PubMed Health⌕ Search

Biomedical subjects

Kyoung-Jae Won

Publications and source records attributed to Kyoung-Jae Won.

2 recordsLinked to original sources

Zfp423 binds autoregulatory sites in p19 cell culture model.

Zfp423 is a 30 zinc finger transcription factor that forms regulatory complexes with EBF family members and factors targeted by canonical signaling pathways. Zfp423 mutations produce a range of developmental abnormalities in mice and humans related to the ciliopathies. Surprisingly, computational analysis of clustered Zfp423 and partner motifs in conserved genomic sequences predicts enrichment in Zfp423 and Ebf genes. In cell culture models selected for Zfp423 and EBF expression, we identify strong and reproducible occupancy of two Zfp423 intronic sites using chromatin immunoprecipitation with multiple independent antibodies. Both sites are significantly enriched in either quantitative PCR or massively parallel sequencing assays. A site in intron 5 acts as a classical enhancer in transient assays, but does not require the consensus motif for activity, suggesting a redundant or modulatory role for Zfp423 binding in this context. We speculate that Zfp423 may repress this enhancer as part of a developmental ratchet.

Animals↗

Training HMM structure with genetic algorithm for biological sequence analysis.

SUMMARY: Hidden Markov models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimizing the structure of HMMs would be highly desirable. However, this raises two important issues; first, the new HMMs should be biologically interpretable, and second, we need to control the complexity of the HMM so that it has good generalization performance on unseen sequences. In this paper, we explore the possibility of using a genetic algorithm (GA) for optimizing the HMM structure. GAs are sufficiently flexible to allow incorporation of other techniques such as Baum-Welch training within their evolutionary cycle. Furthermore, operators that alter the structure of HMMs can be designed to favour interpretable and simple structures. In this paper, a training strategy using GAs is proposed, and it is tested on finding HMM structures for the promoter and coding region of the bacterium Campylobacter jejuni. The proposed GA for hidden Markov models (GA-HMM) allows, HMMs with different numbers of states to evolve. To prevent over-fitting, a separate dataset is used for comparing the performance of the HMMs to that used for the Baum-Welch training. The GA-HMM was capable of finding an HMM comparable to a hand-coded HMM designed for the same task, which has been published previously.

Algorithms↗