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Wildish, Tony

Publications and source records attributed to Wildish, Tony.

MAGI: A Method for Metabolite Annotation and Gene Integration

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (that is, gene annotations). Metabolite Annotation and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. Furthermore, this limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions.

59 BASIC BIOLOGICAL SCIENCES↗

Monitoring data transfer latency in CMS computing operations

During the first LHC run, the CMS experiment collected tens of Petabytes of collision and simulated data, which need to be distributed among dozens of computing centres with low latency in order to make efficient use of the resources. While the desired level of throughput has been successfully achieved, it is still common to observe transfer workflows that cannot reach full completion in a timely manner due to a small fraction of stuck files which require operator intervention.For this reason, in 2012 the CMS transfer management system, PhEDEx, was instrumented with a monitoring system to measure file transfer latencies, and to predict the completion time for the transfer of a data set. The operators can detect abnormal patterns in transfer latencies while the transfer is still in progress, and monitor the long-term performance of the transfer infrastructure to plan the data placement strategy.Based on the data collected for one year with the latency monitoring system, we present a study on the different factors that contribute to transfer completion time. As case studies, we analyze several typical CMS transfer workflows, such as distribution of collision event data from CERN or upload of simulated event data from the Tier-2 centres to the archival Tier-1 centres. For each workflow, we present the typical patterns of transfer latencies that have been identified with the latency monitor.We identify the areas in PhEDEx where a development effort can reduce the latency, and we show how we are able to detect stuck transfers which need operator intervention. Lastly, we propose a set of metrics to alert about stuck subscriptions and prompt for manual intervention, with the aim of improving transfer completion times.

97 MATHEMATICS AND COMPUTING↗

Comprehensive Monitoring for Heterogeneous Geographically Distributed Storage

Storage capacity at CMS Tier-1 and Tier-2 sites reached over 100 Petabytes in 2014, and will be substantially increased during Run 2 data taking. The allocation of storage for the individual users analysis data, which is not accounted as a centrally managed storage space, will be increased to up to 40%. For comprehensive tracking and monitoring of the storage utilization across all participating sites, CMS developed a space monitoring system, which provides a central view of the geographically dispersed heterogeneous storage systems. The first prototype was deployed at pilot sites in summer 2014, and has been substantially reworked since then. In this paper we discuss the functionality and our experience of system deployment and operation on the full CMS scale.

Ratnikova, N.↗