An object model and database for functional genomics

MOTIVATION: Large-scale functional genomics analysis is now feasible and presents significant challenges in data analysis, storage and querying. Data standards are required to enable the development of public data repositories and to improve data sharing. There is an established data format for microarrays (microarray gene expression markup language, MAGE-ML) and a draft standard for proteomics (PEDRo). We believe that all types of functional genomics experiments should be annotated in a consistent manner, and we hope to open up new ways of comparing multiple datasets used in functional genomics. RESULTS: We have created a functional genomics experiment object model (FGE-OM), developed from the microarray model, MAGE-OM and two models for proteomics, PEDRo and our own model (Gla-PSI-Glasgow Proposal for the Proteomics Standards Initiative). FGE-OM comprises three namespaces representing (i) the parts of the model common to all functional genomics experiments; (ii) microarray-specific components; and (iii) proteomics-specific components. We believe that FGE-OM should initiate discussion about the contents and structure of the next version of MAGE and the future of proteomics standards. A prototype database called RNA And Protein Abundance Database (RAPAD), based on FGE-OM, has been implemented and populated with data from microbial pathogenesis. AVAILABILITY: FGE-OM and the RAPAD schema are available from http://www.gusdb.org/fge.html, along with a set of more detailed diagrams. RAPAD can be accessed by registration at the site.     

Comparative Mouse Genomics Centers Consortium: the Mouse Genotype Database

The Comparative Mouse Genomics Centers Consortium (CMGCC) is a branch of the Environmental Genome Project sponsored by the National Institute of Environmental Health Sciences (NIEHS) focusing upon the identification of human single nucleotide polymorphisms (SNPs) that may confer disease susceptibility within the human population. The goal of the CMGCC (http://www.niehs.nih.gov/cmgcc/) is to make genetic mouse models for human SNPs within cell cycle control, DNA replication and DNA repair genes that may be associated with human pathologies. In order to facilitate information sharing and analysis within the consortium a set of informatics resources have been generated to support the mouse model development efforts. The primary entry point for information about the mouse models developed by the consortium is through the CMGCC Genotype Database (http://mrages.niehs.nih.gov/genotype/), which maintains both a consortium specific and public access display of the available and developing mouse models.     

ORIOGEN: order restricted inference for ordered gene expression data

SUMMARY: ORIOGEN is a user-friendly Java-based software package for selecting and clustering genes according to their profiles across various treatment groups. In particular, ORIOGEN is useful for analyzing data obtained from time-course or dose-response type experiments. AVAILABILITY: The ORIOGEN software can be downloaded freely from http://dir.niehs.nih.gov/dirbb/oriogen/index.cfm CONTACT: peddada@niehs.nih.gov (for statistical questions) and oriogen@constellagroup.com (for software support) SUPPLEMENTARY INFORMATION: ORIOGEN has a full set of help files. Also, an example input file is provided with the download.     

Application of the GA/KNN method to SELDI proteomics data

SUMMARY: Proteomics technology has shown promise in identifying biomarkers for disease, toxicant exposure and stress. We show by example that the genetic algorithm/k-nearest neighbors method, developed for mining high-dimensional microarray gene expression data, is also capable of mining surface enhanced laser desorption/ionization-time-of-flight proteomics data. AVAILABILITY: The source code of the program and documentation on how to use it are freely available to non-commercial users at http://dir.niehs.nih.gov/dirbb/lifiles/softlic.htm     

Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method.

MOTIVATION: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. METHODS: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. RESULTS: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. AVAILABILITY: The method is available at http://dir.niehs.nih.gov/microarray/datamining CONTACT: LI3@niehs.nih.gov     

caCORE: a common infrastructure for cancer informatics

MOTIVATION:Sites with substantive bioinformatics operations are challenged to build data processing and delivery infrastructure that provides reliable access and enables data integration. Locally generated data must be processed and stored such that relationships to external data sources can be presented. Consistency and comparability across data sets requires annotation with controlled vocabularies and, further, metadata standards for data representation. Programmatic access to the processed data should be supported to ensure the maximum possible value is extracted. Confronted with these challenges at the National Cancer Institute Center for Bioinformatics, we decided to develop a robust infrastructure for data management and integration that supports advanced biomedical applications. RESULTS: We have developed an interconnected set of software and services called caCORE. Enterprise Vocabulary Services (EVS) provide controlled vocabulary, dictionary and thesaurus services. The Cancer Data Standards Repository (caDSR) provides a metadata registry for common data elements. Cancer Bioinformatics Infrastructure Objects (caBIO) implements an object-oriented model of the biomedical domain and provides Java, Simple Object Access Protocol and HTTP-XML application programming interfaces. caCORE has been used to develop scientific applications that bring together data from distinct genomic and clinical science sources. AVAILABILITY: caCORE downloads and web interfaces can be accessed from links on the caCORE web site (http://ncicb.nci.nih.gov/core). caBIO software is distributed under an open source license that permits unrestricted academic and commercial use. Vocabulary and metadata content in the EVS and caDSR, respectively, is similarly unrestricted, and is available through web applications and FTP downloads. SUPPLEMENTARY INFORMATION: http://ncicb.nci.nih.gov/core/publications contains links to the caBIO 1.0 class diagram and the caCORE 1.0 Technical Guide, which provide detailed information on the present caCORE architecture, data sources and APIs. Updated information appears on a regular basis on the caCORE web site (http://ncicb.nci.nih.gov/core).     

ART: a next-generation sequencing read simulator

ART is a set of simulation tools that generate synthetic next-generation sequencing reads. This functionality is essential for testing and benchmarking tools for next-generation sequencing data analysis including read alignment, de novo assembly and genetic variation discovery. ART generates simulated sequencing reads by emulating the sequencing process with built-in, technology-specific read error models and base quality value profiles parameterized empirically in large sequencing datasets. We currently support all three major commercial next-generation sequencing platforms: Roche's 454, Illumina's Solexa and Applied Biosystems' SOLiD. ART also allows the flexibility to use customized read error model parameters and quality profiles. AVAILABILITY: Both source and binary software packages are available at http://www.niehs.nih.gov/research/resources/software/art.     

CDD: a curated Entrez database of conserved domain alignments

The Conserved Domain Database (CDD) is now indexed as a separate database within the Entrez system and linked to other Entrez databases such as MEDLINE(R). This allows users to search for domain types by name, for example, or to view the domain architecture of any protein in Entrez's sequence database. CDD can be accessed on the WorldWideWeb at http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=cdd. Users may also employ the CD-Search service to identify conserved domains in new sequences, at http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi. CD-Search results, and pre-computed links from Entrez's protein database, are calculated using the RPS-BLAST algorithm and Position Specific Score Matrices (PSSMs) derived from CDD alignments. CD-Searches are also run by default for protein-protein queries submitted to BLAST(R) at http://www.ncbi.nlm.nih.gov/BLAST. CDD mirrors the publicly available domain alignment collections SMART and PFAM, and now also contains alignment models curated at NCBI. Structure information is used to identify the core substructure likely to be present in all family members, and to produce sequence alignments consistent with structure conservation. This alignment model allows NCBI curators to annotate 'columns' corresponding to functional sites conserved among family members.     

The MGED Ontology: a resource for semantics-based description of microarray experiments

MOTIVATION: The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. RESULTS: Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. AVAILABILITY: The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). CONTACT: Stoeckrt@pcbi.upenn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

OMSSA Parser: An open‐source library to parse and extract data from OMSSA MS/MS search results

Protein identification by MS is an important technique in both gel‐based and gel‐free proteome studies. The Open Mass Spectrometry Search Algorithm (OMSSA) ( http://pubchem.ncbi.nlm.nih.gov/omssa ) is an open‐source search engine that can be used to identify MS/MS spectra acquired in these experiments. We here present a lightweight, open‐source Java software library, OMSSA Parser ( http://code.google.com/p/omssa‐parser ), which parses OMSSA omx result files into easy accessible and fully functional object models. In addition, we also provide examples illustrating the usage of our library.     

Collated mutations in mitochondrial DNA (mtDNA) depletion syndrome (excluding the mitochondrial gamma polymerase,POLG1)

These tables list both published and a number of unpublished mutations in genes associated with early onset defects in mitochondrial DNA (mtDNA) maintenance including C10orf2, SUCLG1, SUCLA2, TYMP, RRM2B, MPV17, DGUOK and TK2. The list should not be taken as evidence that any particular mutation is pathogenic. We have included genes known to cause mtDNA depletion, excluding POLG1, because of the existing database (http://tools.niehs.nih.gov/polg/). We have also excluded mutations in C10orf2 associated with dominant adult onset disorders.     

TAGster: efficient selection of LD tag SNPs in single or multiple populations

Genetic association studies increasingly rely on the use of linkage disequilibrium (LD) tag SNPs to reduce genotyping costs. We developed a software package TAGster to select, evaluate and visualize LD tag SNPs both for single and multiple populations. We implement several strategies to improve the efficiency of current LD tag SNP selection algorithms: (1) we modify the tag SNP selection procedure of Carlson et al. to improve selection efficiency and further generalize it to multiple populations. (2) We propose a redundant SNP elimination step to speed up the exhaustive tag SNP search algorithm proposed by Qin et al. (3) We present an additional multiple population tag SNP selection algorithm based on the framework of Howie et al., but using our modified exhaustive search procedure. We evaluate these methods using resequenced candidate gene data from the Environmental Genome Project and show improvements in both computational and tagging efficiency. AVAILABILITY: The software Package TAGster is freely available at http://www.niehs.nih.gov/research/resources/software/tagster/     

A random variance model for detection of differential gene expression in small microarray experiments

MOTIVATION: Microarray techniques provide a valuable way of characterizing the molecular nature of disease. Unfortunately expense and limited specimen availability often lead to studies with small sample sizes. This makes accurate estimation of variability difficult, since variance estimates made on a gene by gene basis will have few degrees of freedom, and the assumption that all genes share equal variance is unlikely to be true. RESULTS: We propose a model by which the within gene variances are drawn from an inverse gamma distribution, whose parameters are estimated across all genes. This results in a test statistic that is a minor variation of those used in standard linear models. We demonstrate that the model assumptions are valid on experimental data, and that the model has more power than standard tests to pick up large changes in expression, while not increasing the rate of false positives. AVAILABILITY: This method is incorporated into BRB-ArrayTools version 3.0 (http://linus.nci.nih.gov/BRB-ArrayTools.html). SUPPLEMENTARY MATERIAL: ftp://linus.nci.nih.gov/pub/techreport/RVM_supplement.pdf     

BLAST 2 Sequences, a new tool for comparing protein and nucleotide sequences

'BLAST 2 Sequences', a new BLAST-based tool for aligning two protein or nucleotide sequences, is described. While the standard BLAST program is widely used to search for homologous sequences in nucleotide and protein databases, one often needs to compare only two sequences that are already known to be homologous, coming from related species or, e.g. different isolates of the same virus. In such cases searching the entire database would be unnecessarily time-consuming. 'BLAST 2 Sequences' utilizes the BLAST algorithm for pairwise DNA-DNA or protein-protein sequence comparison. A World Wide Web version of the program can be used interactively at the NCBI WWW site (http://www.ncbi.nlm.nih.gov/gorf/bl2.++ +html). The resulting alignments are presented in both graphical and text form. The variants of the program for PC (Windows), Mac and several UNIX-based platforms can be downloaded from the NCBI FTP site (ftp://ncbi.nlm.nih.gov).     

GeneMachine: gene prediction and sequence annotation

MOTIVATION: A number of free-standing programs have been developed in order to help researchers find potential coding regions and deduce gene structure for long stretches of what is essentially 'anonymous DNA'. As these programs apply inherently different criteria to the question of what is and is not a coding region, multiple algorithms should be used in the course of positional cloning and positional candidate projects to assure that all potential coding regions within a previously-identified critical region are identified. RESULTS: We have developed a gene identification tool called GeneMachine which allows users to query multiple exon and gene prediction programs in an automated fashion. BLAST searches are also performed in order to see whether a previously-characterized coding region corresponds to a region in the query sequence. A suite of Perl programs and modules are used to run MZEF, GENSCAN, GRAIL 2, FGENES, RepeatMasker, Sputnik, and BLAST. The results of these runs are then parsed and written into ASN.1 format. Output files can be opened using NCBI Sequin, in essence using Sequin as both a workbench and as a graphical viewer. The main feature of GeneMachine is that the process is fully automated; the user is only required to launch GeneMachine and then open the resulting file with Sequin. Annotations can then be made to these results prior to submission to GenBank, thereby increasing the intrinsic value of these data. AVAILABILITY: GeneMachine is freely-available for download at http://genome.nhgri.nih.gov/genemachine. A public Web interface to the GeneMachine server for academic and not-for-profit users is available at http://genemachine.nhgri.nih.gov. The Web supplement to this paper may be found at http://genome.nhgri.nih.gov/genemachine/supplement/.     

dbSNP: a database of single nucleotide polymorphisms

In response to a need for a general catalog of genome variation to address the large-scale sampling designs required by association studies, gene mapping and evolutionary biology, the National Cancer for Biotechnology Information (NCBI) has established the dbSNP database. Submissions to dbSNP will be integrated with other sources of information at NCBI such as GenBank, PubMed, LocusLink and the Human Genome Project data. The complete contents of dbSNP are available to the public at website: http://www.ncbi.nlm.nih.gov/SNP. Submitted SNPs can also be downloaded via anonymous FTP at ftp://ncbi.nlm.nih.gov/snp/     

PROSIT,AN ONLINE SERVICE TO CALCULATE PSEUDOROTATIONAL PARAMETERS OF NUCLEOSIDES AND NUCLEOTIDES

The online service PROSIT (Pseudo-Rotational Online Service and Interactive Tool) is a free service available at http://cactus.nci.nih.gov/prosit/ that performs pseudorotational analysis of nucleosides(tides). PROSIT reads the 3D coordinates of nucleosides and returns the pseudorotational phase angle P, puckering amplitude ν_max, and other related information. As examples, the sugar conformations in a parallel-stranded guanine tetraplex and a four-way Holliday junction are presented here.     

The Homeodomain Resource: 2003 update

The Homeodomain Resource is a searchable, curated collection of information for the homeodomain protein family. The resource is organized in a compact form and provides user-friendly interfaces for both querying the component databases and assembling customized datasets. The current release (version 5.0, October 2002) contains 1056 full-length homeodomain-containing sequences, 37 experimentally-derived structures, 81 homeodomain interactions, 84 homeodomain DNA-binding sites and 114 homeodomain proteins implicated in human genetic disorders. A new feature of this new release is the inclusion of experimentally-derived protein-protein interaction data for homeodomain family members. All entries are cross-linked for easy retrieval of the original records from source databases. The Homeodomain Resource is freely available through the World Wide Web at http://research.nhgri.nih.gov/homeodomain/.