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At least 487 records · Page 27Linked to original sources
PubMed2003

Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms.

MOTIVATION: Information from fully sequenced genomes makes it possible to reconstruct strain-specific global metabolic network for structural and functional studies. These networks are often very large and complex. To properly understand and analyze the global properties of metabolic networks, methods for rationally representing and quantitatively analyzing their structure are needed. RESULTS: In this work, the metabolic networks of 80 fully sequenced organisms are in silico reconstructed from genome data and an extensively revised bioreaction database. The networks are represented as directed graphs and analyzed by using the 'breadth first searching algorithm to identify the shortest pathway (path length) between any pair of the metabolites. The average path length of the networks are then calculated and compared for all the organisms. Different from previous studies the connections through current metabolites and cofactors are deleted to make the path length analysis physiologically more meaningful. The distribution of the connection degree of these networks is shown to follow the power law, indicating that the overall structure of all the metabolic networks has the characteristics of a small world network. However, clear differences exist in the network structure of the three domains of organisms. Eukaryotes and archaea have a longer average path length than bacteria. AVAILABILITY: The reaction database in excel format and the programs in VBA (Visual Basic for Applications) are available upon request. SUPPLEMENTARY MATERIAL: Bioinformatics Online.

Archaea↗
PubMed2005

AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data.

AntiJen is a database system focused on the integration of kinetic, thermodynamic, functional, and cellular data within the context of immunology and vaccinology. Compared to its progenitor JenPep, the interface has been completely rewritten and redesigned and now offers a wider variety of search methods, including a nucleotide and a peptide BLAST search. In terms of data archived, AntiJen has a richer and more complete breadth, depth, and scope, and this has seen the database increase to over 31,000 entries. AntiJen provides the most complete and up-to-date dataset of its kind. While AntiJen v2.0 retains a focus on both T cell and B cell epitopes, its greatest novelty is the archiving of continuous quantitative data on a variety of immunological molecular interactions. This includes thermodynamic and kinetic measures of peptide binding to TAP and the Major Histocompatibility Complex (MHC), peptide-MHC complexes binding to T cell receptors, antibodies binding to protein antigens and general immunological protein-protein interactions. The database also contains quantitative specificity data from position-specific peptide libraries and biophysical data, in the form of diffusion co-efficients and cell surface copy numbers, on MHCs and other immunological molecules. The uses of AntiJen include the design of vaccines and diagnostics, such as tetramers, and other laboratory reagents, as well as helping parameterize the bioinformatic or mathematical in silico modeling of the immune system. The database is accessible from the URL: http://www.jenner.ac.uk/antijen.

Journal Article↗
PubMed2005

Evaluation of BioCreAtIvE assessment of task 2.

BACKGROUND: Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and stored in biological databases to build up annotations for large collections of gene products. Those annotation databases are crucial for the interpretation of large scale analysis approaches using bioinformatics or experimental techniques. Due to the growing accumulation of functional descriptions in biomedical literature the need for text mining tools to facilitate the extraction of such annotations is urgent. In order to make text mining tools useable in real world scenarios, for instance to assist database curators during annotation of protein function, comparisons and evaluations of different approaches on full text articles are needed. RESULTS: The Critical Assessment for Information Extraction in Biology (BioCreAtIvE) contest consists of a community wide competition aiming to evaluate different strategies for text mining tools, as applied to biomedical literature. We report on task two which addressed the automatic extraction and assignment of Gene Ontology (GO) annotations of human proteins, using full text articles. The predictions of task 2 are based on triplets of protein--GO term--article passage. The annotation-relevant text passages were returned by the participants and evaluated by expert curators of the GO annotation (GOA) team at the European Institute of Bioinformatics (EBI). Each participant could submit up to three results for each sub-task comprising task 2. In total more than 15,000 individual results were provided by the participants. The curators evaluated in addition to the annotation itself, whether the protein and the GO term were correctly predicted and traceable through the submitted text fragment. CONCLUSION: Concepts provided by GO are currently the most extended set of terms used for annotating gene products, thus they were explored to assess how effectively text mining tools are able to extract those annotations automatically. Although the obtained results are promising, they are still far from reaching the required performance demanded by real world applications. Among the principal difficulties encountered to address the proposed task, were the complex nature of the GO terms and protein names (the large range of variants which are used to express proteins and especially GO terms in free text), and the lack of a standard training set. A range of very different strategies were used to tackle this task. The dataset generated in line with the BioCreative challenge is publicly available and will allow new possibilities for training information extraction methods in the domain of molecular biology.

Computational Biology↗
PubMed2005

Identification and quantification of glycerolipids in cotton fibers: reconciliation with metabolic pathway predictions from DNA databases.

The lipid profiles of cotton fiber cells were determined from total lipid extracts of elongating and maturing cotton fiber cells to see whether the membrane lipid composition changed during the phases of rapid cell elongation or secondary cell wall thickening. Total FA content was highest or increased during elongation and was lower or decreased thereafter, likely reflecting the assembly of the expanding cell membranes during elongation and the shift to membrane maintenance (and increase in secondary cell wall content) in maturing fibers. Analysis of lipid extracts by electrospray ionization and tandem MS (ESI-MS/MS) revealed that in elongating fiber cells (7-10 d post-anthesis), the polar lipids-PC, PE, PI, PA, phosphatidylglycerol, monogalactosyldiacylglycerol, digalactosyldiacylglycerol, and phosphatidylglycerol-were most abundant. These same glycerolipids were found in similar proportions in maturing fiber cells (21 dpa). Detailed molecular species profiles were determined by ESI-MS/MS for all glycerolipid classes, and ESI-MS/MS results were consistent with lipid profiles determined by HPLC and ELSD. The predominant molecular species of PC, PE, PI, and PA was 34:3 (16:0, 18:3), but 36:6 (18:3,18:3) also was prevalent. Total FA analysis of cotton lipids confirmed that indeed linolenic (18:3) and palmitic (16:0) acids were the most abundant FA in these cell types. Bioinformatics data were mined from cotton fiber expressed sequence tag databases in an attempt to reconcile expression of lipid metabolic enzymes with lipid metabolite data. Together, these data form a foundation for future studies of the functional contribution of lipid metabolism to the development of this unusual and economically important cell type.

Chromatography, High Pressure Liquid↗
PubMed2003

Biopipe: a flexible framework for protocol-based bioinformatics analysis.

We identify several challenges facing bioinformatics analysis today. Firstly, to fulfill the promise of comparative studies, bioinformatics analysis will need to accommodate different sources of data residing in a federation of databases that, in turn, come in different formats and modes of accessibility. Secondly, the tsunami of data to be handled will require robust systems that enable bioinformatics analysis to be carried out in a parallel fashion. Thirdly, the ever-evolving state of bioinformatics presents new algorithms and paradigms in conducting analysis. This means that any bioinformatics framework must be flexible and generic enough to accommodate such changes. In addition, we identify the need for introducing an explicit protocol-based approach to bioinformatics analysis that will lend rigorousness to the analysis. This makes it easier for experimentation and replication of results by external parties. Biopipe is designed in an effort to meet these goals. It aims to allow researchers to focus on protocol design. At the same time, it is designed to work over a compute farm and thus provides high-throughput performance. A common exchange format that encapsulates the entire protocol in terms of the analysis modules, parameters, and data versions has been developed to provide a powerful way in which to distribute and reproduce results. This will enable researchers to discuss and interpret the data better as the once implicit assumptions are now explicitly defined within the Biopipe framework.

Amino Acid Sequence↗
PubMed2006

Broad issues to consider for library involvement in bioinformatics.

BACKGROUND: The information landscape in biological and medical research has grown far beyond literature to include a wide variety of databases generated by research fields such as molecular biology and genomics. The traditional role of libraries to collect, organize, and provide access to information can expand naturally to encompass these new data domains. METHODS: This paper discusses the current and potential role of libraries in bioinformatics using empirical evidence and experience from eleven years of work in user services at the National Center for Biotechnology Information. FINDINGS: Medical and science libraries over the last decade have begun to establish educational and support programs to address the challenges users face in the effective and efficient use of a plethora of molecular biology databases and retrieval and analysis tools. As more libraries begin to establish a role in this area, the issues they face include assessment of user needs and skills, identification of existing services, development of plans for new services, recruitment and training of specialized staff, and establishment of collaborations with bioinformatics centers at their institutions. CONCLUSIONS: Increasing library involvement in bioinformatics can help address information needs of a broad range of students, researchers, and clinicians and ultimately help realize the power of bioinformatics resources in making new biological discoveries.

Computational Biology↗
PubMed2005

Human glutathione transferase zeta.

Zeta-class glutathione transferases (GSTZs) were recently discovered by a bioinformatics approach and the availability of human expressed sequence tag databases. Although GSTZ showed little activity with conventional GST substrates (1-chloro-2,4-dinitrobenzene; organic hydroperoxides), GSTZ was found to catalyze the oxygenation of dichloroacetic acid (DCA) to glyoxylic acid and the cis-trans isomerization of maleylacetoacetate to fumarylacetoacetate. Hence, GSTZ plays a critical role in the tyrosine degradation pathway and in alpha-haloacid metabolism. The GSTZ-catalyzed biotransformation of DCA is of particular interest, because DCA is used in the human clinical management of congenital lactic acidosis and because DCA is a common drinking water contaminant. Substrate selectivity studies showed that GSTZ catalyzes the glutathione-dependent biotransformation of a range of dihaloacetic acids along with fluoroacetic acid, 2-halopropanoic acids, and 2,2-dichloropropanoic acid. Human clinical studies showed that the elimination half-life of DCA increases with repeated doses of DCA; also, rats given DCA show low GSTZ activity with DCA as the substrate. DCA was found to be a mechanism-based inactivator of GSTZ, and proteomic studies showed that Cys-16 of human GSTZ1-1 is covalently modified by a reactive intermediate that contains glutathione and the carbon skeleton of DCA. Bioinformatics studies also showed the presence of at least four polymorphic variants of human GSTZ; these variants differ considerably in the rates of catalysis and in their susceptibility to inactivation by DCA. Finally, Gstz1(-/-) mouse strains have been developed; these mice fail to biotransform DCA or maleylacetone. Although the mice have no obvious phenotype, a high incidence of lethality is observed in young mice given phenylalanine in their drinking water. Gstz1(-/-) mice should prove useful in expanding the role of GSTZ in alpha-haloacid metabolism and in the tyrosine degradation pathway.

Acids↗
PubMed2000

Coping with change: intellectual property rights, new legislation, and the human mutation database initiative.

In 1996, the European Union issued a directive requiring member states to protect databases against unauthorized copying. Similar legislation is currently being considered and will probably be enacted in the US. Such database legislation 1) will almost certainly increase existing pressures on human mutation databases to commercialize, and 2) could inadvertently make human mutation data harder to acquire and use. Strategies for minimizing these difficulties are discussed. Alternatively, a nonprofit, community-wide "depository" could probably support itself by selling sophisticated bioinformatic products to the private sector. The proposed depository would offer substantially similar databases to academic and government users at little or no cost.

Databases, Factual↗
PubMed2006

Genome Annotation Transfer Utility (GATU): rapid annotation of viral genomes using a closely related reference genome.

BACKGROUND: Since DNA sequencing has become easier and cheaper, an increasing number of closely related viral genomes have been sequenced. However, many of these have been deposited in GenBank without annotations, severely limiting their value to researchers. While maintaining comprehensive genomic databases for a set of virus families at the Viral Bioinformatics Resource Center http://www.biovirus.org and Viral Bioinformatics - Canada http://www.virology.ca, we found that researchers were unnecessarily spending time annotating viral genomes that were close relatives of already annotated viruses. We have therefore designed and implemented a novel tool, Genome Annotation Transfer Utility (GATU), to transfer annotations from a previously annotated reference genome to a new target genome, thereby greatly reducing this laborious task. RESULTS: GATU transfers annotations from a reference genome to a closely related target genome, while still giving the user final control over which annotations should be included. GATU also detects open reading frames present in the target but not the reference genome and provides the user with a variety of bioinformatics tools to quickly determine if these ORFs should also be included in the annotation. After this process is complete, GATU saves the newly annotated genome as a GenBank, EMBL or XML-format file. The software is coded in Java and runs on a variety of computer platforms. Its user-friendly Graphical User Interface is specifically designed for users trained in the biological sciences. CONCLUSION: GATU greatly simplifies the initial stages of genome annotation by using a closely related genome as a reference. It is not intended to be a gene prediction tool or a "complete" annotation system, but we have found that it significantly reduces the time required for annotation of genes and mature peptides as well as helping to standardize gene names between related organisms by transferring reference genome annotations to the target genome. The program is freely available under the General Public License and can be accessed along with documentation and tutorial from http://www.virology.ca/gatu.

Amino Acid Sequence↗
PubMed2024

A novel frameshift variant in the TMPRSS3 gene causes nonsyndromic hearing loss in a consanguineous family.

BACKGROUND: Hearing Loss (HL) is the most common sensorineural condition in humans. Mutations in the TMPRSS3 gene (DNFB8/10 locus) have been linked to autosomal recessive non-syndromic hearing loss (ARNSHL). METHODS: Whole-exome sequencing (WES) was utilized to identify disease-causing variants in a proband from Iran with ARNSHL who presented clinically with sensorineural, bilateral, and prelingual HL. The pathogenicity and novelty of the identified variant were assessed using various databases. A co-segregation study was also performed to confirm the presence of the variant in the proband's parents. Additionally, the secondary and tertiary structures of the mutant TMPRSS3 protein were predicted using bioinformatics tools. Furthermore, a global mutational spectrum of TMPRSS3 was created and statistically analyzed. The Iranome database was also used to identify other putative mutations in the TMPRSS3 gene in the Iranian population. RESULTS: We identified a novel homozygous single nucleotide deletion in TMPRSS3 (c.297delA, p.Asp100ThrfsTer52) in the proband. This is the first report of this mutation in a patient with ARNSHL. Sanger sequencing confirmed that this variant co-segregated from the proband's parents. Bioinformatic tools classified this novel variant as likely pathogenic. Additionally, 49.55% of families with TMPRSS3-related HL patients were shown to have consanguinity, consistent with our study. The Iranome database also revealed the c.268G > A variant as a putative novel mutation in TMPRSS3. CONCLUSION: This research expanded the pool of evidence regarding the association between mutations in the TMPRSS3 gene and ARNSHL. The finding confirmed that a single nucleotide deletion caused HL in the proband, suggesting that genetic testing, such as WES, is a robust technique for diagnosing patients with this condition.

Humans↗
PubMed2001

What is bioinformatics? A proposed definition and overview of the field.

BACKGROUND: The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. OBJECTIVES: Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. METHODS: Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. RESULTS AND CONCLUSIONS: Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (e.g. expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

Computational Biology↗
PubMed2006

[High throughput screening and analysis of prostate cancer-related genes through mining databases].

BACKGROUND & OBJECTIVE: Investigation of differentially expressed genes in prostate cancer tissues may help to understand the molecular mechanism of prostate cancer and provide diagnostic markers or new targets for therapy. The Cancer Genome Anatomy Project (CGAP) public database provides an unprecedented opportunity for cancer researchers to mine genes differentially expressed in cancer tissues by bioinformatic methods. This study was to explore the feasibility of incorporating the Internet-available Serial Analysis of Gene Expression (SAGE) and cDNA databases to find human prostate cancer-related genes. METHODS: SAGE digital gene expression displayer (DGED) and cDNA DGED were used to analyze differentially expressed (>5 folds) genes in malignant prostate tissues compared with normal prostate tissues. The SAGE tags were filtered by their confidence and our 3 criteria. To test the confidence of tags of virtual digital analysis for gene identification, the modified GLGI (generation of longer cDNA fragments from SAGE tags for gene identification) was used to get the cDNA 3' end downstream of 20 tags. Main functions of all candidate genes and their relations to prostate cancer were annotated. RESULTS: Fifty-three differentially expressed genes were screened out by SAGE DGED, 26 of them were up-regulated and 27 were down-regulated in prostate cancer; 28 differentially expressed genes were got by cDNA DGED, 15 of them were up-regulated and 13 were down-regulated in prostate cancer. CONCLUSION: Reasonable use of public databases by the Internet-available tools is a simple, effective approach to get cancer-related genes, and might provide useful clues for further investigation although the results require experimental validation.

DNA, Complementary↗
PubMed2026

Integrated bioinformatics analysis reveals cross-talking hub genes and therapeutic agents between sepsis and acute myocardial infarction.

BACKGROUND: Sepsis and acute myocardial infarction (AMI) are two significant diseases that may share overlapping etiological mechanisms. This study aims to systematically identify core genes common to both conditions and to explore their potential as therapeutic targets and drug candidates through an integrative analysis of clinical data and bioinformatics. METHODS: The AMI dataset was obtained from the GEO database, and RNA sequencing data were collected from blood samples of patients with sepsis at our hospital. Common genes were identified using differential expression gene analysis (DEG) and weighted gene co-expression network analysis (WGCNA). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were performed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified using the MCC/Degree algorithm. Diagnostic value was assessed via receiver operating characteristic curve analysis. Immune infiltration patterns, single-cell sequencing data, and molecular docking simulations were employed to evaluate immune relevance and identify potential therapeutic compounds. RESULTS: A total of 417 genes were identified between sepsis and AMI, with enrichment analysis revealing significant involvement in inflammatory responses. Three hub genes-JAK2, MYD88, and TIMP1-were selected for further investigation. ROC curves confirmed their strong diagnostic performance for both diseases. Immune infiltration analysis showed that these core genes were significantly correlated with the infiltration levels of various immune cell types. Molecular docking indicated that quercetin exhibited stable binding affinity with the proteins encoded by these genes. qPCR validation further confirmed the upregulation of these three genes, supporting the anti-inflammatory effects of quercetin as a potential targeted therapy. CONCLUSION: JAK2, MYD88, and TIMP1 were identified as shared core genes in sepsis and AMI. These genes not only serve as potential diagnostic biomarkers but also offer novel targets for developing common therapeutic strategies for both conditions. Furthermore, quercetin emerges as a promising candidate for targeted treatment.

Humans↗
PubMed2003

Overlapping translation of nucleic acid sequences for bioinformatics applications.

SUMMARY: An alternative method to TblastX has been developed. Nucleic acids in database and query sequences were translated into overlapping protein-like sequences (overlappingly translated sequences or OTSs) before searching with BlastP. Thus, each nucleic acid sequences is represented by a single 'protein like' sequence instead of three 'proteins' in different reading frames. The 3x3 comparison of TblastX is represented by a single comparison, giving faster results. Additional advantages are: (1) it can be more sensitive to detect weak sequence similarities than either blastN or TblastX; (2) codon redundancy is eliminated; (3) the sensitivity to single nucleotide polymorphism, mutation and sequencing errors is reduced; (4) it is insensitive to frame shifts. RESULTS: BlastP using OTS detected about two thirds of blastN and TblastX matches but discovered additional similarities. When blastN and TblastX against nucleic acids were compared to blastP against OTS, identical matches discovered by blastP were generally longer (602, respectively. 213 letters, p<0.01), had higher scores (748 respectively 460 bits, p<0.05) and lower E values (3.16E-20 vs. 1.17E+03, p<0.01) but the percentage identity was lower (25% respectively 61%, p<0.001). A qualitative evaluation with LALIGN showed an improvement of the visualization when OTS-s were used instead of nucleic acids. Many extensive sequence similarities became better visible, for example the repeating similarity between prion protein and human insulin gene micro-satellite, and the surprising similarity between the first part of prion protein coding region and the human pro-insulin (34.4% identity and additional 17.2% similarity through 238 residues, score >295 which is expected 4.6e-18 times by chance).

Amino Acid Sequence↗