Integrating regulatory motif discovery and genome-wide expression analysis

By E. Conlon, Shirley Liu, J. Lieb, Jun S. Liu. Proceedings of the National Academy of Sciences of the United States of America, 2003

374 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ab187a757d536371f0ca909aad8e5d995f04fac9

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Citations in the corpus

Transposable elements have rewired the core regulatory network of human embryonic stem cells (2010, methodology): “To find the motifs over-represented in the binding regions, we used the repeat-masked sequence from the regions 100 bp around the top 1,000 peaks of each transcription factor as input for the MDmodule progra”
p-Values for High-Dimensional Regression (2008, methodology): “We apply the multisplit method to a real data set related to motif regression (Conlon et al. 2003).”
Node harvest: simple and interpretable regression and classication (2009, methodology): “Besides these examples, the method is applied to motif regression [Conlon et al. (2003)], where the task is to identify transcription factor binding sites from gene expression measurements.”
ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells (2009, background): “Thus far, the fraction of variation in gene expression ( R 2 ) explained by TF binding has been very moderate, varying between 9.6% and 36.9% on various datasets from yeast to human (5, 7, 10, 11), even after considering TF–TF interaction.”
High-dimensional additive modeling (2008, background): “In motif regression problems [11], the aim is to predict gene expression levels or binding intensities based on information on the DNA sequence.”
Identification of Partially Linear Structure in Additive Models with an Application to Gene Expression Prediction from Sequences (2012, methodology): “For each (se- quence, motif) pair, we also obtain a score xij that represents how well the sequence matches the motif as in Conlon et al. (2003).”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, background): “Pioneering efforts to identify regulatory interactions on a genome scale have used machine-learning algorithms to identify cis-regulatory motifs or transcription factor target genes using a large set of expression arrays [2–18], genomewide location analysis chromatin immunoprecipitation (ChIP-Chip) ”
In Embryonic Stem Cells, ZFP57/KAP1 Recognize a Methylated Hexanucleotide to Affect Chromatin and DNA Methylation of Imprinting Control Regions (2011, methodology): “Themotif-finding program,MDmodule from theMotifRegressor software (Conlon et al., 2003), was used to identify possible consensus DNA motifs.”
Controlling false positive selections in high-dimensional regression and causal inference (2013, methodology): “1 Example about motif regression: subsampling and stability We use the Lasso on a motif regression problem [5] for finding the binding sites in DNA sequences of the so-called HIF1α transcription factor.”
Group SCAD regression analysis for microarray time course gene expression data (2007, positive): “[4] Conlon EM, Liu XS, Lieb JD and Liu JS (2003): Integrating regulatory motif discovery and genome-wide expression analysis Proceedings of National Academy of Sciences, 100: 3339-3344; [5] Das D, Nahle Z and Zhang MQ (2006): Adaptively inferring human transcriptional subnetworks.”