TFInfer: a tool for probabilistic inference of transcription factor activities

H. M. Shahzad Asif, Matthew D. Rolfe, Jeff Green, Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti
Bioinformatics, 26:2635-2636, 2010.

Abstract

Summary: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.

Availability:

Contact: [gsanguin@staffmail.ed.ac.uk](gsanguin@staffmail.ed.ac.uk)

Cite this Paper

BibTeX
@Article{Asif-tfinfer10, title = {{TFInfer}: a tool for probabilistic inference of transcription factor activities}, author = {Asif, H. M. Shahzad and Rolfe, Matthew D. and Green, Jeff and Lawrence, Neil D. and Rattray, Magnus and Sanguinetti, Guido}, journal = {Bioinformatics}, pages = {2635--2636}, year = {2010}, volume = {26}, pdf = {http://bioinformatics.oxfordjournals.org/content/26/20/2635.full.pdf+html}, url = {http://inverseprobability.com/publications/asif-tfinfer10.html}, abstract = {Summary: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.

Availability:

Contact: [gsanguin@staffmail.ed.ac.uk](gsanguin@staffmail.ed.ac.uk)} }
Endnote
%0 Journal Article %T TFInfer: a tool for probabilistic inference of transcription factor activities %A H. M. Shahzad Asif %A Matthew D. Rolfe %A Jeff Green %A Neil D. Lawrence %A Magnus Rattray %A Guido Sanguinetti %J Bioinformatics %D 2010 %F Asif-tfinfer10 %P 2635--2636 %U http://inverseprobability.com/publications/asif-tfinfer10.html %V 26 %X Summary: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.

Availability:

Contact: [gsanguin@staffmail.ed.ac.uk](gsanguin@staffmail.ed.ac.uk)
RIS
TY - JOUR TI - TFInfer: a tool for probabilistic inference of transcription factor activities AU - H. M. Shahzad Asif AU - Matthew D. Rolfe AU - Jeff Green AU - Neil D. Lawrence AU - Magnus Rattray AU - Guido Sanguinetti DA - 2010/10/15 ID - Asif-tfinfer10 VL - 26 SP - 2635 EP - 2636 L1 - http://bioinformatics.oxfordjournals.org/content/26/20/2635.full.pdf+html UR - http://inverseprobability.com/publications/asif-tfinfer10.html AB - Summary: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.

Availability:

Contact: [gsanguin@staffmail.ed.ac.uk](gsanguin@staffmail.ed.ac.uk) ER -
APA
Asif, H.M.S., Rolfe, M.D., Green, J., Lawrence, N.D., Rattray, M. & Sanguinetti, G.. (2010). TFInfer: a tool for probabilistic inference of transcription factor activities. Bioinformatics 26:2635-2636 Available from http://inverseprobability.com/publications/asif-tfinfer10.html.

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