000114833 001__ 114833
000114833 005__ 20190608070214.0
000114833 0247_ $$2doi$$a10.1016/0022-2836(90)90223-9
000114833 037__ $$aARTICLE
000114833 245__ $$aWeight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences
000114833 269__ $$a1990
000114833 260__ $$c1990
000114833 336__ $$aJournal Articles
000114833 500__ $$aDepartment of Polymer Research, Weizmann Institute of Science, Rehovot, Israel.
000114833 520__ $$aOptimized weight matrices defining four major eukaryotic promoter elements, the TATA-box, cap signal, CCAAT-, and GC-box, are presented; they were derived by comparative sequence analysis of 502 unrelated RNA polymerase II promoter regions. The new TATA-box and cap signal descriptions differ in several respects from the only hitherto available base frequency Tables. The CCAAT-box matrix, obtained with no prior assumption but CCAAT being the core of the motif, reflects precisely the sequence specificity of the recently discovered nuclear factor NY-I/CP1 but does not include typical recognition sequences of two other purported CCAAT-binding proteins, CTF and CBP. The GC-box description is longer than the previously proposed consensus sequences but is consistent with Sp1 protein-DNA binding data. The notion of a CACCC element distinct from the GC-box seems not to be justified any longer in view of the new weight matrix. Unlike the two fixed-distance elements, neither the CCAAT- nor the GC-box occurs at significantly high frequency in the upstream regions of non-vertebrate genes. Preliminary attempts to predict promoters with the aid of the new signal descriptions were unexpectedly successful. The new TATA-box matrix locates eukaryotic transcription initiation sites as reliably as do the best currently available methods to map Escherichia coli promoters. This analysis was made possible by the recently established Eukaryotic Promoter Database (EPD) of the EMBL Nucleotide Sequence Data Library. In order to derive the weight matrices, a novel algorithm has been devised that is generally applicable to sequence motifs positionally correlated with a biologically defined position in the sequences. The signal must be sufficiently over-represented in a particular region relative to the given site, but need not be present in all members of the input sequence collection. The algorithm iteratively redefines the set of putative motif representatives from which a weight matrix is derived, so as to maximize a quantitative measure of local over-representation, an optimization criterion that naturally combines structural and positional constancy. A comprehensive description of the technique is presented in Methods and Data.
000114833 700__ $$g113607$$aBucher, P.$$0244404
000114833 773__ $$j212$$tJournal of Molecular Biology$$k4$$q563-78
000114833 909C0 $$xU11780$$0252244$$pGR-BUCHER
000114833 909CO $$pSV$$particle$$ooai:infoscience.tind.io:114833
000114833 937__ $$aGR-BUCHER-ARTICLE-1990-001
000114833 973__ $$rREVIEWED$$sPUBLISHED$$aOTHER
000114833 980__ $$aARTICLE