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CpGProD / CpG island Promoter Detection
An application for identifying mammalian promoter regions associated with CpG islands in large genomic sequences. Although it is strictly dedicated to this particular promoter class corresponding to approximately 50% of the genes, CpGProD exhibits a higher sensitivity and specificity than other tools used for promoter prediction. Notably, CpGProD uses different parameters according to species (human, mouse) studied. Moreover, CpGProD predicts the promoter orientation on the DNA strand.
newcpgreport
Identifies CpG islands in one or more nucleotide sequences. newcpgreport is an Emboss tool. The ratio of observed to expected number of GC dinucleotides patterns is calculated over a window (sequence region) which is moved along the sequence. The calculated ratios are used to identify regions which match the program's definition of a "CpG island" (a CG dinucleotide rich area). A report file is written giving the input sequence name, CpG island parameters and data on any CpG islands that are found.
cpgplot
Identifies and plots CpG islands in nucleotide sequences. cpgplot identifies CpG islands in one or more nucleotide sequences. The ratio of observed to expected number of GC dinucleotides patterns is calculated over a window (sequence region) which is moved along the sequence. The calculated ratios are plotted graphically, together with the regions which match this program's definition of a "CpG island" (a CG dinucleotide rich area). A report file is written giving the input sequence name, CpG island parameters and data on any CpG islands that are found.
CpGPAP / CpG island Predictor Analysis Platform
Obsolete
A web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences. The prediction algorithms supported in CpGPAP include complementary particle swarm optimization (CPSO), a complementary genetic algorithm (CGA) and other methods (CpGPlot, CpGProD and CpGIS) found in the literature. The CpGPAP platform is easy to use and has three main features (1) selection of the prediction algorithm; (2) graphic visualization of results; and (3) application of related tools and dataset downloads. These features allow the user to easily view CpG island results and download the relevant island data.
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