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Träfflista för sökning "WFRF:(Aidinis V.) srt2:(2005-2009)"

Sökning: WFRF:(Aidinis V.) > (2005-2009)

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1.
  • Carninci, P, et al. (författare)
  • The transcriptional landscape of the mammalian genome
  • 2005
  • Ingår i: Science (New York, N.Y.). - : American Association for the Advancement of Science (AAAS). - 1095-9203 .- 0036-8075. ; 309:5740, s. 1559-1563
  • Tidskriftsartikel (refereegranskat)abstract
    • This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5′ and 3′ boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.
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2.
  • Argyropoulos, C., et al. (författare)
  • Operational criteria for selecting a cDNA microarray data normalization algorithm
  • 2006
  • Ingår i: Oncology Reports. - 1021-335X .- 1791-2431. ; 15:4, s. 983-996
  • Tidskriftsartikel (refereegranskat)abstract
    • Microarray technology allows gene expression profiling at a global level. Many algorithms for the normalization of raw microarray data have been proposed, but no attempt has yet been made to propose operationally verifiable criteria for their comparative evaluation, which is necessary for the selection of the most appropriate method for a given dataset. This study develops a set of operational criteria for assessing the impact of various normalization algorithms in terms of accuracy (bias), precision (variance) and over-fitting (information reduction). The use of these criteria is illustrated by applying the three most widely used algorithms (global median normalization, spiked-in based normalization and lowess) on a specifically designed, multiply-controlled dataset.
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