PhyME: A probabilistic algorithm for finding motifs in sets of orthologous sequences. Background: This paper addresses the problem of discovering transcription factor binding sites in heterogeneous sequence data, which includes regulatory sequences of one or more genes, as well as their orthologs in other species. Results: We propose an algorithm that integrates two important aspects of a motif’s significance – overrepresentation and cross-species conservation – into one probabilistic score. The algorithm allows the input orthologous sequences to be related by any user-specified phylogenetic tree. It is based on the Expectation-Maximization technique, and scales well with the number of species and the length of input sequences. We evaluate the algorithm on synthetic data, and also present results for data sets from yeast, fly, and human. Conclusions: The results demonstrate that the new approach improves motif discovery by exploiting multiple species information.

References in zbMATH (referenced in 9 articles )

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  1. Fu, Bin; Fu, Yunhui; Xue, Yuan: Sublinear time motif discovery from multiple sequences (2013)
  2. Jia, Hui; Li, Jinming: Finding transcription factor binding motifs for coregulated genes by combining sequence overrepresentation with cross-species conservation (2012)
  3. Bailey, Timothy L.; Bodén, Mikael; Whitington, Tom; Machanick, Philip: The value of position-specific priors in motif discovery using MEME (2010) ioport
  4. Chen, Gong; Zhou, Qing: Heterogeneity in DNA multiple alignments: modeling, inference, and applications in motif finding (2010)
  5. Fan, Xiaodan; Yuan, Yuan; Liu, Jun S.: The EM algorithm and the rise of computational biology (2010)
  6. Rendon, Gloria; Ger, Mao-Feng; Kantorovitz, Ruth; Natarajan, Shreedhar; Tilson, Jeffrey; Jakobsson, Eric: Understanding the “horizontal dimension” of molecular evolution to annotate, classify, and discover proteins with functional domains (2009) ioport
  7. Janky, Rekin’s; Van Helden, Jacques: Evaluation of phylogenetic footprint discovery for predicting bacterial cis-regulatory elements and revealing their evolution (2008) ioport
  8. Ji, Hongkai; Wong, Wing Hung: Computational biology: toward deciphering gene regulatory information in mammalian genomes (2006)
  9. Sinha, Saurabh; Blanchette, Mathieu; Tompa, Martin: Phyme: A probabilistic algorithm for finding motifs in sets of orthologous sequences (2004) ioport