sars-cov-2

codes for sars-cov-2 analysis

Requirements

tidyverse and ips package are requiring to run varcall.R code

Data preprocessing

Goals for DATA manipulation

• Remove individual specific variants which we do not interests. Those may result of processing bias (base error etc).
• Filter out the sequences which occur to rare. Those may output of bias.

dividing fasta files into single accessions

  perl fasta.divider.pl <fasta file/gzipped fasta file> <output folder> ## divide fasta files into individual files

NTs statistics are calculated by followng scripts.

  perl fasta.char.stat.pl $(ls *) > NTS_stats.tsv ## count nts statistics

the output, NTS_stats.tsv is a headerless tab-separated values files containing following 3 columnes :

Accession	NTs	n
LC529905.1	G	5864
LC529905.1	T	9596
LC529905.1	A	8954
LC529905.1	C	5489
MN938384.1	G	5859
MN938384.1	C	5484
...	...	...

• the output, character.stats, were manipulated by tidyverse package` in R.
• 2-letter or 3-letter codes are considered as Ambiguous letter counted in column nAMB.
• genome size ratio (gRATE) is the ratio against Wuhan-1 (29,903 bps)

outlier checks

    library(tidyverse)
    df <- read_tsv("NTS_stats.tsv",col_names=c("accession","NTs","n"))
    df <- df %>% spread(NTs,n,fill=0) %>% mutate(nAMB=B + D + H + K + M + R + S + V + W + Y) %>%   ## make new column which has counts of non-ACGT character
                                          select(-B,-D,-H,-K,-M,-R,-S,-V,-W, -Y) %>%               ## removing nts columns
                                          mutate(SIZE=A+C+G+T+nAMB,CG=C+G) %>%                     ## calculate genome size and # of CG
                                          mutate(GCRatio=CG/SIZE,gRATE=SIZE/29903)                 ## calculating GCratio and genome size ratio
    summary(df$gRATE)
    
    ## calculate IQR
    QT1 <- summary(df$gRATE)[2]
    QT3 <- summary(df$gRATE)[5]
    IQR <- QT3-QT1
    upperboundary <- QT3 + (3 * IQR)
    lowerboundary <- QT1 - (3 * IQR)
    
    ## check genome size outliers
    df <- df %>%  mutate(outlier=ifelse(gRATE > upperboundary | gRATE < lowerboundary, TRUE,FALSE))   
    
    ## calculate IQR
    QT1 <- summary(df$GCRatio)[2]
    QT3 <- summary(df$GCRatio)[5]
    IQR <- QT3-QT1
    upperboundary <- QT3 + (3 * IQR)
    lowerboundary <- QT1 - (3 * IQR)
    
    df <- df %>% mutate(GCR_outlier=ifelse(GCRatio > upperboundary | GCRatio < lowerboundary, TRUE,FALSE)) %>%
            select(accession,A,C,G,T,nAMB,SIZE,gRATE,outlier,GCRatio,GCR_outlier) %>% rename("gSIZE"="SIZE","gSIZE_outlier"="outlier","gSIZE_ratio"="gRATE")
            
    write_tsv("char.stat-2022-04-14.tsv") ## save file

Pangolin Lineage determination

  cat * > ../FILE.fa
  pangolin FILE.fa --outfile FILE.lineage.csv

Variant calling

This is R code, named varcall.R, which read fasta file and align it to given reference sequence and write .tsv file contaiing position, Reference Allele, and Alternative Allele.

  Rscript varcall.R <Reference> <variant file list> <outfile folder>

Onehot encoding

  Rscript varcall2Onehot.R <FILELIST> <OUTFILE>