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 29 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. Mahdevar, Ghasem; Sadeghi, Mehdi; Nowzari-Dalini, Abbas: Transcription factor binding sites detection by using alignment-based approach (2012)
  4. Wang, Dianhui; Do, Hai Thanh: Computational localization of transcription factor binding sites using extreme learning machines (2012) ioport
  5. Angelov, Stanislav; Inenaga, Shunsuke; Kivioja, Teemu; Mäkinen, Veli: Missing pattern discovery (2011)
  6. Chen, Gong; Zhou, Qing: Heterogeneity in DNA multiple alignments: modeling, inference, and applications in motif finding (2010)
  7. Liu, Li-Fang; Jiao, Li-Cheng: Detection of over-represented motifs corresponding to known TFBSs via motif clustering and matching (2010)
  8. Bi, Chengpeng: DNA motif alignment by evolving a population of Markov chains (2009) ioport
  9. Chan, Tak-Ming; Li, Gang; Leung, Kwong-Sak; Lee, Kin-Hong: Discovering multiple realistic TFBS motifs based on a generalized model (2009) ioport
  10. Erill, Ivan; O’neill, Michael C.: A reexamination of information theory-based methods for DNA-binding site identification (2009) ioport
  11. Hernandez, David; Gras, Robin; Appel, Ron: Neighborhood functions and hill-climbing strategies dedicated to the generalized ungapped local multiple alignment (2008)
  12. Janky, Rekin’s; Van Helden, Jacques: Evaluation of phylogenetic footprint discovery for predicting bacterial cis-regulatory elements and revealing their evolution (2008) ioport
  13. Li, Sierra M.; Wakefield, Jon; Self, Steve: A transdimensional Bayesian model for pattern recognition in DNA sequences (2008)
  14. Bembom, Oliver; Keles, Sunduz; Van der Laan, Mark J.: Supervised detection of conserved motifs in DNA sequences with cosmo (2007)
  15. Feng, Xiucheng; Wan, Lin; Deng, Minghua; Sun, Fengzhu; Qian, Minping: An efficient algorithm for deciphering regulatory motifs (2007)
  16. Gupta, Mayetri: Generalized hierarchical Markov models for the discovery of length-constrained sequence features from genome tiling arrays (2007)
  17. Larsson, Erik; Lindahl, Per; Mostad, Petter: Helicis: a DNA motif discovery tool for colocalized motif pairs with periodic spacing (2007) ioport
  18. Rouchka, Eric C.; Hardin, C. Timothy: Rmotifgen: Random motif generator for DNA and protein sequences (2007) ioport
  19. Andersson, Samuel A.; Lagergren, Jens: Motif Yggdrasil: sampling from a tree mixture model (2006)
  20. Ji, Hongkai; Wong, Wing Hung: Computational biology: toward deciphering gene regulatory information in mammalian genomes (2006)

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