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PubMed2005

Self-organizing and self-correcting classifications of biological data.

MOTIVATION: Rapid, automated means of organizing biological data are required if we hope to keep abreast of the flood of data emanating from sequencing, microarray and similar high-throughput analyses. Faced with the need to validate the annotation of thousands of sequences and to generate biologically meaningful classifications based on the sequence data, we turned to statistical methods in order to automate these processes. RESULTS: An algorithm for automated classification based on evolutionary distance data was written in S. The algorithm was tested on a dataset of 1436 small subunit ribosomal RNA sequences and was able to classify the sequences according to an extant scheme, use statistical measurements of group membership to detect sequences that were misclassified within this scheme and produce a new classification. In this study, the use of the algorithm to address problems in prokaryotic taxonomy is discussed. AVAILABILITY: S-Plus is available from Insightful, Inc. An S-Plus implementation of the algorithm and the associated data are available at http://taxoweb.mmg.msu.edu/datasets

Algorithms↗
PubMed2004

MODBASE, a database of annotated comparative protein structure models, and associated resources.

MODBASE (http://salilab.org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on the MODELLER package for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE uses the MySQL relational database management system for flexible querying and CHIMERA for viewing the sequences and structures (http://www.cgl.ucsf.edu/chimera/). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets. The largest data set contains 1,26,629 models for domains in 659,495 out of 1,182,126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with significant similarity scores and models assessed to have the correct fold despite insignificant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e.g. the 53 new structures produced by the consortium allowed us to characterize structurally 24,113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.org/dbali) as well as web servers for automated comparative modeling with MODPIPE (MODWEB, http://salilab. org/modweb), modeling of loops in protein structures (MODLOOP, http://salilab.org/modloop) and predicting functional consequences of single nucleotide polymorphisms (SNPWEB, http://salilab. org/snpweb).

Amino Acid Sequence↗
PubMed2002

The Ensembl genome database project.

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.

Computational Biology↗
PubMed2005

Chemical and biological profiling of an annotated compound library directed to the nuclear receptor family.

Nuclear receptors form a family of ligand-activated transcription factors that regulate a wide variety of biological processes and are thus generally considered relevant targets in drug discovery. We have constructed an annotated compound library directed to nuclear receptors (NRacl) as a means for integrating the chemical and biological data being generated within this family. Special care has been put in the appropriate storage of annotations by using hierarchical classification schemes for both molecules and nuclear receptors, which takes the ability to extract knowledge from annotated compound libraries to another level. Analysis of NRacl has ultimately led to the identification of scaffolds with highly promiscuous nuclear receptor profiles and to the classification of nuclear receptor groups with similar scaffold promiscuity patterns. This information can be exploited in the design of probing libraries for deorphanization activities as well as for devising screening batteries to address selectivity issues.

Combinatorial Chemistry Techniques↗
PubMed2006

Protein kinase resource: an integrated environment for phosphorylation research.

The protein kinase superfamily is an important group of enzymes controlling cellular signaling cascades. The increasing amount of available experimental data provides a foundation for deeper understanding of details of signaling systems and the underlying cellular processes. Here, we describe the Protein Kinase Resource, an integrated online service that provides access to information relevant to cell signaling and enables kinase researchers to visualize and analyze the data directly in an online environment. The data set is synchronized with Uniprot and Protein Data Bank (PDB) databases and is regularly updated and verified. Additional annotation includes interactive display of domain composition, cross-references between orthologs and functional mapping to OMIM records. The Protein Kinase Resource provides an integrated view of the protein kinase superfamily by linking data with their visual representation. Thus, human kinases can be mapped onto the human kinome tree via an interactive display. Sequence and structure data can be easily displayed using applications developed for the PKR and integrated with the website and the underlying database. Advanced search mechanisms, such as multiparameter lookup, sequence pattern, and blast search, enable fast access to the desired information, while statistics tools provide the ability to analyze the relationships among the kinases under study. The integration of data presentation and visualization implemented in the Protein Kinase Resource can be adapted by other online providers of scientific data and should become an effective way to access available experimental information.

Amino Acid Sequence↗
PubMed2005

Text-based analysis of genes, proteins, aging, and cancer.

The diverse nature of cancer- and aging-related genes presents a challenge for large-scale studies based on molecular sequence and profiling data. An underexplored source of data for modeling and analysis is the textual descriptions and annotations present in curated gene-centered biomedical corpora. Here, 450 genes designated by surveys of the scientific literature as being associated with cancer and aging were analyzed using two complementary approaches. The first, ensemble attribute profile clustering, is a recently formulated, text-based, semi-automated data interpretation strategy that exploits ideas from statistical information retrieval to discover and characterize groups of genes with common structural and functional properties. Groups of genes with shared and unique Gene Ontology terms and protein domains were defined and examined. Human homologs of a group of known Drosphila aging-related genes are candidates for genes that may influence lifespan (hep/MAPK2K7, bsk/MAPK8, puc/LOC285193). These JNK pathway-associated proteins may specify a molecular hub that coordinates and integrates multiple intra- and extracellular processes via space- and time-dependent interactions with proteins in other pathways. The second approach, a qualitative examination of the chromosomal locations of 311 human cancer- and aging-related genes, provides anecdotal evidence for a "phenotype position effect": genes that are proximal in the linear genome often encode proteins involved in the same phenomenon. Comparative genomics was employed to enhance understanding of several genes, including open reading frames, identified as new candidates for genes with roles in aging or cancer. Overall, the results highlight fundamental molecular and mechanistic connections between progenitor/stem cell lineage determination, embryonic morphogenesis, cancer, and aging. Despite diversity in the nature of the molecular and cellular processes associated with these phenomena, they seem related to the architectural hub of tissue polarity and a need to generate and control this property in a timely manner.

Aging↗
PubMed2006

Validation and functional annotation of expression-based clusters based on gene ontology.

BACKGROUND: The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group. RESULTS: In this work, we suggest the information-theoretic concept of mutual information to investigate the relationship between groups of genes, as given by data-driven clustering, and their respective functional categories. Drawing upon related approaches (Gibbons and Roth, Genome Research 12:1574-1581, 2002), we seek to quantify to what extent individual attributes are sufficient to characterize a given group or cluster of genes. CONCLUSION: We show that the mutual information provides a systematic framework to assess the relationship between groups or clusters of genes and their functional annotations in a quantitative way. Within this framework, the mutual information allows us to address and incorporate several important issues, such as the interdependence of functional annotations and combinatorial combinations of attributes. It thus supplements and extends the conventional search for overrepresented attributes within a group or cluster of genes. In particular taking combinations of attributes into account, the mutual information opens the way to uncover specific functional descriptions of a group of genes or clustering result. All datasets and functional annotations used in this study are publicly available. All scripts used in the analysis are provided as additional files.

Algorithms↗
PubMed2006

Integrating image data into biomedical text categorization.

Categorization of biomedical articles is a central task for supporting various curation efforts. It can also form the basis for effective biomedical text mining. Automatic text classification in the biomedical domain is thus an active research area. Contests organized by the KDD Cup (2002) and the TREC Genomics track (since 2003) defined several annotation tasks that involved document classification, and provided training and test data sets. So far, these efforts focused on analyzing only the text content of documents. However, as was noted in the KDD'02 text mining contest-where figure-captions proved to be an invaluable feature for identifying documents of interest-images often provide curators with critical information. We examine the possibility of using information derived directly from image data, and of integrating it with text-based classification, for biomedical document categorization. We present a method for obtaining features from images and for using them-both alone and in combination with text-to perform the triage task introduced in the TREC Genomics track 2004. The task was to determine which documents are relevant to a given annotation task performed by the Mouse Genome Database curators. We show preliminary results, demonstrating that the method has a strong potential to enhance and complement traditional text-based categorization methods.

Artificial Intelligence↗
PubMed2025

Metabolic network reconstruction as a resource for analyzing Salmonella Typhimurium SL1344 growth in the mouse intestine.

Nontyphoidal Salmonella strains (NTS) are among the most common foodborne enteropathogens and constitute a major cause of global morbidity and mortality, imposing a substantial burden on global health. The increasing antibiotic resistance of NTS bacteria has attracted a lot of research on understanding their modus operandi during infection. Growth in the gut lumen is a critical phase of the NTS infection. This might offer opportunities for intervention. However, the metabolic richness of the gut lumen environment and the inherent complexity and robustness of the metabolism of NTS bacteria call for modeling approaches to guide research efforts. In this study, we reconstructed a thermodynamically constrained and context-specific genome-scale metabolic model (GEM) for S. Typhimurium SL1344, a model strain well-studied in infection research. We combined sequence annotation, optimization methods and in vitro and in vivo experimental data. We used GEM to explore the nutritional requirements, the growth limiting metabolic genes, and the metabolic pathway usage of NTS bacteria in a rich environment simulating the murine gut. This work provides insight and hypotheses on the biochemical capabilities and requirements of SL1344 beyond the knowledge acquired through conventional sequence annotation and can inform future research aimed at better understanding NTS metabolism and identifying potential targets for infection prevention.

Salmonella typhimurium↗
PubMed2003

Congruence of tissue expression profiles from Gene Expression Atlas, SAGEmap and TissueInfo databases.

BACKGROUND: Extracting biological knowledge from large amounts of gene expression information deposited in public databases is a major challenge of the postgenomic era. Additional insights may be derived by data integration and cross-platform comparisons of expression profiles. However, database meta-analysis is complicated by differences in experimental technologies, data post-processing, database formats, and inconsistent gene and sample annotation. RESULTS: We have analysed expression profiles from three public databases: Gene Expression Atlas, SAGEmap and TissueInfo. These are repositories of oligonucleotide microarray, Serial Analysis of Gene Expression and Expressed Sequence Tag human gene expression data respectively. We devised a method, Preferential Expression Measure, to identify genes that are significantly over- or under-expressed in any given tissue. We examined intra- and inter-database consistency of Preferential Expression Measures. There was good correlation between replicate experiments of oligonucleotide microarray data, but there was less coherence in expression profiles as measured by Serial Analysis of Gene Expression and Expressed Sequence Tag counts. We investigated inter-database correlations for six tissue categories, for which data were present in the three databases. Significant positive correlations were found for brain, prostate and vascular endothelium but not for ovary, kidney, and pancreas. CONCLUSION: We show that data from Gene Expression Atlas, SAGEmap and TissueInfo can be integrated using the UniGene gene index, and that expression profiles correlate relatively well when large numbers of tags are available or when tissue cellular composition is simple. Finally, in the case of brain, we demonstrate that when PEM values show good correlation, predictions of tissue-specific expression based on integrated data are very accurate.

Brain↗
PubMed2001

Experimental data of a single promoter can be used for in silico detection of genes with related regulation in the absence of sequence similarity.

Gene expression is presently a major focus in genome analysis, and the experimental data on regulatory mechanisms and functional transcription factor binding sites are steadily growing. However, the annotation of transcriptional regulation of sequences cannot keep pace with the exponential growth of sequence databases. Employing detailed experimental data of a single promoter or enhancer to predict genes with similar regulation would provide a powerful method to link the literature about transcriptional regulation and sequence databases. To this end, we used information on individual functional transcription factor binding sites to compose in silico promoter and enhancer models of muscle-specific genes and to analyze the rodents section of EMBL with these models. Exhaustive evaluation of all hits revealed every second to third match to be a muscle-associated gene. Moreover, functionally related regulatory regions were detected by our model-based approach even in the absence of sequence similarity. We believe that this new approach is a substanial extension to database analysis by BLAST or FASTA, which are restricted to sequence similarity.

Animals↗
PubMed2004

The CDKN2A database: Integrating allelic variants with evolution, structure, function, and disease association.

In this report, we introduce the CDKN2A Database, an online database of germline and somatic variants of the CDKN2A tumor suppressor gene recorded in human disease through the year 2002, annotated with evolutionary, structural, and functional information. The CDKN2A Database improves upon existing resources by: 1) including both somatic mutations and germline variants, thereby adding the perspective of somatic cell carcinogenesis to that of hereditary cancer predisposition; 2) including information that assists with the interpretation of allelic variants, such as other primary data (sequences, structures, alignments, functional measurements, and literature references) and annotations (extensive text, figures, and a tree-based phylogenetic classification); and 3) providing the information in a format that allows a user to either download the database or to easily manipulate it online. We describe the database structure, content, current uses, and potential implications (http://biodesktop.uvm.edu/perl/p16).

Alleles↗
PubMed2005

Improving missing value estimation in microarray data with gene ontology.

MOTIVATION: Gene expression microarray experiments produce datasets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete dataset or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation. RESULTS: We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray datasets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments. AVAILABILITY: Java and Matlab codes are available on request from the authors. SUPPLEMENTARY MATERIAL: Available online at http://users.utu.fi/jotatu/GOImpute.html.

Algorithms↗
PubMedDate not supplied

A framework for querying a database for structural information on 3D images of macromolecules: A web-based query-by-content prototype on the BioImage macromolecular server.

Nowadays we are experiencing a remarkable growth in the number of databases that have become accessible over the Web. However, in a certain number of cases, for example, in the case of BioImage, this information is not of a textual nature, thus posing new challenges in the design of tools to handle these data. In this work, we concentrate on the development of new mechanisms aimed at "querying" these databases of complex data sets by their intrinsic content, rather than by their textual annotations only. We concentrate our efforts on a subset of BioImage containing 3D images (volumes) of biological macromolecules, implementing a first prototype of a "query-by-content" system. In the context of databases of complex data types the term query-by-content makes reference to those data modeling techniques in which user-defined functions aim at "understanding" (to some extent) the informational content of the data sets. In these systems the matching criteria introduced by the user are related to intrinsic features concerning the 3D images themselves, hence, complementing traditional queries by textual key words only. Efficient computational algorithms are required in order to "extract" structural information of the 3D images prior to storing them in the database. Also, easy-to-use interfaces should be implemented in order to obtain feedback from the expert. Our query-by-content prototype is used to construct a concrete query, making use of basic structural features, which are then evaluated over a set of three-dimensional images of biological macromolecules. This experimental implementation can be accessed via the Web at the BioImage server in Madrid, at http://www.bioimage.org/qbc/index.html.

Animals↗
PubMed2003

Correlating protein-DNA and protein-protein interaction networks.

Here, we search protein-DNA binding data for prevalent pairs and higher-order tuples of co-occurring transcription factors (TF) in Saccharomyces cerevisiae. While the identification of such modules is dependent on uncertainties of genome-wide data sets, we find several biologically meaningful examples, which allow putative annotation of yet unclassified genes. For the frequently occurring transcriptional module Mcm1-Fkh2-Ndd1, we identified several new target genes involved in cell-cycle control and filament formation. Using large-scale protein interaction data, we demonstrate a significant correlation between co-occurrence of TF binding sites and the vicinity in the protein interaction network. In particular we find that directly interacting transcription factors and those which are members of a protein complex are more likely to occur together as putative DNA-binding modules.

DNA↗
PubMed2006

A hitchhiker's guide to expressed sequence tag (EST) analysis.

Expressed sequence tag (EST) sequencing projects are underway for numerous organisms, generating millions of short, single-pass nucleotide sequence reads, accumulating in EST databases. Extensive computational strategies have been developed to organize and analyse both small- and large-scale EST data for gene discovery, transcript and single nucleotide polymorphism analysis as well as functional annotation of putative gene products. We provide an overview of the significance of ESTs in the genomic era, their properties and the applications of ESTs. Methods adopted for each step of EST analysis by various research groups have been compared. Challenges that lie ahead in organizing and analysing the ever increasing EST data have also been identified. The most appropriate software tools for EST pre-processing, clustering and assembly, database matching and functional annotation have been compiled (available online from http://biolinfo.org/EST). We propose a road map for EST analysis to accelerate the effective analyses of EST data sets. An investigation of EST analysis platforms reveals that they all terminate prior to downstream functional annotation including gene ontologies, motif/pattern analysis and pathway mapping.

Animals↗
PubMed2004

Whole genome sequence comparisons and "full-length" cDNA sequences: a combined approach to evaluate and improve Arabidopsis genome annotation.

To evaluate the existing annotation of the Arabidopsis genome further, we generated a collection of evolutionary conserved regions (ecores) between Arabidopsis and rice. The ecore analysis provides evidence that the gene catalog of Arabidopsis is not yet complete, and that a number of these annotations require re-examination. To improve the Arabidopsis genome annotation further, we used a novel "full-length" enriched cDNA collection prepared from several tissues. An additional 1931 genes were covered by new "full-length" cDNA sequences, raising the number of annotated genes with a corresponding "full-length" cDNA sequence to about 14,000. Detailed comparisons between these "full-length" cDNA sequences and annotated genes show that this resource is very helpful in determining the correct structure of genes, in particular, those not yet supported by "full-length" cDNAs. In addition, a total of 326 genomic regions not included previously in the Arabidopsis genome annotation were detected by this cDNA resource, providing clues for new gene discovery. Because, as expected, the two data sets only partially overlap, their combination produces very useful information for improving the Arabidopsis genome annotation.

Arabidopsis↗
PubMed2026

Multi-omics analysis identifies key genes and functional loci affecting teat number in American Large White and Landrace pigs and their application in optimizing genomic selection models.

BACKGROUND: Teat number is a crucial economic trait in pigs. It directly affects the ability of sows to lactate, which in turn influences the survival and health of piglets. The teat number of French Large White pigs is close to 16, while the teat number of American Large White and Landrace pigs is about 14. In order to improve the teat number of American Landrace and Large White pigs through molecular approaches and precise breeding techniques, we genotyped 2,131 American Landrace and 4,564 American Large White with teat number phenotype using a 50 K SNP chip. Then, the SNP-chip data was imputed to the level of whole-genome sequencing (iWGS). Based on iWGS data, we conducted GWAS to identify novel, significant SNPs associated with teat number and to incorporate them into genomic selection. RESULTS: In Landrace pigs, significant SNPs for TTN mapped to SSC2, SSC7, SSC8, and SSC14; the SSC8 and SSC14 effects are novel. LTN mapped to SSC7, RTN to SSC7 and SSC8. The lead SSC7 SNP explained 2.60% of TTN phenotypic variance. In Large White pigs, significant SNPs were detected on SSC7 and SSC10 for TTN; SSC7, SSC10, and SSC12 for LTN; and SSC7 and SSC10 for RTN. The most significant locus on SSC7 accounted for 2.99% of the phenotypic variance in TTN. Additionally, a multi-population meta-analysis detected significant novel SNPs for LTN on SSC1 and SSC8. By utilizing Bayesian fine mapping, the most precise QTL confidence interval on SSC7 for both TTN and RTN in Large White pigs was reduced to 40 kb. By integrating functional gene annotation with RNA-seq and ATAC-seq data from Erhualian and Bamaxiang pigs mammary placodes at embryonic day 26, we prioritized PTPN13, TRPV3, ZDHHC13, and BRD2 as novel candidate genes for teat number. We then incorporated the significant SNPs to GBLUP and benchmarked genomic-selection accuracy. In both breeds, fitting the top SNP as fixed maximized prediction for TTN and RTN, whereas treating all significant loci as an additional random effect optimized LTN. CONCLUSIONS: Our findings provide a theoretical basis for dissecting new key genes affecting teat number and for advancing molecular breeding of teat number in pigs.

Animals↗