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At least 505 records · Page 28Linked to original sources
PubMed2004

Survey and analysis of microsatellites in the silkworm, Bombyx mori: frequency, distribution, mutations, marker potential and their conservation in heterologous species.

We studied microsatellite frequency and distribution in 21.76-Mb random genomic sequences, 0.67-Mb BAC sequences from the Z chromosome, and 6.3-Mb EST sequences of Bombyx mori. We mined microsatellites of >/=15 bases of mononucleotide repeats and >/=5 repeat units of other classes of repeats. We estimated that microsatellites account for 0.31% of the genome of B. mori. Microsatellite tracts of A, AT, and ATT were the most abundant whereas their number drastically decreased as the length of the repeat motif increased. In general, tri- and hexanucleotide repeats were overrepresented in the transcribed sequences except TAA, GTA, and TGA, which were in excess in genomic sequences. The Z chromosome sequences contained shorter repeat types than the rest of the chromosomes in addition to a higher abundance of AT-rich repeats. Our results showed that base composition of the flanking sequence has an influence on the origin and evolution of microsatellites. Transitions/transversions were high in microsatellites of ESTs, whereas the genomic sequence had an equal number of substitutions and indels. The average heterozygosity value for 23 polymorphic microsatellite loci surveyed in 13 diverse silkmoth strains having 2-14 alleles was 0.54. Only 36 (18.2%) of 198 microsatellite loci were polymorphic between the two divergent silkworm populations and 10 (5%) loci revealed null alleles. The microsatellite map generated using these polymorphic markers resulted in 8 linkage groups. B. mori microsatellite loci were the most conserved in its immediate ancestor, B. mandarina, followed by the wild saturniid silkmoth, Antheraea assama.

Animals↗
PubMed2004

High throughput multiple combination extraction from large scale polymorphism data by exact tree method.

Single nucleotide polymorphisms (SNPs) are increasingly becoming important in clinical settings as useful genetic markers. For the evaluation of genetic risk factors of multifactorial diseases, it is not sufficient to focus on individual SNPs. It is preferable to evaluate combinations of multiple markers, because it allows us to examine the interactions between multiple factors. If all the combinations possible were evaluated round-robin, the number of calculations would rapidly explode as the number of markers analyzed increased. To overcome this limitation, we devised the exact tree method based on decision tree analysis and applied it to 14 SNP data from 68 Japanese stroke patients and 189 healthy controls. From the obtained tree models, we succeeded in extracting multiple statistically significant combinations that elevate the risk of stroke. From this result, we inferred that this method would work more efficiently in the whole genome study, which handles thousands of genetic markers. This exploratory data mining method will facilitate the extraction of combinations from large-scale genetic data and provide a good foothold for further verificatory research.

Adult↗
PubMed2026

Discovery of antimicrobial peptides from incomplete biosynthetic gene clusters to combat multidrug-resistant bacteria.

The escalating crisis of multidrug-resistant bacteria necessitates innovative antibiotic discovery platforms. Conventional antimicrobial peptide (AMP) mining often relies on complete biosynthetic gene clusters (BGCs), leaving fragmented genomic resources underexplored. Here, we present an evolution-inspired approach to reconstruct and predict AMPs from partial BGCs. Applying this strategy to 954 Paenibacillus genomes identifies five polymyxin-like peptides, NP001-NP005, with broad in vitro activity. Crucially, in murine models of polymyxin-resistant infection, NP001 reduced bacterial burdens by up to 1,000-fold in a thigh infection model and improved survival (50% vs. 0%) in a lethal peritonitis model. Structural simulations and biophysical assays revealed that NP001 maintains high affinity for bacterial membranes and effectively binds to MCR-1-modified lipid A, a key colistin-resistance mechanism. Moreover, Leu at position 10 of NP001 plays a key role in antibacterial activity against MCR-1-resistant bacteria. Our work establishes a generalizable framework for AMP discovery and introduces a promising therapeutic candidate, NP001, which effectively counteracts polymyxin-resistant pathogens.

Multigene Family↗
PubMed2002

Discovery informatics: its evolving role in drug discovery.

Drug discovery and development is a highly complex process requiring the generation of very large amounts of data and information. Currently this is a largely unmet informatics challenge. The current approaches to building information and knowledge from large amounts of data has been addressed in cases where the types of data are largely homogeneous or at the very least well-defined. However, we are on the verge of an exciting new era of drug discovery informatics in which methods and approaches dealing with creating knowledge from information and information from data are undergoing a paradigm shift. The needs of this industry are clear: Large amounts of data are generated using a variety of innovative technologies and the limiting step is accessing, searching and integrating this data. Moreover, the tendency is to move crucial development decisions earlier in the discovery process. It is crucial to address these issues with all of the data at hand, not only from current projects but also from previous attempts at drug development. What is the future of drug discovery informatics? Inevitably, the integration of heterogeneous, distributed data are required. Mining and integration of domain specific information such as chemical and genomic data will continue to develop. Management and searching of textual, graphical and undefined data that are currently difficult, will become an integral part of data searching and an essential component of building information- and knowledge-bases.

Artificial Intelligence↗
PubMed2006

Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries.

High-throughput screening (HTS) of chemical compounds to identify modulators of molecular targets is a mainstay of pharmaceutical development. Increasingly, HTS is being used to identify chemical probes of gene, pathway, and cell functions, with the ultimate goal of comprehensively delineating relationships between chemical structures and biological activities. Achieving this goal will require methodologies that efficiently generate pharmacological data from the primary screen and reliably profile the range of biological activities associated with large chemical libraries. Traditional HTS, which tests compounds at a single concentration, is not suited to this task, because HTS is burdened by frequent false positives and false negatives and requires extensive follow-up testing. We have developed a paradigm, quantitative HTS (qHTS), tested with the enzyme pyruvate kinase, to generate concentration-response curves for >60,000 compounds in a single experiment. We show that this method is precise, refractory to variations in sample preparation, and identifies compounds with a wide range of activities. Concentration-response curves were classified to rapidly identify pyruvate kinase activators and inhibitors with a variety of potencies and efficacies and elucidate structure-activity relationships directly from the primary screen. Comparison of qHTS with traditional single-concentration HTS revealed a high prevalence of false negatives in the single-point screen. This study demonstrates the feasibility of qHTS for accurately profiling every compound in large chemical libraries (>10(5) compounds). qHTS produces rich data sets that can be immediately mined for reliable biological activities, thereby providing a platform for chemical genomics and accelerating the identification of leads for drug discovery.

Combinatorial Chemistry Techniques↗
PubMed2007

BRENDA, AMENDA and FRENDA: the enzyme information system in 2007.

The BRENDA (BRaunschweig ENzyme DAtabase) enzyme information system (http://www.brenda.uni-koeln.de) is the largest publicly available enzyme information system worldwide. The major parts of its contents are manually extracted from primary literature. It is not restricted to specific groups of enzymes, but includes information on all identified enzymes irrespective of the enzyme's source. The range of data encompasses functional, structural, sequence, localisation, disease-related, isolation, stability information on enzyme and ligand-related data. Each single entry is linked to the enzyme source and to a literature reference. Recently the data repository was complemented by text-mining data in AMENDA (Automatic Mining of ENzyme DAta) and FRENDA (Full Reference ENzyme DAta). A genome browser, membrane protein prediction and full-text search capacities were added. The newly implemented web service provides instant access to the data for programmers via a SOAP (Simple Object Access Protocol) interface. The BRENDA data can be downloaded in the form of a text file from the beginning of 2007.

Animals↗
PubMed2004

Role of brain-derived neurotrophic factor and NF-kappaB in neuronal plasticity and survival: From genes to phenotype.

PURPOSE: Brain-derived neurotrophic factor (BDNF) is a member of the family of neurotrophins and promotes diverse effects in neurons including development, maintenance of function, synaptic plasticity, and survival in different animal models. We present advances in our understanding of the genomics of the BDNF gene (bdnf) and its regulation by calcium-activated transcription factors, including cAMP response element binding protein (CREB) and more recently, nuclear factor kappaB (NF-kappaB) and discuss these findings in the context of neuronal plasticity and survival. METHODS: We used amplified bdnf complementary DNAs (cDNAs) and genomic DNA templates for direct sequencing and sequence variant discovery, information mining of public databases, and conventional molecular and cellular biology approaches to screen bdnf for novel regulatory elements, alternatively spliced exons, and functional sequence variants. RESULTS: We discovered a candidate NF-kappaB site in promoter 3 of bdnf and showned that activation of N-methyl-D-aspartate (NMDA) inotropic glutamate receptors increased bdnf expression through an NF-kappaB-dependent pathway and extended the finding to show that NF-kappaB was required for NMDA neuroprotection in vitro. In addition, sequence analysis of bdnf cDNAs from different brain regions predicted at least three pre-pro-BDNF protein isoforms, two of which were previously unknown. Each isoform differs at the amino terminus and may have functional importance. CONCLUSIONS: Given the central role that BDNF plays in the developing and adult nervous system, understanding how BDNF is regulated and how it functions will enhance our knowledge of its diverse effects, which may lead to more effective treatments for neurodegenerative disorders and reveal the role of BDNF in complex phenotypes related to behavior.

Animals↗
PubMed2003

Computational inference of regulatory pathways in microbes: an application to phosphorus assimilation pathways in Synechococcus sp. WH8102.

We present a computational protocol for inference of regulatory and signaling pathways in a microbial cell, through literature search, mining "high-throughput'' biological data of various types, and computer-assisted human inference. This protocol consists of four key components: (a) construction of template pathways for microbial organisms related to the target genome, which either have been extensively studied and/or have a significant amount of (relevant) experimental data, (b) inference of initial pathway models for the target genome, through combining the template pathway models and target genome-specific information, (c) refinement and expansion of the initial pathway models through applications of various data mining tools, including phylogenetic profile analysis, inference of protein-protein interactions, and prediction of transcription factor binding sites, and (d) validation and refinement of the pathway models using pathway-specific experimental data or other information. To demonstrate the effectiveness of this procedure, we have applied it to the construction of the phosphorus assimilation pathways in cyanobacterium sp. WH8102. We present, in this paper, a model of the core components of this pathway.

Bacterial Proteins↗
PubMed2003

ToxoDB: accessing the Toxoplasma gondii genome.

ToxoDB (http://ToxoDB.org) provides a genome resource for the protozoan parasite Toxoplasma gondii. Several sequencing projects devoted to T. gondii have been completed or are in progress: an EST project (http://genome.wustl.edu/est/index.php?toxoplasma=1), a BAC clone end-sequencing project (http://www.sanger.ac.uk/Projects/T_gondii/) and an 8X random shotgun genomic sequencing project (http://www.tigr.org/tdb/e2k1/tga1/). ToxoDB was designed to provide a central point of access for all available T. gondii data, and a variety of data mining tools useful for the analysis of unfinished, un-annotated draft sequence during the early phases of the genome project. In later stages, as more and different types of data become available (microarray, proteomic, SNP, QTL, etc.) the database will provide an integrated data analysis platform facilitating user-defined queries across the different data types.

Animals↗
PubMed2002

yMGV: helping biologists with yeast microarray data mining.

yMGV (yeast Microarray Global Viewer) was designed to provide biologists with meaningful information from genome-wide yeast expression data. The database includes most of the available expression data published on yeast microarrays over the last 4 years. It provides customizable tools for the rapid visualization of expression profiles associated with a set of genes from all published experiments. It also allows users to compare the results from different publications so that they can identify genes with common expression profiles. We used yMGV to perform global analyses to find a gene expression profile specific for given biological conditions and to locate functional gene clusters on chromosomes. Other organisms will be added to this database. yMGV is accessible on the web at http://transcriptome.ens.fr/ymgv.

Computer Graphics↗
PubMed2006

Mining ORESTES no-match database: can we still contribute to cancer transcriptome?

The Human Cancer Genome Project generated about 1 million expressed sequence tags by the ORESTES method, principally with the aim of obtaining data from cancer. Of this total, 341,680 showed no similarity with sequences in the public transcript databases, referred to as "no-match". Some of them represent low abundance or difficult to detect human transcripts, but part of these sequences represent genomic contamination or immature mRNA. We performed a bioinformatics pipeline to determine the novelty of ORESTES "no-match" datasets from prostate or breast tissues. We started with 14,908 clusters mapped on the human genome. A total of 2226 clusters originating from more than two libraries or singletons with gaps upon genome alignment were selected. Ninety-four clusters with canonical splice sites representing the most stringent criteria to be considered a gene were subjected to manual inspection regarding genomic hits. Of the manually inspected clusters, 49.6% contained new sequences where 42.2% were probable low-expression alternative forms of the characterized genes and 7.4% unpredicted genes. RT-PCR followed by sequencing was performed to validate the largest spliced sequence from 8 clusters, resulting in the confirmation of five sequences as true human transcript fragments. Some of them were differentially expressed between tumor and normal tissue by an in silico analysis. We can conclude that after clean up of the no-match dataset, we still have about 939 new exons and 165 unpredicted genes that could complete the prostate or breast transcriptome.

Breast Neoplasms↗
PubMed2005

The use of RAPD genomic fingerprinting to study relatedness in strains of Acidithiobacillus ferrooxidans.

Twelve strains of Acidithiobacillus ferrooxidans were recovered from acid mine drainage (AMD) sites from three different geographical locations: Copper Cliff, Ontario, Canada; Mineral City, OH, USA; and Cornwall, England. The spread-plate technique and various culture media were used to isolate and purify all strains. DNA was extracted from each purified culture and amplified using PCR and twenty, 10-mer primers. Amplification products were separated by gel electrophoresis and photographed under UV light. The RAPD (Randomly Amplified Polymorphic DNA) profiles were compared on the basis of the presence or absence of each DNA band and a data matrix was constructed. Strain diversity was calculated using the Jaccard's coefficient and UPGMA (Unweighted Pair-Group Arithmetic Average Clustering) cluster analysis. The variations in the banding patterns indicated genomic variability among the twelve A. ferrooxidans strains tested. The primers used in this study grouped the twelve strains into five major groups. Similarities between the strains ranged from 5.49% to 85.14%. These results show that the strains have a high degree of genomic diversity and that the RAPD procedure is a powerful technique to assess strain variability in this bacterium.

Acidithiobacillus↗
PubMed2005

Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research.

SUMMARY: We present here Blast2GO (B2G), a research tool designed with the main purpose of enabling Gene Ontology (GO) based data mining on sequence data for which no GO annotation is yet available. B2G joints in one application GO annotation based on similarity searches with statistical analysis and highlighted visualization on directed acyclic graphs. This tool offers a suitable platform for functional genomics research in non-model species. B2G is an intuitive and interactive desktop application that allows monitoring and comprehension of the whole annotation and analysis process. AVAILABILITY: Blast2GO is freely available via Java Web Start at http://www.blast2go.de. SUPPLEMENTARY MATERIAL: http://www.blast2go.de -> Evaluation.

Algorithms↗
PubMed2003

Shopping in the genome market with EnsMart.

Life scientists who work with the supermarket of genome data will find the EnsMart database and software package offers a valuable door to a wealth of genes and genome features. Not only available to lab biologists on the web, this popular multi-organism genome database can be installed and used on your own Unix computer with relative ease. It offers a flexible, fast and practical data-mining framework for computer-savvy biologists and bioinformaticians.

Animals↗
PubMed2006

Blast2GO goes grid: developing a grid-enabled prototype for functional genomics analysis.

The vast amount in complexity of data generated in Genomic Research implies that new dedicated and powerful computational tools need to be developed to meet their analysis requirements. Blast2GO (B2G) is a bioinformatics tool for Gene Ontology-based DNA or protein sequence annotation and function-based data mining. The application has been developed with the aim of affering an easy-to-use tool for functional genomics research. Typical B2G users are middle size genomics labs carrying out sequencing, ETS and microarray projects, handling datasets up to several thousand sequences. In the current version of B2G. The power and analytical potential of both annotation and function data-mining is somehow restricted to the computational power behind each particular installation. In order to be able to offer the possibility of an enhanced computational capacity within this bioinformatics application, a Grid component is being developed. A prototype has been conceived for the particular problem of speeding up the Blast searches to obtain fast results for large datasets. Many efforts have been done in the literature concerning the speeding up of Blast searches, but few of them deal with the use of large heterogeneous production Grid Infrastructures. These are the infrastructures that could reach the largest number of resources and the best load balancing for data access. The Grid Service under development will analyse requests based on the number of sequences, splitting them accordingly to the available resources. Lower-level computation will be performed through MPIBLAST. The software architecture is based on the WSRF standard.

Computational Biology↗
PubMed2005

Mining and survey of simple sequence repeats in expressed sequence tags of dicotyledonous species.

Simple sequence repeat (SSR) markers are widely used in many plant and animal genomes due to their abundance, hypervariability, and suitability for high-throughput analysis. Development of SSR markers using molecular methods is time consuming, laborious, and expensive. Use of computational approaches to mine ever-increasing sequences such as expressed sequence tags (ESTs) in public databases permits rapid and economical discovery of SSRs. Most of such efforts to date focused on mining SSRs from monocotyledonous ESTs. In this study, we have computationally mined and examined the abundance of SSRs in more than 1.54 million ESTs belonging to 55 dicotyledonous species. The frequency of ESTs containing SSRs among species ranged from 2.65% to 16.82%. Dinucleotide repeats were found to be the most abundant followed by tri- or mono-nucleotide repeats. The motifs A/T, AG/GA/CT/TC, and AAG/AGA/GAA/CTT/TTC/TCT were the predominant mono-, di-, and tri-nucleotide SSRs, respectively. Most of the mononucleotide SSRs contained 15-25 repeats, whereas the majority of the di- and tri-nucleotide SSRs contained 5-10 repeats. The comprehensive SSR survey data presented here demonstrates the potential of in silico mining of ESTs for rapid development of SSR markers for genetic analysis and applications in dicotyledonous crops.

Computational Biology↗
PubMed2005

INFOBIOMED: European Network of Excellence on Biomedical Informatics to support individualised healthcare.

INFOBIOMED is an European Network of Excellence (NoE) funded by the Information Society Directorate-General of the European Commission (EC). A consortium of European organizations from ten different countries is involved within the network. Four pilots, all related to linking clinical and genomic information, are being carried out. From an informatics perspective, various challenges, related to data integration and mining, are included.

Biomedical Research↗
PubMed2006

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences.

BACKGROUND: The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. RESULTS: There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. CONCLUSION: The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart.

Algorithms↗