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TTD: Therapeutic Target Database.

A number of proteins and nucleic acids have been explored as therapeutic targets. These targets are subjects of interest in different areas of biomedical and pharmaceutical research and in the development and evaluation of bioinformatics, molecular modeling, computer-aided drug design and analytical tools. A publicly accessible database that provides comprehensive information about these targets is therefore helpful to the relevant communities. The Therapeutic Target Database (TTD) is designed to provide information about the known therapeutic protein and nucleic acid targets described in the literature, the targeted disease conditions, the pathway information and the corresponding drugs/ligands directed at each of these targets. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3D structure, function, nomenclature, drug/ligand binding properties, drug usage and effects, and related literature for each target. This database can be accessed at http://xin.cz3.nus.edu.sg/group/ttd/ttd.asp and it currently contains entries for 433 targets covering 125 disease conditions along with 809 drugs/ligands directed at each of these targets. Each entry can be retrieved through multiple methods including target name, disease name, drug/ligand name, drug/ligand function and drug therapeutic classification.

Databases, Nucleic Acid↗

Bioinformatics as a critical prerequisite to transcriptome and proteome studies.

Large-scale genomic studies rely strongly on annotations available in databases to design experimental supports such as arrays or to explain results in term of biological meaning. Most of this information originates from bioinformatic predictions. Their accuracy as well as their relevance to existing biological data are critical in avoiding the misinterpretation of experimental results.

Computational Biology↗

Antiviral potential of chemokines.

In the past few years, a large number of new chemokines (chemotactic cytokines) and chemokine receptors have been discovered. The growth in knowledge about these molecules has been achieved largely through advances in bioinformatics and the expansion of expression sequence tag (EST) databases. It is now clear that chemokines are crucial in controlling both the development and functioning of leukocytes and that their role is not restricted to cell attraction, as originally assumed. In particular, recent findings provide strong support for the idea that chemokines and their receptors are especially important in the control of viral infection and replication. Thus, specific chemokines are now known to enhance the cytotoxic activity of infected cells, thus inhibiting further virus replication. In addition, some chemokines orchestrate the recruitment of activated leukocytes to foci of infection to aid viral clearance. Viruses, in turn, have evolved various defences against chemokines. These range from the production of proteins that inhibit biological activity of the host chemokine to the hijacking of the chemokine system, whereby certain viruses utilize chemokine receptors for their entry. The latter viral defence can itself be blocked by chemokines. Altogether, these findings illustrate the central role of chemokines in many different phases of the immune response, particularly those aspects involving antiviral defence, a variety and versatility that was not fully appreciated even a few years ago.

Animals↗

Bioinformatics in protein analysis.

The chapter gives an overview of bioinformatic techniques of importance in protein analysis. These include database searches, sequence comparisons and structural predictions. Links to useful World Wide Web (WWW) pages are given in relation to each topic. Databases with biological information are reviewed with emphasis on databases for nucleotide sequences (EMBL, GenBank, DDBJ), genomes, amino acid sequences (Swissprot, PIR, TrEMBL, GenePept), and three-dimensional structures (PDB). Integrated user interfaces for databases (SRS and Entrez) are described. An introduction to databases of sequence patterns and protein families is also given (Prosite, Pfam, Blocks). Furthermore, the chapter describes the widespread methods for sequence comparisons, FASTA and BLAST, and the corresponding WWW services. The techniques involving multiple sequence alignments are also reviewed: alignment creation with the Clustal programs, phylogenetic tree calculation with the Clustal or Phylip packages and tree display using Drawtree, njplot or phylo_win. Finally, the chapter also treats the issue of structural prediction. Different methods for secondary structure predictions are described (Chou-Fasman, Garnier-Osguthorpe-Robson, Predator, PHD). Techniques for predicting membrane proteins, antigenic sites and postranslational modifications are also reviewed.

Computational Biology↗

Kleisli: a new tool for data integration in biology.

One of the central problems in bioinformatics is data retrieval and integration. The existing biological databases are geographically distributed across the Internet, complex and heterogeneous in data types and data structures, and constantly changing. With the current rapid growth of biomedical data, the challenge is how large volumes of data retrieved from multiple databases can be transformed and integrated automatically and flexibly. This article describes a powerful new tool, the Kleisli system, for complex queries across multiple databases and data integration.

Computational Biology↗

The path to enlightenment: making sense of genomic and proteomic information.

Whereas genomics describes the study of genome, mainly represented by its gene expression on the DNA or RNA level, the term proteomics denotes the study of the proteome, which is the protein complement encoded by the genome. In recent years, the number of proteomic experiments increased tremendously. While all fields of proteomics have made major technological advances, the biggest step was seen in bioinformatics. Biological information management relies on sequence and structure databases and powerful software tools to translate experimental results into meaningful biological hypotheses and answers. In this resource article, I provide a collection of databases and software available on the Internet that are useful to interpret genomic and proteomic data. The article is a toolbox for researchers who have genomic or proteomic datasets and need to put their findings into a biological context.

Computational Biology↗

EST-PAGE--managing and analyzing EST data.

UNLABELLED: EST-PAGE provides a bioinformatics solution for expressed sequence tags (EST) data entry, database management, GenBank submission, process control and data retrieval from a unified web interface that can be easily customized and adapted by groups working on diverse EST sequencing projects. AVAILABILITY: The system and source code are available upon request from the authors. SUPPLEMENTARY INFORMATION: http://EST-PAGE.binf.gmu.edu

Database Management Systems↗

ExPASy: The proteomics server for in-depth protein knowledge and analysis.

The ExPASy (the Expert Protein Analysis System) World Wide Web server (http://www.expasy.org), is provided as a service to the life science community by a multidisciplinary team at the Swiss Institute of Bioinformatics (SIB). It provides access to a variety of databases and analytical tools dedicated to proteins and proteomics. ExPASy databases include SWISS-PROT and TrEMBL, SWISS-2DPAGE, PROSITE, ENZYME and the SWISS-MODEL repository. Analysis tools are available for specific tasks relevant to proteomics, similarity searches, pattern and profile searches, post-translational modification prediction, topology prediction, primary, secondary and tertiary structure analysis and sequence alignment. These databases and tools are tightly interlinked: a special emphasis is placed on integration of database entries with related resources developed at the SIB and elsewhere, and the proteomics tools have been designed to read the annotations in SWISS-PROT in order to enhance their predictions. ExPASy started to operate in 1993, as the first WWW server in the field of life sciences. In addition to the main site in Switzerland, seven mirror sites in different continents currently serve the user community.

Databases, Protein↗

An ordered comparative map of the cattle and human genomes.

A cattle-human whole-genome comparative map was constructed using parallel radiation hybrid (RH) mapping in conjunction with EST sequencing, database mining for unmapped cattle genes, and a predictive bioinformatics approach (COMPASS) for targeting specific homologous regions. A total of 768 genes were placed on the RH map in addition to 319 microsatellites used as anchor markers. Of these, 638 had human orthologs with mapping data, thus permitting construction of an ordered comparative map. The large number of ordered loci revealed > or =105 conserved segments between the two genomes. The comparative map suggests that 41 translocation events, a minimum of 54 internal rearrangements, and repositioning of all but one centromere can account for the observed organizations of the cattle and human genomes. In addition, the COMPASS in silico mapping tool was shown to be 95% accurate in its ability to predict cattle chromosome location from random sequence data, demonstrating this tool to be valuable for efficient targeting of specific regions for detailed mapping. The comparative map generated will be a cornerstone for elucidating mammalian chromosome phylogeny and the identification of genes of agricultural importance."Ought we, for instance, to begin by discussing each separate species-in virtue of some common element of their nature, and proceed from this as a basis for the consideration of them separately?" from Aristotle, On the Parts of Animals, 350 B.C.E.

Animals↗

TMEM33 regulates the proliferation and migration of lung adenocarcinoma by promoting the PI3K/AKT/mTOR signaling pathway.

Lung adenocarcinoma is a common type of lung cancer with high incidence and mortality rates. TMEM33, a tumor-associated protein, has not been fully elucidated in lung adenocarcinoma. To explore the expression of TMEM33 in lung adenocarcinoma, its impact on tumor progression, and its role in the PI3K/AKT/mTOR signaling pathway. Key genes associated with lung adenocarcinoma were screened using the Gene Expression Omnibus (GEO) dataset GSE140797 in combination with a weighted correlation network analysis (WGCNA). Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on these key genes. TMEM33 expression in Lung adenocarcinoma patients was validated using data from the TCGA database. The directly interactive molecules were screened through a bioinformatics method. Cellular assays (Western Blot, CCK8, colony formation, scratch assay, Trans well assay) and a nude mouse model were used to investigate the effects of TMEM33 on cell proliferation, migration, and tumor growth. TMEM33 is highly expressed in lung adenocarcinoma tissues (P&#x2009;<&#x2009;0.05) and associated with poor prognosis. Overexpression of TMEM33 promotes cell proliferation and migration, and activates the PI3K/AKT/mTOR signaling pathway (P&#x2009;<&#x2009;0.01). In the nude mouse model, TMEM33 overexpression increases tumor volume (P&#x2009;<&#x2009;0.001), and PI3K/AKT pathway inhibition suppresses these effects (P&#x2009;<&#x2009;0.05, P&#x2009;<&#x2009;0.01). TMEM33 acts as an oncogene in lung adenocarcinoma by activating the PI3K/AKT/mTOR pathway, providing new therapeutic targets.

Cell migration↗

Soybean genomic survey: BAC-end sequences near RFLP and SSR markers.

We are building a framework physical infrastructure across the soybean genome by using SSR (simple sequence repeat) and RFLP (restriction fragment length polymorphism) markers to identify BACs (bacterial artificial chromosomes) from two soybean BAC libraries. The libraries were prepared from two genotypes, each digested with a different restriction enzyme. The BACs identified by each marker were grouped into contigs. We have obtained BAC- end sequence from BACs within each contig. The sequences were analyzed by the University of Minnesota Center for Computational Genomics and Bioinformatics using BLAST algorithms to search nucleotide and protein databases. The SSR-identified BACs had a higher percentage of significant BLAST hits than did the RFLP-identified BACs. This difference was due to a higher percentage of hits to repetitive-type sequences for the SSR-identified BACs that was offset in part, however, by a somewhat larger proportion of RFLP-identified significant hits with similarity to experimentally defined genes and soybean ESTs (expressed sequence tags). These genes represented a wide range of metabolic functions. In these analyses, only repetitive sequences from SSR-identified contigs appeared to be clustered. The BAC-end sequences also allowed us to identify microsynteny between soybean and the model plants Arabidopsis thaliana and Medicago truncatula. This map-based approach to genome sampling provides a means of assaying soybean genome structure and organization.

Algorithms↗

Mutation profiling of mismatch repair-deficient colorectal cncers using an in silico genome scan to identify coding microsatellites.

Human colorectal, endometrial, and gastric cancers with defective DNA mismatch repair (MMR) have microsatellite instability, a unique molecular alteration characterized by widespread frameshift mutations of repetitive DNA sequences. We developed "Kangaroo," a bioinformatics program for searches in nucleotide and protein sequence databases, and performed an in silico genome scan for DNA coding microsatellites that may have novel mutations in MMR-deficient cancers. Examination of 29 previously untested coding polyadenines revealed widespread mutations in MMR-deficient colorectal cancers, with the highest frequencies in ERCC5, CASP8AP2, p72, RAD50, CDC25, RECQL1, CBF2, RACK7, GRK4, and DNAPK (range, 10-33%). This algorithm allows comprehensive mutation profiling of MMR-deficient cancers, an important step in understanding the pathogenesis of these neoplasms.

Algorithms↗

Analysis of genetic variation in the GenomEUtwin project.

Multiallelic short tandem repeat polymorphisms, or microsatellites, are useful markers in genome wide scans to identify chromosomal regions containing genes underlying disease loci. The biallelic single nucleotide polymorphism (SNP) can be used to fine map previously identified large candidate regions or to test functional candidate genes by association analysis. In the GenomEUtwin project the population based impact of susceptibility genes for six multifactorial traits will be studied. A genome wide panel of informative human microsatellite markers will be analyzed by fluorescent capillary electrophoresis in well characterized twin and population samples. Contrary to microsatellites, selection of the most informative panels of SNPs is hampered by imperfect data on the allele frequencies and population distribution of SNPs markers in the databases. Therefore, selection of SNPs requires a substantial amount of bioinformatics, and, the SNPs need to be validated experimentally in the relevant populations prior to genotyping large sample sets. In the GenomEUtwin project, large scale genotyping of SNPs will be performed using the SNPstreamUHT and MassARRAY genotyping systems that are based on the primer extension reaction principle combined with fluorescent and mass spectrometric detection, respectively. Production of the genotyping data will be a joint effort by GenomEUtwin partners at the University of Helsinki, the National Public Health Institute in Helsinki, Finland and Uppsala University, Sweden. All genotyping data will be stored in a common database established specifically for the GenomEUtwin project, from where it can be accessed by the twin research centres that provided the samples for genotyping.

Databases, Genetic↗

Discovering motif pairs at interaction sites from protein sequences on a proteome-wide scale.

MOTIVATION: Protein-protein interaction, mediated by protein interaction sites, is intrinsic to many functional processes in the cell. In this paper, we propose a novel method to discover patterns in protein interaction sites. We observed from protein interaction networks that there exist a kind of significant substructures called interacting protein group pairs, which exhibit an all-versus-all interaction between the two protein-sets in such a pair. The full-interaction between the pair indicates a common interaction mechanism shared by the proteins in the pair, which can be referred as an interaction type. Motif pairs at the interaction sites of the protein group pairs can be used to represent such interaction type, with each motif derived from the sequences of a protein group by standard motif discovery algorithms. The systematic discovery of all pairs of interacting protein groups from large protein interaction networks is a computationally challenging problem. By a careful and sophisticated problem transformation, the problem is solved using efficient algorithms for mining frequent patterns, a problem extensively studied in data mining. RESULTS: We found 5349 pairs of interacting protein groups from a yeast interaction dataset. The expected value of sequence identity within the groups is only 7.48%, indicating non-homology within these protein groups. We derived 5343 motif pairs from these group pairs, represented in the form of blocks. Comparing our motifs with domains in the BLOCKS and PRINTS databases, we found that our blocks could be mapped to an average of 3.08 correlated blocks in these two databases. The mapped blocks occur 4221 out of total 6794 domains (protein groups) in these two databases. Comparing our motif pairs with iPfam consisting of 3045 interacting domain pairs derived from PDB, we found 47 matches occurring in 105 distinct PDB complexes. Comparing with another putative domain interaction database InterDom, we found 203 matches. AVAILABILITY: http://research.i2r.a-star.edu.sg/BindingMotifPairs/resources. SUPPLEMENTARY INFORMATION: http://research.i2r.a-star.edu.sg/BindingMotifPairs and Bioinformatics online.

Algorithms↗

Expression analysis of members of the neuronal calcium sensor protein family: combining bioinformatics and Western blot analysis.

We have used in silico mining of public databases (NCBI UniGene and NCI SAGE Anatomic Viewer) as a tool to obtain the tissue distribution pattern of three members of the neuronal calcium sensor protein family, namely VILIP-1, hippocalcin, and NCS-1 in humans. The theoretical human mRNA expression profile of the calcium sensor proteins derived from expressed sequence tag (EST) and serial analysis of gene expression (SAGE) data was compared with expression data from human tissues obtained by Western blot analysis. Since the EST databank searches do not yet give comparable results for rat which is often used as model animal, we have also analyzed the protein expression in rat tissues. Similar to the human expression profile in rat tissues calcium sensor proteins are mainly detected in the nervous system, but the data consistently implicated the additional expression in peripheral tissues with remarkable differences between the calcium sensor proteins.

Animals↗

The quest to deduce protein function from sequence: the role of pattern databases.

In the wake of the numerous now-fruitful genome projects, we have witnessed a 'tsunami' of sequence data and with it the birth of the field of bioinformatics. Bioinformatics involves the application of information technology to the management and analysis of biological data. For many of us, this means that databases and their search tools have become an essential part of the research environment. However, the rate of sequence generation and the haphazard proliferation of databases have made it difficult to keep pace with developments, even for the cognoscenti. Moreover, increasing amounts of sequence information do not necessarily equate with an increase in knowledge, and in the panic to automate the route from raw data to biological insight, we may be generating and propagating innumerable errors in our precious databases. In the genome era upon us, researchers want rapid, easy-to-use, reliable tools for functional characterisation of newly determined sequences. For the pharmaceutical industry in particular, the Pandora's box of bioinformatics harbours an information-rich nugget, ripe with potential drug targets and possible new avenues for the development of therapeutic agents. This review outlines the current status of the major pattern databases now used routinely in the analysis of protein sequences. The review is divided into three main sections. In the first, commonly used terms are defined and the methods behind the databases are briefly described; in the second, the structure and content of the principal pattern databases are discussed; and in the final part, several alignment databases, which are frequently confused with pattern databases, are mentioned. For the new-comer, the array of resources, the range of methods behind them and the different tools required to search them can be confusing. The review therefore also briefly mentions a current international endeavour to integrate the diverse databases, which effort should facilitate sequence analysis in the future. This is particularly important for target-discovery programmes, where the challenge is to rationalise the enormous numbers of potential targets generated by sequence database searches. This problem may be addressed, at least in part, by reducing search outputs to the more focused and manageable subsets suggested by searches of integrated groups of family-specific pattern databases.

Amino Acid Motifs↗

Viral genome organizer: a system for analyzing complete viral genomes.

The viral genome organizer (VGO) is designed to simplify the characterization and annotation of complete viral genomes (particularly those of large poxviruses) and to help researchers discover new genes and detect gene fragmentation. VGO is based on Genotator [Harris, N.L., 1997. Genome Res. 7, 754-762], an annotation workbench designed for the analysis of eukaryotic genomic sequences. VGO automates a number of database search routines (FASTA, BLASTP, PSI-BLAST and TBLASTN), processes the results through a multiple-alignment viewer (MView; [Brown, N.P., Leroy, C., Sander, C. , 1998. Bioinformatics 14, 380-381]) and serves to manage the hundreds of DNA, protein and database search results files that must be organized when dealing with large complete poxviral genomes. It also directs the generation a self-dotplot of the genome by Dotter [Sonnhammer, E.L.L., Durbin, R., 1995. A dot-matrix program with dynamic threshold control suited for genomic DNA and protein sequence analysis. Gene 167: GC1-10. http://www.sanger.ac. uk/Software/Dotter/] to uncover repeated genes and sequences and provides Internet links to programs for generation of restriction maps and analysis of potential PCR primers. The user-friendly graphical interface displays DNA and protein sequences, links to search results, ORFs, stop-start codons, restriction sites and flags of database searches. Currently, VGO and associated programs run in an X-windows environment on commonly available UNIX machines.

Databases, Factual↗

A comparison of microarray databases.

Microarray technology has become one of the most important functional genomics technologies. A proliferation of microarray databases has resulted. It can be difficult for researchers exploring this technology to know which bioinformatics systems best meet their requirements. In order to obtain a better understanding of the available systems, a survey and comparative analysis of microarray databases was undertaken. The survey included databases that are currently available, as well as databases that should become available in early 2001. Databases fall into three categories: (i) those that can be installed locally, (ii) those available for public data submission and (iii) those available for public query. Developers of microarray gene-expression databases were asked questions regarding the scope and availability of their database, its system requirements, its future compliance with MGED (Microarray Gene Expression Database) standards, and its associated analytical tools. Participants included AMAD (Stanford/Berkeley/UCSF), ArrayExpress (EBI), ChipDB (MIT/Whitehead), GeneX (NCGR), GeNet (Silicon Genetics), GeneDirector (BioDiscovery), GEO (NCBI), GXD (Jackson Laboratory), mAdb (NCI), maxdSQL (University of Manchester), NOMAD (UCSF), RAD (University of Pennsylvania) and SMD (Stanford University). Other database developers were contacted but data was not available at the time of manuscript preparation. Each database fulfils a different role, reflecting the widely varying needs of microarray users.

Animals↗