Gene expression rate comparison for multiple high-throughput datasets

Valproic Acid Initiates Transdifferentiation of the Human Ductal Adenocarcinoma Cell-line Panc-1 Into α-Like Cells

Non-mesenchymal pancreatic cells are a potential source for cell replacement. Their transdifferentiation can be achieved by triggering epigenetic remodeling through e. g. post-translational modification of histones. Valproic acid, a branched-chain saturated fatty acid with histone deacetylase inhibitor activity, was linked to the expression of key transcription factors of pancreatic lineage in epithelial cells and insulin transcription. However, the potential of valproic acid to cause cellular reprogramming is not fully understood. To shed further light on it we employed next-generation RNA sequencing, real-time PCR, and protein analyses by ELISA and western blot, to assess the impact of valproic acid on transcriptome and function of Panc-1-cells. Our results indicate that valproic acid has a significant impact on the cell cycle, cell adhesion, histone H3 acetylation, and metabolic pathways as well as the initiation of epithelial-mesenchymal transition through acetylation of histone H3 resulting in α-cell-like characteristics. We conclude that human epithelial pancreatic cells can be transdifferentiated into cells with endocrine properties through epigenetic regulation by valproic acid favoring an α-cell-like phenotype.

A graph-based algorithm for RNA-seq data normalization

The use of RNA-sequencing has garnered much attention in recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from a large number of RNA-seq experiments collectively, due to the normalization problem. Normalization has been challenging due to an inherent circularity, requiring that RNA-seq data be normalized before any pattern of differential (or non-differential) expression can be ascertained; meanwhile, the prior knowledge of non-differential transcripts is crucial to the normalization process. Some methods have successfully overcome this problem by the assumption that most transcripts are not differentially expressed. However, when RNA-seq profiles become more abundant and heterogeneous, this assumption fails to hold, leading to erroneous normalization. We present a normalization procedure that does not rely on this assumption, nor prior knowledge about the reference transcripts. This algorithm is based on a graph constructed from intrinsic correlations among RNA-seq transcripts and seeks to identify a set of densely connected vertices as references. Application of this algorithm on our synthesized validation data showed that it could recover the reference transcripts with high precision, thus resulting in high-quality normalization. On a realistic data set from the ENCODE project, this algorithm gave good results and could finish in a reasonable time. These preliminary results imply that we may be able to break the long persisting circularity problem in RNA-seq normalization.

Gene Ontology based housekeeping gene selection for RNA-seq normalization

RNA-seq analysis provides a powerful tool for revealing relationships between gene expression level and biological function of proteins. In order to identify differentially expressed genes among various RNA-seq datasets obtained from different experimental designs, an appropriate normalization method for calibrating multiple experimental datasets is the first challenging problem. We propose a novel method to facilitate biologists in selecting a set of suitable housekeeping genes for inter-sample normalization. The approach is achieved by adopting user defined experimentally related keywords, GO annotations, GO term distance matrices, orthologous housekeeping gene candidates, and stability ranking of housekeeping genes. By identifying the most distanced GO terms from query keywords and selecting housekeeping gene candidates with low coefficients of variation among different spatio-temporal datasets, the proposed method can automatically enumerate a set of functionally irrelevant housekeeping genes for pratical normalization. Novel and benchmark testing RNA-seq datasets were applied to demostrate that different selections of housekeeping gene lead to strong impact on differential gene expression analysis, and compared results have shown that our proposed method outperformed other traditional approaches in terms of both sensitivity and specificity. The proposed mechanism of selecting appropriate houskeeping genes for inter-dataset normalization is robust and accurate for differential expression analyses.