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Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study.
Li, Sheng; Tighe, Scott W; Nicolet, Charles M; Grove, Deborah; Levy, Shawn; Farmerie, William; Viale, Agnes; Wright, Chris; Schweitzer, Peter A; Gao, Yuan; Kim, Dewey; Boland, Joe; Hicks, Belynda; Kim, Ryan; Chhangawala, Sagar; Jafari, Nadereh; Raghavachari, Nalini; Gandara, Jorge; Garcia-Reyero, Natàlia; Hendrickson, Cynthia; Roberson, David; Rosenfeld, Jeffrey; Smith, Todd; Underwood, Jason G; Wang, May; Zumbo, Paul; Baldwin, Don A; Grills, George S; Mason, Christopher E.
Afiliación
  • Li S; Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA.
  • Tighe SW; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, USA.
  • Nicolet CM; Vermont Cancer Center, University of Vermont, Burlington, Vermont, USA.
  • Grove D; Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
  • Levy S; The Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, USA.
  • Farmerie W; HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA.
  • Viale A; Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, Florida, USA.
  • Wright C; Memorial Sloan-Kettering Cancer Institute, New York, New York, USA.
  • Schweitzer PA; Roy J. Carver Biotechnology Center, University of Illinois, Urbana, Illinois, USA.
  • Gao Y; Biotechnology Resource Center, Institute of Biotechnology, Cornell University, Ithaca, New York, USA.
  • Kim D; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
  • Boland J; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
  • Hicks B; NIH/NCI/SAIC-Frederick, Gaithersburg, Maryland, USA.
  • Kim R; NIH/NCI/SAIC-Frederick, Gaithersburg, Maryland, USA.
  • Chhangawala S; Genome Center, University of California, Davis, Davis, California, USA.
  • Jafari N; Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA.
  • Raghavachari N; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, USA.
  • Gandara J; Center for Genetic Medicine, Northwestern University, Chicago, Illinois, USA.
  • Garcia-Reyero N; NIH/NHLBI, Bethesda, Maryland, USA.
  • Hendrickson C; Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA.
  • Roberson D; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, USA.
  • Rosenfeld J; Institute for Genomics, Biocomputing and Biotechnology, Mississippi State University, Starkville, Mississippi, USA.
  • Smith T; HudsonAlpha Institute for Biotechnology, Huntsville, Alabama, USA.
  • Underwood JG; NIH/NCI/SAIC-Frederick, Gaithersburg, Maryland, USA.
  • Wang M; Division of High Performance and Research Computing, University of Medicine and Dentistry of New Jersey, Newark, New Jersey, USA.
  • Zumbo P; PerkinElmer Inc., Seattle, Washington, USA.
  • Baldwin DA; University of Washington, Department of Genome Sciences. Seattle, Washington, USA.
  • Grills GS; Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
  • Mason CE; Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA.
Nat Biotechnol ; 32(9): 915-925, 2014 Sep.
Article en En | MEDLINE | ID: mdl-25150835
ABSTRACT
High-throughput RNA sequencing (RNA-seq) greatly expands the potential for genomics discoveries, but the wide variety of platforms, protocols and performance capabilitites has created the need for comprehensive reference data. Here we describe the Association of Biomolecular Resource Facilities next-generation sequencing (ABRF-NGS) study on RNA-seq. We carried out replicate experiments across 15 laboratory sites using reference RNA standards to test four protocols (poly-A-selected, ribo-depleted, size-selected and degraded) on five sequencing platforms (Illumina HiSeq, Life Technologies PGM and Proton, Pacific Biosciences RS and Roche 454). The results show high intraplatform (Spearman rank R > 0.86) and inter-platform (R > 0.83) concordance for expression measures across the deep-count platforms, but highly variable efficiency and cost for splice junction and variant detection between all platforms. For intact RNA, gene expression profiles from rRNA-depletion and poly-A enrichment are similar. In addition, rRNA depletion enables effective analysis of degraded RNA samples. This study provides a broad foundation for cross-platform standardization, evaluation and improvement of RNA-seq.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Perfilación de la Expresión Génica / Secuenciación de Nucleótidos de Alto Rendimiento / Transcriptoma Idioma: En Revista: Nat Biotechnol Asunto de la revista: BIOTECNOLOGIA Año: 2014 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Añadir a Mi BVS
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Perfilación de la Expresión Génica / Secuenciación de Nucleótidos de Alto Rendimiento / Transcriptoma Idioma: En Revista: Nat Biotechnol Asunto de la revista: BIOTECNOLOGIA Año: 2014 Tipo del documento: Article País de afiliación: Estados Unidos