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PubMed2006

Autism and environmental genomics.

Autism spectrum disorders (ASD) are defined by behavior and diagnosed by clinical history and observation but have no biomarkers and are presumably, etiologically and biologically heterogeneous. Given brain abnormalities and high monozygotic concordance, ASDs have been framed as neurobiologically based and highly genetic, which has shaped the research agenda and in particular criteria for choosing candidate ASD genes. Genetic studies to date have not uncovered genes of strong effect, but a move toward "genetic complexity" at the neurobiological level may not suffice, as evidence of systemic abnormalities (e.g. gastrointestinal and immune), increasing rates and less than 100% monozygotic concordance support a more inclusive reframing of autism as a multisystem disorder with genetic influence and environmental contributors. We review this evidence and also use a bioinformatic approach to explore the possibility that "environmentally responsive genes" not specifically associated with the nervous system, but potentially associated with systemic changes in autism, have not hitherto received sufficient attention in autism genetics investigations. We overlapped genes from NIEHS Environmental Genome Project, the Comparative Toxicogenomics Database, and the SeattleSNPs database of genes relevant to the human immune and inflammatory response with linkage regions identified in published autism genome scans. We identified 135 genes in overlap regions, of which 56 had never previously been studied in relation to autism and 47 had functional SNPs (in coding regions). Both our review and the bioinformatics exercise support the expansion of criteria for evaluating the relevance of genes to autism risk to include genes related to systemic impact and environmental responsiveness. This review also suggests the utility of environmental genomic resources in highlighting the potential relevance of particular genes within linkage regions. Environmental responsiveness and systems impacts consistent with system-wide findings in autism are thus supported as important considerations in identifying the numerous and complex modes of gene-environment interaction in autism.

Animals↗
PubMed2006

Bioinformatics and microbial biodiversity: analysis of vibrios by the GenEnv system.

Sequence-based approaches to prokaryotic systematics and typing represent a modern and promising strategy in epidemiology and environmental microbiology. GenEnv, a database-driven system for bacterial typing, was developed in order to provide user friendly tools for supporting biomolecular analysis of bacteria. The family Vibrionaceae represents a heterogeneous taxon of aquatic microrganisms, harbouring a plethora of genomes currently analyzed by different molecular techniques. Under the query "Vibrio", GenEnv retrieved 256 organisms, included in a total number of 19 families. Overall, 548 sequences, comprising 16S rRNA (n=402), rpoB (n=1), gyrB (n=145) were available. In addition, GenEnv system allowed primer design, homology analysis and restriction maps, for immediate applications to the study of Vibrionaceae.

Bacterial Typing Techniques↗
PubMed2006

EST-PAC a web package for EST annotation and protein sequence prediction.

With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST) from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST) annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1) searching local or remote biological databases for sequence similarities using Blast services, 2) predicting protein coding sequence from EST data and, 3) annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics.

Journal Article↗
PubMed2006

Lipopolysaccharide-stimulated responses in rat aortic endothelial cells by a systems biology approach.

The vascular endothelium plays an important role in regulating immune and inflammatory responses to resist pathogens infection. Although it has been known that lipopolysaccharide (LPS) is a critical inducer of sepsis or endotoxemia, the systematic responses of LPS-stimulation in endothelial cells (ECs) are still unclear. The present study aims to analyze the late-phase responses of LPS-induced rat aortic ECs by using systematic biology approaches, including rat cDNA microarray, 2-DE and MALDI-TOF MS/MS, and cytokine protein array. Furthermore, to improve the efficiency of analysis of the bulk systematic data of rat, we designed a set of bioinformatic tools to convert and integrate these rat data into the corresponding human genes or proteins IDs based on BioCarta, KEGG, and Gene Ontology databases. Using the systematic analysis, it was shown that LPS could promote some signaling or metabolic pathways as well as pathophysiologic phenomena of proliferation, atherogenesis, inflammation, and apoptosis through activated nuclear factor-kappaB pathway in ECs. Interestingly, ECs also activated the mediators of anti-inflammation, antiapoptosis, and antioxidation to protect themselves. Moreover, the expressions of altered genes, proteins, and their involvement in the hypothetical signaling pathway can provide further understanding of inflammation associated responses in ECs.

Animals↗
PubMed2004

Bioinformatics integration and agent technology.

Vast amounts of life sciences data are scattered around the world in the form of a variety of heterogeneous data sources. The need to be able to co-relate relevant information is fundamental to increase the overall knowledge and understanding of a specific subject. Bioinformaticians aspire to find ways to integrate biological data sources for this purpose and system integration is a very important research topic. The purpose of this paper is to provide an overview of important integration issues that should be considered when designing a bioinformatics integration system. The currently prevailing approach for integration is presented with examples of bioinformatics information systems together with their main characteristics. Here, we introduce agent technology and we argue why it provides an appropriate solution for designing bioinformatics integration systems.

Algorithms↗
PubMed2002

Multi-class cancer classification via partial least squares with gene expression profiles.

MOTIVATION: Discrimination between two classes such as normal and cancer samples and between two types of cancers based on gene expression profiles is an important problem which has practical implications as well as the potential to further our understanding of gene expression of various cancer cells. Classification or discrimination of more than two groups or classes (multi-class) is also needed. The need for multi-class discrimination methodologies is apparent in many microarray experiments where various cancer types are considered simultaneously. RESULTS: Thus, in this paper we present the extension to the classification methodology proposed earlier Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50) to classify cancer samples from multiple classes. The methodologies proposed in this paper are applied to four gene expression data sets with multiple classes: (a) a hereditary breast cancer data set with (1) BRCA1-mutation, (2) BRCA2-mutation and (3) sporadic breast cancer samples, (b) an acute leukemia data set with (1) acute myeloid leukemia (AML), (2) T-cell acute lymphoblastic leukemia (T-ALL) and (3) B-cell acute lymphoblastic leukemia (B-ALL) samples, (c) a lymphoma data set with (1) diffuse large B-cell lymphoma (DLBCL), (2) B-cell chronic lymphocytic leukemia (BCLL) and (3) follicular lymphoma (FL) samples, and (d) the NCI60 data set with cell lines derived from cancers of various sites of origin. In addition, we evaluated the classification algorithms and examined the variability of the error rates using simulations based on randomization of the real data sets. We note that there are other methods for addressing multi-class prediction recently and our approach is along the line of Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50). CONTACT: dnguyen@stat.tamu.edu; dmrocke@ucdavis.edu

Algorithms↗
PubMed2006

Automatic extension of Gene Ontology with flexible identification of candidate terms.

MOTIVATION: Gene Ontology (GO) has been manually developed to provide a controlled vocabulary for gene product attributes. It continues to evolve with new concepts that are compiled mostly from existing concepts in a compositional way. If we consider the relatively slow growth rate of GO in the face of the fast accumulation of the biological data, it is much desirable to provide an automatic means for predicting new concepts from the existing ones. RESULTS: We present a novel method that predicts more detailed concepts by utilizing syntactic relations among the existing concepts. We propose a validation measure for the automatically predicted concepts by matching the concepts to biomedical articles. We also suggest how to find a suitable direction for the extension of a constantly growing ontology such as GO. AVAILABILITY: http://autogo.biopathway.org SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Algorithms↗
PubMed2003

The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro.

Gene Ontology Annotation (GOA) is a project run by the European Bioinformatics Institute (EBI) that aims to provide assignments of terms from the Gene Ontology (GO) resource to gene products in a number of its databases (http://www.ebi.ac.uk/GOA). In the first stage of this project, GO assignments have been applied to a data set representing the complete human proteome by a combination of electronic mappings and manual curation. This vocabulary has also been applied to the nonredundant proteome sets for all other completely sequenced organisms as well as to proteins from a wide range of organisms where the proteome is not yet complete.

Computational Biology↗
PubMed2000

Fast quantum search algorithms in protein sequence comparisons: quantum bioinformatics.

Quantum search algorithms are considered in the context of protein sequence comparison in bioinformatics. Given a sample protein sequence of length m (i.e., m residues), the problem considered is to find an optimal match in a large database containing N residues. Initially, Grover's quantum search algorithm is applied to a simple illustrative case-namely, where the database forms a complete set of states over the 2(m) basis states of a m qubit register, and thus is known to contain the exact sequence of interest. This example demonstrates explicitly the typical O(square root of [N]) speedup on the classical O(N) requirements. An algorithm is then presented for the (more realistic) case where the database may contain repeat sequences, and may not necessarily contain an exact match to the sample sequence. In terms of minimizing the Hamming distance between the sample sequence and the database subsequences the algorithm finds an optimal alignment, in O(square root of [N]) steps, by employing an extension of Grover's algorithm, due to Boyer et al. for the case when the number of matches is not a priori known.

Algorithms↗
PubMed2002

Sequence analysis for assessing potential allergenicity.

Sequence analysis plays an important role in assessing the potential allergenicity of proteins used in transgenic foods, particularly for proteins that have not previously been part of the food supply. Sequence comparisons are used to indicate potential unexpected cross reactivity to existing allergens and to assess the potential for developing new sensitivities. Although the concept of using sequence analysis is straightforward, implementing a bioinformatic analysis that is accurate and complete can be complex. Several factors need to be considered, including the design and content of the sequence database, the analysis strategy, and the criteria for evaluating the results.

Algorithms↗
PubMed2004

MolTalk--a programming library for protein structures and structure analysis.

BACKGROUND: Two of the mostly unsolved but increasingly urgent problems for modern biologists are a) to quickly and easily analyse protein structures and b) to comprehensively mine the wealth of information, which is distributed along with the 3D co-ordinates by the Protein Data Bank (PDB). Tools which address this issue need to be highly flexible and powerful but at the same time must be freely available and easy to learn. RESULTS: We present MolTalk, an elaborate programming language, which consists of the programming library libmoltalk implemented in Objective-C and the Smalltalk-based interpreter MolTalk. MolTalk combines the advantages of an easy to learn and programmable procedural scripting with the flexibility and power of a full programming language. An overview of currently available applications of MolTalk is given and with PDBChainSaw one such application is described in more detail. PDBChainSaw is a MolTalk-based parser and information extraction utility of PDB files. Weekly updates of the PDB are synchronised with PDBChainSaw and are available for free download from the MolTalk project page http://www.moltalk.org following the link to PDBChainSaw. For each chain in a protein structure, PDBChainSaw extracts the sequence from its co-ordinates and provides additional information from the PDB-file header section, such as scientific organism, compound name, and EC code. CONCLUSION: MolTalk provides a rich set of methods to analyse and even modify experimentally determined or modelled protein structures. These methods vary in complexity and are thus suitable for beginners and advanced programmers alike. We envision MolTalk to be most valuable in the following applications:1) To analyse protein structures repetitively in large-scale, i.e. to benchmark protein structure prediction methods or to evaluate structural models. The quality of the resulting 3D-models can be assessed by e.g. calculating a Ramachandran-Sasisekharan plot.2) To quickly retrieve information for (a limited number of) macro-molecular structures, i.e. H-bonds, salt bridges, contacts between amino acids and ligands or at the interface between two chains.3) To programme more complex structural bioinformatics software and to implement demanding algorithms through its portability to Objective-C, e.g. iMolTalk.4) To be used as a front end to databases, e.g. PDBChainSaw.

Artificial Intelligence↗
PubMed2006

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.

BACKGROUND: Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations. METHODS: We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. RESULTS: Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. CONCLUSION: A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.

Abstracting and Indexing↗
PubMed2005

Text-mining and information-retrieval services for molecular biology.

Text-mining in molecular biology -- defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents -- has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators.

Computational Biology↗
PubMed2005

The Nuclear Receptor Signaling Atlas: development of a functional atlas of nuclear receptors.

The Nuclear Receptor Signaling Atlas (NURSA) was developed by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the National Institute on Aging (NIA), and the National Cancer Institute (NCI) of the National Institutes of Health (NIH); the aim of NURSA is to utilize classical approaches to validate existing hypotheses and exploit new and emerging technologies to formulate and test new hypotheses that might elucidate the program of nuclear receptor (NR) structure, function, and role in disease. The means for carrying out this ambitious program required development of interactions among investigators and the combined application of new high-throughput technologies and existing approaches to allow for both mechanistic studies and accrual of large datasets in a discovery-based research effort, all leading to advances with implications for the missions of the NIDDK, NIA, and NCI. A team-based multidisciplinary approach has allowed for both objectives to proceed simultaneously, tied together via a central bioinformatics resource and one web-accessible venue (www.nursa.org). The ultimate goals for the NURSA consortium are to: 1) establish the mechanistic principles of NR function, 2) characterize NR-coregulator complex formation and regulation, 3) map protein-protein interactions for coregulators, 4) identify candidate downstream target genes of NR action, 5) identify target tissue expression of NRs, 6) understand the regulation of NR expression and, 7) integrate existing and emerging information through NURSA bioinformatics tools.

Animals↗
PubMed2005

Comparative plant genomics resources at PlantGDB.

PlantGDB (http://www.plantgdb.org/) is a database of plant molecular sequences. Expressed sequence tag (EST) sequences are assembled into contigs that represent tentative unique genes. EST contigs are functionally annotated with information derived from known protein sequences that are highly similar to the putative translation products. Tentative Gene Ontology terms are assigned to match those of the similar sequences identified. Genome survey sequences are assembled similarly. The resulting genome survey sequence contigs are matched to ESTs and conserved protein homologs to identify putative full-length open reading frame-containing genes, which are subsequently provisionally classified according to established gene family designations. For Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa), the exon-intron boundaries for gene structures are annotated by spliced alignment of ESTs and full-length cDNAs to their respective complete genome sequences. Unique genome browsers have been developed to present all available EST and cDNA evidence for current transcript models (for Arabidopsis, see the AtGDB site at http://www.plantgdb.org/AtGDB/; for rice, see the OsGDB site at http://www.plantgdb.org/OsGDB/). In addition, a number of bioinformatic tools have been integrated at PlantGDB that enable researchers to carry out sequence analyses on-site using both their own data and data residing within the database.

Computational Biology↗
PubMed2025

COL5A1 in the tumor microenvironment predicts the prognosis of head and neck cancer.

ObjectivesThis study aims to investigate the significance of tumor microenvironment (TME)-related genes and signal transduction pathways in head and neck cancer (HNC).MethodsGene expression and clinical data of HNC patients were obtained from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened through a multi-step filtration approach to obtain candidate predictors. The biological role of COL5A1 in HNC was verified through rigorous bioinformatic analysis, experimental validation using quantitative real-time PCR (qRT-PCR), immunohistochemical (IHC) analysis from HNC samples, and IHC data from the Human Protein Atlas (HPA) database.ResultsCOL5A1 was significantly upregulated in HNC tissues and cell lines. High COL5A1 expression was significantly associated with advanced tumor grade (P&#x2009;<&#x2009;.05) and shorter survival (TCGA: P&#x2009;<&#x2009;.001; GSE42743: P&#x2009;=&#x2009;.004). COL5A1 was an independent prognostic indicator (univariate analysis: HR&#x2009;=&#x2009;1.324, P&#x2009;=&#x2009;.001; Multivariate analysis: HR&#x2009;=&#x2009;1.326, P&#x2009;=&#x2009;.005). It was enriched in pathways related to tumor invasion and immune responses, and its expression was associated with decreased levels of CD8+ T cells and increased levels of macrophages and neutrophils. Spatial distribution analysis revealed higher expression at the tumor's leading edge (vs. tumor core: P&#x2009;<&#x2009;.001). COL5A1 expression is associated with tumor stage, with more pronounced expression in advanced-stage tumors.ConclusionCOL5A1 represented a novel potential prognostic indicator and therapeutic target in an HNC database sample, as its expression is closely linked to tumor progression, immune cell infiltration, and adverse clinical outcomes. These findings, primarily derived from squamous cell carcinoma-dominated cohorts, warrant further functional validation.

Humans↗
PubMed2003

DNA and the revolutions of molecular evolution, computational biology, and bioinformatics.

The discovery of the structure of DNA was a necessary prerequisite for determining the sequence of DNA molecules. Technological advances have now made it possible to sequence DNA rapidly and has resulted in public databases with over 30 billion nucleotides of known sequence. The analysis of these data has lead to new fields of science and to amazing advances in our understanding of evolution.

Animals↗