Dec 02, 2022
Version 1
Methodology to Define the Origin of SARS-CoV-2 V.1
- David Maison1,2,3,
- Sean Cleveland4,5,
- Vivek R. Nerurkar1,2,3
- 1Department of Tropical Medicine, Medical Microbiology, and Pharmacology;
- 2Pacific Center for Emerging Infectious Diseases Research;
- 3John A. Burns School of Medicine, University of Hawai’i - System, Honolulu, Hawai’i 96813;
- 4Hawai’i Data Science Institute;
- 5Information Technology Services - Cyberinfrastructure, University of Hawai’i - System, Honolulu, Hawai’i 96813
External link: https://doi.org/10.1371/journal.pone.0278287
Protocol Citation: David Maison, Sean Cleveland, Vivek R. Nerurkar 2022. Methodology to Define the Origin of SARS-CoV-2. protocols.io https://dx.doi.org/10.17504/protocols.io.x54v9yqz4g3e/v1
Manuscript citation:
Maison DP, Cleveland SB, et al. Genomic Analysis of SARS-CoV-2 Variants of Concern Circulating in Hawai’i to Facilitate Public-Health Policies. Res Sq. Published online June 9, 2021. doi: 10.21203/rs.3.rs-378702/v3
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: August 07, 2022
Last Modified: December 02, 2022
Protocol Integer ID: 68322
Keywords: SARS-CoV-2, phylogenetics, public health, COVID-19
Funders Acknowledgement:
Pacific Center for Emerging Infectious Diseases Research, COBRE
Grant ID: P30GM114737-05
INBRE, National Institute of General Medical Sciences, NIH
Grant ID: P20GM103466-20S1
NSF grant on the University of Hawai’i MANA High Performance Computing Cluster
Grant ID: #1920304
Abstract
Using the CDC-classified SARS-CoV-2 VOC (B.1.1.7, B.1.351, B.1.427, B.1.429, and P.1), identified in Hawai’i as an example, we demonstrate a method to define the origin of SARS-CoV-2 lineages and VOC. This method works using either open-source or licensed software with either a personal computer or a supercomputer.
Image Attribution
Maison DP, Cleveland SB, et al. Genomic Analysis of SARS-CoV-2 Variants of Concern Circulating in Hawai’i to Facilitate Public-Health Policies. Res Sq. Published online June 9, 2021. doi: 10.21203/rs.3.rs-378702/v3. Created with BioRender.com.
The lineage-defining sequences of SARS-CoV-2 Lineage A and Lineage B act as the most ancestral roots. Lineage A (EPI_ISL_406801) is from GISAID, and Lineage B (MN908947) is from GenBank.
Identify lineages of interest in an area:
filter GISAID by location (e.g.: North America/USA/Hawai’i) and download all sequences. For VOC with >10,000 sequences, GISAID sequences were downloaded in batches due to GISAID maximum download size. Similarly, all geographically similar sequences reported in GenBank were downloaded using the search term SARS-CoV-2 and state abbreviation (e.g., “SARS-CoV-2 HI”) and the sequence length filter (20,000 - 40,000).
Combine the GISAID and GenBank sequences into one .fasta file using AliView, Geneious Prime, or a text editor, and assign lineages using Pangolin Lineage Assigner (pangolin.cog-uk.io).
Determine prevalence of Each Lineage:
Download the results to Microsoft Excel, use advanced filter to copy unique records of lineages to a new column (ex: column M), then use COUNTIF (e.g., =COUNTIF($B$2:$B$1432,M2)) to determine prevalence of each lineage. Alternatively, upload the results to Google Sheets and use the =UNIQUE command (e.g., =UNIQUE(B2:B1432) followed by the above COUNTIF command.
Filter GISAID and GenBank by the lineage of interest (e.g., B.1.429) and download all sequences.
Combine lineage of interest (B.1.429) GenBank sequences, GISAID (B.1.429) sequences, and EPI_ISL_406801 into one fasta file.
Align sequences using multiple alignment using fast Fourier transform (MAFFT) program or server with MN908947 as a reference and do not remove any uninformative sequences and all parameters set as “same as input.
Remove the newly added MN908947 sequence that MAFFT places at the beginning of the alignment using AliView, Geneious Prime, or a text editor. If not, the sRNA toolbox will remove the MN908947 sequence during the duplicate removal step, and Lineage B will not serve as an ancestral root in the phylogenetic tree.
Import Multiple Sequence Alignment (MSA) file into Geneious Prime or AliView, search for the orf1a 5’ start of the entire alignment (5’-atggagagccttgtccctggtttca-3’) and remove the 5’ untranslated region (UTR) by deleting the upstream region (~265 bp) from the MSA. Next, search for ORF10 3’ end (5’-tgtagttaactttaatctcacatag-3’) and remove the entire 3’ UTR by deleting the downstream region (~229 bp) from the MSA.
Create a duplicate file for the MN908947 sequence and remove the 5’ UTR and 3’ UTR from MN908947 as described above.
Using MAFFT, align the trimmed MSA with the trimmed MN908947 as a reference and delete sequences with uncalled nucleotides ‘n’. Set the “remove uninformative sequences” parameter in the MAFFT at >0%.
Using sRNAtoolbox program or server, load the updated alignment to remove duplicate sequences and merge identifications (also referred to as sequence accession numbers) of duplicates. This merger will create “appendages” in the phylogenetic tree where the sRNA toolbox will line up identical sequences together with equal signs (=).
Import the final alignment into Geneious Prime and create an approximately maximum-likelihood phylogenetic tree using the FastTree program. Alternatively, FastTree can run as standalone software, and FastTreeMP is appropriate when multiple CPU cores/threads are available.
Root the tree with Lineage A (EPI_ISL_406801), which should then be the most recent common ancestor (MRCA) to Lineage B (MN908947) if performing phylogenetics on a Lineage B subgroup. Identify the MRCA of each sequence of interest.