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Jungmin Seo

Publications and source records attributed to Jungmin Seo.

3 recordsLinked to original sources

Impact of transcriptional properties on essentiality and evolutionary rate.

We characterized general transcriptional activity and variability of eukaryotic genes from global expression profiles of human, mouse, rat, fly, plants, and yeast. The variability shows a higher degree of divergence between distant species, implying that it is more closely related to phenotypic evolution, than the activity. More specifically, we show that transcriptional variability should be a true indicator of evolutionary rate. If we rule out the effect of translational selection, which seems to operate only in yeast, the apparent slow evolution of highly expressed genes should be attributed to their low variability. Meanwhile, rapidly evolving genes may acquire a high level of transcriptional variability and contribute to phenotypic variations. Essentiality also seems to be correlated with the variability, not the activity. We show that indispensable or highly interactive proteins tend to be present in high abundance to maintain a low variability. Our results challenge the current theory that highly expressed genes are essential and evolve slowly. Transcriptional variability, rather than transcriptional activity, might be a common indicator of essentiality and evolutionary rate, contributing to the correlation between the two variables.

Animals↗

TFExplorer: integrated analysis database for predicted transcription regulatory elements.

UNLABELLED: TFExplorer is a web-based integrated database for predicted regulatory elements in human, mouse and rat. It shows predicted binding sites of transcription factors in the promoter regions, along with their phylogenetic footprinting information. In addition, TFExplorer can search for genes that have a given sequence pattern in their promoter regions using the motif-searching method. AVAILABILITY: TFExplorer is freely available at http://mars.kribb.re.kr:8080/tfExplorer/ CONTACT: sskimb@kribb.re.kr.

Algorithms↗

A domain combination based probabilistic framework for protein-protein interaction prediction.

In this paper, we propose a probabilistic framework to predict the interaction probability of proteins. The notion of domain combination and domain combination pair is newly introduced and the prediction model in the framework takes domain combination pair as a basic unit of protein interactions to overcome the limitations of the conventional domain pair based prediction systems. The framework largely consists of prediction preparation and service stages. In the prediction preparation stage, two appearance probability matrices are constructed. Each matrix holds information on appearance frequencies of domain combination pairs in the interacting and non-interacting sets of protein pairs, respectively. Based on the appearance probability matrix, a probability equation is devised. The equation maps a protein pair to a real number in the range of 0 to 1. Two distributions of interacting and non-interacting sets of protein pairs are obtained using the equation. In the prediction service stage, the interaction probability of a protein pair is predicted using the distributions and the equation. The validity of the prediction model is evaluated for the interacting set of protein pairs in a Yeast organism and artificially generated non-interacting set of protein pairs. When 80% of the set of interacting protein pairs in DIP (Database of Interacting Proteins) is used as a learning set of interacting protein pairs, very high sensitivity (86%) and moderate specificity (56%) are achieved within our framework.

Amino Acid Sequence↗