Bedtools是处理基因组信息分析的强大工具集合，其主要功能如下：
bedtools: flexible tools for genome arithmetic and DNA sequence analysis.
usage: bedtools <subcommand> [options]
The bedtools sub-commands include:
[ Genome arithmetic ]
intersect Find overlapping intervals in various ways.
求区域之间的交集，可以用来注释peak，计算reads比对到的基因组区域
不同样品的peak之间的peak重叠情况。
window Find overlapping intervals within a window around an interval.
closest Find the closest, potentially non-overlapping interval.
寻找最近但可能不重叠的区域
coverage Compute the coverage over defined intervals.
计算区域覆盖度
map Apply a function to a column for each overlapping interval.
genomecov Compute the coverage over an entire genome.
merge Combine overlapping/nearby intervals into a single interval.
合并重叠或相接的区域
cluster Cluster (but don't merge) overlapping/nearby intervals.
complement Extract intervals _not_ represented by an interval file.
获得互补区域
subtract Remove intervals based on overlaps b/w two files.
计算区域差集
slop Adjust the size of intervals.
调整区域大小，如获得转录起始位点上下游3 K的区域
flank Create new intervals from the flanks of existing intervals.
sort Order the intervals in a file.
排序，部分命令需要排序过的bed文件
random Generate random intervals in a genome.
获得随机区域，作为背景集
shuffle Randomly redistrubute intervals in a genome.
根据给定的bed文件获得随机区域，作为背景集
sample Sample random records from file using reservoir sampling.
spacing Report the gap lengths between intervals in a file.
annotate Annotate coverage of features from multiple files.
[ Multi-way file comparisons ]
multiinter Identifies common intervals among multiple interval files.
unionbedg Combines coverage intervals from multiple BEDGRAPH files.
[ Paired-end manipulation ]
pairtobed Find pairs that overlap intervals in various ways.
pairtopair Find pairs that overlap other pairs in various ways.
[ Format conversion ]
bamtobed Convert BAM alignments to BED (& other) formats.
bedtobam Convert intervals to BAM records.
bamtofastq Convert BAM records to FASTQ records.
bedpetobam Convert BEDPE intervals to BAM records.
bed12tobed6 Breaks BED12 intervals into discrete BED6 intervals.
[ Fasta manipulation ]
getfasta Use intervals to extract sequences from a FASTA file.
提取给定位置的FASTA序列
maskfasta Use intervals to mask sequences from a FASTA file.
nuc Profile the nucleotide content of intervals in a FASTA file.
[ BAM focused tools ]
multicov Counts coverage from multiple BAMs at specific intervals.
tag Tag BAM alignments based on overlaps with interval files.
[ Statistical relationships ]
jaccard Calculate the Jaccard statistic b/w two sets of intervals.
计算数据集相似性
reldist Calculate the distribution of relative distances b/w two files.
fisher Calculate Fisher statistic b/w two feature files.
[ Miscellaneous tools ]
overlap Computes the amount of overlap from two intervals.
igv Create an IGV snapshot batch script.
用于生成一个脚本，批量捕获IGV截图
links Create a HTML page of links to UCSC locations.
makewindows Make interval "windows" across a genome.
把给定区域划分成指定大小和间隔的小区间 (bin)
groupby Group by common cols. & summarize oth. cols. (~ SQL "groupBy")
分组结算，不只可以用于bed文件。
expand Replicate lines based on lists of values in columns.
split Split a file into multiple files with equal records or base pairs.安装bedtools
ct@ehbio:~$ conda install bedtools获得测试数据集(http://quinlanlab.org/tutorials/bedtools/bedtools.html)
ct@ehbio:~$ mkdir bedtools
ct@ehbio:~$ cd bedtools
ct@ehbio:~$ url=https://s3.amazonaws.com/bedtools-tutorials/web
ct@ehbio:~/bedtools$ curl -O ${url}/maurano.dnaseI.tgz
ct@ehbio:~/bedtools$ curl -O ${url}/cpg.bed
ct@ehbio:~/bedtools$ curl -O ${url}/exons.bed
ct@ehbio:~/bedtools$ curl -O ${url}/gwas.bed
ct@ehbio:~/bedtools$ curl -O ${url}/genome.txt
ct@ehbio:~/bedtools$ curl -O ${url}/hesc.chromHmm.bed交集 (intersect)
查看输入文件，bed格式，至少三列，分别是染色体，起始位置(0-based,
包括)，终止位置
(1-based，不包括)。第四列一般为区域名字，第五列一般为空，第六列为链的信息。更详细解释见http://www.genome.ucsc.edu/FAQ/FAQformat.html#format1。
自己做研究CpG岛信息可以从UCSC的Table Browser获得，具体操作见http://blog.genesino.com/2013/05/ucsc-usages/。
ct@ehbio:~/bedtools$ head -n 3 cpg.bed exons.bed
==> cpg.bed <==
chr1 28735 29810 CpG:_116
chr1 135124 135563 CpG:_30
chr1 327790 328229 CpG:_29
==> exons.bed <==
chr1 11873 12227 NR_046018_exon_0_0_chr1_11874_f 0 +
chr1 12612 12721 NR_046018_exon_1_0_chr1_12613_f 0 +
chr1 13220 14409 NR_046018_exon_2_0_chr1_13221_f 0 +获得重叠区域(既是外显子，又是CpG岛的区域)
ct@ehbio:~/bedtools$ bedtools intersect -a cpg.bed -b exons.bed | head -5
chr1 29320 29370 CpG:_116
chr1 135124 135563 CpG:_30
chr1 327790 328229 CpG:_29
chr1 327790 328229 CpG:_29
chr1 327790 328229 CpG:_29输出重叠区域对应的原始区域(与外显子存在交集的CpG岛)
ct@ehbio:~/bedtools$ bedtools intersect -a cpg.bed -b exons.bed -wa -wb > | head -5- chr1 28735 29810 CpG:_116 chr1 29320 29370
NR_024540_exon_10_0_chr1_29321_r 0 - - chr1 135124 135563 CpG:_30 chr1 134772 139696
NR_039983_exon_0_0_chr1_134773_r 0 - - chr1 327790 328229 CpG:_29 chr1 324438 328581
NR_028322_exon_2_0_chr1_324439_f 0 + - chr1 327790 328229 CpG:_29 chr1 324438 328581
NR_028325_exon_2_0_chr1_324439_f 0 + - chr1 327790 328229 CpG:_29 chr1 327035 328581
NR_028327_exon_3_0_chr1_327036_f 0 +
计算重叠碱基数
ct@ehbio:~/bedtools$ bedtools intersect -a cpg.bed -b exons.bed -wo | head -10- chr1 28735 29810 CpG:_116 chr1 29320 29370
NR_024540_exon_10_0_chr1_29321_r 0 - 50 - chr1 135124 135563 CpG:_30 chr1 134772 139696
NR_039983_exon_0_0_chr1_134773_r 0 - 439 - chr1 327790 328229 CpG:_29 chr1 324438 328581
NR_028322_exon_2_0_chr1_324439_f 0 + 439 - chr1 327790 328229 CpG:_29 chr1 324438 328581
NR_028325_exon_2_0_chr1_324439_f 0 + 439 - chr1 327790 328229 CpG:_29 chr1 327035 328581
NR_028327_exon_3_0_chr1_327036_f 0 + 439 - chr1 713984 714547 CpG:_60 chr1 713663 714068
NR_033908_exon_6_0_chr1_713664_r 0 - 84 - chr1 762416 763445 CpG:_115 chr1 761585 762902
NR_024321_exon_0_0_chr1_761586_r 0 - 486 - chr1 762416 763445 CpG:_115 chr1 762970 763155
NR_015368_exon_0_0_chr1_762971_f 0 + 185 - chr1 762416 763445 CpG:_115 chr1 762970 763155
NR_047519_exon_0_0_chr1_762971_f 0 + 185 - chr1 762416 763445 CpG:_115 chr1 762970 763155
NR_047520_exon_0_0_chr1_762971_f 0 + 185
计算第一个(-a)bed区域有多少个重叠的第二个(-b)bed文件中有多少个区域
ct@ehbio:~/bedtools$ bedtools intersect -a cpg.bed -b exons.bed -c | head
chr1 28735 29810 CpG:_116 1
chr1 135124 135563 CpG:_30 1
chr1 327790 328229 CpG:_29 3
chr1 437151 438164 CpG:_84 0
chr1 449273 450544 CpG:_99 0
chr1 533219 534114 CpG:_94 0
chr1 544738 546649 CpG:_171 0
chr1 713984 714547 CpG:_60 1
chr1 762416 763445 CpG:_115 10
chr1 788863 789211 CpG:_28 9另外还有-v取出不重叠的区域,
-f限定重叠最小比例，-sorted可以对按sort -k1,1 -k2,2n排序好的文件加速操作。
同时对多个区域求交集 (可以用于peak的多维注释)
# -names标注注释来源
# -sorted: 如果使用了这个参数，提供的一定是排序好的bed文件
ct@ehbio:~/bedtools$ bedtools intersect -a exons.bed \
-b cpg.bed gwas.bed hesc.chromHmm.bed -sorted -wa -wb -names cpg gwas chromhmm \
| head -10000 | tail -10- chr1 27632676 27635124
NM_001276252_exon_15_0_chr1_27632677_chromhmm chr1 27633213
27635013 5_Strong_Enhancer - chr1 27632676 27635124
NM_001276252_exon_15_0_chr1_27632677_chromhmm chr1 27635013
27635413 7_Weak_Enhancer - chr1 27632676 27635124 NM_015023_exon_15_0_chr1_27632677_f
chromhmm chr1 27632613 27632813 6_Weak_Enhancer - chr1 27632676 27635124 NM_015023_exon_15_0_chr1_27632677_f
chromhmm chr1 27632813 27633213 7_Weak_Enhancer - chr1 27632676 27635124 NM_015023_exon_15_0_chr1_27632677_f
chromhmm chr1 27633213 27635013 5_Strong_Enhancer - chr1 27632676 27635124 NM_015023_exon_15_0_chr1_27632677_f
chromhmm chr1 27635013 27635413 7_Weak_Enhancer - chr1 27648635 27648882 NM_032125_exon_0_0_chr1_27648636_f cpg
chr1 27648453 27649006 CpG:_63 - chr1 27648635 27648882 NM_032125_exon_0_0_chr1_27648636_f
chromhmm chr1 27648613 27649413 1_Active_Promoter - chr1 27648635 27648882 NR_037576_exon_0_0_chr1_27648636_f cpg
chr1 27648453 27649006 CpG:_63 - chr1 27648635 27648882 NR_037576_exon_0_0_chr1_27648636_f
chromhmm chr1 27648613 27649413 1_Active_Promoter
合并区域
bedtools merge输入的是按sort -k1,1 -k2,2n排序好的bed文件。
只需要输入一个排序好的bed文件，默认合并重叠或邻接区域。
ct@ehbio:~/bedtools$ bedtools merge -i exons.bed | head -n 5
chr1 11873 12227
chr1 12612 12721
chr1 13220 14829
chr1 14969 15038
chr1 15795 15947合并区域并输出此合并后区域是由几个区域合并来的
ct@ehbio:~/bedtools$ bedtools merge -i exons.bed -c 1 -o count | head -n 5
chr1 11873 12227 1
chr1 12612 12721 1
chr1 13220 14829 2
chr1 14969 15038 1
chr1 15795 15947 1合并相距90 nt内的区域，并输出是由哪些区域合并来的
# -c: 指定对哪些列进行操作
# -o: 与-c对应，表示对指定列进行哪些操作
# 这里的用法是对第一列做计数操作，输出这个区域是由几个区域合并来的
# 对第4列做收集操作，记录合并的区域的名字，并逗号分隔显示出来
ct@ehbio:~/bedtools$ bedtools merge -i exons.bed -d 340 -c 1,4 -o count,collapse | head -4
chr1 11873 12227 1 NR_046018_exon_0_0_chr1_11874_f
chr1 12612 12721 1 NR_046018_exon_1_0_chr1_12613_f
chr1 13220 15038 3 NR_046018_exon_2_0_chr1_13221_f,NR_024540_exon_0_0_chr1_14362_r,NR_024540_exon_1_0_chr1_14970_r
chr1 15795 15947 1 NR_024540_exon_2_0_chr1_15796_r计算互补区域
给定一个全集，再给定一个子集，求另一个子集。比如给定每条染色体长度和外显子区域，求非外显子区域。给定基因区，求非基因区。给定重复序列，求非重复序列等。
重复序列区域的获取也可以用上面提供的链接
http://blog.genesino.com/2013/05/ucsc-usages/。
ct@ehbio:~/bedtools$ head genome.txt
chr1 249250621
chr10 135534747
chr11 135006516
chr11_gl000202_random 40103
chr12 133851895
chr13 115169878
chr14 107349540
chr15 102531392
ct@ehbio:~/bedtools$ bedtools complement -i exons.bed -g genome.txt | head -n 5
chr1 0 11873
chr1 12227 12612
chr1 12721 13220
chr1 14829 14969
chr1 15038 15795基因组覆盖广度和深度
计算基因组某个区域是否被覆盖，覆盖深度多少。有下图多种输出格式，也支持RNA-seq数据，计算junction-reads覆盖。
genome.txt里面的内容就是染色体及对应的长度。
# 对单行FASTA，可如此计算
# 如果是多行FASTA，则需要累加
ct@ehbio:~/bedtools$ awk 'BEGIN{OFS=FS="\t"}{\
if($0~/>/) {seq_name=$0;sub(">","",seq_name);} \
else {print seq_name,length;} }' ../bio/genome.fa | tee ../bio/genome.txt
chr1 60001
chr2 54001
chr3 54001
chr4 60001
ct@ehbio:~/bedtools$ bedtools genomecov -ibam ../bio/map.sortP.bam -bga \
-g ../bio/genome.txt | head
# 这个warning很有意思，因为BAM中已经有这个信息了，就不需要提供了
*****
*****WARNING: Genome (-g) files are ignored when BAM input is provided.
*****
# bedgraph文件，前3列与bed相同，最后一列表示前3列指定的区域的覆盖度。
chr1 0 11 0
chr1 11 17 1
chr1 17 20 2
chr1 20 31 3
chr1 31 36 4
chr1 36 43 6
chr1 43 44 7
chr1 44 46 8
chr1 46 48 9
chr1 48 54 10两个思考题：
- 怎么计算有多少基因组区域被测到了？
- 怎么计算平均测序深度是多少？
- 数据集相似性
bedtools jaccard计算的是给定的两个bed文件之间交集区域(intersection)占总区域(union-intersection)的比例(jaccard)和交集的数目(n_intersections)。
ct@ehbio:~/bedtools$ bedtools jaccard \
-a fHeart-DS16621.hotspot.twopass.fdr0.05.merge.bed \
-b fHeart-DS15839.hotspot.twopass.fdr0.05.merge.bed
intersection union-intersection jaccard n_intersections
81269248 160493950 0.50637 130852小思考：1. 如何用bedtools其它工具算出这个结果？2.
如果需要比较的文件很多，怎么充分利用计算资源？
一个办法是使用for循环,
双层嵌套。这种用法也很常见，不管是单层还是双层for循环，都有利于简化重复运算。
ct@ehbio:~/bedtools$ for i in *.merge.bed; do \
for j in *.merge.bed; do \
bedtools jaccard -a $i -b $j | cut -f3 | tail -n +2 | sed "s/^/$i\t$j\t/"; \
done; done >total.similarity另一个办法是用parallel，不只可以批量，更可以并行。
root@ehbio:~# yum install parallel.noarch
# parallel 后面双引号("")内的内容为希望用parallel执行的命令，
# 整体写法与Linux下命令写法一致。
# 双引号后面的 三个相邻冒号 (:::)默认用来传递参数的，可多个连写。
# 每个三冒号后面的参数会被循环调用，而在命令中的引用则是根据其出现的位置，分别用{1}, {2}
# 表示第一个三冒号后的参数，第二个三冒号后的参数。
#
# 这个命令可以替换原文档里面的整合和替换, 相比于原文命令生成多个文件，这里对每个输出结果
# 先进行了比对信息的增加，最后结果可以输入一个文件中。
#
ct@ehbio:~/bedtools$ parallel "bedtools jaccard -a {1} -b {2} | awk 'NR> | cut -f 3 \
| sed 's/^/{1}\t{2}\t/'" ::: `ls *.merge.bed` ::: `ls *.merge.bed` >totalSimilarity.2
# 上面的命令也有个小隐患，并行计算时的输出冲突问题，可以修改为输出到单个文件,再cat到一起
ct@ehbio:~/bedtools$ parallel "bedtools jaccard -a {1} -b {2} | awk 'NR> | cut -f 3 \
| sed 's/^/{1}\t{2}\t/' >{1}.{2}.totalSimilarity_tmp" ::: `ls *.merge.bed` ::: `ls *.merge.bed`
ct@ehbio:~/bedtools$ cat *.totalSimilarity_tmp >totalSimilarity.2
# 替换掉无关信息
ct@ehbio:~/bedtools$ sed -i -e 's/.hotspot.twopass.fdr0.05.merge.bed//' \
-e 's/.hg19//' totalSimilarity.2原文档的命令，稍微有些复杂，利于学习不同命令的组合。使用时推荐使用上面的命令。
ct@ehbio:~/bedtools$ parallel "bedtools jaccard -a {1} -b {2} \
| awk 'NR>1' | cut -f 3 \
> {1}.{2}.jaccard" \
::: `ls *.merge.bed` ::: `ls *.merge.bed`This command will create a single file containing the pairwise Jaccard
measurements from all 400 tests.
find . \
| grep jaccard \
| xargs grep "" \
| sed -e s"/\.\///" \
| perl -pi -e "s/.bed./.bed\t/" \
| perl -pi -e "s/.jaccard:/\t/" \
> pairwise.dnase.txtA bit of cleanup to use more intelligible names for each of the samples.
cat pairwise.dnase.txt \
| sed -e 's/.hotspot.twopass.fdr0.05.merge.bed//g' \
| sed -e 's/.hg19//g' \
> pairwise.dnase.shortnames.txtNow let’s make a 20x20 matrix of the Jaccard statistic. This will allow
the data to play nicely with R.
awk 'NF==3' pairwise.dnase.shortnames.txt \
| awk '$1 ~ /^f/ && $2 ~ /^f/' \
| python make-matrix.py \
> dnase.shortnames.distance.matrix标签：chr1,测序,bed,CpG,exon,ehbio,高通量,Bedtools,bedtools From： https://blog.51cto.com/u_16077014/6252717