Richard Shippy
GE Healthcare
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Publication
Featured researches published by Richard Shippy.
Nature Biotechnology | 2006
Lei Guo; Edward K. Lobenhofer; Charles Wang; Richard Shippy; Stephen Harris; Lu Zhang; Nan Mei; Tao Chen; Damir Herman; Federico Goodsaid; Patrick Hurban; Kenneth L. Phillips; Jun Xu; Xutao Deng; Yongming Andrew Sun; Weida Tong; Leming Shi
To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed.
Nature Methods | 2005
Shawn C. Baker; Steven R. Bauer; Richard P. Beyer; James D. Brenton; Bud Bromley; John Burrill; Helen C. Causton; Michael P Conley; Rosalie K. Elespuru; Michael Fero; Carole Foy; James C. Fuscoe; Xiaolian Gao; David Gerhold; Patrick Gilles; Federico Goodsaid; Xu Guo; Joe Hackett; Richard D. Hockett; Pranvera Ikonomi; Rafael A. Irizarry; Ernest S. Kawasaki; Tamma Kaysser-Kranich; Kathleen F. Kerr; Gretchen Kiser; Walter H. Koch; Kathy Y Lee; Chunmei Liu; Z Lewis Liu; Chitra Manohar
Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.
Nature Biotechnology | 2006
Richard Shippy; Stephanie Fulmer-Smentek; Roderick V. Jensen; Wendell D. Jones; Paul K. Wolber; Charles D. Johnson; P. Scott Pine; Cecilie Boysen; Xu Guo; Eugene Chudin; Yongming Andrew Sun; James C. Willey; Jean Thierry-Mieg; Danielle Thierry-Mieg; Robert A. Setterquist; Michael Wilson; Natalia Novoradovskaya; Adam Papallo; Yaron Turpaz; Shawn C. Baker; Janet A. Warrington; Leming Shi; Damir Herman
We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.
Nature Biotechnology | 2006
Weida Tong; Richard Shippy; Xiaohui Fan; Hong Fang; Huixiao Hong; Michael S. Orr; Tzu-Ming Chu; Xu Guo; Patrick J. Collins; Yongming Andrew Sun; Sue-Jane Wang; Wenjun Bao; Russell D. Wolfinger; Svetlana Shchegrova; Lei Guo; Janet A. Warrington; Leming Shi
External RNA controls (ERCs), although important for microarray assay performance assessment, have yet to be fully implemented in the research community. As part of the MicroArray Quality Control (MAQC) study, two types of ERCs were implemented and evaluated; one was added to the total RNA in the samples before amplification and labeling; the other was added to the copyRNAs (cRNAs) before hybridization. ERC concentration-response curves were used across multiple commercial microarray platforms to identify problematic assays and potential sources of variation in the analytical process. In addition, the behavior of different ERC types was investigated, resulting in several important observations, such as the sample-dependent attributes of performance and the potential of using these control RNAs in a combinatorial fashion. This multiplatform investigation of the behavior and utility of ERCs provides a basis for articulating specific recommendations for their future use in evaluating assay performance across multiple platforms.
Archive | 2003
David Dorris; Chang-Gong Liu; Ramesh Ramakrishnan; Richard Shippy; Sangeet Singh-Gasson; Anna Lublinsky; Edward Touma; Marc Domanus; Luis Allegri; Hong Fei; Abhijit Mazumder
We report on expression profiling technologies that employ arrays of oligonucleotides covalently attached to polymeric surfaces. These surfaces can be comprised of photochemically polymerized slabs or film-type structures. We demonstrate that the expression profiling technologies can be used for toxicology and biological discovery. Refinement of the entire platform (probe design, array manufacturing, assays and software analysis) has led to a minimal detectable sensitivity of one copy per cell, a dynamic range of two to three logs, a specificity of greater than 90%, a minimal fold change of 1.8–2 fold and the majority of CVs below 40%. The performance, throughputs and flexibility of this system should enable widespread use in biological and pharmaceutical applications.
BMC Bioinformatics | 2008
Leming Shi; Wendell D. Jones; Roderick V. Jensen; Stephen Harris; Roger Perkins; Federico Goodsaid; Lei Guo; Lisa J. Croner; Cecilie Boysen; Hong Fang; Feng Qian; Shashi Amur; Wenjun Bao; Catalin Barbacioru; Vincent Bertholet; Xiaoxi Megan Cao; Tzu Ming Chu; Patrick J. Collins; Xiaohui Fan; Felix W. Frueh; James C. Fuscoe; Xu Guo; Jing Han; Damir Herman; Huixiao Hong; Ernest S. Kawasaki; Quan Zhen Li; Yuling Luo; Yunqing Ma; Nan Mei
Archive | 2006
David Dorris; Abhijit Mazumder; Richard Shippy
Nature Precedings | 2007
Leming Shi; Wendell D. Jones; Roderick V. Jensen; Stephen Harris; Roger Perkins; Federico Goodsaid; Lei Guo; Lisa J. Croner; Cecilie Boysen; Hong Fang; Shashi Amur; Wenjun Bao; Catalin Barbacioru; Vincent Bertholet; Xiaoxi Megan Cao; Tzu-Ming Chu; Patrick J. Collins; Xiaohui Fan; Felix W. Frueh; James C. Fuscoe; Xu Guo; Jing Han; Damir Herman; Huixiao Hong; Ernest S. Kawasaki; Quan Zhen Li; Yuling Luo; Yunqing Ma; Nan Mei; Ron L. Peterson
computational systems bioinformatics | 2005
Manohar Kollegal; Sudeshna Adak; Richard Shippy; Timothy Sendera
Archive | 2002
David Dorris; Abhijit Mazumder; Richard Shippy