Combining phylogenetic data with co-regulated genes to identify regulatory motifs
By Ting Wang, Gary D. Stormo. Bioinform., 2003
310 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/2b77ea2195696f301931d6802870fbb59f3c9f8e
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In Molecular Biology
- Wei Wang
- Bing Ren
- Kyoung-Jae Won
- Modan K. Das
- Ho-Kwok Dai
- Shaun Mahony
- Panayiotis V. Benos
- E. van Nimwegen
Citations in the corpus
- PhyloGibbs: A Gibbs Sampling Motif Finder That Incorporates Phylogeny (2005, methodology): “PhyloCon operates on sets of co-regulated genes and their orthologs.”
- iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections (2014, unknown): “These methods could be improved by phylogenetic footprinting [97–103] and by applying genome-scale methods that exploit the entire gene expression data set rather than a set of co-expressed genes [104–106].”
- An improved map of conserved regulatory sites for Saccharomyces cerevisiae (2006, background): “Of these, Rox1 [26] and Zap1 [12] have been previously shown to regulate their own expression.”
- A survey of DNA motif finding algorithms (2007, methodology): “Most recently, algorithms that integrate DNA sequence data from coregulated genes and phylogenetic footprinting have significantly improved motif finding from genomic sequences [9-16].”
- Genome-wide prediction of transcription factor binding sites using an integrated model (2010, methodology): “Many computational methods [6-15] (for a survey, see [16]) have been developed to identify DNA segments recognized by TFs.”
- STAMP: a web tool for exploring DNA-binding motif similarities (2007, methodology): “Two motifs can be aligned using Needleman–Wunsch (global) or Smith–Waterman (local) alignment methods, based on column comparison scores calculated by one of the five supported distance metrics: (i) Pearson’s correlation coefficient (9), (ii) Kullback–Leibler information content (3), b sum of square”
- Quantifying similarity between motifs (2007, unknown): “Previous methods begin by defining a score between two motif columns, and then they combine these scores either by summing (as we do) [ 7-9 ,14] or by taking the mean [11-13] or geometric mean [10] of the column scores.”