JASPAR: an open-access database for eukaryotic transcription factor binding profiles
Abstract:The analysis of regulatory regions in genome sequences is strongly based on the detection of potential transcription factor binding sites. The preferred models for representation of transcription factor binding specificity have been termed position-specific scoring matrices. JASPAR is an open-access database of annotated, high-quality, matrix-based transcription factor binding site profiles for multicellular eukaryotes. The profiles were derived exclusively from sets of nucleotide sequences experimentally demonstrated to bind transcription factors. The database is complemented by a web interface for browsing, searching and subset selection, an online sequence analysis utility and a suite of programming tools for genome-wide and comparative genomic analysis of regulatory regions. JASPAR is available at http://jaspar. cgb.ki.se.
暂无分享,去 创建一个
[1] S. B. Needleman,et al. A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.
[2] Nir Friedman,et al. Modeling dependencies in protein-DNA binding sites , 2003, RECOMB '03.
[3] Wyeth W. Wasserman,et al. In silico identification of metazoan transcriptional regulatory regions , 2003, Naturwissenschaften.
[4] Jon D. McAuliffe,et al. Phylogenetic Shadowing of Primate Sequences to Find Functional Regions of the Human Genome , 2003, Science.
[5] A. Sandelin,et al. Identification of conserved regulatory elements by comparative genome analysis , 2003, Journal of biology.
[6] Gary D. Stormo,et al. DNA binding sites: representation and discovery , 2000, Bioinform..
[7] Wyeth W. Wasserman,et al. Identification of functional clusters of transcription factor binding motifs in genome sequences: the MSCAN algorithm , 2003, ISMB.
[8] Wynand Alkema,et al. Understanding the language of gene regulation , 2003, Genome Biology.
[9] G. Stormo,et al. Ann-spec: a Method for Discovering Transcription Factor Binding Sites with Improved Specificity , 2022 .
[10] T. D. Schneider,et al. Sequence logos: a new way to display consensus sequences. , 1990, Nucleic acids research.
[11] R. Treisman,et al. A sensitive method for the determination of protein-DNA binding specificities. , 1990, Nucleic acids research.
[12] William Stafford Noble,et al. Searching for statistically significant regulatory modules , 2003, ECCB.
[13] W. Wasserman,et al. A predictive model for regulatory sequences directing liver-specific transcription. , 2001, Genome research.
[14] Saurabh Sinha,et al. YMF: a program for discovery of novel transcription factor binding sites by statistical overrepresentation , 2003, Nucleic Acids Res..
[15] Michael Q. Zhang. Computational prediction of eukaryotic protein-coding genes , 2002, Nature Reviews Genetics.
[16] Wyeth W. Wasserman,et al. TFBS: Computational framework for transcription factor binding site analysis , 2002, Bioinform..
[17] William Noble Grundy,et al. Meta-MEME: motif-based hidden Markov models of protein families , 1997, Comput. Appl. Biosci..
[18] A. Sandelin,et al. Integrated analysis of yeast regulatory sequences for biologically linked clusters of genes , 2003, Functional & Integrative Genomics.
[19] Martin C. Frith,et al. Cluster-Buster: finding dense clusters of motifs in DNA sequences , 2003, Nucleic Acids Res..
[20] Alexander E. Kel,et al. TRANSFAC®: transcriptional regulation, from patterns to profiles , 2003, Nucleic Acids Res..