BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. The development of high throughput genome sequencing and gene expression techniques gives rise to the demand for data-mining tools. BioProspector, a C program using a Gibbs sampling strategy, examines the upstream region of genes in the same gene expression pattern group and looks for regulatory sequence motifs. BioProspector uses Markov background to model the base dependencies of non-motif bases, which greatly improved the specificity of the reported motifs. The parameters of the Markov background model are either estimated from user-specified sequences or pre-computed from the whole genome sequences. A new motif scoring function is adopted to allow each input sequences to contain zero to multiple copies of the motif. In addition, BioProspector can model gapped motifs and motifs with palindromic patterns, which are prevalent motif patterns in prokaryotes. All these modifications greatly improve the performance of the program. Besides showing preliminary success in finding the binding motifs for S. cerevisiae RAP1, B. subtilis RNA polymerase, and E. coli CRP, we have used BioProspector to find s54 motif from M. xanthus genome, many B. subtilis motifs from DBTBS collection of promoters, and motifs from yeast expression data.

References in zbMATH (referenced in 17 articles )

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  1. Baragatti, Meïli; Grimaud, Agnès; Pommeret, Denys: Parallel tempering with equi-energy moves (2013)
  2. Woodard, Dawn B.; Rosenthal, Jeffrey S.: Convergence rate of Markov chain methods for genomic motif discovery (2013)
  3. Wang, Dianhui; Do, Hai Thanh: Computational localization of transcription factor binding sites using extreme learning machines (2012) ioport
  4. Angelov, Stanislav; Inenaga, Shunsuke; Kivioja, Teemu; Mäkinen, Veli: Missing pattern discovery (2011)
  5. Liu, Li-Fang; Jiao, Li-Cheng: Detection of over-represented motifs corresponding to known TFBSs via motif clustering and matching (2010)
  6. Hernandez, David; Gras, Robin; Appel, Ron: Neighborhood functions and hill-climbing strategies dedicated to the generalized ungapped local multiple alignment (2008)
  7. Bembom, Oliver; Keles, Sunduz; van der Laan, Mark J.: Supervised detection of conserved motifs in DNA sequences with cosmo (2007)
  8. Feng, Xiucheng; Wan, Lin; Deng, Minghua; Sun, Fengzhu; Qian, Minping: An efficient algorithm for deciphering regulatory motifs (2007)
  9. Andersson, Samuel A.; Lagergren, Jens: Motif Yggdrasil: sampling from a tree mixture model (2006)
  10. Kou, S.C.; Zhou, Qing; Wong, Wing Hung: Equi-energy sampler with applications in statistical inference and statistical mechanics (2006)
  11. Liu, L.Angela; Bader, Joel S.: Decoding transcriptional regulatory interactions (2006)
  12. Mahony, Shaun; Benos, Panayiotis V.; Smith, Terry J.; Golden, Aaron: Self-organizing neural networks to support the discovery of DNA-binding motifs (2006)
  13. Wang, Chuancai; Xie, Jun; Craig, B.A.: Context dependent models for discovery of transcription factor binding sites (2006)
  14. Mahony, Shaun; Hendrix, David; Smith, Terry J.; Golden, Aaron: Self-organizing maps of position weight matrices for motif discovery in biological sequences (2005) ioport
  15. Hernandez, David; Gras, Robin; Appel, Ron: MoDEL: an efficient strategy for ungapped local multiple alignment (2004)
  16. Jensen, Shane T.; Liu, X.Shirley; Zhou, Qing; Liu, Jun S.: Computational discovery of gene regulatory binding motifs: a Bayesian perspective (2004)
  17. Maruyama, Osamu; Bannai, Hideo; Tamada, Yoshinori; Kuhara, Satoru; Miyano, Satoru: Fast algorithm for extracting multiple unordered short motifs using bit operations (2002)