Introduction and detailed usage of ViralCC, a virus sequence analysis tool that can quickly and accurately detect and classify viruses, with application script included

introduce

viralcc is a genomic virus analysis tool that can be used to quickly and accurately detect and classify viral sequences.

github：dyxstat/ViralCC: ViralCC: leveraging metagenomic proximity-ligation to retrieve complete viral genomes (github.com)

 Instruction of reproducing results in ViralCC paper：dyxstat/Reproduce_ViralCC: Instruction of reproducing results in ViralCC paper (github.com)

Install viralcc:

First, make sure you have Python 3.6 or higher installed.

Download the code for viralcc from GitHub. Enter the following command in the terminal:

git clone https://github.com/dyxstat/ViralCC.git

Enter the viralcc folder:

cd viralcc

It is recommended to use mamba or conda to install directly:

#安装前先修改配置文件viralcc_linux_env.yaml，将环境名称修改为自己想要的
#其他的东西不要动

name: viralcc    //修改这个就行了，原来为ViralCC_ENV
channels:
  - bioconda
  - conda-forge
  - defaults
  - r
dependencies:
  - _libgcc_mutex=0.1
  - _openmp_mutex=4.5
  - _r-mutex=1.0.1
  - binutils_impl_linux-64=2.35.1
  - binutils_linux-64=2.35
  - biopython=1.78
  - bwidget=1.9.14
  - bzip2=1.0.8

mamba installation:

mamba env create -f viralcc_linux_env.yaml

 Use viralcc:

Enter the following command in the terminal to view the available commands and options of viralcc:

mamba activate viralcc

 python ./viralcc.py  -h
usage: viralcc.py [-h] {pipeline} ...

ViralCC: a metagenomic proximity-based tool to retrieve complete viral genomes

optional arguments:
  -h, --help  show this help message and exit

commands:
  Valid commands

Prepare input files. viralcc supports input files in FASTA and FASTQ formats, and you can prepare your virus sequence files.

Run viralcc for virus analysis testing. Enter the following command in the terminal:

python ./viralcc.py pipeline -v Test/final.contigs.fa Test/MAP_SORTED.bam Test/viral_contigs.txt Test/out_test

Use the analysis process:

Instructions: Process Raw Data Follow the instructions in this section to process raw shotgun and Hi-C data and generate input for ViralCC:

1. Clean the original shotgun and Hi-C reads using the bbduk tool in the BBTools suite to remove adapter sequences, with the parameter ktrim=rk=23 mink=11 hdist=1 minlen=50 tpe tbo; and also use bbduk for quality trimming, with the parameter trimq= 10 qtrim=r ftm=5 minlen=50. Additionally, cut the first 10 nucleotides of the Hi-C read by setting the bbduk parameter ftl=10. Use the clumpify.sh script from the BBTools suite to remove identical PCR optical repeats and Tile edge repeats in Hi-C reads.

2. Assemble shotgun reads For the shotgun library, use de novo assembly software such as MEGAHIT for metagenomic assembly.

   megahit -1 SG1.fastq.gz -2 SG2.fastq.gz -o ASSEMBLY --min-contig-len 1000 --k-min 21 --k-max 141 --k-step 12 --merge-level 20,0.95

3. Align Hi-C paired-end reads to assembled contigs Use DNA alignment software such as BWA MEM to align Hi-C paired-end reads to assembled contigs. Then apply samtools (parameter 'view -F 0x904') to remove unaligned, supplementary and secondary aligned reads. Need to use 'samtools sort' to sort BAM files by name.

   bwa index final.contigs.fa
   bwa mem -5SP final.contigs.fa hic_read1.fastq.gz hic_read2.fastq.gz > MAP.sam
   samtools view -F 0x904 -bS MAP.sam > MAP_UNSORTED.bam
   samtools sort -n MAP_UNSORTED.bam -o MAP_SORTED.bam

4. Identify viral contigs from assembled contigs Use viral sequence detection software such as VirSorter to screen assembled contigs to identify viral contigs.

   wrapper_phage_contigs_sorter_iPlant.pl -f final.contigs.fa --db 1 --wdir virsorter_output --data-dir virsorter-data

Instructions: Run ViralCC

python ./viralcc.py pipeline [参数] FASTA文件 BAM文件 VIRAL文件 输出目录

Parameter description: --min-len: Minimum acceptable contig length (default value is 1000) --min-mapq: Minimum acceptable alignment quality (default value is 30) --min-match: Accepted alignment There must be at least N matches (default value is 30) --min-k: Determine the lower limit of k value of the host neighbor graph (default value is 4) --random-seed: Random seed of Leiden clustering algorithm (default value is 42 ) --cover (optional): Overwrite existing files. If this option is not specified, an error will be returned if the output file is detected to already exist. -v (optional): Display verbose output with more details about the ViralCC process.

Input file: FASTA_file: fasta file of assembled contigs (for example: Test/final.contigs.fa) BAM_file: bam file of Hi-C comparison results (for example: Test/MAP_SORTED.bam) VIRAL_file: Contains the identified virus contigs txt file of names, one name per line and no header (for example: Test/viral_contigs.txt)

Output file: VIRAL_BIN: fasta folder containing draft virus bin cluster_viral_contig.txt: clustering result, including two columns, the first column is the virus contig name, the second column is the group number viral_contig_info.csv: virus contig information, including three columns (contig name, contig length and GC content) prokaryotic_contig_info.csv: non-viral contig information, containing three columns (contig name, contig length and GC content) viralcc.log: ViralCC log file

Example:

python ./viralcc.py pipeline -v final.contigs.fa MAP_SORTED.bam viral_contigs.txt out_directory

Practical script location: Reproduce_ViralCC/Scripts at main · dyxstat/Reproduce_ViralCC (github.com)

concatenation.py

import os
import io
import sys
import argparse
import Bio.SeqIO as SeqIO
import gzip
import numpy as np
import pandas as pd

def get_no_hidden_folder_list(wd):
    folder_list = []
    for each_folder in os.listdir(wd):
        if not each_folder.startswith('.'):
            folder_list.append(each_folder)

    folder_list_sorte = sorted(folder_list)
    return folder_list_sorte

def main(path ,  output_file):
    file_list = get_no_hidden_folder_list(path)
    bin_num = len(file_list)	    
    for k in range(bin_num):
        seq_file = '%s/%s' % (path , file_list[k])
        if k==0:
            op1 = 'echo ' + '\">BIN_' + str(k) + '\" ' + '> ' + output_file
        else:
            op1 = 'echo ' + '\">BIN_' + str(k) + '\" ' + '>> ' + output_file

        os.system(op1)
        op2 = 'grep ' + '-v ' + '\'>\' ' + seq_file  + ' >> ' + output_file
        os.system(op2)    

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("-p",help="path")
    parser.add_argument("-o",help="output_file")
    args=parser.parse_args()
    main(args.p,args.o)

 find_viral_contig.R

virsorterfile = 'VIRSorter_global-phage-signal.csv'
vs.pred <- read.csv(virsorterfile,quote="",head=F)
vs.head <- read.table(virsorterfile,sep=",",quote="",head=T,comment="",skip=1,nrows=1)
colnames(vs.pred) <- colnames(vs.head)
colnames(vs.pred)[1] <- "vs.id"
vs.cats <- do.call(rbind,strsplit(x=as.character(vs.pred$vs.id[grep("category",vs.pred$vs.id)]),split=" - ",fixed=T))[,2]
vs.num <- grep("category",vs.pred$vs.id)
vs.pred$Category <- paste(c("",rep.int(vs.cats, c(vs.num[-1],nrow(vs.pred)) - vs.num)), vs.pred$Category)
vs.pred <- vs.pred[-grep("#",vs.pred$vs.id),]

vs.pred$node <- gsub(pattern="VIRSorter_",replacement="",x=vs.pred$vs.id)
vs.pred$node <- gsub(pattern="-circular",replacement="",x=vs.pred$node)
vs.pred$node <- gsub(pattern="cov_(\\d+)_",replacement="cov_\\1.",x=vs.pred$node,perl=F)

rownames(vs.pred) = seq(1 , 1393)

vs_phage = vs.pred[1:1338 , ]

phage_name = vs_phage$node

for(i in 1:1338)
{
  temp = paste0(strsplit(phage_name[i],split='_')[[1]][1] , '_' , strsplit(phage_name[i],split='_')[[1]][2])
  phage_name[i] = temp
}

group_name = rep('group0' , 1338)
phage = cbind(phage_name , group_name)

write.table(phage , file = 'viral.txt' ,  sep='\t', row.names = F , col.names = F , quote =FALSE)

plot_graph.R

####################write ggplot figure###############
library(ggplot2)
library(ggpubr)
library(ggforce)

theme_set(theme_bw()+theme(panel.spacing=grid::unit(0,"lines")))


##########柱状图对于不同方法和分类###########
Rank = rep(c('F-score' , 'ARI' , 'NMI' , 'Homogeneity') , each = 5)
Pipeline = rep(c('VAMB' , 'CoCoNet' , 'vRhyme' , 'bin3C' , 'ViralCC'),times = 4)
Number = c(0.198,0.485,0.366,0.404,0.795,
           0.111,0.471,0.302,0.274,0.787,
           0.724,0.742,0.782,0.817,0.929,
           0.570,0.723,0.687,0.691,0.921)

col = c('#8FBC94' , '#4FB0C6', "#4F86C6", "#527F76", '#CC9966')

df <- data.frame(Rank = Rank, Pipeline = Pipeline, Number = Number)
df$Pipeline = factor(df$Pipeline , levels=c('VAMB' , 'CoCoNet' , 'vRhyme' , 'bin3C' , 'ViralCC'))
df$Rank = factor(df$Rank , levels = c('F-score' , 'ARI' , 'NMI', 'Homogeneity'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Pipeline)) + 
  geom_bar(stat = 'identity', position = 'dodge')+
  scale_fill_manual(values = col,limits= c('VAMB' , 'CoCoNet' , 'vRhyme' , 'bin3C' , 'ViralCC'))+
  coord_cartesian(ylim = c(0.05,0.975))+
  labs(x = "Clustering metrics", y = "Scores", title = "The mock human gut dataset")+
  theme(legend.position="bottom",
        legend.title=element_blank(),
        legend.text = element_text(size = 12),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14,face = "bold"),
        axis.title.y = element_text(size = 14,face = "bold"),
        title = element_text(size = 16,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("fig2a.eps", width = 7 , height = 6 , device = cairo_ps)




Rank = rep(c('VAMB' , 'CoCoNet' , 'vRhyme' , 'bin3C' , 'ViralCC'),each = 3)
Pipeline = rep(c('Moderately complete' , 'Substantially complete' ,  'Near-complete'),times = 5)
Number = c(2,4,1,
           1,5,5, 
           6,1,0,
           1,0,5, 
           4,2,26)

col = c("#8FBC94","#77AAAD","#6E7783")

df <- data.frame(Rank = Rank, Pipeline = Pipeline, Number = Number)
df$Pipeline = factor(df$Pipeline , levels=c('Moderately complete' , 'Substantially complete' ,  'Near-complete'))
df$Rank = factor(df$Rank , levels = c('VAMB' , 'CoCoNet' , 'vRhyme' , 'bin3C' , 'ViralCC'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Pipeline)) + 
  geom_bar(stat = 'identity', position = 'stack')+
  scale_fill_manual(values = col,limits= c('Moderately complete' , 'Substantially complete' ,  'Near-complete'))+
  labs(x = "Binning method", y = "Number of viral bins", title = "The mock human gut dataset")+
  theme(legend.position="bottom",
        legend.title=element_blank(),
        legend.text = element_text(size = 12),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14,face = "bold"),
        axis.title.y = element_text(size = 14,face = "bold"),
        title = element_text(size = 16,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("fig2b.eps", width = 7, height = 6, device = cairo_ps)



viral_num = data.frame('number' = c(1, 4 , 1 , 1 , 13),
                         'method' = c('VAMB' , 'CoCoNet' , 'vRhyme' , 'bin3C' , 'ViralCC'))

viral_num$method = factor(viral_num$method , levels=c('VAMB' , 'CoCoNet' , 'vRhyme' , 'bin3C' , 'ViralCC'))



ggplot(data = viral_num, aes(x = method , y = number )) + 
  geom_bar(stat = "identity", position='dodge' , width = 0.9,fill = 'steelblue') +  
  labs(x = 'Binning method', y = 'Number of high-quality vMAGs within the co-host systems', title = "The mock human gut dataset") +
  theme(
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14,face = "bold"),
        axis.title.y = element_text(size = 14,face = "bold"),
        title = element_text(size = 16,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("fig2c.eps", width = 7, height = 6, device = cairo_ps)





##############human gut 2a############
Rank = rep(c('ViralCC' ,'bin3C' , 'vRhyme' , 'CoCoNet' , 'VAMB'),each = 5)
Completeness = rep(c( "≥ 50%", "≥ 60%", "≥ 70%", "≥ 80%" , "≥ 90%"),times = 5)
###Number needs to be 4*5 matrix##
Number = c(11 , 12 , 17  , 7 , 78,
           1 , 0 , 1 , 4 , 33,
           10, 11, 10, 6, 60,
           2, 1 , 3 , 2 , 25,
           10, 11, 14, 15, 69)

col = c("#023FA5" ,"#5465AB" ,"#7D87B9" ,"#A1A6C8" ,"#BEC1D4")[5:1]
df <- data.frame(Rank = Rank, Completeness = Completeness, Number = Number)
df$Completeness = factor(df$Completeness , levels=c("≥ 50%", "≥ 60%", "≥ 70%", "≥ 80%" , "≥ 90%"))
df$Rank = factor(df$Rank , levels = c('ViralCC' ,'bin3C' , 'vRhyme' , 'CoCoNet' , 'VAMB'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Completeness)) + 
  geom_bar(stat = 'identity', position = 'stack')+
  scale_fill_manual(values = col , limits= c("≥ 50%", "≥ 60%", "≥ 70%", "≥ 80%" , "≥ 90%"))+
  labs(x = "Binning method", y = "Number of bins", 
       title = "CheckV results on the real human gut dataset")+
  coord_flip()+
  theme(legend.position="bottom",
        legend.title=element_text(size = 11),
        legend.text = element_text(size = 11),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 13,face = "bold"),
        axis.title.y = element_text(size = 13,face = "bold"),
        title = element_text(size = 14,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("fig3a.eps", width = 6.3, height = 5, device = cairo_ps)



##############cow fecal 2b############
Rank = rep(c('ViralCC' ,'bin3C' , 'vRhyme' , 'CoCoNet' , 'VAMB'),each = 5)
Completeness = rep(c( "≥ 50%", "≥ 60%", "≥ 70%", "≥ 80%" , "≥ 90%"),times = 5)
###Number needs to be 4*5 matrix##
Number = c(21 , 14 , 21  , 9 , 60,
           14 , 17 , 12 , 8 , 31,
           18, 14 , 16 , 14 , 36,
           3, 3 , 2 , 2 , 25,
           19,17,10,8,23)

col = c("#023FA5" ,"#5465AB" ,"#7D87B9" ,"#A1A6C8" ,"#BEC1D4")[5:1]
df <- data.frame(Rank = Rank, Completeness = Completeness, Number = Number)
df$Completeness = factor(df$Completeness , levels=c("≥ 50%", "≥ 60%", "≥ 70%", "≥ 80%" , "≥ 90%"))
df$Rank = factor(df$Rank , levels = c('ViralCC' ,'bin3C' , 'vRhyme' , 'CoCoNet' , 'VAMB'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Completeness)) + 
  geom_bar(stat = 'identity', position = 'stack')+
  scale_fill_manual(values = col , limits= c("≥ 50%", "≥ 60%", "≥ 70%", "≥ 80%" , "≥ 90%"))+
  labs(x = "Binning method", y = "Number of bins", 
       title = "CheckV results on the real cow fecal dataset")+
  coord_flip()+
  theme(legend.position="bottom",
        legend.title=element_text(size = 11),
        legend.text = element_text(size = 11),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 13,face = "bold"),
        axis.title.y = element_text(size = 13,face = "bold"),
        title = element_text(size = 14,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("fig3b.eps", width = 6.3, height = 5, device = cairo_ps)


##############wastewater 2c############
Rank = rep(c('ViralCC' ,'bin3C' , 'vRhyme' , 'CoCoNet' , 'VAMB'),each = 5)
Completeness = rep(c( "≥ 50%", "≥ 60%", "≥ 70%", "≥ 80%" , "≥ 90%"),times = 5)
###Number needs to be 3*5 matrix##
Number = c(30 , 27 , 21  , 17 , 77,
           19, 20 , 11 , 11 , 28,
           14,16,14,15,32,
           2, 8 , 8 , 6 , 38,
           20,34,14,13,58)


col = c("#023FA5" ,"#5465AB" ,"#7D87B9" ,"#A1A6C8" ,"#BEC1D4")[5:1]
df <- data.frame(Rank = Rank, Completeness = Completeness, Number = Number)
df$Completeness = factor(df$Completeness , levels=c("≥ 50%", "≥ 60%", "≥ 70%", "≥ 80%" , "≥ 90%"))
df$Rank = factor(df$Rank , levels = c('ViralCC' ,'bin3C' , 'vRhyme' , 'CoCoNet' , 'VAMB'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Completeness)) + 
  geom_bar(stat = 'identity', position = 'stack')+
  scale_fill_manual(values = col , limits= c("≥ 50%", "≥ 60%", "≥ 70%", "≥ 80%" , "≥ 90%"))+
  labs(x = "Binning method", y = "Number of bins", 
       title = "CheckV results on the real wastewater dataset")+
  coord_flip()+
  theme(legend.position="bottom",
        legend.title=element_text(size = 11),
        legend.text = element_text(size = 11),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 13,face = "bold"),
        axis.title.y = element_text(size = 13,face = "bold"),
        title = element_text(size = 14,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("fig3c.eps", width = 6.35, height = 5, device = cairo_ps)



########Fraction of host by different number of viruses#########

df<-data.frame(group=c('infected by one virus' , 'infected by two viruses', 'infected by three viruses'),
               value=c(25,35,45))
df$group = as.vector(df$group)

ggplot(df,aes(x="",y=value,fill=group))+
  geom_bar(stat="identity")+
  coord_polar("y",start=1) + 
  geom_text(aes(y=
                  c(0,cumsum(value)[-length(value)]),
                label=percent(value/100)),size=5)+
  theme_minimal()+
  theme(axis.title=element_blank(),
        axis.ticks=element_blank(),
        axis.text = element_blank(),
        legend.title = element_blank())+
  scale_fill_manual(values=c("darkgreen","orange","deepskyblue"))




##########Supplementary material###########
########Mock cow fecal dataset#######
Rank = rep(c('F-score' , 'ARI' , 'NMI' , 'Homogeneity') , each = 4)
Pipeline = rep(c( 'CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'),times = 4)
Number = c(0.564, 0.763 , 0.936 , 0.936,
           0.455 ,0.719, 0.926 , 0.926,
           0.796 , 0.885 , 0.969 , 0.963,
           0.661 ,0.806, 0.940 , 1)

col = c('#4FB0C6', "#4F86C6", "#527F76", '#CC9966')

df <- data.frame(Rank = Rank, Pipeline = Pipeline, Number = Number)
df$Pipeline = factor(df$Pipeline , levels=c('CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'))
df$Rank = factor(df$Rank , levels = c('F-score' , 'ARI' , 'NMI', 'Homogeneity'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Pipeline)) + 
  geom_bar(stat = 'identity', position = 'dodge')+
  scale_fill_manual(values = col,limits= c('CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'))+
  coord_cartesian(ylim = c(0.3,1))+
  labs(x = "Clustering metrics", y = "Scores", 
       title = "The mock cow fecal dataset")+
  theme(legend.position="bottom",
        legend.title=element_blank(),
        legend.text = element_text(size = 12),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14,face = "bold"),
        axis.title.y = element_text(size = 14,face = "bold"),
        title = element_text(size = 16,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("sp1a.eps", width = 6, height = 5, device = cairo_ps)




Rank = rep(c('CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'),each = 3)
Pipeline = rep(c('Moderately complete' , 'Substantially complete' ,  'Near-complete'),times = 4)
Number = c(1 , 1 , 3 , 
           3,2,2,
           1, 3 , 5 , 
           0 ,0 , 8 )

col = c("#8FBC94","#77AAAD","#6E7783")

df <- data.frame(Rank = Rank, Pipeline = Pipeline, Number = Number)
df$Pipeline = factor(df$Pipeline , levels=c('Moderately complete' , 'Substantially complete' ,  'Near-complete'))
df$Rank = factor(df$Rank , levels = c('CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Pipeline)) + 
  geom_bar(stat = 'identity', position = 'stack')+
  coord_cartesian(ylim = c(0 , 9))+
  scale_y_discrete(limits = c(0 , 3 , 6 , 9))+
  scale_fill_manual(values = col,limits= c('Moderately complete' , 'Substantially complete' ,  'Near-complete'))+
  labs(x = "Binning method", y = "Number of viral bins", title = "The mock cow fecal dataset")+
  theme(legend.position="bottom",
        legend.title=element_blank(),
        legend.text = element_text(size = 12),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14,face = "bold"),
        axis.title.y = element_text(size = 14,face = "bold"),
        title = element_text(size = 16,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("sp1b.eps", width = 6, height = 5, device = cairo_ps)


##########Supplementary material###########
########Mock wastewater fecal#######
Rank = rep(c('F-score' , 'ARI' , 'NMI' , 'Homogeneity') , each = 4)
Pipeline = rep(c('CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'),times = 4)
Number = c(0.667,0.657,0.858,0.903,
           0.602 ,0.596,0.828,0.891,
           0.806 ,0.843, 0.898,0.937,
           0.687 ,0.746, 0.816,0.881)

col = c('#4FB0C6', "#4F86C6", "#527F76", '#CC9966')

df <- data.frame(Rank = Rank, Pipeline = Pipeline, Number = Number)
df$Pipeline = factor(df$Pipeline , levels=c('CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'))
df$Rank = factor(df$Rank , levels = c('F-score' , 'ARI' , 'NMI', 'Homogeneity'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Pipeline)) + 
  geom_bar(stat = 'identity', position = 'dodge')+
  scale_fill_manual(values = col,limits= c('CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'))+
  coord_cartesian(ylim = c(0.1,0.97))+
  labs(x = "Clustering metrics", y = "Scores", 
       title = "The mock wastewater dataset")+
  theme(legend.position="bottom",
        legend.title=element_blank(),
        legend.text = element_text(size = 12),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14,face = "bold"),
        axis.title.y = element_text(size = 14,face = "bold"),
        title = element_text(size = 16,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("sp1c.eps", width = 6, height = 5, device = cairo_ps)




Rank = rep(c('CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'),each = 3)
Pipeline = rep(c('Moderately complete' , 'Substantially complete' ,  'Near-complete'),times = 4)
Number = c( 5 , 3 , 1 , 
            1,2,2,
           1, 3 , 1 , 
           1 ,3 , 12 )

col = c("#8FBC94","#77AAAD","#6E7783")

df <- data.frame(Rank = Rank, Pipeline = Pipeline, Number = Number)
df$Pipeline = factor(df$Pipeline , levels=c('Moderately complete' , 'Substantially complete' ,  'Near-complete'))
df$Rank = factor(df$Rank , levels = c('CoCoNet' , 'vRhyme', 'bin3C' , 'ViralCC'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Pipeline)) + 
  geom_bar(stat = 'identity', position = 'stack')+
  scale_fill_manual(values = col,limits= c('Moderately complete' , 'Substantially complete' ,  'Near-complete'))+
  labs(x = "Binning method", y = "Number of viral bins", title = "The mock wastewater dataset")+
  theme(legend.position="bottom",
        legend.title=element_blank(),
        legend.text = element_text(size = 12),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14,face = "bold"),
        axis.title.y = element_text(size = 14,face = "bold"),
        title = element_text(size = 16,face = "bold"),
        plot.title = element_text(hjust = 0.5))

ggsave("sp1d.eps", width = 6, height = 5, device = cairo_ps)


##########CheckM results#############
Rank = rep(c('MetaBAT2' , 'CoCoNet' , 'bin3C' , 'ViralCC'),each = 3)
Pipeline = rep(c('Moderately complete' , 'Substantially complete' ,  'Near-complete'),times = 4)
Number = c(3 , 4 , 4  , 
           5 , 3 , 1 , 
           1, 3 , 1 , 
           2 ,2 , 12 )

col = c("#8FBC94","#77AAAD","#6E7783")

df <- data.frame(Rank = Rank, Pipeline = Pipeline, Number = Number)
df$Pipeline = factor(df$Pipeline , levels=c('Moderately complete' , 'Substantially complete' ,  'Near-complete'))
df$Rank = factor(df$Rank , levels = c('MetaBAT2' , 'CoCoNet' , 'bin3C' , 'ViralCC'))


ggplot(data = df, mapping = aes(x = Rank, y = Number, fill = Pipeline)) + 
  geom_bar(stat = 'identity', position = 'stack')+
  scale_fill_manual(values = col,limits= c('Moderately complete' , 'Substantially complete' ,  'Near-complete'))+
  labs(x = "Binning method", y = "Number of bins", title = "Mock wastewater dataset")+
  theme(legend.position="top",
        legend.title=element_blank(),
        legend.text = element_text(size = 11),
        panel.grid.major = element_blank(),   #不显示网格线
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(size = 11),
        axis.text.y = element_text(size = 11),
        axis.title.x = element_text(size = 14,face = "bold"),
        axis.title.y = element_text(size = 14,face = "bold"),
        title = element_text(size = 14,face = "bold"),
        plot.title = element_text(hjust = 0.5))





#######Compute the length of viral contigs########
contig_info = read.csv('contig_viral_info_ww.csv' , sep = ',' , header = F)
min(contig_info[,3])
max(contig_info[,3])



#######Chi-square testing############
tableR = matrix(c(72,96,264,36,38,90,21,24,49,38,42,80),nrow=3)
chisq.test(tableR,correct = F)

removesmalls.pl

## removesmalls.pl
##!/usr/bin/perl
## perl removesmalls.pl 200 contigs.fasta > contigs-l200.fasta
use strict;
use warnings;

my $minlen = shift or die "Error: `minlen` parameter not provided\n";
{
		local $/=">";
    while(<>) {
				chomp;
        next unless /\w/;
        s/>$//gs;
        my @chunk = split /\n/;
        my $header = shift @chunk;
        my $seqlen = length join "", @chunk;
        print ">$_" if($seqlen >= $minlen);
    }
    local $/="\n";
}