Version 1.2

New Features in SABINE version 1.2

Updated training set: The support vector regression models which play a central role in the SABINE algorithm were updated and additional transcription factors from more recent versions of public databases were incorporated as training data.

New Features in SABINE version 1.1

• Superclass auto-detection: The superclass of a given transcription factor can now be auto-detected based on homology to transcription factors in the training set of SABINE. Alternatively, the tool TFpredict can be used for superclass prediction.
• Dynamic Best Match Threshold: The Best Match Threshold corresponds to a cutoff for the predicted PFM similarity of known PFMs to the unknown PFM of the input transcription factor. This cutoff is now by default chosen dynamically depending on the PFM similarities predicted for the best matches. If best matches with very high DNA-motif similarity to the input factor were found, only these factors are used for the PFM transfer. If a lower but still significant DNA-motif similarity was found, SABINE can still perform a prediction using a lower cutoff value. For this purpose, three different cutoff values were predefined and associated to different confidence levels (high, medium, and low).
• DNA-binding domain prediction: The DNA-binding domains of a transcription factor can now be predicted using an alignment-based approach. As this is still an exploratory feature, it is currently only implemented in the command-line interface of the stand-alone version of SABINE. A viable alternative method is provided by the tool TFpredict which can also be employed for the prediction of DNA-binding domains.
• Installation validator: A new function was implemented which allows to automatically validate a local installation of SABINE.

Requirements

• SABINE requires a Linux platform.
• SABINE needs the latest JAVA JRE release (version 6 or later). You can get Java from java.com.