protein Data Bank (PDB) database as a gold standard, we show that NdPASA has improved pairwise alignment. Statistical analyses of the performance of NdPASA indicate that the introduction of sequence patterns of secondary structure derived from neighbor-dependent sequence analysis clearly improves alignment performance for sequence pairs sharing less than 20% sequence identity. For sequence pairs sharing 13-21% sequence identity, NdPASA improves the accuracy of alignment over the conventional global alignment (GA) algorithm using the BLOSUM62 by an average of 8.6%. NdPASA is most effective for aligning query sequences with template sequences whose structure is known. NdPASA can be accessed online at http://astro.temple.edu/feng/Servers/BioinformaticServers.htm.

Algorithms↗

PubMed2001

Re-identification of DNA through an automated linkage process.

This work demonstrates how seemingly anonymous DNA database entries can be related to publicly available health information to uniquely and specifically identify the persons who are the subjects of the information even though the DNA information contains no accompanying explicit identifiers such as name, address, or Social Security number and contains no additional fields of personal information. The software program, REID (Re-Identification of DNA), iteratively uncovers unique occurrences in visit-disease patterns across data collections that reveal inferences about the identities of the patients who are the subject of the DNA. Using real-world data, REID established identifiable linkages in 33-100% of the 10,886 cases explicitly surveyed over 8 gene-based diseases.

Algorithms↗

PubMed2003

Bioinformatics for medical diagnostics: assessment of microarray data in the context of clinical databases.

MOTIVATION: To identify genes suitable for medical diagnostics microarray data is assessed in the context of clinical databases, which store complex information about the patient phenotype. The wealth of data and lacking standards make it difficult to analyse this kind of data. RESULTS: We present a workflow for exploratory analysis of microarray data together with clinical data consisting of four steps: definition of clinically meaningful research questions in a masterfile, generation of analysis files, selection and characterization of differentially expressed genes, and estimation of classification accuracy. We applied this workflow to large data sets from the field of cardiology and oncology (n~500 patients). Systematic data management of microarray data and clinical data helps to make results more transparent and comparable.

Cardiology↗

PubMed2005

Plasma Proteome Database as a resource for proteomics research.

Plasma is one of the best studied compartments in the human body and serves as an ideal body fluid for the diagnosis of diseases. This report provides a detailed functional annotation of all the plasma proteins identified to date. In all, gene products encoded by 3778 distinct genes were annotated based on proteins previously published in the literature as plasma proteins and the identification of multiple peptides from proteins under HUPO's Plasma Proteome Project. Our analysis revealed that 51% of these genes encoded more than one protein isoform. All single nucleotide polymorphisms involving protein-coding regions were mapped onto the protein sequences. We found a number of examples of isoform-specific subcellular localization as well as tissue expression. This database is an attempt at comprehensive annotation of a complex subproteome and is available on the web at http://www.plasmaproteomedatabase.org.

Amino Acid Motifs↗

PubMed1998

Feature selection for genetic sequence classification.

MOTIVATION: Most of the existing methods for genetic sequence classification are based on a computer search for homologies in nucleotide or amino acid sequences. The standard sequence alignment programs scale very poorly as the number of sequences increases or the degree of sequence identity is <30%. Some new computationally inexpensive methods based on nucleotide or amino acid compositional analysis have been proposed, but prediction results are still unsatisfactory and depend on the features chosen to represent the sequences. RESULTS: In this paper, a feature selection method based on the Gamma (or near-neighbour) test is proposed. If there is a continuous or smooth map from feature space to the classification target values, the Gamma test gives an estimate for the mean-squared error of the classification, despite the fact that one has no a priori knowledge of the smooth mapping. We can search a large space of possible feature combinations for a combination which gives a smallest estimated mean-squared error using a genetic algorithm. The method was used for feature selection and classification of the large subunits of rRNA according to RDP (Ribosomal Database Project) phylogenetic classes. The sequences were represented by dinucleotide frequency distribution. The nearest-neighbour criterion has been used to estimate the predictive accuracy of the classification based on the selected features. For examples discussed, we found that the classification according to the first nearest neighbour is correct for 80% of the test samples. If we consider the set of the 10 nearest neighbours, then 94% of the test samples are classified correctly. AVAILABILITY: The principal novel component of this method is the Gamma test and this can be downloaded compiled for Unix Sun 4, Windows 95 and MS-DOS from http://www.cs.cf.ac.uk/ec/ CONTACT: s.margetts@cs.cf.ac.uk

Algorithms↗

PubMed2006

Integration of gel-based proteome data with pProRep.

UNLABELLED: pProRep is a web application integrating electrophoretic and mass spectral data from proteome analyses into a relational database. The graphical web-interface allows users to upload, analyse and share experimental proteome data. It offers researchers the possibility to query all previously analysed datasets and can visualize selected features, such as the presence of a certain set of ions in a peptide mass spectrum, on the level of the two-dimensional gel. AVAILABILITY: The pProRep package and instructions for its use can be downloaded from http://www.ptools.ua.ac.be/pProRep. The application requires a web server that runs PHP 5 (http://www.php.net) and MySQL. Some (non-essential) extensions need additional freely available libraries: details are described in the installation instructions.

Computational Biology↗

PubMed2006

Prediction of buried helices in multispan alpha helical membrane proteins.

Analysis of a database of structures of membrane proteins shows that membrane proteins composed of 10 or more transmembrane (TM) helices often contain buried helices that are inaccessible to phospholipids. We introduce a method for identifying TM helices that are least phospholipid accessible and for prediction of fully buried TM helices in membrane proteins from sequence information alone. Our method is based on the calculation of residue lipophilicity and evolutionary conservation. Given that the number of buried helices in a membrane protein is known, our method achieves an accuracy of 78% and a Matthew's correlation coefficient of 0.68. A server for this tool (RANTS) is available online at http://gila.bioengr.uic.edu/lab/.

Algorithms↗

PubMed2002

Rational drug discovery: what can we learn from regulatory networks?

To enable the list of genes and proteins contained within genomic databases to be useful for drug discovery, we need to understand how the genome maps into the phenome. An essential, but not explicitly listed ingredient of the genome is the regulatory interactions between genes and proteins that form a genome-wide network. How can the concept of regulatory networks increase our understanding of living systems? Networks are more than just static "wiring diagrams". Gene interactions impose dynamic constraints, which, although obvious in emergent phenotypic properties, are not captured by traditional one gene-one trait approaches. Understanding the nature of these constraints in gene-activation state space will pave the way to a holistic yet formal and genomics-based approach to rational drug development.

Computational Biology↗

PubMed2006

VISTA Enhancer Browser--a database of tissue-specific human enhancers.

Despite the known existence of distant-acting cis-regulatory elements in the human genome, only a small fraction of these elements has been identified and experimentally characterized in vivo. This paucity of enhancer collections with defined activities has thus hindered computational approaches for the genome-wide prediction of enhancers and their functions. To fill this void, we utilize comparative genome analysis to identify candidate enhancer elements in the human genome coupled with the experimental determination of their in vivo enhancer activity in transgenic mice [L. A. Pennacchio et al. (2006) Nature, in press]. These data are available through the VISTA Enhancer Browser (http://enhancer.lbl.gov). This growing database currently contains over 250 experimentally tested DNA fragments, of which more than 100 have been validated as tissue-specific enhancers. For each positive enhancer, we provide digital images of whole-mount embryo staining at embryonic day 11.5 and an anatomical description of the reporter gene expression pattern. Users can retrieve elements near single genes of interest, search for enhancers that target reporter gene expression to a particular tissue, or download entire collections of enhancers with a defined tissue specificity or conservation depth. These experimentally validated training sets are expected to provide a basis for a wide range of downstream computational and functional studies of enhancer function.

Animals↗

PubMed2001

DBAli: a database of protein structure alignments.

SUMMARY: The DBAli database includes approximately 35000 alignments of pairs of protein structures from SCOP (Lo Conte et al., Nucleic Acids Res., 28, 257-259, 2000) and CE (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998). DBAli is linked to several resources, including Compare3D (Shindyalov and Bourne, http://www.sdsc.edu/pb/software.htm, 1999) and ModView (Ilyin and Sali, http://guitar.rockefeller.edu/ModView/, 2001) for visualizing sequence alignments and structure superpositions. A flexible search of DBAli by protein sequence and structure properties allows construction of subsets of alignments suitable for a number of applications, such as benchmarking of sequence-sequence and sequence-structure alignment methods under a variety of conditions. AVAILABILITY: http://guitar.rockefeller.edu/DBAli/

Computational Biology↗

PubMed1999

The CATH Database provides insights into protein structure/function relationships.

We report the latest release (version 1.4) of the CATH protein domains database (http://www.biochem.ucl.ac.uk/bsm/cath). This is a hierarchical classification of 13 359 protein domain structures into evolutionary families and structural groupings. We currently identify 827 homologous families in which the proteins have both structual similarity and sequence and/or functional similarity. These can be further clustered into 593 fold groups and 32 distinct architectures. Using our structural classification and associated data on protein functions, stored in the database (EC identifiers, SWISS-PROT keywords and information from the Enzyme database and literature) we have been able to analyse the correlation between the 3D structure and function. More than 96% of folds in the PDB are associated with a single homologous family. However, within the superfolds, three or more different functions are observed. Considering enzyme functions, more than 95% of clearly homologous families exhibit either single or closely related functions, as demonstrated by the EC identifiers of their relatives. Our analysis supports the view that determining structures, for example as part of a 'structural genomics' initiative, will make a major contribution to interpreting genome data.

Algorithms↗

PubMedDate not supplied

Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers.

The growth of nursing databases necessitates new approaches to data analyses. These databases, which are known to be massive and multidimensional, easily exceed the capabilities of both human cognition and traditional analytical approaches. One innovative approach, knowledge discovery in large databases (KDD), allows investigators to analyze very large data sets more comprehensively in an automatic or a semi-automatic manner. Among KDD techniques, Bayesian networks, a state-of-the art representation of probabilistic knowledge by a graphical diagram, has emerged in recent years as essential for pattern recognition and classification in the healthcare field. Unlike some data mining techniques, Bayesian networks allow investigators to combine domain knowledge with statistical data, enabling nurse researchers to incorporate clinical and theoretical knowledge into the process of knowledge discovery in large datasets. This tailored discussion presents the basic concepts of Bayesian networks and their use as knowledge discovery tools for nurse researchers.

Artificial Intelligence↗

PubMed2001

Automatic rule generation for protein annotation with the C4.5 data mining algorithm applied on SWISS-PROT.

MOTIVATION: The gap between the amount of newly submitted protein data and reliable functional annotation in public databases is growing. Traditional manual annotation by literature curation and sequence analysis tools without the use of automated annotation systems is not able to keep up with the ever increasing quantity of data that is submitted. Automated supplements to manually curated databases such as TrEMBL or GenPept cover raw data but provide only limited annotation. To improve this situation automatic tools are needed that support manual annotation, automatically increase the amount of reliable information and help to detect inconsistencies in manually generated annotations. RESULTS: A standard data mining algorithm was successfully applied to gain knowledge about the Keyword annotation in SWISS-PROT. 11 306 rules were generated, which are provided in a database and can be applied to yet unannotated protein sequences and viewed using a web browser. They rely on the taxonomy of the organism, in which the protein was found and on signature matches of its sequence. The statistical evaluation of the generated rules by cross-validation suggests that by applying them on arbitrary proteins 33% of their keyword annotation can be generated with an error rate of 1.5%. The coverage rate of the keyword annotation can be increased to 60% by tolerating a higher error rate of 5%. AVAILABILITY: The results of the automatic data mining process can be browsed on http://golgi.ebi.ac.uk:8080/Spearmint/ Source code is available upon request. CONTACT: kretsch@ebi.ac.uk.

Algorithms↗

PubMed2004

MaizeGDB, the community database for maize genetics and genomics.

The Maize Genetics and Genomics Database (MaizeGDB) is a central repository for maize sequence, stock, phenotype, genotypic and karyotypic variation, and chromosomal mapping data. In addition, MaizeGDB provides contact information for over 2400 maize cooperative researchers, facilitating interactions between members of the rapidly expanding maize community. MaizeGDB represents the synthesis of all data available previously from ZmDB and from MaizeDB-databases that have been superseded by MaizeGDB. MaizeGDB provides web-based tools for ordering maize stocks from several organizations including the Maize Genetics Cooperation Stock Center and the North Central Regional Plant Introduction Station (NCRPIS). Sequence searches yield records displayed with embedded links to facilitate ordering cloned sequences from various groups including the Maize Gene Discovery Project and the Clemson University Genomics Institute. An intuitive web interface is implemented to facilitate navigation between related data, and analytical tools are embedded within data displays. Web-based curation tools for both designated experts and general researchers are currently under development. MaizeGDB can be accessed at http://www.maizegdb.org/.

Computational Biology↗

PubMed2006

APID: Agile Protein Interaction DataAnalyzer.

Agile Protein Interaction DataAnalyzer (APID) is an interactive bioinformatics web tool developed to integrate and analyze in a unified and comparative platform main currently known information about protein-protein interactions demonstrated by specific small-scale or large-scale experimental methods. At present, the application includes information coming from five main source databases enclosing an unified sever to explore >35 000 different proteins and 111 000 different proven interactions. The web includes search tools to query and browse upon the data, allowing selection of the interaction pairs based in calculated parameters that weight and qualify the reliability of each given protein interaction. Such parameters are for the 'proteins': connectivity, cluster coefficient, Gene Ontology (GO) functional environment, GO environment enrichment; and for the 'interactions': number of methods, GO overlapping, iPfam domain-domain interaction. APID also includes a graphic interactive tool to visualize selected sub-networks and to navigate on them or along the whole interaction network. The application is available open access at http://bioinfow.dep.usal.es/apid/.

Computational Biology↗

PubMed2001

Functional versatility and molecular diversity of the metabolic map of Escherichia coli.

We have analyzed the known metabolic enzymes of Escherichia coli in relation to their biochemical reaction properties and their involvement in biochemical pathways. All enzymes involved in small-molecule metabolism and their corresponding protein sequences have been extracted from the EcoCyc database. These 548 metabolic enzymes are clustered into 405 protein families according to sequence similarity. In this study, we examine the functional versatility within enzyme families in terms of their reaction capabilities and pathway participation. In addition, we examine the molecular diversity of reactions and pathways according to their presence across enzyme families. These complex, many-to-many relationships between protein sequence and biochemical function reveal a significant degree of correlation between enzyme families and reactions. Pathways, however, appear to require more than one enzyme type to perform their complex biochemical transformations. Finally, the distribution of enzyme family members across different pathways provides support for the "recruitment" hypothesis of biochemical pathway evolution.

Amino Acid Sequence↗