Mining the transcriptome of target tissues of autoimmune and degenerative pancreatic β-cell and brain diseases to discover therapies

RedRibbon: A new rank-rank hypergeometric overlap for gene and transcript expression signatures

High-throughput omics technologies have generated a wealth of large protein, gene, and transcript datasets that have exacerbated the need for new methods to analyse and compare big datasets. Rank-rank hypergeometric overlap is an important threshold-free method to combine and visualize two ranked lists of P-values or fold-changes, usually from differential gene expression analyses. Here, we introduce a new rank-rank hypergeometric overlap-based method aimed at gene level and alternative splicing analyses at transcript or exon level, hitherto unreachable as transcript numbers are an order of magnitude larger than gene numbers. We tested the tool on synthetic and real datasets at gene and transcript levels to detect correlation and anticorrelation patterns and found it to be fast and accurate, even on very large datasets thanks to an evolutionary algorithm-based minimal P-value search. The tool comes with a ready-to-use permutation scheme allowing the computation of adjusted P-values at low time cost. The package compatibility mode is a drop-in replacement to previous packages. RedRibbon holds the promise to accurately extricate detailed information from large comparative analyses.

REL-NPMI: Exploring genotype and phenotype relationship of pancreatitis based on improved normalized point-by-point mutual information

Pancreatitis is a relatively serious disease caused by the self-digestion of trypsin in the pancreas. The generation of diseases is closely related to gene and phenotype information. Generally, gene-phenotype relations are mainly obtained through clinical experiments, but the cost is huge. With the amount of published biomedical literature increasing exponentially, it carries a wealth of disease-related gene and phenotype information. This study provided an effective way to obtain disease-related gene and phenotype information. To our best knowledge, this work first attempted to explore relationships between genotype and phenotype about the pancreatitis from the computational perspective. It mined 6152 genes and 76,753 pairs of genotype and phenotype extracted from the biomedical literature about pancreatitis using text mining. Based on the above 76,753 pairs, the study proposed an improved normalized point-wise mutual information (REL-NPMI) model to optimize gene-phenotype relations related to pancreatitis, and obtained 12,562 gene-phenotype pairs which may be related to pancreatitis. The extracted top 20 results were validated and evaluated. The experimental results show that the method is promising for exploring pancreatitis' molecular mechanism, thus it provides a computational way for studying pancreatitis' disease pathogenesis. Data resources and the Pancreatitis Gene-Phenotype Association Database are available at http://114.116.4.45:8081/and resources are also available at https://github.com/polipoptbe8023/REL-NPMI.git.