Wang, Junwen

Abstract Motivation: Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. Results: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. Availability and Implementation: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS. Contact: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

AbstractThe cranial neural crest and the cranial mesoderm are the source of tissues from which the bone and cartilage of the skull, face and jaws are constructed. The development of the cranial mesoderm is not well studied, which is inconsistent with its importance in craniofacial morphogenesis as a source of precursor tissue of the chondrocranium, muscles, vasculature and connective tissues, mechanical support for tissue morphogenesis, and the signaling activity that mediate interactions with the cranial neural crest. Phenotypic analysis of conditional knockout mouse mutants, complemented by the transcriptome analysis of differentially enriched genes in the cranial mesoderm and cranial neural crest, have identified signaling pathways that may mediate cross-talk between the two tissues. In the cranial mesenchyme, Bmp4 is expressed in the mesoderm cells while its signaling activity could impact on both the mesoderm and the neural crest cells. In contrast, Fgf8 is predominantly expressed in the cranial neural crest cells and it influences skeletal development and myogenesis in the cranial mesoderm. WNT signaling, which emanates from the cranial neural crest cells, interacts with BMP and FGF signaling in monitoring the switch between tissue progenitor expansion and differentiation. The transcription factor Twist1, a critical molecular regulator of many aspects of craniofacial development, coordinates the activity of the above pathways in cranial mesoderm and cranial neural crest tissue compartments.