Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors
By G. Yvert, Rachel B. Brem, J. Whittle, Joshua M. Akey, E. Foss, Erin N. Smith, R. Mackelprang, Leonid Kruglyak. Nature Genetics, 2003
673 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/39ea5e02f5393cba8585f715bb7aec2fbb2acca1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- John D. Storey
- Jeffrey T. Leek
- Frank Emmert-Streib
- Lin S. Chen
- Aldons J. Lusis
- Leonid Kruglyak
- Eric E. Schadt
- Rico A. Caldo
Citations in the corpus
- Population genomics of human gene expression (2007, neutral): “In general, the detection of trans effects has been less successful in humans than in yeas”
- Harnessing naturally randomized transcription to infer regulatory relationships among genes (2007, background): “Because DNA variation has a substantial and widespread effect on transcriptional variation [12-15,21-25], we show that randomizing DNA content provides a natural mechanism for randomizing RNA levels.”
- Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis (2007, background): “, the quantitative trait locus (QTL) linkage peak coincided with the physical location of the open reading frame for the expression trait [36].”
- An integrative genomics approach to infer causal associations between gene expression and disease (2005, methodology): “Microarray data have recently been combined with other experimental approaches to facilitate identification of key mechanistic drivers of complex trait”
- Estimating the number of true null hypotheses from a histogram of p values (2006, methodology): “Combining the two technologies (see, e.g., Jansen and Nap 2001; Brem, Yvert, Clinton, and Kruglyak 2002; Yvert et al. 2003; Schadt et al. 2003 a,b; Pomp, Allan, and Wesolowski 2004; Hubner et al. 2005; Bystrykh et al. 2005; Chesler et al. 2005; DeCook, Lall, Nettleton, and Howell 2006) can result in”
- Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks (2008, result): “After partitioning the yeast genome into 603 bins, each 20 kb in length, we identified 23 bins in chromosomes 1, 2, 3, 5, 8, 12, 13, 14 and 15 that contained at least 15 eQTLs each (defined as eQTL hot spots), 22 of which were identical to the previously reported results from this datase”