To analyze gene promoters, we used the web-based Genomatix software (Genomatix, Ann Harbor, Michigan, USA; ), which is a bioinformatics tool that utilizes a large library of matrix descriptions for known transcription factor binding sites and algorithms to locate matches in DNA sequences.
Motifs were compared to a library of search matrices for 9 known Mtb motifs (Acr, Crp, CsoR, DosR, FurA, IdeR, KstR, MprAB, and ZurB), as well as a library of 55 E. coli motifs [ 98] and 22 Corynebacterial motifs [ 99].
Transcription factor binding sites within the regions were predicted by the on-line program Match using a library of mononucleotide weight matrices from TRANSFAC 6.0 (www.gene-regulation.com) with both core similarity and matrix similarity of the transcription factor binding sites set higher than 0.85.
Match uses a library of mononucleotide weight matrices from the database TRANSFAC 11.4 professional [ 43].
MATCH uses a library of mononucleotide weight matrices from TRANSFAC 6.0.
TFBS orientation depends on the Genomatix library of position weight matrices (PWM).
In addition, it predicts the effects of an SNP in a regulatory region based on a library of position-weighted matrices collected from JASPER (31), TRANSFAC (32) and protein-binding microarray experiments (33– 33).
In order to study a possible similarity of the overrepresented CNS motifs with known binding sites for transcription factors (TF), we applied our recently developed method m2transfac [ 16], and compared all the motifs found at the previous step with the TRANSFAC library of positional weight matrices (PWMs).
We developed a Java library for matrix computations and show that using a set of optimization techniques Java can achieve performance comparable with other libraries developed in C or Fortran.
