The metabolism of some derivatives of benzimidazole

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An integrative approach to ortholog prediction for disease-focused and other functional studies

Background Mapping of orthologous genes among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one species based on what is known about the functions of orthologs in other species. Several tools for predicting orthologous gene relationships are available. However, these tools can give different results and identification of predicted orthologs is not always straightforward. Results We report a simple but effective tool, the Drosophila RNAi Screening Center Integrative Ortholog Prediction Tool (DIOPT; http://www.flyrnai.org/diopt webcite), for rapid identification of orthologs. DIOPT integrates existing approaches, facilitating rapid identification of orthologs among human, mouse, zebrafish, C. elegans, Drosophila, and S. cerevisiae. As compared to individual tools, DIOPT shows increased sensitivity with only a modest decrease in specificity. Moreover, the flexibility built into the DIOPT graphical user interface allows researchers with different goals to appropriately 'cast a wide net' or limit results to highest confidence predictions. DIOPT also displays protein and domain alignments, including percent amino acid identity, for predicted ortholog pairs. This helps users identify the most appropriate matches among multiple possible orthologs. To facilitate using model organisms for functional analysis of human disease-associated genes, we used DIOPT to predict high-confidence orthologs of disease genes in Online Mendelian Inheritance in Man (OMIM) and genes in genome-wide association study (GWAS) data sets. The results are accessible through the DIOPT diseases and traits query tool (DIOPT-DIST; http://www.flyrnai.org/diopt-dist webcite). Conclusions DIOPT and DIOPT-DIST are useful resources for researchers working with model organisms, especially those who are interested in exploiting model organisms such as Drosophila to study the functions of human disease genes.Harvard CatalystNational Institutes of Health (U.S.) (NIH R01 GM067761)National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (5K08DK78361)Dana-Farber/Harvard Cancer Cente

FlyPrimerBank: An Online Database for Drosophila melanogaster Gene Expression Analysis and Knockdown Evaluation of RNAi Reagents

The evaluation of specific endogenous transcript levels is important for understanding transcriptional regulation. More specifically, it is useful for independent confirmation of results obtained by the use of microarray analysis or RNA-seq and for evaluating RNA interference (RNAi)-mediated gene knockdown. Designing specific and effective primers for high-quality, moderate-throughput evaluation of transcript levels, i.e., quantitative, real-time PCR (qPCR), is nontrivial. To meet community needs, predefined qPCR primer pairs for mammalian genes have been designed and sequences made available, e.g., via PrimerBank. In this work, we adapted and refined the algorithms used for the mammalian PrimerBank to design 45,417 primer pairs for 13,860 Drosophila melanogaster genes, with three or more primer pairs per gene. We experimentally validated primer pairs for ~300 randomly selected genes expressed in early Drosophila embryos, using SYBR Green-based qPCR and sequence analysis of products derived from conventional PCR. All relevant information, including primer sequences, isoform specificity, spatial transcript targeting, and any available validation results and/or user feedback, is available from an online database (www.flyrnai.org/flyprimerbank). At FlyPrimerBank, researchers can retrieve primer information for fly genes either one gene at a time or in batch mode. Importantly, we included the overlap of each predicted amplified sequence with RNAi reagents from several public resources, making it possible for researchers to choose primers suitable for knockdown evaluation of RNAi reagents (i.e., to avoid amplification of the RNAi reagent itself). We demonstrate the utility of this resource for validation of RNAi reagents in vivo

FlyRNAi: the Drosophila RNAi screening center database

RNA interference (RNAi) has become a powerful tool for genetic screening in Drosophila. At the Drosophila RNAi Screening Center (DRSC), we are using a library of over 21 000 double-stranded RNAs targeting known and predicted genes in Drosophila. This library is available for the use of visiting scientists wishing to perform full-genome RNAi screens. The data generated from these screens are collected in the DRSC database () in a flexible format for the convenience of the scientist and for archiving data. The long-term goal of this database is to provide annotations for as many of the uncharacterized genes in Drosophila as possible. Data from published screens are available to the public through a highly configurable interface that allows detailed examination of the data and provides access to a number of other databases and bioinformatics tools

Presence of DNA from Chlamydia-like organisms in the nasal cavities of grey seal pups (Halichoerus grypus) and three different substrates present in a breeding colony

BackgroundChlamydia-like organisms (CLO) have been found to be present in many environmental niches, including human sewage and agricultural run-off, as well as in a number of aquatic species worldwide. Therefore, monitoring their presence in sentinel wildlife species may be useful in assessing the wider health of marine food webs in response to habitat loss, pollution and disease. We used nasal swabs from live (n?=?42) and dead (n?=?50) pre-weaned grey seal pups and samples of differing natal substrates (n?=?8) from an off-shore island devoid of livestock and permanent human habitation to determine if CLO DNA is present in these mammals and to identify possible sources.ResultsWe recovered CLO DNA from 32/92 (34.7%) nasal swabs from both live (n?=?17) and dead (n?=?15) seal pups that clustered most closely with currently recognised species belonging to three chlamydial families: Parachlamydiaceae (n?=?22), Rhabdochlamydiaceae (n?=?6), and Simkaniaceae (n?=?3). All DNA positive sediment samples (n?=?7) clustered with the Rhabdochlamydiaceae. No difference was found in rates of recovery of CLO DNA in live versus dead pups suggesting the organisms are commensal but their potential as opportunistic secondary pathogens could not be determined.ConclusionThis is the first report of CLO DNA being found in marine mammals. This identification warrants further investigation in other seal populations around the coast of the UK and in other areas of the world to determine if this finding is unique or more common than shown by this data. Further investigation would also be warranted to determine if they are present as purely commensal organisms or whether they could also be opportunistic pathogens in seals, as well as to investigate possible sources of origin, including whether they originated as a result of anthropogenic impacts, including human waste and agricultural run-off

False negative rates in Drosophila cell-based RNAi screens: a case study

<p>Abstract</p> <p>Background</p> <p>High-throughput screening using RNAi is a powerful gene discovery method but is often complicated by false positive and false negative results. Whereas false positive results associated with RNAi reagents has been a matter of extensive study, the issue of false negatives has received less attention.</p> <p>Results</p> <p>We performed a meta-analysis of several genome-wide, cell-based <it>Drosophila </it>RNAi screens, together with a more focused RNAi screen, and conclude that the rate of false negative results is at least 8%. Further, we demonstrate how knowledge of the cell transcriptome can be used to resolve ambiguous results and how the number of false negative results can be reduced by using multiple, independently-tested RNAi reagents per gene.</p> <p>Conclusions</p> <p>RNAi reagents that target the same gene do not always yield consistent results due to false positives and weak or ineffective reagents. False positive results can be partially minimized by filtering with transcriptome data. RNAi libraries with multiple reagents per gene also reduce false positive and false negative outcomes when inconsistent results are disambiguated carefully.</p

Fiber Bragg gratings inscribed using 800nm femtosecond laser and a phase mask in singleand multi-core mid-IR glass fibers

For the first time, Fiber Bragg grating (FBG) structures have been inscribed in single-core passive germanate and three-core passive and active tellurite glass fibers using 800 nm femtosecond (fs) laser and phase mask technique. With fs peak power intensity in the order of 10(11)W/cm(2), the FBG spectra with 2nd and 3rd order resonances at 1540 and 1033 nm in the germanate glass fiber and 2nd order resonances at approximately 1694 and approximately 1677 nm with strengths up to 14 dB in all three cores in the tellurite fiber were observed. Thermal responsivities of the FBGs made in these mid-IR glass fibers were characterized, showing average temperature responsivity approximately 20 pm/ degrees C. Strain responsivities of the FBGs in germanate glass fiber were measured to be 1.219 pm/microepsilon

GA-MINER: Parallel Data Mining with Hierarchical Genetic Algorithms - Final Report

Many organisations now routinely gather vast and ever-increasing amounts of data in the ordinary course of their business. While much of this information is collected for day-to-day operational reasons, many businesses are now realising that this data has much additional value for improving operational processes. Large databases can form the basis of decision support systems, often based around a data warehouse. Such systems may then be used for a variety of applications such as trend spotting, pattern recognition, behavioral modeling and customer worth assessment. Against this backdrop, the term data mining is used to refer to the process of searching through a large volume of data to discover interesting and useful information. The authors have traditionally sought to divide data mining into three types or levels---undirected or pure data mining, where the system is left almost entirely unconstrained to discover patterns in the data free of prejudices from the user; directed data mi..

A Genetic Algorithm-Based Approach to Data Mining

Most data mining systems to date have used variants of traditional machine-learning algorithms to tackle the task of directed knowledge discovery. This paper presents an approach which, as well as being useful for such directed data mining, can also be applied to the further tasks of undirected data mining and hypothesis refinement. This approach exploits parallel genetic algorithms as the search mechanism and seeks to evolve explicit &quot;rules&quot; for maximum comprehensibility. Example rules found in real commercial datasets are presented