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At least 811 records · Page 45Linked to original sources

PubMed2006

Structure alignment based on coding of local geometric measures.

BACKGROUND: A structure alignment method based on a local geometric property is presented and its performance is tested in pairwise and multiple structure alignments. In this approach, the writhing number, a quantity originating from integral formulas of Vassiliev knot invariants, is used as a local geometric measure. This measure is used in a sliding window to calculate the local writhe down the length of the protein chain. By encoding the distribution of writhing numbers across all the structures in the protein databank (PDB), protein geometries are represented in a 20-letter alphabet. This encoding transforms the structure alignment problem into a sequence alignment problem and allows the well-established algorithms of sequence alignment to be employed. Such geometric alignments offer distinct advantages over structural alignments in Cartesian coordinates as it better handles structural subtleties associated with slight twists and bends that distort one structure relative to another. RESULTS: The performance of programs for pairwise local alignment (TLOCAL) and multiple alignment (TCLUSTALW) are readily adapted from existing code for Smith-Waterman pairwise alignment and for multiple sequence alignment using CLUSTALW. The alignment algorithms employed a blocked scoring matrix (TBLOSUM) generated using the frequency of changes in the geometric alphabet of a block of protein structures. TLOCAL was tested on a set of 10 difficult proteins and found to give high quality alignments that compare favorably to those generated by existing pairwise alignment programs. A set of protein comparison involving hinged structures was also analyzed and TLOCAL was seen to compare favorably to other alignment methods. TCLUSTALW was tested on a family of protein kinases and reveal conserved regions similar to those previously identified by a hand alignment. CONCLUSION: These results show that the encoding of the writhing number as a geometric measure allow high quality structure alignments to be generated using standard algorithms of sequence alignment. This approach provides computationally efficient algorithms that allow fast database searching and multiple structure alignment. Because the geometric measure can employ different window sizes, the method allows the exploration of alignments on different, well-defined length scales.

Algorithms↗

PubMed2003

Gene expression in the mouse preimplantation embryo.

Mouse preimplantation development represents a tightly controlled programme of gene expression and cell division, which starts with the fertilized egg and ends with implantation of the blastocyst approximately 4.5 days later. Spatial and temporal differences in gene expression underpin establishment of axes at the two-cell stage and development of the trophectoderm and inner cell mass after embryo compaction at the eight-cell stage. Approximately 15 700 mouse genes expressed during preimplantation development have been identified from cDNA sequences deposited in the UniGene database of the National Institutes of Health. This inventory of preimplantation genes is the starting point for identifying signalling modules that function in preimplantation development.

Animals↗

PubMed2000

[Development of research support environment for molecular pharmacology and toxicology].

The Chem-Bio Informatics division has developed the institute-wide computer network which is connected to the Internet via IMnet (Inter Ministry Network) sponsored by the Government. We call the network and associated information and computing infrastructure as NICI, the NIHS Information and Computing Infrastructure. The primary goal of NICI is to provide general communication environment such as e-mails, and the second goal is to provide WWW environment. Accomplishing these two goals the division is now challenging to develop information and computing environment that directly supports pharmaceutical and toxicological research. The prototype of this environment which consists of various chemical and biomolecular databases and computing tools has been developed under the name of MOBI-DICS. The concept and vision on how to extend the prototype to institute-wide infrastructure was discussed.

Computational Biology↗

PubMed2004

Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management.

Recent technical advances in combinatorial chemistry, genomics, and proteomics have made available large databases of biological and chemical information that have the potential to dramatically improve our understanding of cancer biology at the molecular level. Such an understanding of cancer biology could have a substantial impact on how we detect, diagnose, and manage cancer cases in the clinical setting. One of the biggest challenges facing clinical oncologists is how to extract clinically useful knowledge from the overwhelming amount of raw molecular data that are currently available. In this paper, we discuss how the exploratory data analysis techniques of machine learning and high-dimensional visualization can be applied to extract clinically useful knowledge from a heterogeneous assortment of molecular data. After an introductory overview of machine learning and visualization techniques, we describe two proprietary algorithms (PURS and RadViz) that we have found to be useful in the exploratory analysis of large biological data sets. We next illustrate, by way of three examples, the applicability of these techniques to cancer detection, diagnosis, and management using three very different types of molecular data. We first discuss the use of our exploratory analysis techniques on proteomic mass spectroscopy data for the detection of ovarian cancer. Next, we discuss the diagnostic use of these techniques on gene expression data to differentiate between squamous and adenocarcinoma of the lung. Finally, we illustrate the use of such techniques in selecting from a database of chemical compounds those most effective in managing patients with melanoma versus leukemia.

Artificial Intelligence↗

PubMed1999

Helminth genome analysis: the current status of the filarial and schistosome genome projects. Filarial Genome Project. Schistosome Genome Project.

Genome projects for the parasitic helminths Brugia malayi (a representative filarial nematode) and Schistosoma were initiated in 1995 by the World Health Organization with the ultimate objectives of identifying new vaccine candidates and drug targets and of developing low resolution genome maps. Because no genetic maps are available, and very few genes have been characterized from either parasite group, the first goal of both Initiatives has been to catalogue new genes for future placement on chromosome and physical maps. These genes have been identified by the expressed sequence tag (EST) approach, utilising cDNA libraries constructed from diverse life cycle stages. To date, the Initiatives have deposited over 16,000 Brugia ESTs and nearly 8000 Schistosoma ESTs in Genbank's dbEST database, corresponding to 6000 and over 3600 genes respectively (33% of Brugia's estimated gene compliment, 18-24% of that of Schistosoma). Large fragment, genomic libraries have been constructed in BAC and YAC vectors for studies of genomic organization and for physical and chromosome mapping, and public, hypertext genomic databases have been established to facilitate data access. We present a summary of progress within the helminth genome initiatives and give several examples of important gene discoveries and future applications of these data.

Animals↗

PubMed2002

LigBase: a database of families of aligned ligand binding sites in known protein sequences and structures.

A database comprising all ligand-binding sites of known structure aligned with all related protein sequences and structures is described. Currently, the database contains approximately 50000 ligand-binding sites for small molecules found in the Protein Data Bank (PDB). The structure-structure alignments are obtained by the Combinatorial Extension (CE) program (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998) and sequence-structure alignments are extracted from the ModBase database of comparative protein structure models for all known protein sequences (Sanchez et al., Nucleic Acids Res., 28, 250-253, 2000). It is possible to search for binding sites in LigBase by a variety of criteria. LigBase reports summarize ligand data including relevant structural information from the PDB file, such as ligand type and size, and contain links to all related protein sequences in the TrEMBL database. Residues in the binding sites are graphically depicted for comparison with other structurally defined family members. LigBase provides a resource for the analysis of families of related binding sites.

Binding Sites↗

PubMed2004

SNP500Cancer: a public resource for sequence validation and assay development for genetic variation in candidate genes.

The SNP500Cancer Database provides sequence and genotype assay information for candidate single nucleotide polymorphisms (SNPs) useful in mapping complex diseases, such as cancer. The database is an integral component of the NCI's Cancer Genome Anatomy Project. SNP500Cancer provides bi-directional sequencing information on a set of control DNA samples derived from anonymized subjects (102 Coriell samples representing four self-described ethnic groups: African/African-American, Caucasian, Hispanic and Pacific Rim). All SNPs are chosen from public databases and reports, and the choice of genes includes a bias towards non-synonymous and promoter SNPs in genes that have been implicated in one or more cancers. The web site is searchable by gene, chromosome, gene ontology pathway and by known dbSNP ID. As of July 2003, the database contains over 3400 SNPs, 2490 of which have been sequenced in the SNP500Cancer population. For each analyzed SNP, gene location and over 200 bp of surrounding annotated sequence (including nearby SNPs) are provided, with frequency information in total and per subpopulation, and calculation of Hardy-Weinberg Equilibrium (HWE) for each subpopulation. Sequence validated SNPs with minor allele frequency > 5% are entered into a high-throughput pipeline for genotyping analysis to determine concordance for the same 102 samples. The website provides the conditions for validated genotyping assays. SNP500Cancer provides an invaluable resource for investigators to select SNPs for analysis, design genotyping assays using validated sequence data, choose selected assays already validated on one or more genotyping platforms, and select reference standards for genotyping assays. The SNP500Cancer Database is freely accessible via the web page at http://snp500cancer.nci.nih.gov/.

Computational Biology↗

PubMed2003

The ontology of the gene ontology.

The rapidly increasing wealth of genomic data has driven the development of tools to assist in the task of representing and processing information about genes, their products and their functions. One of the most important of these tools is the Gene Ontology (GO), which is being developed in tandem with work on a variety of bioinformatics databases. An examination of the structure of GO, however, reveals a number of problems, which we believe can be resolved by taking account of certain organizing principles drawn from philosophical ontology. We shall explore the results of applying such principles to GO with a view to improving GO's consistency and coherence and thus its future applicability in the automated processing of biological data.

Computational Biology↗

PubMed2006

A case study of the utility of the HapMap database for pharmacogenomic haplotype analysis in the Taiwanese population.

BACKGROUND: Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. The goal of this work was to evaluate the feasibility of using SNP information of the Han Chinese in Beijing (CHB) population from the HapMap database for clinical association studies in the Taiwanese (TWN) population. METHODS: We compared the HapMap populations with our TWN study population with regard to allele frequencies for common SNPs in two candidate genes for antidepressant treatment response to determine the applicability of the HapMap CHB data for SNP selection in the TWN population. RESULTS AND CONCLUSION: Our preliminary results suggest that there was no significant difference, in terms of allele and haplotype frequencies, between the CHB population of the HapMap database and the TWN population collected by Vita Genomics Inc. Therefore, it is possible to use the CHB population of the HapMap database for SNP selection in association studies for the TWN population. Using haplotype analysis, we generated a panel of SNPs that may be strongly relevant to antidepressant response in this population.

Asian People↗

PubMed2006

Drought tolerance genes in rice.

Quantitative trait loci (QTLs) for drought tolerance (DT) can be readily identified in available databases and in this paper, these QTLs were summarized in the form of a consensus map. An in silico strategy was then deployed to mine for candidate genes associated with DT QTLs using rice dbEST and rice genome databases. DT QTLs on rice chromosomes 1, 2, 4, 8, and 9 were selected to test the method. The result showed candidate genes associated with DT could be readily identified.

Acclimatization↗

PubMed2000

Identification of novel multi-transmembrane proteins from genomic databases using quasi-periodic structural properties.

MOTIVATION: Identification of novel G protein-coupled receptors and other multi-transmembrane proteins from genomic databases using structural features. RESULTS: Here we describe a new algorithm for identifying multi-transmembrane proteins from genomic databases with a specific application to identifying G protein-coupled receptors (GPCRs) that we call quasi-periodic feature classifier (QFC). The QFC algorithm uses concise statistical variables as the 'feature space' to characterize the quasi-periodic physico-chemical properties of multi-transmembrane proteins. For the case of identifying GPCRs, the variables are then used in a non-parametric linear discriminant function to separate GPCRs from non-GPCRs. The algorithm runs in time linearly proportional to the number of sequences, and performance on a test dataset shows 96% positive identification of known GPCRs. The QFC algorithm also works well with short random segments of proteins and it positively identified GPCRs at a level greater than 90% even with segments as short as 100 amino acids. The primary advantage of the algorithm is that it does not directly use primary sequence patterns which may be subject to sampling bias. The utility of the new algorithm has been demonstrated by the isolation from the Drosophila genome project database of a novel class of seven-transmembrane proteins which were shown to be the elusive olfactory receptor genes of Drosophila.

Algorithms↗

PubMed2001

Transcription-associated protein families are primarily taxon-specific.

The mechanisms controlling gene regulation appear to be fundamentally different in eukaryotes and prokaryotes (Struhl (1999) CELL, 98, 1-4). To investigate this diversity further, we have analysed the distribution of all known transcription-associated proteins (TAPs), as reflected by sequence database annotations. Our results for the primary phylogenetic domains (Archaea, Bacteria and Eukaryota) show that TAP families are mostly taxon-specific and very few transcriptional regulators are common across these domains.

Computational Biology↗

PubMed2004

GeneDB: a resource for prokaryotic and eukaryotic organisms.

GeneDB (http://www.genedb.org/) is a genome database for prokaryotic and eukaryotic organisms. The resource provides a portal through which data generated by the Pathogen Sequencing Unit at the Wellcome Trust Sanger Institute and other collaborating sequencing centres can be made publicly available. It combines data from finished and ongoing genome and expressed sequence tag (EST) projects with curated annotation, that can be searched, sorted and downloaded, using a single web based resource. The current release stores 11 datasets of which six are curated and maintained by biologists, who review and incorporate information from the scientific literature, public databases and the respective research communities.

Animals↗

PubMed2004

THGS: a web-based database of Transmembrane Helices in Genome Sequences.

Transmembrane Helices in Genome Sequences (THGS) is an interactive web-based database, developed to search the transmembrane helices in the user-interested gene sequences available in the Genome Database (GDB). The proposed database has provision to search sequence motifs in transmembrane and globular proteins. In addition, the motif can be searched in the other sequence databases (Swiss-Prot and PIR) or in the macromolecular structure database, Protein Data Bank (PDB). Further, the 3D structure of the corresponding queried motif, if it is available in the solved protein structures deposited in the Protein Data Bank, can also be visualized using the widely used graphics package RASMOL. All the sequence databases used in the present work are updated frequently and hence the results produced are up to date. The database THGS is freely available via the world wide web and can be accessed at http:// pranag.physics.iisc.ernet.in/thgs/ or http://144.16. 71.10/thgs/.

Animals↗

PubMed2006

BASC: an integrated bioinformatics system for Brassica research.

The BASC system provides tools for the integrated mining and browsing of genetic, genomic and phenotypic data. This public resource hosts information on Brassica species supporting the Multinational Brassica Genome Sequencing Project, and is based upon five distinct modules, ESTDB, Microarray, MarkerQTL, CMap and EnsEMBL. ESTDB hosts expressed gene sequences and related annotation derived from comparison with GenBank, UniRef and the genome sequence of Arabidopsis. The Microarray module hosts gene expression information related to genes annotated within ESTDB. MarkerQTL is the most complex module and integrates information on genetic markers, maps, individuals, genotypes and traits. Two further modules include an Arabidopsis EnsEMBL genome viewer and the CMap comparative genetic map viewer for the visualization and integration of genetic and genomic data. The database is accessible at http://bioinformatics.pbcbasc.latrobe.edu.au.

Arabidopsis↗

PubMed2000

CART classification of human 5' UTR sequences.

A nonredundant database of 2312 full-length human 5'-untranslated regions (UTRs) was carefully prepared using state-of-the-art experimental and computational technologies. A comprehensive computational analysis of this data was conducted for characterizing the 5' UTR features. Classification and regression tree (CART) analysis was used to classify the data into three distinct classes. Class I consists of mRNAs that are believed to be poorly translated with long 5' UTRs filled with potential inhibitory features. Class II consists of terminal oligopyrimidine tract (TOP) mRNAs that are regulated in a growth-dependent manner, and class III consists of mRNAs with favorable 5' UTR features that may help efficient translation. The most accurate tree we found has 92.5% classification accuracy as estimated by cross validation. The classification model included the presence of TOP, a secondary structure, 5' UTR length, and the presence of upstream AUGs (uAUGs) as the most relevant variables. The present classification and characterization of the 5' UTRs provide precious information for better understanding the translational regulation of human mRNAs. Furthermore, this database and classification can help people build better computational models for predicting the 5'-terminal exon and separating the 5' UTR from the coding region.

5' Untranslated Regions↗

PubMed2005

Defining genes in the genome of the hyperthermophilic archaeon Pyrococcus furiosus: implications for all microbial genomes.

The original genome annotation of the hyperthermophilic archaeon Pyrococcus furiosus contained 2,065 open reading frames (ORFs). The genome was subsequently automatically annotated in two public databases by the Institute for Genomic Research (TIGR) and the National Center for Biotechnology Information (NCBI). Remarkably, more than 500 of the originally annotated ORFs differ in size in the two databases, many very significantly. For example, more than 170 of the predicted proteins differ at their N termini by more than 25 amino acids. Similar discrepancies were observed in the TIGR and NCBI databases with the other archaeal and bacterial genomes examined. In addition, the two databases contain 60 (NCBI) and 221 (TIGR) ORFs not present in the original annotation of P. furiosus. In the present study we have experimentally assessed the validity of 88 previously unannotated ORFs. Transcriptional analyses showed that 11 of 61 ORFs examined were expressed in P. furiosus when grown at either 95 or 72 degrees C. In addition, 7 of 54 ORFs examined yielded heat-stable recombinant proteins when they were expressed in Escherichia coli, although only one of the seven ORFs was expressed in P. furiosus under the growth conditions tested. It is concluded that the P. furiosus genome contains at least 17 ORFs not previously recognized in the original annotation. This study serves to highlight the discrepancies in the public databases and the problems of accurately defining the number and sizes of ORFs within any microbial genome.

Archaeal Proteins↗

PubMed2004

The status of structural genomics defined through the analysis of current targets and structures.

Structural genomics--large-scale macromolecular 3-dimenional structure determination--is unique in that major participants report scientific progress on a weekly basis. The target database (TargetDB) maintained by the Protein Data Bank (http://targetdb.pdb.org) reports this progress through the status of each protein sequence (target) under consideration by the major structural genomics centers worldwide. Hence, TargetDB provides a unique opportunity to analyze the potential impact that this major initiative provides to scientists interested in the sequence-structure-function-disease paradigm. Here we report such an analysis with a focus on: (i) temporal characteristics--how is the project doing and what can we expect in the future? (ii) target characteristics--what are the predicted functions of the proteins targeted by structural genomics and how biased is the target set when compared to the PDB and to predictions across complete genomes? (iii) structures solved--what are the characteristics of structures solved thus far and what do they contribute? The analysis required a more extensive database of structure predictions using different methods integrated with data from other sources. This database, associated tools and related data sources are available from http://spam.sdsc.edu.

Computational Biology↗