SARS-CoV-2 sequence typing, evolution and signatures of selection using CoVa, a Python-based command-line utility

Abstract

The current global pandemic COVID-19, caused by SARS-CoV-2, has resulted in millions of infections worldwide in a few months. Global efforts to tackle this situation have produced a tremendous body of genomic data, which can be used for tracing transmission routes, characterization of isolates, and monitoring variants with potential for unusual virulence. Several groups have analyzed these genomes using different approaches. However, as new data become available, the research community needs a pipeline to perform a set of routine analyses, that can quickly incorporate new genome sequences and update the analysis reports. We developed a programmatic tool, CoVa, with this objective. It is a fast, accurate and user-friendly utility to perform a variety of genome analyses on hundreds of SARS-CoV-2 sequences. Using CoVa, we define a modified sequence typing nomenclature and identify sites under positive selection. Further analysis identified some peptides and sites showing geographical patterns of selection. Specifically, we show differences in sequence type distribution between sequences from India and those from the rest of the world. We also show that several sites show signatures of positive selection uniquely in sequences from India. Preliminary evolutionary analysis, using features that will be incorporated into CoVa in the near future, show a mutation rate of 7.4 x 10 -4 substitutions/site/year, confirm a temporal signal with a November 2019 origin of SARS-CoV-2, and a heterogeneity in the geographical distribution of Indian samples.

The Impact of Accumulated Mutations in SARS-CoV-2 Variants on the qPCR Detection Efficiency

“…Multiple sequences alignments were performed using the progressive method (FFT-NS-2) implemented in MAFFT (version 7.4) (Katoh et al, 2002). The whole genome mutation analysis was carried out used the pipeline provided by CoVa (version 0.2) software (Ali et al 2020). For substitution analysis, we first deleted the gap generated in the multi-sequence alignments based on the ref NC_045512.2 and then used the CoVa pipeline for substitution calculation (Figure S1).…”

PAN-INDIA 1000 SARS-CoV-2 RNA Genome Sequencing Reveals Important Insights into the Outbreak

“…To understand temporal and geographic patterns in the development of various genetic variants across India, we used a set of ~1,700 genomes combining those sequences by this consortium and others available in GISAID. We first defined sequence types based on the nucleotide sequence of 10 sites as defined by Guan et al (16), and numbered according to the date of their first emergence globally, as described earlier for the CoVa pipeline (17). The most predominant sequence type among Indian samples was ST4 ( Figure 13) which is characterized by two non-synonymous and 1 synonymous mutations (S D614G + RdRp P323L + nsp3 F106F).…”