In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 Variants of Interest (preprint)

Rapidly evolving viral pathogens such as SARS-CoV-2 continuously accumulate amino acid changes, some of which affect transmissibility, virulence or improve the virus' ability to escape host immunity. Since the beginning of the pandemic and establishment of SARS-CoV-2 as a human pathogen, multiple lineages with concerning phenotypic alterations, so called Variants of Concern (VOCs), have emerged and risen to predominance. To optimize public health management and to ensure the continued efficacy of vaccines, the early detection of such variants of interest is essential. Therefore, large-scale viral genomic surveillance programs have been initiated worldwide, with data being deposited in public repositories in a timely manner. However, technologies for their continuous interpretation are currently lacking. Here, we describe the CoVerage system (www.sarscoverage.org) for viral genomic surveillance, which continuously predicts and characterizes novel and emerging potential Variants of Interest (pVOIs) together with their antigenic and evolutionary alterations. Using the establishment of Omicron and its current sublineages as an example, we demonstrate how CoVerage can be used to quickly identify and characterize such variants. CoVerage can facilitate the timely identification and assessment of future SARS-CoV-2 Variants of Concern.

Smart-Plexer: a breakthrough workflow for hybrid development of multiplex PCR assays (preprint)

Developing multiplex PCR assays requires an extensive amount of experimental testing, the number of which exponentially increases by the number of multiplexed targets. Dedicated efforts must be devoted to the design of optimal multiplex assays for specific and sensitive identification of multiple analytes in a single well reaction. Inspired by data-driven approaches, we reinvent the way of designing and developing multiplex assays by proposing a hybrid, easy-to-use workflow, named Smart-Plexer, which couples empirical testing of singleplex assays and computer simulation of multiplexing. The Smart-Plexer leverages kinetic inter-target distances among amplification curves to generate optimal multiplex PCR primer sets for accurate multi-pathogen identification. The optimal single-channel assays, together with a novel data-driven approach, Amplification Curve Analysis (ACA), were demonstrated to be capable of classifying the presence of desired targets in a single test for seven common respiratory infection pathogens.

The unignorable N protein of COVID-19 (preprint)

With more and more people infected with COVID-19, it was found that the SARS-CoV-2 virus was quite different from SARS-CoV. Most researches have focused on ACE2 and S protein, however, the known variations of these proteins are not enough to explain why there were so many different clinical manifestations between patients infected with such two viruses. Here, the N protein of the two coronaviruses was parallelly analyzed. Through the analysis of N protein structure, protein conserved binding domain, binding site and ligand, and CTL epitope, it was found that N protein may have a unique expression profile in the SARS-CoV-2. For example, mirror structure and RNA binding tendency.