SEARCH · Search PubMed

Results for “bioinformatic database”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,261 records · Page 70Linked to original sources

PubMed2004

Bioinformatics and cellular signaling.

The understanding of cellular function requires an integrated analysis of context-specific, spatiotemporal data from diverse sources. Recent advances in describing the genomic and proteomic 'parts list' of the cell and deciphering the interrelationship of these parts are described, including genome-wide location analysis, standards for microarray data analysis, and two-hybrid and mass spectrometry approaches. This information is being collected and curated in databases such as the Alliance for Cellular Signaling (AfCS) Molecule Pages, which will serve as vital tools for the reconstruction and analysis of cellular signaling networks.

Cell Physiological Phenomena↗

PubMed2001

Design and implementation of an introductory course for computer applications in molecular genetics. A case study.

Formal training in computational biology was initiated at Wayne State University in 1990 to meet the needs of the faculty. This was still at a time when the molecular databases and analysis tools could be housed in what is now equivalent to a modern but dated desktop computer. In 1995 the course was expanded to include graduate students to provide these senior students with a foundation in computational biology. This course has armed our students with a requisite set of basic skills that are necessary for a successful career in molecular genetics. It is now an integral component of the graduate program of the Center for Molecular Medicine and Genetics and our experiences in course delivery have been detailed (BioInformatics Methods and Protocols, S. Misener and S. A. Krawetz, eds., Humana Press, Totowa, NJ, 2000.). The course was expanded to a campus-wide unlimited enrollment program for the summer of 2000 to address the needs of our student body. In this review we present our experience with delivering a multidisciplinary campus-wide computational biology course to a new and widely diverse student body.

Computational Biology↗

PubMed2026

Comprehensive Analysis of miRNAs and Predicted Protein Interaction Networks in Skeletal Muscle Development of Myostatin-Deficient Rabbits.

Myostatin (MSTN), encoded by the MSTN gene, is a critical negative regulator of skeletal muscle mass. This study aims to identify and characterize the miRNAs involved in the development of the double-muscling phenotype in MSTN-deficient rabbits. We performed high-throughput sequencing to analyze the miRNA expression profiles in gluteus maximus tissue from wild type (MSTN+/+) and MSTN-KO (MSTN+/- and MSTN-/- inclusive) rabbits. Differentially expressed miRNAs (DEmiRNAs) were identified, and their potential target genes were predicted. Functional enrichment analysis of these target mRNAs was conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to elucidate the involved biological pathways and regulatory networks. A total of 25 DEmiRNAs (13 downregulated and 12 upregulated, |log2FC| ≥ 1.0, adjusted p < 0.05) and 1178 differentially expressed mRNAs (408 upregulated and 770 downregulated, |log2FC| ≥ 2.0, adjusted p < 0.05) were identified in MSTN-KO compared to MSTN+/+ rabbits. Bioinformatics analysis revealed that the target genes of these DEmiRNAs were significantly enriched in key pathways governing muscle growth and metabolism, including the PI3K-Akt signaling pathway, MAPK signaling pathway, and pathways related to ECM-receptor interaction and insulin signaling. Notably, many predicted target mRNAs are expressed by genes that encode key inhibitors of myogenesis (e.g., HDAC4) and major extracellular matrix components (e.g., COL4A3, POSTN). Our results demonstrate that MSTN deficiency induces a distinct and widespread change in the miRNA expression landscape of skeletal muscle.

Animals↗

PubMed2003

Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation.

MOTIVATION: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or 'semantic similarity' between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. A measure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repertoire of analyses. RESULTS: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. AVAILABILITY: Software available from http://www.russet.org.uk.

Artificial Intelligence↗

PubMed2006

AlgPred: prediction of allergenic proteins and mapping of IgE epitopes.

In this study a systematic attempt has been made to integrate various approaches in order to predict allergenic proteins with high accuracy. The dataset used for testing and training consists of 578 allergens and 700 non-allergens obtained from A. K. Bjorklund, D. Soeria-Atmadja, A. Zorzet, U. Hammerling and M. G. Gustafsson (2005) Bioinformatics, 21, 39-50. First, we developed methods based on support vector machine using amino acid and dipeptide composition and achieved an accuracy of 85.02 and 84.00%, respectively. Second, a motif-based method has been developed using MEME/MAST software that achieved sensitivity of 93.94 with 33.34% specificity. Third, a database of known IgE epitopes was searched and this predicted allergenic proteins with 17.47% sensitivity at specificity of 98.14%. Fourth, we predicted allergenic proteins by performing BLAST search against allergen representative peptides. Finally hybrid approaches have been developed, which combine two or more than two approaches. The performance of all these algorithms has been evaluated on an independent dataset of 323 allergens and on 101 725 non-allergens obtained from Swiss-Prot. A web server AlgPred has been developed for the predicting allergenic proteins and for mapping IgE epitopes on allergenic proteins (http://www.imtech.res.in/raghava/algpred/). AlgPred is available at www.imtech.res.in/raghava/algpred/.

Algorithms↗

PubMed2006

Functional annotation prediction: all for one and one for all.

In an era of rapid genome sequencing and high-throughput technology, automatic function prediction for a novel sequence is of utter importance in bioinformatics. While automatic annotation methods based on local alignment searches can be simple and straightforward, they suffer from several drawbacks, including relatively low sensitivity and assignment of incorrect annotations that are not associated with the region of similarity. ProtoNet is a hierarchical organization of the protein sequences in the UniProt database. Although the hierarchy is constructed in an unsupervised automatic manner, it has been shown to be coherent with several biological data sources. We extend the ProtoNet system in order to assign functional annotations automatically. By leveraging on the scaffold of the hierarchical classification, the method is able to overcome some frequent annotation pitfalls.

Algorithms↗

PubMed2026

Identification of a necroptosis-related lncRNA prognostic signature and the hub RBP HNRNPK in esophageal squamous cell carcinoma.

ObjectiveEsophageal squamous cell carcinoma (ESCC) is a malignant tumor with poor prognosis. Necroptosis is important for tumor immunity, but its role in ESCC remains unclear. This retrospective bioinformatics study aimed to investigate the prognostic value of necroptosis-related long non-coding RNAs (lncRNAs) and to identify key lncRNA-binding proteins (RBPs) in ESCC patients.MethodsRNA transcriptome and clinical data of ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Necroptosis-related lncRNAs were identified through correlation analysis with necroptosis-related genes, subjected to consensus cluster analysis, and used to construct a prognostic risk model via least absolute shrinkage and selection operator (LASSO) regression. The hub RBP was experimentally validated by quantitative polymerase chain reaction (qPCR) using 30 pairs of ESCC and adjacent normal tissues from patients who underwent surgical resection.ResultsA total of 30 necroptosis-related lncRNAs were significantly correlated with overall survival (OS). The upregulated lncRNAs in the risk model were associated with high immune scores, innate immune cell infiltration, cluster 2 classification, and advanced T-stage disease (p < 0.05). Three hub RBPs (HNRNPA1, HNRNPC, and HNRNPK) were identified through protein-protein interaction network analysis. qPCR confirmed that HNRNPK was significantly overexpressed in ESCC tissues compared to adjacent normal tissues (p < 0.05).ConclusionsThe necroptosis-related lncRNA risk model is an independent prognostic factor for ESCC patients. HNRNPK was identified as a hub RBP significantly overexpressed in ESCC tissues. We hypothesize that HNRNPK may promote tumor progression through regulating proto-oncogene expression or modulating the immune microenvironment, though this requires further mechanistic validation.

Humans↗

PubMedDate not supplied

A bioinformatics perspective on proteomics: data storage, analysis, and integration.

The field of proteomics is advancing rapidly as a result of powerful new technologies and proteomics experiments yield a vast and increasing amount of information. Data regarding protein occurrence, abundance, identity, sequence, structure, properties, and interactions need to be stored. Currently, a common standard has not yet been established and open access to results is needed for further development of robust analysis algorithms. Databases for proteomics will evolve from pure storage into knowledge resources, providing a repository for information (meta-data) which is mainly not stored in simple flat files. This review will shed light on recent steps towards the generation of a common standard in proteomics data storage and integration, but is not meant to be a comprehensive overview of all available databases and tools in the proteomics community.

Computational Biology↗

PubMed2004

Is it functional? Report on the first BSPR/EBI meeting on functional proteomics.

This report describes the first scientific meeting of the British Society for Proteome Research (BSPR), which was organised jointly with the European Bioinformatics Institute (EBI) and held in July 2004. The focus of the conference was functional proteomics with an emphasis on possible clinical application. The main subjects described here are: the need to simplify samples, the use of biological fluids versus tissue, consideration of biological and experimental variation and the creation of databases to achieve meaningful functional analysis.

Animals↗

PubMed2004

The bioinformatics challenges in comparative analysis of cereal genomes-an overview.

Comparative genomic analysis is the cornerstone of in silico-based approaches to understanding biological systems and processes across cereal species, such as rice, wheat and barley, in order to identify genes of agronomic interest. The size of the genomic repositories is nearly doubling every year, and this has significant implications on the way bioinformatics analyses are carried out. In this overview the concepts and technology underpinning bioinformatics as applied to comparative genomic analysis are considered in the context of other manuscripts appearing in this issue of Functional and Integrative Genomics.

Computational Biology↗

PubMed2006

Genomic analysis of influenza A viruses, including avian flu (H5N1) strains.

This study was designed to conduct genomic analysis in two steps, such as the overall relative synonymous codon usage (RSCU) analysis of the five virus species in the orthomyxoviridae family, and more intensive pattern analysis of the four subtypes of influenza A virus (H1N1, H2N2, H3N2, and H5N1) which were isolated from human population. All the subtypes were categorized by their isolated regions, including Asia, Europe, and Africa, and most of the synonymous codon usage patterns were analyzed by correspondence analysis (CA). As a result, influenza A virus showed the lowest synonymous codon usage bias among the virus species of the orthomyxoviridae family, and influenza B and influenza C virus were followed, while suggesting that influenza A virus might have an advantage in transmitting across the species barrier due to their low codon usage bias. The ENC values of the host-specific HA and NA genes represented their different HA and NA types very well, and this reveals that each influenza A virus subtype uses different codon usage patterns as well as the amino acid compositions. In NP, PA and PB2 genes, most of the virus subtypes showed similar RSCU patterns except for H5N1 and H3N2 (A/HK/1774/1999) subtypes which were suspected to be transmitted across the species barrier, from avian and porcine species to human beings, respectively. This distinguishable synonymous codon usage patterns in non-human origin viruses might be useful in determining the origin of influenza A viruses in genomic levels as well as the serological tests. In this study, all the process, including extracting sequences from GenBank flat file and calculating codon usage values, was conducted by Java codes, and these bioinformatics-related methods may be useful in predicting the evolutionary patterns of pandemic viruses.

Africa↗

PubMed2001

Which craft is best in bioinformatics?

'Silicon-based' biology has gathered momentum as the world-wide sequencing projects have made possible the investigation and comparative analysis of complete genomes. Central to the quest to elucidate and characterise the genes and gene products encoded within genomes are pivotal concepts concerning the processes of evolution, the mechanisms of protein folding, and, crucially, the manifestation of protein function. Our use of computers to model such concepts is limited by, and must be placed in the context of, the current limits of our understanding of these biological processes. It is important to recognise that we do not have a common understanding of what constitutes a gene; we cannot invariably say that a particular sequence or fold has arisen via divergence or convergence; we do not fully understand the rules of protein folding, so we cannot predict protein structure; and we cannot invariably diagnose protein function, given knowledge only of its sequence or structure in isolation. Accepting what we cannot do with computers plays an essential role in forming an appreciation of what we can do. Without this understanding, it is easy to be misled, as spurious arguments are often used to promote over-enthusiastic notions of what particular programs can achieve. There are valuable lessons to be learned here from the field of artificial intelligence, principal among which is the realisation that capturing and representing complex knowledge is time consuming, expensive and hard. If bioinformatics is to tackle biological complexity meaningfully, the road ahead must therefore be paved with caution, rigour and pragmatism.

Amino Acid Sequence↗

PubMed2005

Integration of text- and data-mining using ontologies successfully selects disease gene candidates.

Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (+/-18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.

Anatomy↗

PubMed2005

InterProScan: protein domains identifier.

InterProScan [E. M. Zdobnov and R. Apweiler (2001) Bioinformatics, 17, 847-848] is a tool that combines different protein signature recognition methods from the InterPro [N. J. Mulder, R. Apweiler, T. K. Attwood, A. Bairoch, A. Bateman, D. Binns, P. Bradley, P. Bork, P. Bucher, L. Cerutti et al. (2005) Nucleic Acids Res., 33, D201-D205] consortium member databases into one resource. At the time of writing there are 10 distinct publicly available databases in the application. Protein as well as DNA sequences can be analysed. A web-based version is accessible for academic and commercial organizations from the EBI (http://www.ebi.ac.uk/InterProScan/). In addition, a standalone Perl version and a SOAP Web Service [J. Snell, D. Tidwell and P. Kulchenko (2001) Programming Web Services with SOAP, 1st edn. O'Reilly Publishers, Sebastopol, CA, http://www.w3.org/TR/soap/] are also available to the users. Various output formats are supported and include text tables, XML documents, as well as various graphs to help interpret the results.

Databases, Protein↗

PubMed2004

Bioinformatics in the post-genome era.

Recent years saw a dramatic increase in genomic and proteomic data in public archives. Now with the complete genome sequences of human and other species in hand, detailed analyses of the genome sequences will undoubtedly improve our understanding of biological systems and at the same time require sophisticated bioinformatic tools. Here we review what computational challenges are ahead and what are the new exciting developments in this exciting field.

Animals↗

PubMed2005

Identification and characterization of human archaemetzincin-1 and -2, two novel members of a family of metalloproteases widely distributed in Archaea.

Systematic analysis of degradomes, the complete protease repertoires of organisms, has demonstrated the large and growing complexity of proteolytic systems operating in all cells and tissues. We report here the identification of two new human metalloproteases that have been called archaemetzincin-1 (AMZ1) and archaemetzincin-2 (AMZ2) to emphasize their close relationship to putative proteases predicted by bioinformatic analysis of archaeal genomes. Both human proteins contain a catalytic domain with a core motif (HEXXHXXGX3CX4CXMX17CXXC) that includes an archetypal zinc-binding site, the methionine residue characteristic of metzincins, and four conserved cysteine residues that are not present at the equivalent positions of other human metalloproteases. Analysis of genome sequence databases revealed that AMZs are widely distributed in Archaea and vertebrates and contribute to the defining of a new metalloprotease family that has been called archaemetzincin. However, AMZ-like sequences are absent in a number of model organisms from bacteria to nematodes. Phylogenetic analysis showed that these enzymes have undergone a complex evolutionary process involving a series of lateral gene transfer, gene loss, and genetic duplication events that have shaped this novel family of metalloproteases. Northern blot analysis showed that AMZ1 and AMZ2 exhibit distinct expression patterns in human tissues. AMZ1 is mainly detected in liver and heart whereas AMZ2 is predominantly expressed in testis and heart, although both are also detectable at lower levels in other tissues. Both human enzymes were produced in Escherichia coli, and the purified recombinant proteins hydrolyzed synthetic substrates and bioactive peptides, demonstrating that they are functional proteases. Finally, these activities were abolished by inhibitors of metalloproteases, providing further evidence that AMZs belong to this catalytic class of proteolytic enzymes.

Amino Acid Motifs↗

PubMed2005

Endogenous ouabain: upregulation of steroidogenic genes in hypertensive hypothalamus but not adrenal.

BACKGROUND: Mammalian tissues contain a presumed endogenous Na+, K(+)-ATPase inhibitor that binds reversibly to the Na+ pump with high affinity and specificity. The inhibitor has been linked to the pathogenesis of experimental volume-expanded and human essential hypertension. This compound has been isolated from mammalian hypothalamus and appears to be an isomer of the plant-derived cardiac glycoside ouabain, if not ouabain itself. The objective of this study was to test the hypothesis that a biosynthetic pathway exists in mammalian tissues to produce a steroid derivative closely related to plant cardiac glycosides. METHODS AND RESULTS: Using bioinformatics and genomic techniques, Milan hypertensive rat tissues were studied because this strain has a 10-fold increase in hypothalamic ouabain-like compound that is linked to the pathogenesis of the hypertension. A putative steroid biosynthetic pathway was constructed and candidate genes encoding enzymes in this pathway were identified from sequence databases. Differential expression of selected genes in the pathway was studied by microarray analysis and quantitative polymerase chain reaction, with functional validation by gene silencing using small interfering RNAs. Marked upregulation of genes coding for P450 side chain cleavage and Delta5-3beta-hydroxysteroid dehydrogenase/Delta5-Delta4- isomerase enzymes in hypertensive hypothalamus but not adrenal was found, compared with normotensive Milan rats. Knockdown of the latter gene decreased production of ouabain-like factor from neural tissue. CONCLUSIONS: Our findings support the possibility that a unique steroid biosynthetic circuit exists in Milan rat brain, functioning independently from adrenal, which could account for the overproduction of the hypothalamic ouabain-like compound in this species.

Adrenal Glands↗

PubMed2005

Overview of BioCreAtIvE: critical assessment of information extraction for biology.

BACKGROUND: The goal of the first BioCreAtIvE challenge (Critical Assessment of Information Extraction in Biology) was to provide a set of common evaluation tasks to assess the state of the art for text mining applied to biological problems. The results were presented in a workshop held in Granada, Spain March 28-31, 2004. The articles collected in this BMC Bioinformatics supplement entitled "A critical assessment of text mining methods in molecular biology" describe the BioCreAtIvE tasks, systems, results and their independent evaluation. RESULTS: BioCreAtIvE focused on two tasks. The first dealt with extraction of gene or protein names from text, and their mapping into standardized gene identifiers for three model organism databases (fly, mouse, yeast). The second task addressed issues of functional annotation, requiring systems to identify specific text passages that supported Gene Ontology annotations for specific proteins, given full text articles. CONCLUSION: The first BioCreAtIvE assessment achieved a high level of international participation (27 groups from 10 countries). The assessment provided state-of-the-art performance results for a basic task (gene name finding and normalization), where the best systems achieved a balanced 80% precision / recall or better, which potentially makes them suitable for real applications in biology. The results for the advanced task (functional annotation from free text) were significantly lower, demonstrating the current limitations of text-mining approaches where knowledge extrapolation and interpretation are required. In addition, an important contribution of BioCreAtIvE has been the creation and release of training and test data sets for both tasks. There are 22 articles in this special issue, including six that provide analyses of results or data quality for the data sets, including a novel inter-annotator consistency assessment for the test set used in task 2.

Computational Biology↗