Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence

By Justin B. Kinney, A. Murugan, C. Callan, E. Cox. Proceedings of the National Academy of Sciences of the United States of America, 2010

344 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ed428faf2828d6587d82e5cf0802b88bf50b2ff6

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Citations in the corpus

Inferring fitness landscapes by regression produces biased estimates of epistasis (2014, background): “(52) and Otwinowski and Nemenman (65) (Materials and Methods).”
Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters (2012, methodology): “Several medium-scale 1–3 and large-scale 4–6 libraries have been created in bacteria and yeast in which transcription regulatory elements were randomly ligated or muta-genized and the expression of the resulting promoters was measured.”
Equitability, mutual information, and the maximal information coefficient (2013, background): “Not only does it naturally quantify the strength of relationships between arbitrary variables, its close connection to likelihood [44, 45] makes it a proper objective function for ﬁtting parameterized models to a wide variety of data sets [33, 34, 35, 36, 37].”
Protein model discrimination using mutational sensitivity derived from deep sequencing. (2012, background): “, 2011), determining protein:DNA interacting residues (Kinney et al., 2010), studying codon bias effects on fitness (Hietpas et al.”
Predicting evolution (2017, background): “For example, specific interactions between antigens and immune receptors can be inferred by deep mutational scans 99,100 , similar scans map regulatory DNA–protein interactions 98,101–103 .”
Logomaker: beautiful sequence logos in Python (2019, methodology): “Data (unpublished; collected by JBK) are from a mutARS-seq experiment analogous to the one reported by Liachko et al. (2013). (F) A saliency logo (Shrikumar et al.”