Build a Dictionary, Learn a Grammar, Decipher Stegoscripts, and Discover Genomic Regulatory Elements

It has been a challenge to discover transcription factor (TF) binding motifs (TFBMs), which are short cis-regulatory DNA sequences playing essential roles in transcriptional regulation. We approach the problem of discovering TFBMs from a steganographic perspective. We view the regulatory regions of a genome as if they constituted a stegoscript with conserved words (i.e., TFBMs) being embedded in a covertext, and model the stegoscript with a statistical model consisting of a dictionary and a grammar. We develop an efficient algorithm, WordSpy, to learn such a model from a stegoscript and to recover conserved motifs. Subsequently, we select biologically meaningful motifs based on a motif's specificity to the set of genes of interest and/or the expression coherence of the genes whose promoters contain the motif. From the promoters of 645 distinct cell-cycle related genes of S. cerevisiae, our method is able to identify all known cell-cycle related TFBMs among its top ranking motifs. Our method can also be directly applied to discriminative motif finding. By utilizing the ChIP-chip data of Lee et al., we predicted potential binding motifs of 113 known transcription factors of budding yeast.

[1]  Satoru Miyano,et al.  Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection , 2003, ECCB.

[2]  M. Q. Zhang.  Large-scale gene expression data analysis: a new challenge to computational biologists. , 1999, Genome research.

[3]  C. Lawrence,et al.  Human-mouse genome comparisons to locate regulatory sites , 2000, Nature Genetics.

[4]  J. Collado-Vides,et al.  Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. , 1998, Journal of molecular biology.

[5]  Jun S. Liu,et al.  Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. , 1993, Science.

[6]  Gary D. Stormo,et al.  Identifying DNA and protein patterns with statistically significant alignments of multiple sequences , 1999, Bioinform..

[7]  Peter C. Hollenhorst,et al.  Forkhead genes in transcriptional silencing, cell morphology and the cell cycle. Overlapping and distinct functions for FKH1 and FKH2 in Saccharomyces cerevisiae. , 2000, Genetics.

[8]  Peter Wayner,et al.  Disappearing Cryptography: Information Hiding: Steganography and Watermarking (2nd Edition) , 2002 .

[9]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

[10]  K. Nasmyth,et al.  A role for the transcription factors Mbp1 and Swi4 in progression from G1 to S phase. , 1993, Science.

[11]  J. Collado-Vides,et al.  A web site for the computational analysis of yeast regulatory sequences , 2000, Yeast.

[12]  Michael Q. Zhang,et al.  SCPD: a promoter database of the yeast Saccharomyces cerevisiae , 1999, Bioinform..

[13]  Saurabh Sinha,et al.  A probabilistic method to detect regulatory modules , 2003, ISMB.

[14]  G. Church,et al.  Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. , 2000, Journal of molecular biology.

[15]  Nicola J. Rinaldi,et al.  Transcriptional Regulatory Networks in Saccharomyces cerevisiae , 2002, Science.

[16]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[17]  Gesine Reinert,et al.  Probabilistic and Statistical Properties of Words: An Overview , 2000, J. Comput. Biol..

[18]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[19]  R. Tjian,et al.  Orchestrated response: a symphony of transcription factors for gene control. , 2000, Genes & development.

[20]  Jun S. Liu,et al.  Discovery of Conserved Sequence Patterns Using a Stochastic Dictionary Model , 2003 .

[21]  Mireille Régnier,et al.  A unified approach to word statistics , 1998, RECOMB '98.

[22]  D. Stillman,et al.  Role of negative regulation in promoter specificity of the homologous transcriptional activators Ace2p and Swi5p , 1996, Molecular and cellular biology.

[23]  Daphne Koller,et al.  Genome-wide discovery of transcriptional modules from DNA sequence and gene expression , 2003, ISMB.

[24]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[25]  Michael Q. Zhang,et al.  Identifying combinatorial regulation of transcription factors and binding motifs , 2004, Genome Biology.

[26]  Jeffrey D. Ullman,et al.  Introduction to automata theory, languages, and computation, 2nd edition , 2001, SIGA.

[27]  B. Birren,et al.  Sequencing and comparison of yeast species to identify genes and regulatory elements , 2003, Nature.

[28]  P. Blaiseau,et al.  Multiple transcriptional activation complexes tether the yeast activator Met4 to DNA , 1998, The EMBO journal.

[29]  G. Church,et al.  Identifying regulatory networks by combinatorial analysis of promoter elements , 2001, Nature Genetics.

[30]  H. Bussemaker,et al.  Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Eric D Siggia,et al.  Computational methods for transcriptional regulation. , 2005, Current opinion in genetics & development.

[32]  S. Fields,et al.  The yeast STE12 protein binds to the DNA sequence mediating pheromone induction. , 1989, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Saurabh Sinha,et al.  A Statistical Method for Finding Transcription Factor Binding Sites , 2000, ISMB.

[34]  Charles Elkan,et al.  Unsupervised learning of multiple motifs in biopolymers using expectation maximization , 1995, Mach. Learn..