716 research outputs found

    Saccharomyces Genome Database provides mutant phenotype data

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    The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is a scientific database for the molecular biology and genetics of the yeast Saccharomyces cerevisiae, which is commonly known as baker’s or budding yeast. The information in SGD includes functional annotations, mapping and sequence information, protein domains and structure, expression data, mutant phenotypes, physical and genetic interactions and the primary literature from which these data are derived. Here we describe how published phenotypes and genetic interaction data are annotated and displayed in SGD

    Bayesian paternity analysis and mating patterns in a parasitic nematode, Trichostrongylus tenuis

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    Mating behaviour is a fundamental aspect of the evolutionary ecology of sexually reproducing species, but one that has been under-researched in parasitic nematodes. We analysed mating behaviour in the parasitic nematode Trichostrongylus tenuis by performing a paternity analysis in a population from a single red grouse host. Paternity of the 150 larval offspring of 25 mothers (sampled from one of the two host caeca) was assigned among 294 candidate fathers (sampled from both caeca). Each candidate father's probability of paternity of each offspring was estimated from 10-locus microsatellite genotypes. Seventy-six (51%) offspring were assigned a father with a probability of >0.8, and the estimated number of unsampled males was 136 (95% credible interval (CI) 77-219). The probability of a male from one caecum fathering an offspring in the other caecum was estimated as 0.024 (95% CI 0.003-0.077), indicating that the junction of the caeca is a strong barrier to dispersal. Levels of promiscuity (defined as the probability of two of an adult's offspring sharing only one parent) were high for both sexes. Variance in male reproductive success was moderately high, possibly because of a combination of random mating and high variance in post-copulatory reproductive success. These results provide the first data on individual mating behaviour among parasitic nematodes

    Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors

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    Genomic DNA copy number alterations are key genetic events in the development and progression of human cancers. Here we report a genome-wide microarray comparative genomic hybridization (array CGH) analysis of DNA copy number variation in a series of primary human breast tumors. We have profiled DNA copy number alteration across 6,691 mapped human genes, in 44 predominantly advanced, primary breast tumors and 10 breast cancer cell lines. While the overall patterns of DNA amplification and deletion corroborate previous cytogenetic studies, the high-resolution (gene-by-gene) mapping of amplicon boundaries and the quantitative analysis of amplicon shape provide significant improvement in the localization of candidate oncogenes. Parallel microarray measurements of mRNA levels reveal the remarkable degree to which variation in gene copy number contributes to variation in gene expression in tumor cells. Specifically, we find that 62% of highly amplified genes show moderately or highly elevated expression, that DNA copy number influences gene expression across a wide range of DNA copy number alterations (deletion, low-, mid- and high-level amplification), that on average, a 2-fold change in DNA copy number is associated with a corresponding 1.5-fold change in mRNA levels, and that overall, at least 12% of all the variation in gene expression among the breast tumors is directly attributable to underlying variation in gene copy number. These findings provide evidence that widespread DNA copy number alteration can lead directly to global deregulation of gene expression, which may contribute to the development or progression of cancer

    National study of colorectal cancer genetics

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    Approximately, a third of all colorectal cancer (CRC) is due to inherited susceptibility. However, high-risk mutations in APC, the mismatch repair (MMR) genes, MUTYH/MYH, SMAD4, ALK3 and STK11/LKB1 are rare and account for <5% of cases. Much of the remaining variation in genetic risk is likely to be explained by combinations of more common gene variants that modestly increase risk. Reliable identification of such ‘low penetrance' alleles would provide insight into the aetiology of CRC and might highlight potential therapeutic and preventative interventions. In 2003, the National Study of Colorectal Cancer Genetics (NSCCG) was established with the aim of collecting DNA and clinicopathological data from 20 000 CRC cases and a series of spouse/partner controls, thereby creating a unique resource for identifying low-penetrance CRC susceptibility alleles. The National Cancer Research Network (NCRN) adopted NSCCG onto its portfolio of trials and 148 centres in the United Kingdom (UK) are now actively participating. Over 8700 cases and 2185 controls have so far been entered into NSCCG. Our experience in developing NSCCG serves to illustrate how world-class DNA databases for genetic analyses can be rapidly developed in the United Kingdom

    From DNA sequence to application: possibilities and complications

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    The development of sophisticated genetic tools during the past 15 years have facilitated a tremendous increase of fundamental and application-oriented knowledge of lactic acid bacteria (LAB) and their bacteriophages. This knowledge relates both to the assignments of open reading frames (ORF’s) and the function of non-coding DNA sequences. Comparison of the complete nucleotide sequences of several LAB bacteriophages has revealed that their chromosomes have a fixed, modular structure, each module having a set of genes involved in a specific phase of the bacteriophage life cycle. LAB bacteriophage genes and DNA sequences have been used for the construction of temperature-inducible gene expression systems, gene-integration systems, and bacteriophage defence systems. The function of several LAB open reading frames and transcriptional units have been identified and characterized in detail. Many of these could find practical applications, such as induced lysis of LAB to enhance cheese ripening and re-routing of carbon fluxes for the production of a specific amino acid enantiomer. More knowledge has also become available concerning the function and structure of non-coding DNA positioned at or in the vicinity of promoters. In several cases the mRNA produced from this DNA contains a transcriptional terminator-antiterminator pair, in which the antiterminator can be stabilized either by uncharged tRNA or by interaction with a regulatory protein, thus preventing formation of the terminator so that mRNA elongation can proceed. Evidence has accumulated showing that also in LAB carbon catabolite repression in LAB is mediated by specific DNA elements in the vicinity of promoters governing the transcription of catabolic operons. Although some biological barriers have yet to be solved, the vast body of scientific information presently available allows the construction of tailor-made genetically modified LAB. Today, it appears that societal constraints rather than biological hurdles impede the use of genetically modified LAB.

    Gene Ontology annotations at SGD: new data sources and annotation methods

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    The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) collects and organizes biological information about the chromosomal features and gene products of the budding yeast Saccharomyces cerevisiae. Although published data from traditional experimental methods are the primary sources of evidence supporting Gene Ontology (GO) annotations for a gene product, high-throughput experiments and computational predictions can also provide valuable insights in the absence of an extensive body of literature. Therefore, GO annotations available at SGD now include high-throughput data as well as computational predictions provided by the GO Annotation Project (GOA UniProt; http://www.ebi.ac.uk/GOA/). Because the annotation method used to assign GO annotations varies by data source, GO resources at SGD have been modified to distinguish data sources and annotation methods. In addition to providing information for genes that have not been experimentally characterized, GO annotations from independent sources can be compared to those made by SGD to help keep the literature-based GO annotations current

    Identification of a Simple Sequence Repeat molecular-marker set for large-scale analyses of pear germplasm

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    Simple Sequence Repeats (SSR) are molecular markers suitable to assess the genetic variation of germplasm resources; however, large-scale SSR use requires protocol optimization. The present work aimed to identify SSR markers, developed for pear and other fruit species that are effective in characterizing pear germplasm collections and in demonstrating their use in providing support for genetic breeding programs. From a total of 62 SSR markers investigated, 23 yielding reproducible and polymorphic patterns were used to genotype a sample of 42 pear accessions of the Brazilian Pear Germplasm Bank (PGB). When compared to these 23 SSR markers, a subset of eleven markers, selected based on He, PIC and PId, was used to distinguish individual accessions and perform cluster analysis with similar efficacy. Genetic diversity analysis clustered the European, Japanese and Chinese accessions in distinct groups. This markers subset constitutes a valuable tool for several applications related to pear genetic resources management and breeding

    Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data

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    Recently, a novel approach has been developed to study gene expression in single cells with high time resolution using RNA Fluorescent In Situ Hybridization (FISH). The technique allows individual mRNAs to be counted with high accuracy in wild-type cells, but requires cells to be fixed; thus, each cell provides only a “snapshot” of gene expression. Here we show how and when RNA FISH data on pairs of genes can be used to reconstruct real-time dynamics from a collection of such snapshots. Using maximum-likelihood parameter estimation on synthetically generated, noisy FISH data, we show that dynamical programs of gene expression, such as cycles (e.g., the cell cycle) or switches between discrete states, can be accurately reconstructed. In the limit that mRNAs are produced in short-lived bursts, binary thresholding of the FISH data provides a robust way of reconstructing dynamics. In this regime, prior knowledge of the type of dynamics – cycle versus switch – is generally required and additional constraints, e.g., from triplet FISH measurements, may also be needed to fully constrain all parameters. As a demonstration, we apply the thresholding method to RNA FISH data obtained from single, unsynchronized cells of Saccharomyces cerevisiae. Our results support the existence of metabolic cycles and provide an estimate of global gene-expression noise. The approach to FISH data presented here can be applied in general to reconstruct dynamics from snapshots of pairs of correlated quantities including, for example, protein concentrations obtained from immunofluorescence assays
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