Type:	Package
Title:	Mutation Analysis Toolkit for COVID-19 (Coronavirus Disease 2019)
Version:	0.1.3
Date:	2020-08-29
Description:	A feasible framework for mutation analysis and reverse transcription polymerase chain reaction (RT-PCR) assay evaluation of COVID-19, including mutation profile visualization, statistics and mutation ratio of each assay. The mutation ratio is conducive to evaluating the coverage of RT-PCR assays in large-sized samples. Mercatelli, D. and Giorgi, F. M. (2020) <doi:10.20944/preprints202004.0529.v1>.
Depends:	R (≥ 3.6)
License:	Artistic-2.0
Encoding:	UTF-8
LazyData:	true
Imports:	ggplot2, cowplot, seqinr, stringr, grDevices, graphics, utils, ggpubr, dplyr, VennDiagram
RoxygenNote:	7.1.1
NeedsCompilation:	no
URL:	https://github.com/MSQ-123/CovidMutations
BugReports:	https://github.com/MSQ-123/CovidMutations/issues
Suggests:	testthat
Packaged:	2020-09-11 08:10:05 UTC; lenovo
Author:	Shaoqian Ma [aut, cre], Yongyou Zhang [aut]
Maintainer:	Shaoqian Ma <shaoqianma@qq.com>
Repository:	CRAN
Date/Publication:	2020-09-18 12:00:39 UTC

Calculate the mutation detection rate using different assays

Description

This function is to use the well established assays information to detect mutations in different SARS-CoV-2 genomic sites. The output will be series of figures presenting the mutation profile using a specific assay and a figure for comparison between the mutation detection rate in each primers binding region.

Usage

AssayMutRatio(
  nucmerr = nucmerr,
  assays = assays,
  totalsample = totalsample,
  plotType = "barplot",
  outdir = NULL
)

Arguments

Value

Plot the selected figure type as output.

Examples

data("nucmerr")
data("assays")
Total <- 52 ## Total Cleared GISAID fasta data, sekitseq
#outdir <- tempdir()
#Output the results
AssayMutRatio(nucmerr = nucmerr,
              assays = assays,
              totalsample = Total,
              plotType = "logtrans",
              outdir = NULL)

Bacth assay analysis for last five Nr of primers

Description

Last five nucleotides of primer mutation count/type for any reverse transcription polymerase chain reaction (RT-PCR) primer.

Usage

LastfiveNrMutation(
  nucmerr = nucmerr,
  assays = assays,
  totalsample = totalsample,
  figurelist = FALSE,
  outdir = NULL
)

Arguments

Value

Plot the mutation counts(last five nucleotides for each primer) for each assay as output.

Examples

data("nucmerr")
data("assays")
totalsample <- 434
#outdir <- tempdir()
LastfiveNrMutation(nucmerr = nucmerr,
                   assays = assays,
                   totalsample = totalsample,
                   figurelist = FALSE,
                   outdir = NULL)

Plot mutation counts for certain genes

Description

After annotating the mutations, this function is to plot the counts of mutational events for each gene in the SARS-CoV-2 genome.

Usage

MutByGene(nucmerr = nucmerr, gff3 = gff3, figurelist = FALSE, outdir = NULL)

Arguments

Value

Plot the mutation counts figure for each gene as output.

Examples

data("nucmerr")
data("gene_position")
#outdir <- tempdir()
MutByGene(nucmerr = nucmerr, gff3 = gene_position, figurelist = FALSE, outdir = NULL)
#if figurelist = TRUE, the recommendation for figure display(in pixel)is: width=1650, height=1300

Assays for mutation detection using different primers and probes

Description

These assays include the primer detection ranges in which mutations may occur.

Usage

data(assays)

Format

A dataframe with 10 rows and 7 columns.

References

Kilic T, Weissleder R, Lee H (2019) iScience 23, 101406. (PubMed)

Examples

data(assays)

A list of places in China

Description

The list is used for displacing some original cities' names with "China" in order to make the downstream analysis easier.

Usage

data(chinalist)

Format

A dataframe with 31 rows and 1 column.

Source

This data is created by Zhanglab in Xiamen University.

Examples

data(chinalist)

Mutation annotation results produced by "indelSNP" function

Description

A dataframe which could be used for downstream analysis like mutation statistics description.

Usage

data(covid_annot)

Format

A dataframe with 394 rows and 10 columns.

Source

https://www.gisaid.org/

Examples

data(covid_annot)

Detection of co-occurring mutations using double-assay information

Description

The detection of SARS-CoV-2 is important for the prevention of the outbreak and management of patients. Real-time reverse-transcription polymerase chain reaction (RT-PCR) assay is one of the most effective molecular diagnosis strategies to detect virus in clinical laboratory. It will be more accurate and practical to use double assays to detect some samples with co-occurring mutations.

Usage

doubleAssay(nucmerr = nucmerr, assay1 = assay1, assay2 = assay2, outdir = NULL)

Arguments

Value

Plot three figures in a single panel, including two results of assays and a "venn" plot for co-occurring mutated samples.

Examples

data("nucmerr")
data("assays")
assay1 <- assays[1,]
assay2 <- assays[2,]
#outdir <- tempdir()
doubleAssay(nucmerr = nucmerr,
            assay1 = assay1,
            assay2 = assay2,
            outdir = NULL)

"GFF3" format gene position data for SARS-Cov-2

Description

This "GFF3" data is used for counting the mutations in each gene in virus sample.

Usage

data(gene_position)

Format

A dataframe with 26 rows and 10 columns.

Source

https://www.ncbi.nlm.nih.gov/

Examples

data(gene_position)

"GFF3" format annotation data for SARS-Cov-2

Description

This "GFF3" data is used for annotating the effects of mutations in virus sample.

Usage

data(gff3)

Format

A dataframe with 26 rows and 10 columns.

Source

https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=2697049

Examples

data(gff3)

Global mutational events profiling of proteins

Description

This function is to visualize the global protein mutational pattern in the SARS-CoV-2 genome.

Usage

globalProteinMut(
  covid_annot = covid_annot,
  outdir = NULL,
  figure_Type = "heatmap",
  top = 10,
  country = "global"
)

Arguments

Value

Plot the selected figure type as output.

Examples

data("covid_annot")
outdir <- tempdir()
# make sure the covid_annot is a dataframe
covid_annot <- as.data.frame(covid_annot)
globalProteinMut(covid_annot = covid_annot,
                 outdir = outdir,
                 figure_Type = "heatmap",
                 top = 10,
                 country = "USA")

Global single nucleotide polymorphism (SNP) profiling in virus genome

Description

This function is to visualize the global SNP pattern in the SARS-CoV-2 genome.

Usage

globalSNPprofile(
  nucmerr = nucmerr,
  outdir = NULL,
  figure_Type = "heatmap",
  country = "global",
  top = 5
)

Arguments

Value

Plot the selected figure type as output.

Examples

data("nucmerr")
outdir <- tempdir()
globalSNPprofile(nucmerr = nucmerr,
                 outdir = outdir,
                 figure_Type = "heatmap",
                 country = "global",
                 top = 5)

Provide effects of each single nucleotide polymorphism (SNP), insertion and deletion in virus genome

Description

This function is to annotate the mutational events and indicate their potential effects on the proteins. Mutational events include SNP, insertion and deletion.

Usage

indelSNP(
  nucmer = nucmer,
  saveRda = FALSE,
  refseq = refseq,
  gff3 = gff3,
  annot = annot,
  outdir = NULL
)

Arguments

Value

Write the result as ".csv" file to the specified directory.

Examples

data("nucmer")
# Fix IUPAC codes
nucmer<-nucmer[!nucmer$qvar%in%c("B","D","H","K","M","N","R","S","V","W","Y"),]
nucmer<- mergeEvents(nucmer = nucmer)## This will update the nucmer object
data("refseq")
data("gff3")
annot <- setNames(gff3[,10],gff3[,9])
#outdir <- tempdir()
nucmer<- indelSNP(nucmer = nucmer,
                  saveRda = FALSE,
                  refseq = refseq,
                  gff3 = gff3,
                  annot = annot,
                  outdir = NULL)

Merge neighboring events of single nucleotide polymorphism (SNP), insertion and deletion.

Description

The first step for handling the nucmer object, then effects of mutations can be analysed using "indelSNP" function.

Usage

mergeEvents(nucmer = nucmer)

Arguments

Value

An updated "nucmer" object.

Examples

#The example data:
data("nucmer")
#options(stringsAsFactors = FALSE)

#The input nucmer object can be made by the comment below:
#nucmer<-read.delim("nucmer.snps",as.is=TRUE,skip=4,header=FALSE)
#colnames(nucmer)<-c("rpos","rvar","qvar","qpos","","","","",
#"rlength","qlength","","","rname","qname")
#rownames(nucmer)<-paste0("var",1:nrow(nucmer))

# Fix IUPAC codes
nucmer<-nucmer[!nucmer$qvar%in%c("B","D","H","K","M","N","R","S","V","W","Y"),]
nucmer<- mergeEvents(nucmer = nucmer)## This will update the nucmer object

Plot mutation statistics for nucleiotide

Description

Visualization for the top mutated samples, average mutational counts, top mutated position in the genome, mutational density across the genome and distribution of mutations across countries.

Usage

mutStat(
  nucmerr = nucmerr,
  outdir = NULL,
  figure_Type = "TopMuSample",
  type_top = 10,
  country = FALSE,
  mutpos = NULL
)

Arguments

Value

Plot the selected figure type as output.

Examples

data("nucmerr")
outdir <- tempdir()
mutStat(nucmerr = nucmerr,
        outdir = outdir,
        figure_Type = "TopCountryMut",
        type_top = 10,
        country = FALSE,
        mutpos = NULL)

Mutation information derived from "nucmer" SNP analysis

Description

The "nucmer.snps" variant file is obtained by processing the SARS-Cov-2 sequence from Gisaid website (complete, high coverage only, low coverage exclusion, Host=human, Virus name = hCoV-19) with "seqkit" software and "nucmer" scripts. The example data is downsampled from complete data in 2020-07-28 (0.001 proportion, 52 samples).

Usage

data(nucmer)

Format

A dataframe with 437 rows (mutation sites) and 14 columns.

Source

https://www.gisaid.org/

Examples

data(nucmer)

Preprocess "nucmer" object to add group information

Description

Manipulate the "nucmer" object to make the analysis easier.

Usage

nucmerRMD(nucmer = nucmer, outdir = NULL, chinalist = chinalist)

Arguments

Value

Saving the updated "nucmer" object.

Examples

data("nucmer")
data("chinalist")
#outdir <- tempdir()
nucmerr<- nucmerRMD(nucmer = nucmer, outdir = NULL, chinalist = chinalist)

Preprocessed "nucmer.snps" file using "nucmerRMD" function

Description

A dataset contains some group information subtracted from the "nucmer" object by "nucmerRMD" function in order to best describe the results.

Usage

data(nucmerr)

Format

A dataframe with 437 rows (downsampled mutation sites) and 10 columns.

Source

https://www.gisaid.org/

Examples

data(nucmerr)

Plot the mutation statistics after annotating the "nucmer" object by "indelSNP" function

Description

Basic descriptions for the mutational events.

Usage

plotMutAnno(covid_annot = covid_annot, figureType = "MostMut", outdir = NULL)

Arguments

Value

Plot the selected figure type as output.

Examples

data("covid_annot")
# make sure the covid_annot is a dataframe
covid_annot <- as.data.frame(covid_annot)
#outdir <- tempdir() specify your output directory
plotMutAnno(covid_annot = covid_annot, figureType = "MostMut", outdir = NULL)

Plot the most frequent mutational events for proteins in the SARS-CoV-2 genome

Description

Plot the most frequent mutational events for proteins selected. The protein name should be specified correctly (only for SARS-CoV-2).

Usage

plotMutProteins(
  covid_annot = covid_annot,
  proteinName = "NSP2",
  top = 20,
  outdir = NULL
)

Arguments

Value

Plot the mutational events for selected proteins as output.

Examples

data("covid_annot")
# make sure the covid_annot is a dataframe
covid_annot <- as.data.frame(covid_annot)
#outdir <- tempdir() specify your output directory
plotMutProteins(covid_annot = covid_annot,proteinName = "NSP2", top = 20, outdir = NULL)

SARS-Cov-2 genomic reference sequence from NCBI

Description

This reference sequence is derived from "fasta" file, preprocessed by "read.fasta" function(refseq<-read.fasta("NC_045512.2.fa",forceDNAtolower=FALSE)[[1]]). It is used for annotating mutations in virus samples.

Usage

data(refseq)

Format

"SeqFastadna" characters.

Source

https://pubmed.ncbi.nlm.nih.gov/32015508/

Examples

data(refseq)