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A framework for collaborative analysis of ENCODE data: making large-scale analyses biologist-friendly.

ABSTRACT: The standardization and sharing of data and tools are the biggest challenges of large collaborative projects such as the Encyclopedia of DNA Elements (ENCODE). Here we describe a compact Web application, Galaxy2(ENCODE), that effectively addresses these issues. It provides an intuitive interface for the deposition and access of data, and features a vast number of analysis tools including operations on genomic intervals, utilities for manipulation of multiple sequence alignments, and molecular evolution algorithms. By providing a direct link between data and analysis tools, Galaxy2(ENCODE) allows addressing biological questions that are beyond the reach of existing software. We use Galaxy2(ENCODE) to show that the ENCODE regions contain >2000 unannotated transcripts under strong purifying selection that are likely functional. We also show that the ENCODE regions are representative of the entire genome by estimating the rate of nucleotide substitution and comparing it to published data. Although each of these analyses is complex, none takes more than 15 min from beginning to end. Finally, we demonstrate how new tools can be added to Galaxy2(ENCODE) with almost no effort. Every section of the manuscript is supplemented with QuickTime screencasts. Galaxy2(ENCODE) and the screencasts can be accessed at http://g2.bx.psu.edu.

SUBMITTER: Blankenberg D

PROVIDER: S-EPMC1891355 | biostudies-literature | 2007 Jun

REPOSITORIES: biostudies-literature

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A framework for collaborative analysis of ENCODE data: making large-scale analyses biologist-friendly.

Blankenberg Daniel D Taylor James J Schenck Ian I He Jianbin J Zhang Yi Y Ghent Matthew M Veeraraghavan Narayanan N Albert Istvan I Miller Webb W Makova Kateryna D KD Hardison Ross C RC Nekrutenko Anton A

Genome research 20070601 6

The standardization and sharing of data and tools are the biggest challenges of large collaborative projects such as the Encyclopedia of DNA Elements (ENCODE). Here we describe a compact Web application, Galaxy2(ENCODE), that effectively addresses these issues. It provides an intuitive interface for the deposition and access of data, and features a vast number of analysis tools including operations on genomic intervals, utilities for manipulation of multiple sequence alignments, and molecular ev ...[more]

PMID: 17568012

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Project description:Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for "virtual" energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings' time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building's daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

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A framework for collaborative analysis of ENCODE data: making large-scale analyses biologist-friendly.

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A framework for collaborative analysis of ENCODE data: making large-scale analyses biologist-friendly.

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