Publications by authors named "Leslie Derr"

5 Publications

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Towards quality assurance and quality control in untargeted metabolomics studies.

Metabolomics 2019 01 3;15(1). Epub 2019 Jan 3.

National Cancer Institute, Rockville, MD, USA.

We describe here the agreed upon first development steps and priority objectives of a community engagement effort to address current challenges in quality assurance (QA) and quality control (QC) in untargeted metabolomic studies. This has included (1) a QA and QC questionnaire responded to by the metabolomics community in 2015 which recommended education of the metabolomics community, development of appropriate standard reference materials and providing incentives for laboratories to apply QA and QC; (2) a 2-day 'Think Tank on Quality Assurance and Quality Control for Untargeted Metabolomic Studies' held at the National Cancer Institute's Shady Grove Campus and (3) establishment of the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) to drive forward developments in a coordinated manner.
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http://dx.doi.org/10.1007/s11306-018-1460-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443086PMC
January 2019

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations.

Cell Syst 2018 01 29;6(1):13-24. Epub 2017 Nov 29.

BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.
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http://dx.doi.org/10.1016/j.cels.2017.11.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5799026PMC
January 2018

The National Institutes of Health's Big Data to Knowledge (BD2K) initiative: capitalizing on biomedical big data.

J Am Med Inform Assoc 2014 Nov-Dec;21(6):957-8. Epub 2014 Jul 9.

National Human Genome Research Institute, NIH, Bethesda, Maryland, USA.

Biomedical research has and will continue to generate large amounts of data (termed 'big data') in many formats and at all levels. Consequently, there is an increasing need to better understand and mine the data to further knowledge and foster new discovery. The National Institutes of Health (NIH) has initiated a Big Data to Knowledge (BD2K) initiative to maximize the use of biomedical big data. BD2K seeks to better define how to extract value from the data, both for the individual investigator and the overall research community, create the analytic tools needed to enhance utility of the data, provide the next generation of trained personnel, and develop data science concepts and tools that can be made available to all stakeholders.
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http://dx.doi.org/10.1136/amiajnl-2014-002974DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215061PMC
May 2015

Detecting false expression signals in high-density oligonucleotide arrays by an in silico approach.

Genomics 2005 Mar;85(3):297-308

Laboratory of Population Genetics, National Cancer Institute/National Institutes of Health, 8424 Helgerman Court, Room 101, MSC 8302, Bethesda, MD 20892-8302, USA.

High-density oligonucleotide arrays have become a popular assay for concurrent measurement of mRNA expression at the genome scale. Much effort has been devoted to the development of statistical analysis tools aimed at reducing experimental noise and normalizing experimental variation in gene expression analysis. However, these investigations do not detect or catalog systematic problems associated with specific oligonucleotide probes. Here, we present an investigation of problematic probes that yield consistent but inaccurate signals across multiple experiments. By evaluating data integrity among gene, probe sequence, and genomic structure we identified a total of 20,696 (10.5%) nonspecific probes that could cross-hybridize to multiple genes and a total of 18,363 (9.3%) probes that miss the target transcript sequences on the Affymetrix GeneChip U95A/Av2 array. The numbers of nonspecific and mistargeted probes on the U133A array are 29,405 (12.1%) and 19,717 (8.0%), respectively. The poor performance of the mistargeted probes was confirmed in two GeneChip experiments, in which these probes showed a 20-30% decrease in detecting present signals compared with normal probes. Comparison of qualitative expression signals obtained from SAGE and EST data with those from GeneChip arrays showed that the consistency of the two platforms is 30% lower in problematic probes than in normal probes. A Web application was developed to apply our results for improving the accuracy of expression analysis.
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http://dx.doi.org/10.1016/j.ygeno.2004.11.004DOI Listing
March 2005