Multiplatform single-sample estimates of transcriptional activation

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 110; no. 44; pp. 17778 - 17783
• Main Authors: Piccolo, Stephen R., Withers, Michelle R., Francis, Owen E., Bild, Andrea H., Johnson, W. Evan
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 29.10.2013; NATIONAL ACADEMY OF SCIENCES; National Acad Sciences
• Subjects: Algorithms; Bar codes; Base Composition; Bioinformatics; Biological markers; Biological Sciences; Biotechnology; computer software; Data normalization; DNA Barcoding, Taxonomic - methods; Gene expression; Gene Expression Profiling - methods; Genes; Genes - genetics; Genomics; high-throughput nucleotide sequencing; Integration; microarray technology; Models, Genetic; Physical Sciences; Ribonucleic acid; RNA; Software; Technology; Tissue samples; transcriptional activation; Transcriptional Activation - physiology

Over the past two decades, many biotechnology platforms have been developed for high-throughput gene expression profiling. However, because each platform is subject to technology-specific biases and produces distinct raw-data distributions, researchers have experienced difficulty in integrating data...