Searching journal content for articles similar to Wu et al. 20 (12): 1730.

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  1. Method: Comparison of CAGE and RNA-seq transcriptome profiling using clonally amplified and single-molecule next-generation sequencing
    • Hideya Kawaji,
    • Marina Lizio,
    • Masayoshi Itoh,
    • Mutsumi Kanamori-Katayama,
    • Ai Kaiho,
    • Hiromi Nishiyori-Sueki,
    • Jay W. Shin,
    • Miki Kojima-Ishiyama,
    • Mitsuoki Kawano,
    • Mitsuyoshi Murata,
    • Noriko Ninomiya-Fukuda,
    • Sachi Ishikawa-Kato,
    • Sayaka Nagao-Sato,
    • Shohei Noma,
    • Yoshihide Hayashizaki,
    • Alistair R.R. Forrest,
    • Piero Carninci,
    • and The FANTOM Consortium
    Genome Res. April 2014 24: 708-717; Published in Advance March 27, 2014, doi:10.1101/gr.156232.113
    ...La). This asymmetric design could allow us to assess sensitivity in measuring differences between cellular expression profiles, even with a low amount of differentially expressed (<1%) ‘‘genes.’’ We profiled these RNA mixtures by each technology and microarray. One exception was RNA-seq, where only the four mixtures...
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  2. Method: Digital gene expression for non-model organisms
    • Lewis Z. Hong,
    • Jun Li,
    • Anne Schmidt-Küntzel,
    • Wesley C. Warren,
    • and Gregory S. Barsh
    Genome Res. November 2011 21: 1905-1915; Published in Advance August 15, 2011, doi:10.1101/gr.122135.111
    ...-quality assembled reference genome sequence is available. We present a method for gene expression profiling called EDGE, or EcoP15I-tagged Digital Gene Expression, based on ultra-high-throughput sequencing of 27-bp cDNA fragments that uniquely tag the corresponding gene, thereby allowing direct quantification...
  3. Research: Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions
    • Nathan C. Sheffield,
    • Robert E. Thurman,
    • Lingyun Song,
    • Alexias Safi,
    • John A. Stamatoyannopoulos,
    • Boris Lenhard,
    • Gregory E. Crawford,
    • and Terrence S. Furey
    Genome Res. May 2013 23: 777-788; Published in Advance March 12, 2013, doi:10.1101/gr.152140.112
    ...diverse biological samples by tissue specificity into 1856 chromatin profiles and found each cluster to have a distinct bias relative to location, evolutionary conservation, CpG islands, and promoter proximity (distal vs. proximal). Gene expression profiling has emerged as a powerful tool to classify...
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