Long Amplicon Nanopore Sequencing for Dual-Typing RdRp and VP1 Genes of Norovirus Genogroups I and II in Wastewater

Pipette 25µL of RNA extract into a 96 well PCR plate

Add 45µL (1.8X) of Mag-Bind® Total Pure NGS beads

Pipette carefully 10x to mix

Leave to stand at room temperature for 0h 5m 0s

If there is liquid on the side of the wells, cover the plate with a PCR adhesive seal and briefly spin down plate

Place on magnetic rack for 0h 3m 0s

Remove 68µL of supernatant

Add 80µL of 80% (v/v) ethanol for 0h 0m 30s (make EtOH fresh every time with nuclease free water)

Remove 82µL of supernatant being careful to avoid the pellet

Repeat clean once more

Set pipette to 20µL and remove any residual ethanol

Leave to dry for 0h 2m 0s

Remove from magnetic rack

Add 27µL of molecular grade water and mix by pipetting 10x

Allow to stand for 0h 5m 0s

Place plate back on magnetic rack for 0h 3m 0s

Recover 25µL of the supernatant

Add 2.5µL of LunaScript® RT SuperMix to a 0.2 mL PCR reaction tube placed on ice or on a cool block

Add 10µL of cleaned nucleic acid extract and mix gently by pipetting 5x

Cover or cap the reaction tubes and briefly spin down

Incubate the combined reaction mixture in a thermocycler with a heated lid (105°C) for:

25°C for 0h 2m 0s

55°C for 0h 45m 0s

95°C for 0h 1m 0s

Hold at 4°C

Briefly spin down the reaction tubes

Add 7.5µL of molecular biology grade water to each sample

Reconstitute the lyophilised primers in Table 1 to 100micromolar (µM)using TE buffer 8

Table 1. Norovirus GI and GII Primers

A	B	C	D
Name	Genogroup	F/R	Sequence (5’-3’)
NV4478m	GI	F1	AARYTVCCHATHAARGTTGGNATG
NV4562m	GI	F2	GATGCDGAYTAYACRGCHTGGG
GISKRm	GI	R1,2	CCIACCCAICCATTRTACA
NV4611	GII	F1	CWGCAGCMCTDGAAATCATGG
NV4692	GII	F2	GTGTGRTKGATGTGGGTGACTT
GIISKR	GII	R1,2	CCRCCNGCATRHCCRTTRTACAT

1. first round primers and 2) semi-nested primers

Dilute primers to a working concentration of 10micromolar (µM)in molecular biology grade water and aliquot into suitable volumes to avoid repeated freeze-thaw cycles

Prepare the mastermixes as indicated in Table 2 and Table 3 for the first round PCRs

Table 2 . Forward and reverse primers for the GI and GII first-round PCRs

A	B	C
Genogroup	Forward Primer	Reverse Primer
GI	NV4478m	GISKRm
GII	NV4611	GIISKR

Table 3. Mastermix Recipe for all PCRs

A	B	C
Reagents	Final concentration	Volume (µL)
H20 (molecular biology grade)	-	15.15
PCR Buffer, without Mg (10X)	1	2.5
dNTP (10 mM)	0.2 mM	0.5
MgCl2 (50 mM)	1.5 mM	0.75
Forward primer (10 µM)	0.2 µM	0.5
Reverse primer (10 µM)	0.2 µM	0.5
Platinum Taq polymerase (10 U/µL)	1 unit/tube	0.1

Remove all mastermix components from the freezer and place the polymerase on On ice

Defrost all other components at Room temperature

Briefly vortex and spin down all components

Add the components as outlined in Table 2 and 3 reserving the polymerase until the end

When adding the polymerase, slowly aspirate and pre-wet pipette tip x3

After dispensing the polymerase rinse the pipette tip 10x

Vortex then briefly spin down the prepared mastermix

Distribute 20µL of PCR mastermix 0.2 mL reaction tubes

Add 5µL of cDNA for each sample

Add5µL of molecular biology grade water or positive control cDNA for your PCR positive and negative controls

Seal or cap the reaction tubes then spin down ensuring no bubbles are present

Run the reactions in a thermocycler using the conditions in Table 4 and Table 5 for GI and GII

Table 4. GI first-round PCR cycling conditions

A	B	C	D
Stage	Temperature (°C)	Time	Cycles
Initial denature	95.0	60 s	1
Denature	95.0	30 s	40
Anneal	47.4	30 s
Elongation	72.0	30 s
Final elongation	72.0	7 min	1
Hold	4.0

Table 5. GII first-round PCR cycling conditions

A	B	C	D
Stage	Temperature (°C)	Time	Cycles
Initial denature	95.0	60 s	1
Denature	95.0	30 s	40
Anneal	55.7	30 s
Elongation	72.0	30 s
Final elongation	72.0	7 min	1
Hold	4.0

Prepare the mastermixes as indicated in Table 3 and Table 6 following

Table 6. GI and GII primer pairs for the semi-nested PCR

A	B	C
Genogroup	Forward Primer	Reverse Primer
GI	NV4562m	GISKRm
GII	NV4692	GIISKR

Remove all mastermix components from the freezer and place the polymerase on On ice

Defrost all other components at Room temperature

Briefly vortex and spin down all components

Add the components as outlined in Table 2 and 3 reserving the polymerase until the end

When adding the polymerase, slowly aspirate and pre-wet pipette tip x3

After dispensing the polymerase rinse the pipette tip 10x

Vortex then briefly spin down the prepared mastermix

Distribute 20µL of PCR mastermix into a 96-well plate

Vortex and spin down the plate from the first round PCR

Add 5µL of first-round PCR product to each of the sample wells

Add 5µL of molecular biology grade water for your negative control

Seal or cap the plate then spin down ensuring no bubbles are present

Run using the conditions in Table 7 and Table 8 for GI and GII

Table 7. GI semi-nested PCR cycling conditions

A	B	C	D
Stage	Temperature (°C)	Time	Cycles
Initial denature	95.0	60 s	1
Denature	95.0	30 s	40
Anneal	57.2	30 s
Elongation	72.0	30 s
Final elongation	72.0	7 min	1
Hold	4.0

Table 8. GII semi-nested PCR cycling conditions

A	B	C	D
Stage	Temperature (°C)	Time	Cycles
Initial denature	95.0	60 s	1
Denature	95.0	30 s	40
Anneal	55.7	30 s
Elongation	72.0	30 s
Final elongation	72.0	7 min	1
Hold	4.0

Remove ScreenTape from packaging, ensure there are no bubbles in any of the electrophoresis lanes, flicking ScreenTape to remove bubbles if neccesary

Place ScreenTape into the TapeStation and allow to come to room temperature 0h 30m 0s

Load 10µL of D5000 reagents into the 0.2 mL TapeStation reaction tubes

Add 1µL of ladder into the tube representing position TapeStation position A1

Load 1µL of your positive and negative PCR and process controls into the 0.2 mL TapeStation reaction tubes

Cap tubes, vortex and then spin down ensuring no bubbles are present

Place tubes into the machine and analyse the samples

Remove ExoSAP-IT from the -20°C freezer and allow to defrost On ice

Briefly vortex and spin down reaction tubes or plates from the semi-nested PCR

Briefly vortex and spin down ExoSap-IT

Aliquot 10µL of semi-nested PCR product into a new reaction tube or plate

Add 4µL of ExoSap-IT to each PCR product

Seal or cap reaction vessel

Incubate with a heated lid (105°C):

37°C for 0h 15m 0s

80°C for 0h 15m 0s

Hold at 4°C

Add 198µL High-Sensitivity dsDNA Qubit™ reagents to Qubit tubes for each sample being quantified

Load 2µL of cleaned, semi-nested PCR product onto the side of the tube ensuring residual nucleic acids aren't present on the exterior of the pipette tip

Add 190µL High-Sensitivity dsDNA Qubit™ reagents to Qubit tubes for your standards

Load 10µL of each standard

Cap tubes, vortex and briefly spin down

Check for presence of bubbles, flick tubes and spin down again if necessary

Incubate for 0h 2m 0s and then measure with the Qubit™

Calculate the volume of water required to dilute 8µL of the cleaned semi-nested GI and GII PCR products to 14.83 fmol/µL and 19.94 fmol/µL.

For each sample, convert the GI and GII Qubit derived PCR product concentrations from ng/µL to fmol/µL by inputting into software such as the NEBioCalculator ensuring to enter the amplicon lengths of 1110 bp for GI and 971 bp for GII.

Calculate the GI dilution factor by dividing the fmol/µL of the PCR product by 14.83

Calculate the GII dilution factor by dividing the fmol/µL of the PCR product by 19.94

Calculate the volumes of water required by subtracting 1 from the dilution factor and then multiplying by 8

Vortex and then briefly spin down the cleaned, semi-nested PCR products

Separately aliquot 8µL of cleaned, semi-nested GI and GII PCR products into new reaction tubes

Add the volumes of water calculated in the previous steps to the GI and GII amplicons

Cap the reaction vessels

Briefly vortex and then spin down

In new reaction tubes, for every sample, combine 5.75µL of both the diluted GI and GII PCR products for every sample undergoing analysis

Briefly vortex and then spin down

Perform end-prep and barcoding following the manufacturer’s instructions for ligation sequencing of amplicons in the Native Barcoding Kit 96 V14 by Oxford Nanopore Technologies

Load 45 fmol of library per flow cell, using an amplicon size of 1 kb for molarity calculation

Run sequencing at 260 bps using super-accurate basecalling until ≈60,000 reads per barcode have been generated

Install all the required software using mamba:

$ mamba create --name=amplion_analysis_norovirus duplex-tools=0.2.14 cutadapt=3.4 seqtk=1.3 minimap2=2.24 yacrd=1.0.0 kma=1.4.9 seqkit=2.3.0 samtools=1.13 bedtools=2.30.0 cd-hit=4.8.1 pyfastx=0.8.4

$ mamba create --name=NGSpeciesID medaka=1.2.2 bcftools=1.11 python=3.6.10 perl=5.32.0 openblas=0.3.3 spoa=4.0.7 racon=1.4.20 minimap2=2.17 tensorflow=2.4.1

$ mamba activate NGSpeciesID

$ pip install NGSpeciesID==0.1.3

Edit the NGSpeciesID script:

$ nano ~/mambaforge/envs/NGSpeciesID/bin/NGSpeciesID

Change the behaviour of the abundance_cutoff parameter so that it is based on the minimum coverage, rather than the relative abundance of a sequence within the sample:

-    abundance_cutoff = int( args.abundance_ratio * len(read_array))
+    abundance_cutoff = args.abundance_ratio

Edit NGSpeciesID's consensus module:

$ nano ~/mambaforge/envs/NGSpeciesID/lib/python3.6/site-packages/modules/consensus.py

Change the behaviour of NGSpeciesID so it is possible to output consensus sequences from spoa without polishing the consensus sequences using Medaka:

+    polishing = False
     if args.medaka:
         polishing_pattern = os.path.join(args.outfolder, "medaka_cl_id_*")
+        polishing = True
     elif args.racon:
         polishing_pattern = os.path.join(args.outfolder, "racon_cl_id_*")
+        polishing = True

-    for folder in glob.glob(polishing_pattern):
-        shutil.rmtree(folder)
+    if (polishing == True):
+        for folder in glob.glob(polishing_pattern):
+            shutil.rmtree(folder)

     spoa_pattern = os.path.join(args.outfolder, "consensus_reference_*")
     for file in glob.glob(spoa_pattern):

Create a folder to store results from the analysis.

$ mkdir Analysis_Results

$ cd Analysis_Results

Copy the fastq_pass or pass folder from the sequencing run to the Analysis_Results folder.

Store the name of the barcode and sample to be analysed as a variable:

$ barcodeID=barcode09

$ sampleID=Sample-4_PCR-Norm_PoolingType-1

Activate the correct software environment:

$ mamba activate amplion_analysis_norovirus

Use duplex_tools to split the reads:

$ mkdir  Split_Reads

$ duplex_tools split_on_adapter --threads 12 --allow_multiple_splits \
fastq_pass/${barcodeID} Split_Reads/${barcodeID} Native

Create a text file to store the sequence of different primers being used in the experiment:

$ nano primer_sequences.fasta

Copy the following into the file and then save it

>Long_GI_PCR1
AARYTVCCHATHAARGTTGGNATG...TGTAYAATGGNTGGGTNGG
>Long_GI_PCR2
GATGCDGAYTAYACRGCHTGGG...TGTAYAATGGNTGGGTNGG
>Long_GII_PCR1
CWGCAGCMCTDGAAATCATGG...ATGTAYAAYGGDYATGCNGGYGG
>Long_GII_PCR2
GTGTGRTKGATGTGGGTGACTT...ATGTAYAAYGGDYATGCNGGYGG

Use cutadapt to trim primer sequences from reads:

$ mkdir Trim_Reads

$ cat Split_Reads/${barcodeID}/*_split.fastq.gz > Split_Reads/${barcodeID}/combined.fastq.gz

$ cutadapt -j12 --action=trim -n 1 -e 0.30 -O 12 --revcomp \
-g file:primer_sequences.fasta --discard-untrimmed \
--output Trim_Reads/trimmed-${sampleID}-{name}.fastq.gz \
Split_Reads/${barcodeID}/combined.fastq.gz \
> Trim_Reads/trimmed-${sampleID}.log 2>&1

Update the sampleID and barcodeID variables, and repeat steps described in this section for any other samples in the sequencing library.

Select reads which include primers from the 2nd round of PCR and are longer than 800bp, then randomly sample 90,000 reads for subsequent filtering and chimera removal:

$ cat Trim_Reads/trimmed-${sampleID}-*_G*_PCR2.fastq.gz \
> Trim_Reads/trimmed-${sampleID}-combined-PCR2.fastq.gz

$ seqtk seq -L 800 Trim_Reads/trimmed-${sampleID}-combined-PCR2.fastq.gz |\
seqtk sample - 90000 > Trim_Reads/trimmed-${sampleID}-combined-PCR2-gt800bp.fastq

Use minimap2 to carry out an all-vs-all alignment of reads:

$ mkdir Detect_Chimeric_Reads

$ minimap2 -k19 -Xw19 -e0 -m100 -r100 -I 30M --cap-kalloc=8000m --cap-sw-mem=100m -t 12 \
Trim_Reads/trimmed-${sampleID}-combined-PCR2-gt800bp.fastq \
Trim_Reads/trimmed-${sampleID}-combined-PCR2-gt800bp.fastq \
> Detect_Chimeric_Reads/trimmed-${sampleID}-combined-PCR2.overlap.paf

Use yacrd to identify chimeras and reads with poor support:

$ yacrd -t12 -c 10 -n 0.2 \
-i Detect_Chimeric_Reads/trimmed-${sampleID}-combined-PCR2.overlap.paf \
-o Detect_Chimeric_Reads/trimmed-${sampleID}-combined-PCR2.overlap-gt800bp.report.yacrd 

$ awk '$1=="NotBad"' \
Detect_Chimeric_Reads/trimmed-${sampleID}-combined-PCR2.overlap-gt800bp.report.yacrd |\
cut -f2 \
> Detect_Chimeric_Reads/trimmed-${sampleID}-combined-PCR2.overlap-gt800bp.report.lst

$ seqtk subseq Trim_Reads/trimmed-${sampleID}-combined-PCR2-gt800bp.fastq \
Detect_Chimeric_Reads/trimmed-${sampleID}-combined-PCR2.overlap-gt800bp.report.lst \
> Trim_Reads/trimmed-${sampleID}-combined-PCR2-gt800bp.filtered.fastq

$ rm  Detect_Chimeric_Reads/trimmed-${sampleID}-combined-PCR2.overlap.paf

Update the sampleID and barcodeID variables, and repeat the steps described in this section for any other samples in the sequencing library.

Cluster reads to generate consensus sequences:

$ mkdir -p Consensus_Reads/${sampleID}/

$ mamba activate NGSpeciesID

$ NGSpeciesID --t 20 --q 15 --ont --consensus --max_seqs_for_consensus 100000 \
--abundance_ratio 100 --m 1100 --s 200 \
--fastq Trim_Reads/trimmed-${sampleID}-combined-PCR2-gt800bp.filtered.fastq \
--outfolder Consensus_Reads/${sampleID}/ \
--rc_identity_threshold 0.9 --aligned_threshold 0.8 --mapped_threshold 0.8 \
> Consensus_Reads/${sampleID}/NGSpeciesID.log 2>&1 \
|| echo "No Assembly"

Align reads against consensus sequences, call variants and identify any poorly supported regions:

$ mamba activate amplion_analysis_norovirus

$ cat Consensus_Reads/${sampleID}/consensus_reference_*.fasta \
> Consensus_Reads/${sampleID}_consensus_seqs.fasta

$ kma index -NI -i Consensus_Reads/${sampleID}_consensus_seqs.fasta \
-o Consensus_Reads/${sampleID}_consensus_seqs

$ mkdir Align_vs_Consensus_Seqs

$ kma -i Trim_Reads/trimmed-${sampleID}-combined-PCR2.fastq.gz \
-o Align_vs_Consensus_Seqs/${sampleID}_consensus_seqs_kma \
-t_db Consensus_Reads/${sampleID}_consensus_seqs \
-vcf 1 -ConClave 2 -bcNano -bc 0.7 -bcd 100 -t 20 -md 100 -1t1 \
-ont -ml 900 -xl 1100 -ef -mrs 0.92 -mrc 0.90 \
> Align_vs_Consensus_Seqs/${sampleID}_consensus_seqs_kma.log 2>&1

Update the sampleID and barcodeID variables, and repeat the last two steps for any other samples in the sequencing library.

Combine consensus sequences from every sample into a single file and rename them:

$ mkdir Cluster_Consensus_Sequences

$ cat Align_vs_Consensus_Seqs/*kma.fsa > Cluster_Consensus_Sequences/combined_seqs.fasta

$ seqtk rename Cluster_Consensus_Sequences/combined_seqs.fasta OTU_ \
> Cluster_Consensus_Sequences/combined_seqs_renamed.fasta

Trim consensus sequences to remove any poorly supported regions identified by kma:

$ seqkit locate --bed -P -r -p '^[agct]+' -p '[agct]+$' \
Cluster_Consensus_Sequences/combined_seqs_renamed.fasta \
> Cluster_Consensus_Sequences/combined_seqs_renamed.bed

$ samtools faidx Cluster_Consensus_Sequences/combined_seqs_renamed.fasta

$ bedtools sort -i Cluster_Consensus_Sequences/combined_seqs_renamed.bed \
-g Cluster_Consensus_Sequences/combined_seqs_renamed.fasta.fai \
> Cluster_Consensus_Sequences/combined_seqs_renamed_sorted.bed

$ bedtools complement -i Cluster_Consensus_Sequences/combined_seqs_renamed_sorted.bed \
-g Cluster_Consensus_Sequences/combined_seqs_renamed.fasta.fai \
> Cluster_Consensus_Sequences/combined_seqs_renamed_sorted_inverse.bed

$ bedtools getfasta -fi Cluster_Consensus_Sequences/combined_seqs_renamed.fasta \
-bed Cluster_Consensus_Sequences/combined_seqs_renamed_sorted_inverse.bed \
-fo Cluster_Consensus_Sequences/combined_seqs_renamed_trimmed.fasta

Cluster consensus sequences at 95 percent sequence identity:

$ cd-hit-est -i Cluster_Consensus_Sequences/combined_seqs_renamed_trimmed.fasta \
  -o Cluster_Consensus_Sequences/combined_seqs_renamed_trimmed_clustered.fasta \
  -G 0 -c 0.95 -n 10 -d 0 -M 100000 -T 80 -g 1 -aL 0.9

Align reads against consensus sequences to calculate coverage:

$ mkdir Align_vs_Single_Set_Consensus_Reads

$ kma index -NI \
-i Cluster_Consensus_Sequences/combined_seqs_renamed_trimmed_clustered.fasta \
-o Cluster_Consensus_Sequences/combined_seqs_renamed_trimmed_clustered

$ kma -i Trim_Reads/trimmed-${sampleID}-combined-PCR2.fastq.gz \
-o Align_vs_Single_Set_Consensus_Reads/${sampleID}_consensus_seqs_kma \
-t_db Cluster_Consensus_Sequences/combined_seqs_renamed_trimmed_clustered \
-nc -vcf 2 -ConClave 2 -bcNano -bc 0.7 -bcd 100 -t 20 -md 100 -1t1 \
-ont -ml 900 -xl 1100 -ef -mrs 0.92 -mrc 0.90 \
> Align_vs_Single_Set_Consensus_Reads/${sampleID}_consensus_seqs_kma.log 2>&1

Use the following files for downstream statistical analysis:

The coverage of each representative sequence in each sample:

Align_vs_Single_Set_Consensus_Reads/*_consensus_seqs_kma.mapstat

The sequence of each representative sequence:

Cluster_Consensus_Sequences/combined_seqs_renamed_trimmed_clustered.fasta

Use the CDC's calicivirus typing tool to type each representative sequence:

https://calicivirustypingtool.cdc.gov/

Upload the entire fasta file

Click on 'type it'

Once the input sequences have been typed click on 'Download EXCEL'

Install R (≥ 4.1.2), RStudio (≥ 1.4.1717) and the openxlsx package (≥ 4.2.5.2)

Setup a new project directory using RStudio

Download the coverage files to a sub-folder within the project directory called 'Coverage Results'

Copy the Excel file downloaded from the CDC's calicivirus typing tool website to the project directory

Create a new R script in your RStudio project

Copy the following code into the R script:

# Load library for opening xlsx files 
library(openxlsx)

# List coverage files
coverage_results = 
  list.files(path = 'Coverage Results', pattern = "\\_consensus_seqs_kma.mapstat", 
             recursive = TRUE, include.dirs = FALSE)

# List samples
samples = gsub(x = coverage_results, 
               pattern = "^(.+)_consensus_seqs_kma.mapstat", replacement = "\\1")

# Collate coverage results into a single file
combined_coverage_results = NULL

for (sampleID in 1:length(samples)) {
  print(sampleID)
  coverage_filename = 
    paste0("Coverage Results/", samples[sampleID], "_consensus_seqs_kma.mapstat")
  
  coverage_file = readLines(con = coverage_filename)
  
  coverage_file[[7]] = gsub(x = coverage_file[[7]], pattern = "# ", replacement = "")
  
  noRecords = sum(!grepl(pattern = "##", x = coverage_file,fixed = TRUE)) - 1
  
  if (noRecords > 0) {
    coverage = read.table(file = textConnection(coverage_file), 
                          header = TRUE, stringsAsFactors = FALSE, sep = "\t", skip = 6)
    
    coverage$sample = samples[sampleID]
    
    combined_coverage_results = rbind(combined_coverage_results, coverage)
  }
  
}

# Import file linking reference sequences to genotype
taxa_results_field_names = 
  c('query','name','sequence','length','Genus','Genotype-B-region-score',
    'Genotype-B-region-plot','Genotype','Genotype-C-region-plot',
    'Genotype-C-region-score','Genotype-plot')

combined_taxa_results = 
  read.xlsx(xlsxFile = "calicivirus_typing_output.xlsx", 
            sheet = "sheet1", startRow = 2)

colnames(combined_taxa_results) = taxa_results_field_names

# Merge coverage results with genotype
combined_coverage_results2 = 
  merge(x = combined_coverage_results, 
        y = combined_taxa_results[,c('name','Genotype')], 
        by.x = c('refSequence'), by.y = c('name'), all.x = TRUE)

combined_coverage_results2[
  is.na(combined_coverage_results2$Genotype),
  c('Genotype')
  ] = "Unknown"

# Summarise by genotype
coverage_per_genotype = 
  aggregate(formula = readCount ~ Genotype + refSequence + sample, 
            data = combined_coverage_results2, 
            FUN = sum)

coverage_per_genotype_wide = 
  reshape(data = coverage_per_genotype, 
          v.names = "readCount", 
          idvar = c("Genotype","refSequence"),
          timevar = "sample", direction = "wide")

coverage_per_genotype_wide[is.na(coverage_per_genotype_wide)] = 0

colnames(coverage_per_genotype_wide) = 
  gsub(x = colnames(coverage_per_genotype_wide),
       pattern = "^readCount\\.(.+)$", replacement = "\\1")

# Save results as a csv file
write.csv(x = coverage_per_genotype_wide, 
          file = "coverage_per_genotype_wide.csv", row.names = FALSE)

Run the R script, check for errors and that the output looks correct.

Using the coverage_per_genotype_wide.csv file generated above, for GI and GII independently, sum the norovirus-aligned reads for each sample

Find the median of the total aligned reads per sample across the data set

Filter data on a sample-to-sample basis removing reads associated with consensus sequences that have fewer than 0.1% of the median total aligned reads per sample

Collate a list of all known types of norovirus GI and GII from the Centre for Disease Control’s (CDC) Human Calicivirus Typing Tool – this should be performed prior the analysis of any new data sets to prevent missing recently observed types

Remove all coverage (coverage_per_genotype_wide.csv) and sequence data (combined_seqs_renamed_trimmed_clustered.fasta) relating to consensus sequences that are types not previously reported by the CDC

For each wastewater sample under analysis, annotate the consensus sequences (combined_seqs_renamed_trimmed_clustered.fasta) with the read depths (coverage_per_genotype_wide.csv)

Perform chimera filtering with USEARCH v11 using the following parameters:

-uchime3_denovo -chimeras chimeras.fa

Open chimeras.fa to identify potentially chimeric sequences for that sample

Remove all coverage (coverage_per_genotype_wide.csv) and sequence data (combined_seqs_renamed_trimmed_clustered.fasta) relating to consensus sequences identified as chimeras in chimeras.fa

For each sample, identify types that may be generated from two other (parent) types in the sample using the data in coverage_per_genotype_wide.csv

Assess the putative chimeras and parent sequences using a multiple sequence alignment tool such as NCBI Multiple Sequence Alignment Viewer v1.25.0  with the putative chimera sequence anchored

Check the parent sequences for breakpoints within the terminal or proximal regions of RdRp and VP1 and child-parent sequence similarities ≥95%

Remove all coverage (coverage_per_genotype_wide.csv) and sequence data (combined_seqs_renamed_trimmed_clustered.fasta) relating to consensus sequences identified as putative chimeras which match these criteria