一、 基本介绍
exomePeak2使用bam文件进行peak calling以及peak统计，它整合了meRIP-seq数据分析的常规分析内容：
- 使用scanMeripBAM检查BAM的index。
- 使用exomePeakCalling识别外显子区域的被修饰的peaks。
- normalizeGC计算GC偏倚。
- glmM或glmDM构建线性模型来计算差异位点。
- exportResults输出peak结果。
exomePeak2识别RNA修饰峰和差异峰，并从MeRIP-seq实验的BAM文件中计算峰统计量。应提供transcript annotation（来自TxDb对象或GFF文件），以对外显子进行分析。需要基因组名称或BSgenome对象来执行GC含量偏差校正。如果未提供基因组参数，分析将在不进行GC校正的情况下进行。如果在参数bam_ip_treated和bam_input_treated处提供了处理样本的BAM文件，则将报告峰/位点上差异修饰检测的统计信息。
在默认设置下，exomePeak2会将（差异）修饰分析的结果保存在名为“exomePeak2_output”的文件夹下。生成的结果包括一个BED文件、一个RDS文件和一个CSV表，该表存储了（差异）修饰的峰/位点的位置和统计数据。
二、 使用方法
(1) 使用方法
# 安装
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("exomePeak2")
# 载入
library(exomePeak2)
# 注释文件
gtf = "annotation/genome/Drosophila_melanogaster.BDGP6.32.109.gtf"
# input样本
input_bam <- c("06_markdup/LF4.Input.markdup.bam",
"06_markdup/LF5.Input.markdup.bam")
# ip样本
ip_bam <- c("06_markdup/LF4.IP.markdup.bam",
"06_markdup/LF5.IP.markdup.bam")
# 变量名
name = c("LF4", "LF5")
# 批量callpeak
lapply(1:2, function(x){
dir.create(paste('11_exomepeak/', name[x], sep = ''))
exomePeak2(gff = gtf,
bam_ip = ip_bam[x],
bam_input = input_bam[x],
save_dir = "11_exomepeak/",
experiment_name = name[x],
strandness = "1st_strand",
parallel = 4,
p_cutoff = 1e-10,
fragment_length = 150)
}) -> result
# 两组比较*
exomePeak2(gff = gtf,
bam_ip = ip_bam[5],
bam_input = input_bam[5],
bam_ip_treated = ip_bam[1],
bam_input_treated = input_bam[1],
save_dir = "11_exomepeak/",
experiment_name = name[1],
strandness = "1st_strand",
parallel = 4,
p_cutoff = 1e-10,
fragment_length = 150)
(2) exomePeak2参数
exomePeak2(
bam_ip = NULL, # IP样本的BAM文件目录的字符向量
bam_input = NULL, # input样本的BAM文件目录的字符向量
bam_ip_treated = NULL, # treated IP样本的BAM文件目录的字符向量，仅在差异甲基化分析中需要
bam_input_treated = NULL, # treated input样本的BAM文件目录的字符向量，仅在差异甲基化分析中需要
txdb = NULL, #转录本注释TxDb object
genome = NULL, #参考基因组BSgenome
gff = NULL, #指定基因注释GFF/GTF文件的目录，当TxDb对象不可用时应用该项
strandness = c("unstrand", "1st_strand", "2nd_strand"),
fragment_length = 100, #a positive integer number for the expected fragment length (in bp); default = 100.
bin_size = 25,
step_size = 25,
test_method = c("Poisson", "DESeq2"),
p_cutoff = 1e-10, #峰识别中的p值。Note that when using the test method of DESeq2, a larger p-value cut-off (e.g. 0.001) is often required.
parallel = 1,
plot_gc = TRUE, #saving the plots of bins' GC content v.s. bins' fitted coverage curves
save_output = TRUE,
save_dir = getwd(),
experiment_name = "exomePeak2_output", #在包含所有结果的输出目录中生成的文件夹名称的字符
mode = c("exon", "full_transcript", "whole_genome"), #a character specifies the scope of peak calling on genome.
motif_based = FALSE, #基于motif位点检测修饰，如果设置为T，滑动窗口将被替换为single based sites of the modification motif
motif_sequence = "DRACH" #a character for the motif sequence used for the reference sites
)
(3) 生物学重复取交集
生物学重复取交集，同时过滤掉overlap小于50%的peaks（使用作者提供的一个python2小脚本）。
$ vi exomePeak2_intersect.py
#to filter intersect with proportion less then 0.5
from __future__ import division
import sys
for line in open(sys.argv[1],"r") :
line = line.strip()
info = line.split("\t")
a_len = sum([int(i) for i in info[10].split(",")])
b_len = sum([int(i) for i in info[22].split(",")])
o_len = int(info[24])
if (int(info[1])-int(info[13]))*(int(info[2])-int(info[14])) <= 0 :
print line
else:
if (o_len/a_len) >= 0.5 or (o_len/b_len) >= 0.5 :
print line
$ for i in 'WTF' 'WTM' ; do (bedtools intersect -a 11_exomepeak/$i\4/peaks.bed -b 11_exomepeak/$i\5/peaks.bed -s -split -wo > tmp_file > 11_intersect/$i\_tmp_file) ; done
$ python shellscript/exomePeak2_intersect.py 11_intersect/WTF_tmp_file | cut -f 1,2,3,4,5,6,7,8,9,10,11,12 | sort | uniq > 11_intersect/WTF_intersect.bed
$ rm 11_intersect/*_tmp_file
$ wc -l 11_intersect/*
