DNA binding sites: representation and discovery

By Gary D. Stormo. 2000

1,652 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Training GANs with Optimism (2017, background): “For many important cellular functions, this distribution can be well modeled by a position-weight matrix (PWM) that speciﬁes the probability of different nucleotides occuring at each position (Stormo, 2000).”
ProteinNet: a standardized data set for machine learning of protein structure (2019, background): “re derived directly from the mmCIF files. In instances where an amino acid is chemically modified or its identity is unknown, the most probable residue in its position-specific scoring matrix (PSSM) (Stormo, 2000) is substituted (see next section for how PSSMs are derived). If a PSSM contains more t”
Biological Sequence Modeling with Convolutional Kernel Networks (2017, neutral): “A related line of work focused on position weight matrices (PWMs) to characterize a set of sequences [42].”
UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein–DNA interactions (2014, methodology): “…in the same manner: the Seed-and-Wobble algorithm, introduced jointly with universal PBM technology (1,22), is used to generate a position weight matrix (PWM) (23,24), which in turn is used to generate sequence logos (25) that are displayed on the protein’s Details page (e.g. see Figure 2A).”
Cluster-Buster: finding dense clusters of motifs in DNA sequences (2003, methodology): “In the details figure, motifs are drawn as color-coded rectangles above or below the line according to the strand they are on (relative to the motif matrix; the ERE appears on only one strand in Fig. 1B because we used a slightly non-palindromic matrix to represent this motif ). The heights of the m”
Transcriptional control of human p53-regulated genes (2008, background): “PHMMs assume that each nucleotide position contributes independently to the overall binding free energy of the binding sit”
AGRIS: Arabidopsis Gene Regulatory Information Server, an information resource of Arabidopsis cis-regulatory elements and transcription factors (2003, background): “(b) Position Weight Matrix Methods (PWM) to scan known binding sites: We have prepared position-weight matrices [15] of those Arabidopsis transcription factors for which at least 5–10 experimentally known binding sites exist.”
Virtual Footprint and PRODORIC: an integrative framework for regulon prediction in prokaryotes (2005, background): “However, in some cases, e.g. when sufﬁcient sequence data are not available, it is necessary to use other pattern deﬁnitions (Stormo, 2000).”
The State of the Art of Mammalian Promoter Recognition (2003, positive): “Another review giving the history of in silico pattern was published by Gary Stormo.(16) The new generation of promoter predictors appears to be several times better than all previous approaches (see the comparisons).”
Natural similarity measures between position frequency matrices with an application to clustering (2008, background): “Stormo (2000) shows that the score of a sequence is proportional to its binding energy.”
Identification of a novel cis-regulatory element involved in the heat shock response in Caenorhabditis elegans using microarray gene expression and computational methods. (2002, background): “From a consideration of the thermodynamics of protein–DNA interactions and the statistics of the scores (Stormo 1998; Stormo and Fields 1998), we expect that the score should be proportional to the free energy of binding.”
Understanding eukaryotic linear motifs and their role in cell signaling and regulation. (2008, background): “In that field, the best results have been achieved by predictive schemes that combine statistical models to represent the site (90) with phylogenetic footprinting (91).”
Transcriptional regulatory elements in the human genome. (2006, neutral): “Multiple examples of experimentally determined TFBSs are then used to build a position-specific scoring matrix for each factor (172).”
The evolution of transcriptional regulation in eukaryotes. (2003, methodology): “We discuss here a few of the many approaches which have been developed (for additional information, see Hardison [2000]; Stormo [2000]; Ohler and Niemann [2001]; and Markstein and Levine [2002]).”