This website contains supplementary material for:

 

T.R. Hvidsten, B. Wilczyński, A. Kryshtafovych, J. Tiuryn, J. Komorowski and K. Fidelis. Discovering regulatory binding site modules using rule-based learning. Genome Research 15: 856-66, 2005.

 

 

Discovering regulatory binding site modules using rule-based learning

 

Torgeir R. Hvidsten (1), Bartosz Wilczyński (2,3), Andriy Kryshtafovych (2),

Jerzy Tiuryn (4), Jan Komorowski (1) and Krzysztof Fidelis (2)

 

(1) The Linnaeus Centre for Bioinformatics, Uppsala University, Sweden

jan.komorowski@lcb.uu.se

(2) Biology and Biotechnology Research Program, Lawrence Livermore National Laboratory, CA, USA

fidelis@llnl.gov

(3) Institute of Mathematics of the Polish Academy of Sciences, Warsaw, Poland

(4) Faculty of Mathematics, Informatics, and Mechanics, Warsaw University, Poland

 

Corresponding authors:

Krzysztof Fidelis, Biology and Biotechnology Research Program,

Lawrence Livermore National Laboratory, 7000 East Ave., L-448, Livermore, CA, 94550, USA.

 

 

Rules and evaluation to binding data and Gene Ontology

 

The induced rules and their evaluation to binding data1 and Gene Ontology2 for six expression studies using known binding sites (file format):

 

  1. cell cycle3  (including putative motifs: cell cycle)
  2. sporulation4
  3. diauxic shift5
  4. heat and cold shock6
  5. pheromone7
  6. DNA-damaging agents8

 

Known binding sites were taken from the database of known and putative motifs published by Pilpel et al9.

Expression data was taken from ExpressDB10.

 

Selecting expression thresholds

Overlap between sequence motifs

Comparisons to other studies

 

Extended tables from the article

 

  1. Table 2 (extended with additional binding data11 and standard deviations for the random tests)
  2. Table 3 (extended with standard deviation for the random tests)
  3. Table 4 (extended to include all rules)
  4. Figure 4 (extended with graphs showing only significant edges)

 

 

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