:: DESCRIPTION
TFBSs is a web server for Predicting transcription factor binding sites.
::DEVELOPER
The Li’s Group of Theoretical Biophysics and Bioinformatics
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Guo-liang Fan and Qianzhong Li, Keli Yang,
TFBSs: a web server for Predicting transcription factor binding sites.
2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2012,1:65-68
:: DESCRIPTION
BSG is a collection of programs used to construct and analyze Binding Site Graphs. Overall,the analysis is a two step process that can be performed on separate computers. First, ensemble Gibbs sampling is performed, preferably on a massively parallel computer, and the results are collected. The resulting output files are then compressed (using gzip) to conserve disk space. Then, in a second step, the compressed output files are analyzed and predictions are made
::DEVELOPER
Bioinformatics and Evolution of Systems Laboratory
:: SCREENSHOTS
n/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Timothy E Reddy, Charles DeLisi, Boris E Shakhnovich
Binding Site Graphs: A New Graph Theoretical Framework for Prediction of Transcription Factor Binding Sites
PLoS Comput Biol 3(5): e90. doi:10.1371/journal.pcbi.0030090
:: DESCRIPTION
SVMotif is a mechine learning based motif finder. It can be classified into K-mer enumeration based methods. the evaluation of each possible K-mer is done by Recursive SVM feature selection
::DEVELOPER
:: SCREENSHOTS
n/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Ensemble machine methods for DNA binding
(with Y. Fan, and C. DeLisi),
Machine Learning and Applications 7, M. Wani, et al., eds. IEEE, Washington (2008),709-716.
:: DESCRIPTION
RIP (regulatory interaction predictor) is a machine learning approach that inferred 73,923 RIs for 301 human TFs and 11,263 target genes with considerably good quality and 4,516 RIs with very high quality. The inference of RIs is independent of any specific condition.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Bioinformatics. 2011 Aug 15;27(16):2239-47. doi: 10.1093/bioinformatics/btr366. Epub 2011 Jun 20.
RIP: the regulatory interaction predictor–a machine learning-based approach for predicting target genes of transcription factors.
Bauer T, Eils R, König R.
:: DESCRIPTION
MACO is a Position Frequency Matrix(PFM) comparing application that implements a gap-allowed alignment algorithm. You may compare your PFM with those collected in our dataset.
::DEVELOPER
Bioinformatics Group in Nanjing University.
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
MACO: a gapped-alignment scoring tool for comparing transcription factor binding sites.
Su G, Mao B, Wang J.
In Silico Biol. 2006;6(4):307-10.
:: DESCRIPTION
WordSpy is a novel, steganalysis-based approach for genome-wide motif finding. The software views regulatory regions as a stegoscript with cis-elements embedded in ‘background’ sequences. WordSpy can discover a complete set of cis-elements and facilitate the systematic study of regulatory networks.
::DEVELOPER
Computational Intelligence Center(CIC)
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Guandong Wang and Weixiong Zhang
A steganalysis-based approach for genome-wide identification of regulatory DNA sequence elements
Genome Biology 2006, 7:R49
:: DESCRIPTION
TFInfer is an open source software for the inference of transcription factor activities from microarray data.
::DEVELOPER
Shahzad Asif (shahzad.asif@ed.ac.uk) , Guido Sanguinetti (gsanguin@staffmail.ed.ac.uk)
:: SCREENSHOTS
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
TFInfer: a tool for probabilistic inference of transcription factor activities.
Asif HM, Rolfe MD, Green J, Lawrence ND, Rattray M, Sanguinetti G.
Bioinformatics. 2010 Oct 15;26(20):2635-6. Epub 2010 Aug 24.
:: DESCRIPTION
LocalMotif is a software tool for discovering transcription factor binding motifs in a collection of DNA sequences. LocalMotif is based on a novel scoring function, called spatial confinement score, which can determine the exact interval of localization of a motif. This score is combined with other existing scoring measures including over-representation and relative entropy to determine the overall prominence of the motif. The approach successfully discovers biologically relevant motifs and their intervals of localization in scenarios where the motifs cannot be discovered by general motif finding tools. It is especially useful for discovering multiple co-localized motifs in a set of regulatory sequences, such as those identified by ChIP-Seq.
::DEVELOPER
:: SCREENSHOTS
Command Line
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Narang, V., Mittal, A., Sung, W.K. (2009)
“Localized motif discovery in gene regulatory sequences“,
Bioinformatics (2010) 26 (9): 1152-1159.
