RBPMetaDB: A comprehensive annotation of mouse RNA-Seq datasets with perturbations of RNA-binding proteins

Abstract

RNA-binding proteins may play a critical role in gene regulation in various diseases or biological processes by controlling post-transcriptional events such as polyadenylation, splicing, and mRNA stabilization via binding activities to RNA molecules. Due to the importance of RNA-binding proteins in gene regulation, a great number of studies have been conducted, resulting in a large amount of RNA-Seq datasets. However, these datasets usually do not have structured organization of metadata, which limits their potentially wide use. To bridge this gap, the metadata of a comprehensive set of publicly available mouse RNA-Seq datasets with perturbed RNA-binding proteins were collected and integrated into a database called RBPMetaDB. This database contains 278 mouse RNA-Seq datasets for a comprehensive list of 163 RNA-binding proteins. These RNAbinding proteins account for only ~10% of all known RNA-binding proteins annotated in Gene Ontology, indicating that most are still unexplored using highthroughput sequencing. This negative information provides a great pool of candidate RNA-binding proteins for biologists to conduct future experimental studies. In addition, we found that DNA-binding activities are significantly enriched among RNA-binding proteins in RBPMetaDB, suggesting that prior studies of these DNA-and RNA-binding factors focus more on DNA-binding activities instead of RNA-binding activities. This result reveals the opportunity to efficiently reuse these data for investigation of the roles of their RNA-binding activities. A web application has also been implemented to enable easy access and wide use of RBPMetaDB. It is expected that RBPMetaDB will be a great 3 resource for improving understanding of the biological roles of RNA-binding proteins.

Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

“…While the present study focused on an application in neuroscience and an application in metabolism, the integrated analysis described here can be generalized to other diseases and BPs. Thus, we expect that similar resources for other regulatory mechanisms and proteins, such as RNA-binding proteins 6 , as well as full-length RNA-Seq and proteogenomics data 39,40,41 , will serve as an important foundation for identifying key factors in human diseases and BPs.…”

“…RNA-related BPs can be a fruitful direction for studying CIT mechanisms. Given that only approximately 15% of SFs currently have related RNA-Seq data 6 , more RNA-Seq data will be generated, which will fuel our integrated analysis to predict more key factors of CIT in the future.…”

“…As a popular method for transcriptome analysis, RNA-sequencing (RNA-Seq) 4 has enabled genome-wide analyses of RNA molecules at a high sequencing depth with high accuracy. It has been used successfully on many mouse models 5,6 , and thousands of RNA-Seq datasets have been generated and released to the public. This massive amount of biological data brings great opportunity for generating prominent biological hypotheses 7,8 .…”

A data mining paradigm for identifying key factors in biological processes using gene expression data

“…For example, around one million series studies are publicly available in GEO. Due to the unstructured nature of the metadata associated with public data, manual curation is required [3][4][5][6][7] , a step that is essential for collecting large-scale gene expression data.…”