MATCHTM: a tool for searching transcription factor binding sites in DNA sequences

By A. Kel, E. Gößling, I. Reuter, E. Cheremushkin, O. Kel-Margoulis, E. Wingender. Nucleic Acids Res., 2003

1,207 citations recorded by the source corpus.

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Citations in the corpus

VISTA: computational tools for comparative genomics (2004, methodology): “rVISTA makes predictions based on using the MatchTM program ( 33 )”
High-Resolution Temporal Profiling of Transcripts during Arabidopsis Leaf Senescence Reveals a Distinct Chronology of Processes and Regulation[C][W][OA] (2011, methodology): “For any given PSSM and promoter, we scanned the sequence and computed a matrix similarity score (Kel et al., 2003) at each position on both strands.”
The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation (2008, neutral): “Sets of binding sites can be summarized into an International Union of Pure and Applied Chemistry (IUPAC) consensus, which can be used for the prediction of TFBSs (PatSearch/Patch [16] [19]), or a simple statistical model such as a weight matrix can be derived and used for this purpose (MatInspector”
The transcription factors Blimp-1 and IRF4 jointly control the differentiation and function of effector regulatory T cells (2011, methodology): “Searches for putative Blimp-1- and IRF4-binding sites in the Il10 locus were made with the MATCH program in the TRANSFAC databas”
rVISTA 2.0: evolutionary analysis of transcription factor binding sites (2004, methodology): “Comparative detection of PWMs in long genomic intervals performed by MATCH (6) and tfSearch programs Region/PWMs MATCH(s) (cut-off = 0.75) tfSearch(s) (cut-off = 0.75) Speed increase MATCH(s) (cut-off = 0.85) tfSearch(s) (cut-off = 0.85) Speed increase 1 Mb/491 PWMs 12243.0 708.4 17· 4029.5 54.6 74·”
An enhanced computational platform for investigating the roles of regulatory RNA and for identifying functional RNA motifs (2013, methodology): “MATCH [25] is an approach for searching transcription factor binding sites with specific position-weight matrices (PWM).”
MatInspector and beyond: promoter analysis based on transcription factor binding sites (2005, methodology): “The program Match (Kel et al. (2003)) uses a similar scoring scheme as MatInspector, but lacks matrix families (see below), resulting in a large number of redundant matches.”
TRANSFAC®: transcriptional regulation, from patterns to profiles (2003, background): “MATCHTM: ENHANCEMENT BY TISSUE- AND STATE-SPECIFIC PROFILES TRANSFAC1 6.0 is accompanied by the new public version of MatchTM ( 12 ).”
Transcriptional regulatory elements in the human genome. (2006, neutral): “Programs such as MatInspector (150) and, more recently, MATCH (88) compare a genomic sequence input to all the matrices in TRANSFAC, and return a list of potential TFBSs based on a statistical match between a region in the sequence and a site matrix.”
The functional landscape of the human phosphoproteome (2019, methodology): “A matrix similarity score (MSS) ranging from 0 to 1 was next computed between the PWMs and each Serine, Threonine, and Tyrosine in the human proteome as described in the MATCH algorithm (Kel et al. 2003).”
g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments (2007, methodology): “First, TFBS are found by matching TRANSFAC position-speciﬁc matrices using the program Match (20) on 1000-bp upstream regions, as provided by the UCSC genome database (21).”
SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies (2009, methodology): “Possible scores for MSS and CSS range from 0 to 1 ( 10 ).”