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Xuan, J.

Publications and source records attributed to Xuan, J..

2 recordsLinked to original sources

ChIP-BIT2: a software tool to detect weak binding events using a Bayesian integration approach

Transcription factor binding events play important functional roles in gene regulation. It is, however, a challenging task to detect weak binding events since the ambiguity in differentiation of weak binding signals from background signals. We present a software package, ChIP-BIT2, to identify weak binding events using a Bayesian integration approach. By integrating signals from sample and input ChIP-seq data, ChIP-BIT2 can detect both strong and weak binding events at gene promoter, enhancer or the whole genome effectively. The ChIP-BIT2 package has been extensively tested on ChIP-seq data, demonstrating its wide applicability in ChIP-seq data analysis.\n\nAvailability and ImplementationThe ChIP-BIT2 package is available at http://sourceforge.net/projects/chipbitc/.

bioinformatics

MSIGNET: a Metropolis sampling-based method for global optimal significant network identification

In this paper, we propose a novel approach namely MSIGNET to identify subnetworks with significantly expressed genes by integrating context specific gene expression and protein-protein interaction (PPI) data. Specifically, we integrate differential expression of each gene and mutual information of gene pairs in a Bayesian framework and use Metropolis sampling to identify functional interactions. During the sampling process, a conditional probability is calculated given a randomly selected gene to control the network state transition. Our method provides global statistics of all genes and their interactions, and finally achieves a global optimal sub-network. We apply MSIGNET to simulated data and have demonstrated its superior performance over comparable network identification tools. Using a validated Parkinson data set we show that the network identified using MSIGNET is consistent to previously reported results but provides more biology meaningful interpretation of Parkinsons disease. Finally, to study networks related to ovarian cancer recurrence, we investigate two patient data sets. Identified networks from independent data sets show functional consistence. And those common genes and interactions are well supported by current biological knowledge.

bioinformatics