Subgenomic RNA profiling suggests novel mechanism in coronavirus gene regulation and host adaption.

Fundamental to viral biology is identification and annotation of viral genes and their function. Determining the level of coronavirus gene expression is inherently difficult due to the positive stranded RNA genome and the identification of subgenomic RNAs (sgRNAs) that are required for expression of most viral genes. We developed a bioinformatic pipeline to analyze metatranscriptomic data from 20 independent studies encompassing 588 individual samples and 10 coronavirus species. This comparative analysis defined a core sgRNA repertoire for SARS-CoV-2 and found novel sgRNAs that could encode functional short peptides. Relevant to coronavirus infectivity and transmission, we also observed that the ratio of Spike sgRNA to Nucleocapsid one is highest in SARS-CoV-2, among the ß-coronaviruses examined. Furthermore, the adjustment of this ratio can be made by modifications to the viral RNA replication machinery, representing a form of viral gene regulation that may be involved in host adaption.

SBTD: A Novel Method for Detecting Topological Associated Domains from Hi-C Data.

The development of Hi-C technology has generated terabytes of chromatin interaction data, which bring possibilities for insight study of chromatin structure. Several studies revealed that mammalian chromosomes are folded into topological associated domains (TADs), which are conserved across cell types. Accurate detection of topological associated domains is now a vital process for revealing the relationship between the structure and function of genome organization. Unfortunately, the current TAD detection methods require massive computing resources, careful parameter adjustment and/or encounter inconsistent results. In this paper, we propose a novel method, Spectral-Based TAD Detector (SBTD), and evaluate its performance with a set of widely accepted statistical methods. We treat the chromatin interaction matrix as a graph and first introduce cosine similarity as a measure of the interaction patterns between bins. The results show that SBTD identifies higher quality TADs than the popular methods (DomainCaller, TopDom and SpectralTAD) and the internal bins of TADs identified by SBTD have higher correlation. Besides, The TADs identified by SBTD show a highly similar histone modification signal enrichment pattern at the boundary as reported in the previous literature. Finally, the motif enrichment analysis shows that compared with the background region, the DNA motifs of known insulator proteins are significantly enriched in the TAD boundary region identified by our method, which proves the high performance of our proposed method. Overall, SBTD is much more effective than existing methods with only one easy-to-adjust parameter, cluster number, for which we provide optimization guidelines.