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At least 649 records · Page 36Linked to original sources
PubMed2006

SSR-based linkage map with new markers using an intraspecific population of common wheat.

Simple sequence repeats (SSRs) are valuable molecular markers in many plant species. In common wheat (Triticum aestivum L.), which is characteristic of its large genomes and alloploidy, SSRs are one of the most useful markers. To increase SSR marker sources and construct an SSR-based linkage map of appropriate density, we tried to develop new SSR markers from SSR-enriched genomic libraries and the public database. SSRs having (GA)n and (GT)n motifs were isolated from enriched libraries, and di- and tri-nucleotide repeats were mined from expressed sequence tags (ESTs) and DNA sequences of Triticum species in the public database. Of the 1,147 primer pairs designed, 842 primers gave accurate amplification products, and 478 primers showed polymorphism among the nine wheat lines examined. Using a doubled haploid (DH) population from an intraspecific cross between Kitamoe and Münstertaler (KM), we constructed an SSR-based linkage map that consisted of 464 loci: 185 loci from genomic libraries, 65 loci from the sequence database including ESTs, 213 loci from the SSR markers already reported, and 1 locus of morphological marker. Although newly developed SSR loci were distributed throughout all chromosomes, clustering of them around putative centromeric regions was found on several chromosomes. The total length of the KM map spanned 3,441 cM and corresponded to approximately 86% genome coverage. The KM map comprised of 23 linkage groups because two gaps of over 50 cM distance remained on chromosome 6A. This is a first report of SSR-based linkage map using single intraspecific population of common wheat. This mapping result suggests that it becomes possible to construct linkage maps with sufficient genome coverage using only SSR markers without RFLP markers, even in an intraspecific population of common wheat. Moreover, the new SSR markers will contribute to the enrichment of molecular marker resources in common wheat.

Chromosome Mapping↗
PubMed2004

RNAiDB and PhenoBlast: web tools for genome-wide phenotypic mapping projects.

RNA interference (RNAi) is being used in large-scale genomic studies as a rapid way to obtain in vivo functional information associated with specific genes. How best to archive and mine the complex data derived from these studies provides a series of challenges associated with both the methods used to elicit the RNAi response and the functional data gathered. RNAiDB (RNAi Database; http://www. rnai.org) has been created for the archival, distribution and analysis of phenotypic data from large-scale RNAi analyses in Caenorhabditis elegans. The database contains a compendium of publicly available data and provides information on experimental methods and phenotypic results, including raw data in the form of images and streaming time-lapse movies. Phenotypic summaries together with graphical displays of RNAi to gene mappings allow quick intuitive comparison of results from different RNAi assays and visualization of the gene product(s) potentially inhibited by each RNAi experiment based on multiple sequence analysis methods. RNAiDB can be searched using combinatorial queries and using the novel tool PhenoBlast, which ranks genes according to their overall phenotypic similarity. RNAiDB could serve as a model database for distributing and navigating in vivo functional information from large-scale systematic phenotypic analyses in different organisms.

Animals↗
PubMedDate not supplied

A role for oligonucleotide-based RNA-knock down technologies in functional genomics.

Functional genomics is inundating the pharmaceutical industry with large numbers of potential gene targets from several sources such as gene expression profiling experiments (DNA microchips, proteomics) or database mining. Oligonucleotide-based RNA-knock down technologies such as antisense or RNA interference can aid in the filtering and prioritization of target candidates in the drug discovery process.

Drug Industry↗
PubMedDate not supplied

Using genomic databases for sequence-based biological discovery.

The inherent potential underlying the sequence data produced by the International Human Genome Sequencing Consortium and other systematic sequencing projects is, obviously, tremendous. As such, it becomes increasingly important that all biologists have the ability to navigate through and cull important information from key publicly available databases. The continued rapid rise in available sequence information, particularly as model organism data is generated at breakneck speed, also underscores the necessity for all biologists to learn how to effectively make their way through the expanding "sequence information space." This review discusses some of the more commonly used tools for sequence discovery; tools have been developed for the effective and efficient mining of sequence information. These include LocusLink, which provides a gene-centric view of sequence-based information, as well as the 3 major genome browsers: the National Center for Biotechnology Information Map Viewer, the University of California Santa Cruz Genome Browser, and the European Bioinformatics Institute's Ensembl system. An overview of the types of information available through each of these front-ends is given, as well as information on tutorials and other documentation intended to increase the reader's familiarity with these tools.

Animals↗
PubMed2026

NCBoost v2: a classifier for non-coding single-nucleotide variants in Mendelian diseases.

MOTIVATION: The current diagnostic rate of rare diseases through whole-genome sequencing has stabilized at around 30% on average, highlighting the need for improved computational scores to identify pathogenic variants. In 2019, we developed NCBoost, a supervised-learning approach that mined a comprehensive set of sequence constraint features and proved particularly well suited to identifying high-effect pathogenic non-coding variants in genetic diseases. Since its first release, the substantial increase in the number of variants available for training, as well as the enhanced capacity to detect purifying selection signals from large-scale genome sequencing projects, motivated an update of NCBoost. RESULTS: We implemented NCBoost v2, a pathogenicity score for non-coding single-nucleotide variants, trained on the largest set of curated pathogenic variants in monogenic Mendelian diseases available to date. It leverages conservation features computed from recent large-scale genomic consortia such as Zoonomia and gnomAD, and incorporates recent splice-altering predictive scores. NCBoost v2 outperformed alternative state-of-the-art methods in a variety of scenarii, providing more consistent scores across non-coding genomic regions and fine-tuning the scoring of pathogenic splice-altering variants in Mendelian disease genes. AVAILABILITY AND IMPLEMENTATION: NCBoost v2 software is implemented in Python 3.10 and is freely available under the GNU General Public License Version 3 at https://doi.org/10.5281/zenodo.16029049 and https://github.com/RausellLab/NCBoost-2, together with precomputed scores for the human genome assembly GRCh38.

Polymorphism, Single Nucleotide↗
PubMed2006

Between a rock and a hard place: trace element nutrition in Chlamydomonas.

Photosynthetic organisms are among the earliest life forms on earth and their biochemistry is strictly dependent on a wide range of inorganic nutrients owing to the use of metal cofactor-dependent enzymes in photosynthesis, respiration, inorganic nitrogen and sulfur assimilation. Chlamydomonas reinhardtii is a photosynthetic eukaryotic model organism for the study of trace metal homeostasis. Chlamydomonas spp. are widely distributed and can be found in soil, glaciers, acid mines and sewage ponds, suggesting that the genus has significant capacity for acclimation to micronutrient availability. Analysis of the draft genome indicates that metal homeostasis mechanisms in Chlamydomonas represent a blend of mechanisms operating in animals, plants and microbes. A combination of classical genetics, differential expression and genomic analysis has led to the identification of homologues of components known to operate in fungi and animals (e.g., Fox1, Ftr1, Fre1, Fer1, Ctr1/2) as well as novel molecules involved in copper and iron nutrition (Crr1, Fea1/2). Besides activating iron assimilation pathways, iron-deficient Chlamydomonas cells re-adjust metabolism by reducing light delivery to photosystem I (to avoid photo-oxidative damage resulting from compromised FeS clusters) and by modifying the ferredoxin profile (perhaps to accommodate preferential allocation of reducing equivalents). Up-regulation of a MnSOD isoform may compensate for loss of FeSOD. Ferritin could function to buffer the iron released from programmed degradation of iron-containing enzymes in the chloroplast. Some metabolic adjustments are made in anticipation of deficiency while others occur only with sustained or severe deficiency. Copper-deficient Chlamydomonas cells induce a copper assimilation pathway consisting of a cell surface reductase and a Cu(+) transporter (presumed CTR homologue). There are metabolic adaptations in addition: the synthesis of "back-up" enzymes for plastocyanin in photosynthesis and the ferroxidase in iron assimilation plus activation of alternative oxidase to handle the electron "overflow" resulting from reduced cytochrome oxidase function. Oxygen-dependent enzymes in the tetrapyrrole pathway (coproporphyrinogen oxidase and aerobic oxidative cyclase) are also increased in expression and activity by as much as 10-fold but the connection between copper nutrition and tetrapyrroles is not understood. The copper-deficiency responses are mediated by copper response elements that are defined by a GTAC core sequence and a novel metalloregulator, Crr1, which uses a zinc-dependent SBP domain to bind to the CuRE. The Chlamydomonas model is ideal for future investigation of nutritional manganese deficiency and selenoenzyme function. It is also suited for studies of trace nutrient interactions, nutrition-dependent metabolic changes, the relationship between photo-oxidative stress and metal homeostasis, and the important questions of differential allocation of limiting metal nutrients (e.g., to respiration vs. photosynthesis).

Animals↗
PubMed2004

Bioinformatics: harvesting information for plant and crop science.

Bioinformatics is an integral aspect of plant and crop science research. Developments in data management and analytical software are reviewed with an emphasis on applications in functional genomics. This includes information resources for Arabidopsis and crop species, and tools available for analysis and visualisation of comparative genomic data. Approaches used to explore relationships between plant genes and expressed sequences are compared, including use of ontologies. The impact of bioinformatics in forward and reverse genetics is described, together with the potential from data mining. The role of bioinformatics is explored in the wider context of plant and crop science.

Algorithms↗
PubMed2003

Disease proteomics.

The sequencing of the human genome and that of numerous pathogens has opened the door for proteomics by providing a sequence-based framework for mining proteomes. As a result, there is intense interest in applying proteomics to foster a better understanding of disease processes, develop new biomarkers for diagnosis and early detection of disease, and accelerate drug development. This interest creates numerous opportunities as well as challenges to meet the needs for high sensitivity and high throughput required for disease-related investigations.

Disease↗
PubMed2001

A new siglec family member, siglec-10, is expressed in cells of the immune system and has signaling properties similar to CD33.

The siglecs (sialic acid-binding Ig-like lectins) are a distinct subset of the Ig superfamily with adhesion-molecule-like structure. We describe here a novel member of the siglec protein family that shares a similar structure including five Ig-like domains, a transmembrane domain, and a cytoplasmic tail containing two ITIM-signaling motifs. Siglec-10 was identified through database mining of an asthmatic eosinophil EST library. Using the Stanford G3 radiation hybrid panel we were able to localize the genomic sequence of siglec-10 within the cluster of genes on chromosome 19q13.3-4 that encode other siglec family members. We have demonstrated that siglec-10 is an immune system-restricted membrane-bound protein that is highly expressed in peripheral blood leukocytes as demonstrated by Northern, RT-PCR and flow cytometry. Binding assays determined that the extracellular domain of siglec-10 was capable of binding to peripheral blood leukocytes. The cytoplasmic tail of siglec-10 contains four tyrosines, two of which are embedded in ITIM-signaling motifs (Y597 and Y667) and are likely involved in intracellular signaling. The ability of tyrosine kinases to phosphorylate the cytoplasmic tyrosines was evaluated by kinase assay using wild-type siglec-10 cytoplasmic domain and Y-->F mutants. The majority of the phosphorylation could be attributed to Y597 andY667. Further experiments with cell extracts suggest that SHP-1 interacts with Y667 and SHP-2 interacts with Y667 in addition to another tyrosine. This is very similar to CD33, which also binds the phosphatases SHP-1 and SHP-2, therefore siglec-10, as CD33, may be characterized as an inhibitory receptor.

Amino Acid Motifs↗
PubMed2002

A bioinformatics tool to select sequences for microarray studies of mouse models of oncogenesis.

UNLABELLED: One of the challenges to the effective utilization of cDNA microarray analysis in mouse models of oncogenesis is the choice of a critical set of probes that are informative for human disease. Given the thousands of genes with a potential role in human oncogenesis and the hundreds of thousands of mouse sequences available for use as probes, selection of an informative set of mouse probes can be an overwhelming task. We have developed a web based sequence mining tool using DataBase Independent (DBI) Perl to annotate publicly available sequences. The Mouse Oncochip Design Tool uses the Mouse Genome Database (MGD) developed and maintained by the Jackson Laboratories for mouse DNA sequences. There are over 380 000 sequences in their database. The output list has been ordered to present the genes more likely to be informative in a mouse model of human cancer using a candidate set of oncogenes to order the list. Mouse sequences that represent genes that are homologous with a member of a human oncogene set are listed first. In addition it provides a set of links for information on clone source gene function. CONTACT: http://nciarray.nci.nih.gov/cgi-bin/me/mouse_design.cgi

Animals↗
PubMed2004

Solution structure of the hypothetical protein Mth677 from Methanobacterium thermoautotrophicum: a novel alpha+beta fold.

The structure of Mth677, a hypothetical protein from Methanobacterium thermoautotrophicum (Mth), has been determined by using heteronuclear nuclear magnetic resonance (NMR) methods on a double-labeled (15)N-(13)C sample. Mth677 adopts a novel alpha+beta fold, consisting of two alpha-helices (one N terminal and one C terminal) packed on the same side of a central beta-hairpin. This structure is likely shared by its three orthologs, detected in three other Archaebacteria. There are no clear features in the sequences of these proteins or in the genome organization of Mth to make a reliable functional assignment to this protein. However, the structural similarity to Escherichia coli MinE, the protein which controls that division occurs at the midcell site, lends support to the proposal that Mth677 might be, in Mth, the counterpart of the topological specificity domain of MinE in E. coli.

Amino Acid Sequence↗
PubMed2006

Gene expression profiling in schizophrenia and related mental disorders.

The etiology and pathophysiology of schizophrenia and related mental disorders such as bipolar disorder and major depression remain largely unclear. Recent advances in mRNA profiling techniques made it possible to perform genome-wide gene expression analysis in a hypothesis-free manner. It was thought that this large-scale data mining approach would reveal unknown molecular cascades involved in mental disorders. Contrary to this initial expectation, however, DNA microarray results in psychiatric fields have been notoriously discordant. Here the authors review the findings of DNA microarray analysis, focusing on systematic gene expression changes in schizophrenia, as well as alterations in the expression of specific genes, that have been reported and replicated. The authors also address the probable causes for the discordance among studies, possible ways to solve the problem, and their preferred approach for data interpretation.

Bipolar Disorder↗
PubMed1999

IMAGEne I: clustering and ranking of I.M.A.G.E. cDNA clones corresponding to known genes.

MOTIVATION: To enhance the usefulness of the I.M.A.G.E. Consortium (Lennon et al., 1996, Genomics, 33, 151-152) cDNA clone collection by directed analysis and organization of their associated Expressed Sequence Tags (ESTs), thus enabling effective mining of the immense amounts of public cDNA information. RESULTS: This paper introduces the IMAGEne suite of tools, which clusters ESTs around known genes, then ranks each clone within a cluster. IMAGEne filters data from known gene sequence databases and the GenBank's EST database (Boguski and Shuler, 1995, Nature Genet., 10, 369-371). It applies biological criteria in connection with judicious use of the BLAST (Altschul et al., 1990, J. Mol. Biol., 215), FASTA (Pearson and Lipman, 1988, Proc. Natl Acad. Sci. USA, 85, 2444-2448; Pearson, 1990, Methods Enzymol., 183, 63-98; Gusfield, 1997, Algorithms on Strings, Trees, and Sequences, Cambridge University Press), and SIM (Huang et al., 1990, Comput. Appl. Biosci., 6, 373-381) tools to form known gene clusters. It then applies criteria derived from experienced biologists to select the best representative I.M.A.G.E. clone for a gene. The tool provides an intuitive Java interface for query and display of the gene and its associated clones, thus directing researchers in selecting a clone that will best enhance their research. An important product is a listing of clones that best represent all known genes. The listing will be used for re-arraying clones into minimally redundant Master Arrays. Both the listings and Master Arrays will be made available to the public, which will be a valuable resource to the genomic community in furthering discovery in the area of gene function.

Algorithms↗
PubMed2002

Molecular classification of breast carcinomas by comparative genomic hybridization: a specific somatic genetic profile for BRCA1 tumors.

In approximately 70% of the families with a high frequency of early-onset breast and/or ovarian cancer, BRCA1 or BRCA2 germline mutations cannot be identified with the current screening regime. Therefore, we used data mining to identify a somatic genetic signature to differentiate BRCA1 mutation carriers from non-BRCA1 carriers based on the genetic characteristics of their breast carcinomas. For this purpose, we developed a molecular classifier, which assigns a given tumor to either the BRCA1 or control group based on somatic genetic profiles as revealed by comparative genomic hybridization. This was performed on breast tumors selected from two groups of patients: 28 proven BRCA1 germline mutation carriers; and a control group consisting of 42 breast tumors from patients with unknown BRCA1 or BRCA2 status. We show that BRCA1 breast carcinomas exhibit specific somatic genetic aberrations and can be distinguished from control tumors with an accuracy of 84% (sensitivity of 96% and specificity of 76%). Chromosomal bands used by this classifier include regions on chromosomes 3p, 3q, and 5q. The classifier miss-assigned one patient with a BRCA1 mutation to the non-BRCA1 class. The germline mutation in this patient is a 62bp deletion in the last exon of BRCA1 (5622del62). Possibly, this mutation may give a different phenotypic effect than do mutations in other regions of the gene. Validation on an independent set of BRCA1 and sporadic tumors showed that the BRCA1 classifier correctly identified all 6 BRCA1 tumors and assigned 4 of the 19 control patients to the BRCA1 class. The resulting accuracy on the validation set is 84%.

Breast Neoplasms↗
PubMed2001

Molecular characterization of Acidithiobacillus ferrooxidans and A. thiooxidans strains isolated from mine wastes in Brazil.

Nineteen strains of Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans, including 12 strains isolated from coal, copper, gold and uranium mines in Brazil, strains isolated from similar sources in other countries and the type strains of the two species were characterized together with the type strain of A. caldus by using a combination of molecular systematic methods, namely ribotyping, BOX- and ERIC-PCR and DNA-DNA hybridization assays. Data derived from the molecular fingerprinting analyses showed that the tested strains encompassed a high degree of genetic variability. Two of the Brazilian A. ferrooxidans organisms (strains SSP and PCE) isolated from acid coal mine waste and uranium mine effluent, respectively, and A. thiooxidans strain DAMS, isolated from uranium mine effluent, were the most genetically divergent organisms. The DNA-DNA hybridization data did not support the allocation of Acidithiobacillus strain SSP to the A. ferrooxidans genomic species, as it shared only just over 40% DNA relatedness with the type strain of the species. Acidithiobacillus strain SSP was not clearly related to A. ferrooxidans in the 16S rDNA tree.

Brazil↗
PubMed2004

In silico identification and expression of SLC30 family genes: an expressed sequence tag data mining strategy for the characterization of zinc transporters' tissue expression.

BACKGROUND: Intracellular zinc concentration and localization are strictly regulated by two main protein components, metallothioneins and membrane transporters. In mammalian cells, two membrane transporters family are involved in intracellular zinc homeostasis: the uptake transporters called SLC39 or Zip family and the efflux transporters called SLC30 or ZnT family. ZnT proteins are members of the cation diffusion facilitator (CDF) family of metal ion transporters. RESULTS: From genomic databanks analysis, we identified the full-length sequences of two novel SLC30 genes, SLC30A8 and SLC30A10, extending the SLC30 family to ten members. We used an expressed sequence tag (EST) data mining strategy to determine the pattern of ZnT genes expression in tissues. In silico results obtained for already studied ZnT sequences were compared to experimental data, previously published. We determined an overall good correlation with expression pattern obtained by RT-PCR or immunomethods, particularly for highly tissue specific genes. CONCLUSION: The method presented herein provides a useful tool to complete gene families from sequencing programs and to produce preliminary expression data to select the proper biological samples for laboratory experimentation.

Amino Acid Sequence↗
PubMedDate not supplied

Clinical and pharmacogenomic data mining: 3. Zeta theory as a general tactic for clinical bioinformatics.

A new approach, a Zeta Theory of observations, data, and data mining, is being forged from a theory of expected information into an even more cohesive and comprehensive form by the challenge of general genomic, pharmacogenomic, and proteomic data. In this paper, the focus is not on studies using the specific tool FANO (CliniMiner) but on extensions to a new broader theoretical approach, aspects of which can easily be implemented into, or otherwise support, excellent existing methods, such as forms of multivariate analysis and IBM's product Intelligent Miner. The theory should perhaps be distinguished from an existing purely number-theoretic area sometimes also known as Zeta Theory, which focuses on the Riemann Zeta Function and the ways in which it governs the distribution of prime numbers. However, Zeta Theory as used here overlaps heavily with it and actually makes use of these same matters. The distinction is that it enters from a Bayesian information theory and data representation perspective. It could thus be considered an application of the 'mathematician's version'. The application is by no means confined to areas of modern biomedicine, and indeed its generality, even merging into quantum mechanics, is a key feature. Other areas with some similar challenges as modern biology, and which have inspired data mining methods such as IBM's Intelligent Miner, include commerce. But for several reasons discussed, modern molecular biology and medicine seem particularly challenging, and this relates to the often irreducible high dimensionality of the data. This thus remains our main target.

Computational Biology↗
PubMed2004

SGMD: the Soybean Genomics and Microarray Database.

The Soybean Genomics and Microarray Database (SGMD) attempts to provide an integrated view of the interaction of soybean with the soybean cyst nematode and contains genomic, EST and microarray data with embedded analytical tools allowing correlation of soybean ESTs with their gene expression profiles. SGMD provides analytical tools to mine the microarray data quickly by integrating many analysis methods within the database itself. The expression profiles of genes at time intervals during the first 8 days of nematode invasion is searchable by gene name or GenBank accession number. Recent developments include the addition of a searchable database for soybean cyst nematode ESTs and photographs of the invasion process at time points examined using microarrays. SGMD is completely accessible from the web at: http://psi081.ba.ars.usda.gov/SGMD/default.htm.

Computational Biology↗