快速、准确地检测和分类病毒序列分析工具 ViralCC的介绍和详细使用方法, 附带应用脚本_samtools 0x904

 使用viralcc：


在终端中输入以下命令，可以查看viralcc的可用命令和选项：

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

准备输入文件。viralcc支持FASTA和FASTQ格式的输入文件，你可以将你的病毒序列文件准备好。


运行viralcc进行病毒分析测试。在终端中输入以下命令：

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

## 使用分析流程：



指令：处理原始数据 按照本节的指示，对原始shotgun和Hi-C数据进行处理，并生成ViralCC的输入：


1. 清理原始shotgun和Hi-C读段 使用BBTools套件中的bbduk工具去除接头序列，参数为ktrim=r k=23 mink=11 hdist=1 minlen=50 tpe tbo；同时使用bbduk进行质量修剪，参数为trimq=10 qtrim=r ftm=5 minlen=50。另外，通过设置bbduk参数ftl=10来剪切Hi-C读段的前10个核苷酸。使用BBTools套件中的clumpify.sh脚本来移除Hi-C读段中的相同PCR光学重复和Tile边缘重复。
2. 组装shotgun读段 对shotgun文库，采用如MEGAHIT之类的de novo组装软件进行元基因组组装。

 

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. 将Hi-C双端读段比对到组装得到的contigs上 使用如BWA MEM这样的DNA比对软件将Hi-C双端读段比对至已组装的contigs。然后应用samtools（参数为‘view -F 0x904’）移除未比对、补充比对以及二级比对的读段。需要使用'samtools sort'按名称对BAM文件进行排序。

 

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. 从组装的contigs中识别病毒contigs 利用如VirSorter这样的病毒序列检测软件对组装后的contigs进行筛选以识别病毒contigs。

 

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

指令：运行ViralCC

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

参数说明： --min-len: 可接受的最小contig长度（默认值为1000） --min-mapq: 最小可接受的比对质量（默认值为30） --min-match: 接受的比对至少要有N个匹配（默认值为30） --min-k: 确定宿主邻近图的k值下限（默认值为4） --random-seed: Leiden聚类算法的随机种子（默认值为42） --cover (可选): 覆盖现有文件。如果不指定此选项，若检测到输出文件已存在，则会返回错误。 -v (可选): 显示有关ViralCC过程更多详细信息的详尽输出。


输入文件： FASTA\_file: 已组装contig的fasta文件（例如：Test/final.contigs.fa） BAM\_file: Hi-C比对结果的bam文件（例如：Test/MAP\_SORTED.bam） VIRAL\_file: 包含识别出的病毒contigs名称的txt文件，每行一个名称且无表头（例如：Test/viral\_contigs.txt）


输出文件： VIRAL\_BIN: 包含草稿病毒bin的fasta文件夹 cluster\_viral\_contig.txt: 聚类结果，包含两列，第一列是病毒contig名称，第二列是组号 viral\_contig\_info.csv: 病毒contig信息，包含三列（contig名称、contig长度和GC含量） prokaryotic\_contig\_info.csv: 非病毒contig信息，包含三列（contig名称、contig长度和GC含量） viralcc.log: ViralCC日志文件


示例：

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

实用脚本位置:[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.predCategory) vs.pred <- vs.pred[-grep("#",vs.predvs.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.prednode <- gsub(pattern="cov_(\\d+)_",replacement="cov_\\1.",x=vs.prednode,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,