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SUMMARY The relationship between the host and its microbiota is challenging to understand because both microbial communities and their environment are highly variable. We developed a set of techniques to address this challenge based on population dynamics and information theory. These methods identified additional bacterial taxa associated with pediatric Crohn's disease and could detect significant changes in microbial communities with fewer samples than previous statistical approaches. We also substantially improved the accuracy of the diagnosis based on the microbiota from stool samples and found that the ecological niche of a microbe predicts its role in Crohn’s disease. Bacteria typically residing in the lumen of healthy patients decrease in disease while bacteria typically residing on the mucosa of healthy patients increase in disease. Our results also show that the associations with Crohn’s disease are evolutionarily conserved and provide a mutual-information-based method to visualize dysbiosis.

The realization of personalized medicine through human induced pluripotent stem cell (iPSC) technology can be advanced by transcriptomics, epigenomics, and bioinformatics that inform on genetic pathways directing tissue development and function. When possible, population diversity should be included in new studies as resources become available. Previously we derived replicate iPSC lines of African American, Hispanic-Latino and Asian self-designated ethnically diverse (ED) origins with normal karyotype, verified teratoma formation, pluripotency biomarkers, and tri-lineage in vitro commitment. Here we perform bioinformatics of RNA-Seq and ChIP-seq pluripotency data sets for two replicate Asian and Hispanic-Latino ED-iPSC lines that reveal differences in generation of contractile cardiomyocytes but similar and robust differentiation to multiple neural, pancreatic, and smooth muscle cell types. We identify shared and distinct genes and contributing pathways in the replicate ED-iPSC lines to enhance our ability to understand how reprogramming to iPSC impacts genes and pathways contributing to cardiomyocyte contractility potential.

Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction’ and is used to calculate the probability of a candidate graph (G) in the structure learning process.Results: Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods.Availability: Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP.Contact: hasan.otu@bilgi.edu.trSupplementary Information: Supplementary data are available at Bioinformatics online.