Abstract
The annotation of transcription binding sites in new sequenced genomes is an important and challenging problem. We have previously shown how a regression model that linearly relates gene expression levels to the matching scores of nucleotide patterns allows us to identify DNA-binding sites from a collection of co-regulated genes and their nearby non-coding DNA sequences. Our methodology uses Bayesian models and stochastic search techniques to select transcription factor binding site candidates. Here we show that this methodology allows us to identify binding sites in nearby species. We present examples of annotation crossing from Schizosaccharomyces pombe to Schizosaccharomyces japonicus. We found that the eng1 motif is also regulating a set of 9 genes in S. japonicus. Our framework may have an effective interest in conveying information in the annotation process of a new species. Finally we discuss a number of statistical and biological issues related to the identification of binding sites through covariates of genes expression and sequences.
Keywords
- Variable Selection Method
- Pattern Score
- Candidate Motif
- Debaryomyces Hansenii
- Markov Chain Monte Carlo Chain
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Angelini, C., Cutillo, L., De Feis, I., van der Wath, R., Lio’, P. (2007). Identifying Regulatory Sites Using Neighborhood Species. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_1
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DOI: https://doi.org/10.1007/978-3-540-71783-6_1
Publisher Name: Springer, Berlin, Heidelberg
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