@article{Eichner2013c,
  author = {Eichner, Johannes and Topf, Florian and Dr\"ager, Andreas and Wrzodek,
	Clemens and Wanke, Dierk and Zell, Andreas},
  title = {{TFpredict and SABINE: Sequence-Based Prediction of Structural and
	Functional Characteristics of Transcription Factors}},
  journal = {PLoS ONE},
  publisher = {Public Library of Science},
  year = {2013},
  volume = {8},
  pages = {e82238},
  number = {12},
  month = dec,
  abstract = {One of the key mechanisms of transcriptional control are the specific
	connections between transcription factors (TF) and \emph{cis}-regulatory
	elements in gene promoters. The elucidation of these specific protein-DNA
	interactions is crucial to gain insights into the complex regulatory
	mechanisms and networks underlying the adaptation of organisms to
	dynamically changing environmental conditions. As experimental techniques
	for determining TF binding sites are expensive and mostly performed
	for selected TFs only, accurate computational approaches are needed
	to analyze transcriptional regulation in eukaryotes on a genome-wide
	level. We implemented a four-step classification workflow which for
	a given protein sequence (1) discriminates TFs from other proteins,
	(2) determines the structural superclass of TFs, (3) identifies the
	DNA-binding domains of TFs and (4) predicts their \emph{cis}-acting
	DNA motif. While existing tools were extended and adapted for performing
	the latter two prediction steps, the first two steps are based on
	a novel numeric sequence representation which allows for combining
	existing knowledge from a BLAST scan with robust machine learning-based
	classification. By evaluation on a set of experimentally confirmed
	TFs and non-TFs, we demonstrate that our new protein sequence representation
	facilitates more reliable identification and structural classification
	of TFs than previously proposed sequence-derived features. The algorithms
	underlying our proposed methodology are implemented in the two complementary
	tools TFpredict and SABINE. The online and stand-alone versions of
	TFpredict and SABINE are freely available to academics at \url{http://www.cogsys.cs.uni-tuebingen.de/software/TFpredict/}
	and \url{http://www.cogsys.cs.uni-tuebingen.de/software/SABINE/}.},
  doi = {10.1371/journal.pone.0082238},
  pdf = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0082238&type=printable},
  url = {https://doi.org/10.1371/journal.pone.0082238}
}