Now that we have a BAM file with only the reads we want included, we can do some more sophisticated analysis using bedtools. Bedtools changes from version to version, and here we are using version 2.22, the newest version, and what is currently installed on stampede. You can check what versions of bedtools are installed by using the following command on stampede:
First, log on to the login8 node on stampede and make a directory in scratch called bedtools in your scratch folder. Then copy your filtered BAM file from the samtools section into this folder.
ssh user@login8.stampede.tacc.utexas.edu #if you are not already logged in!
cd $SCRATCH/core_ngs
mkdir bedtools
cd samtools
cp yeast_pairedend_sort.mapped.q1.bam ../bedtools
cd ../bedtools
If you were unable to make the filtered and sorted BAM file from the previous section, you can copy it over from my scratch directory:
Click here to expand...
cd bedtools
cp /scratch/01786/awh394/core_ngs/bedtools/yeast_pairedend_sort.mapped.q1.bam .
Sometimes, especially when working with external data, we need to go from a BAM file back to a fastq file. This can be useful for re-aligning reads using a different aligner, different settings on the original aligner used. It can also be useful for extracting the sequence of interesting regions of the genome after you have manipulated your BAM file.
For this exercise, you'll be using bamtofastq. This function takes an aligned BAM file as input and outputs a fastq format file. You can use the options if you have paired end data to output R1 and R2 reads for your fastq file. This type of function is especially useful if you need to to analyze sequences after you've compared several BAM or bed files.
bedtools bamtofastq -i input.bam -fq output.fastq
Exercise 1: convert BAM to fastq and look at the quality scores
click here to see the code and output
module load bedtools
bedtools bamtofastq -i yeast_pairedend_sort.mapped.q1.bam -fq yeast_pairedend_sort.mapped.q1.fastq #takes 1-2 minutes
more yeast_pairedend_sort.mapped.q1.fastq
Here is an example of two sequences (and their corresponding quality scores):
@HWI-ST1097:127:C0W5VACXX:5:2212:10568:79659
TACCCTCCAATTACCCATATCCAACCCACTGCCACTTACCCTACCATTACCCTACCATCCACCATGACCTACTCACCATACTGTTCTTCTACCCACCATAT
+
CCCFFFFFHHHHHJJJJJIIJJJJIJJIJJIJJIIIIJJJIJJIJJIJJIJJJJJJJJJJIIGGIGEGAEHFEFFEFFFDEEE@CCEDCDDD>ACBBDCA@
@HWI-ST1097:127:C0W5VACXX:5:2115:19940:13862
TAGGGTAAGTTTGAGATGGTATATACCCTACCATCCACCATGACCTACTCACCATACTGTTCTTCTACCCACCATATTGAAACGCTAACAAATGATCGTAA
+
?B@DF2ADHFHHFHJIIIGCIHIGGIJJJJGHIIIGIJEHHIGGGAHEGGFGHIECGIJIIIJIIIIIJJJJJJE>EHDHEEEBCDD?CDDBDDDDDDCDB
As we discussed earlier, the top line is the identifier for the sequence produced, the second line defines which bases were produced, the third line indicates the strand the sequence is aligned to, and the fourth line indicates the ASCII based quality scores for each character in the second line.
While it's useful to be able to look at the fastq file, many analyses will be easiest to perform in bed format. Bed format is a simple tab delimited format that designates various properties about segments of the genome, defined by the chromosome, start coordinates and end coordinates. Bedtools provides a simple utility to convert BAM files over into bed files, termed bamtobed.
bedtools bamtobed -i input.bam > output.bed #output to a file
bedtools bamtobed -i input.bam | more #output to more
Note that the output will be piped to standard out unless you redirect to a program (head, more, less) or a file (output.bed). Now we'll put this example to use and convert our filtered BAM file from the samtools section into a bed file.
Exercise 2: Convert the filtered yeast paired end BAM to bed using bamtobed, look at your file in more, and find the number of lines in the file
Hint: direct the output to a file first, then use more to look at the converted file visually; use ctrl+c to quit more.
click to see the code and output
module load bedtools #if you haven't loaded it in for this session
bedtools bamtobed -i yeast_pairedend_sort.mapped.q1.bam > yeast_pairedend_sort.mapped.q1.bed
more yeast_pairedend_sort.mapped.q1.bed #to examine the bed file visually
wc -l yeast_pairedend_sort.mapped.q1.bed #to get the number of lines in a file
Here is what my output looks like:
wc -l yeast_pairedend_sort.mapped.q1.bed
528976 yeast_pairedend_sort.mapped.q1.bed
more yeast_pairedend_sort.mapped.q1.bed
chrI 219 320 HWI-ST1097:127:C0W5VACXX:5:2212:10568:79659/1 37 +
chrI 266 344 HWI-ST1097:127:C0W5VACXX:5:2115:19940:13862/2 29 +
chrI 368 469 HWI-ST1097:127:C0W5VACXX:5:2115:19940:13862/1 29 -
chrI 684 785 HWI-ST1097:127:C0W5VACXX:5:2212:10568:79659/2 37 -
chrI 871 955 HWI-ST1097:127:C0W5VACXX:5:1103:4918:43976/2 29 +
chrI 871 948 HWI-ST1097:127:C0W5VACXX:5:1104:2027:42518/2 29 +
chrI 871 948 HWI-ST1097:127:C0W5VACXX:5:1109:3153:38695/2 29 +
chrI 871 948 HWI-ST1097:127:C0W5VACXX:5:2109:6222:11815/2 29 +
chrI 871 948 HWI-ST1097:127:C0W5VACXX:5:2113:5002:59471/2 29 +
chrI 871 948 HWI-ST1097:127:C0W5VACXX:5:2113:7803:87146/2 29 +
chrI 971 1072 HWI-ST1097:127:C0W5VACXX:5:1103:4918:43976/1 29 -
chrI 978 1079 HWI-ST1097:127:C0W5VACXX:5:1104:2027:42518/1 29 -
chrI 978 1079 HWI-ST1097:127:C0W5VACXX:5:1109:3153:38695/1 29 -
chrI 978 1079 HWI-ST1097:127:C0W5VACXX:5:2109:6222:11815/1 29 -
chrI 978 1079 HWI-ST1097:127:C0W5VACXX:5:2113:5002:59471/1 29 -
chrI 978 1079 HWI-ST1097:127:C0W5VACXX:5:2113:7803:87146/1 29 -
chrI 978 1079 HWI-ST1097:127:C0W5VACXX:5:2203:1231:50183/1 37 -
Note the "stacks" of reads that are occurring on similar coordinates on the same strand of the genome. We'll deal with those in the bedtools merge section.
See also: bedtools bedtobam, if you need to get back to a bam file from a bed file (some programs take bam files as input). Documentation here.
One way of characterizing data is to understand what percentage of the genome your data covers. What type of experiment you performed should affect the coverage of your data. A ChIP-seq experiment will cover binding sites, and a RNA-seq experiment will cover expressed transcripts. Bedtools coverage allows you to compare one bed file to another and compute the breadth and depth of coverage.
bedtools coverage -a experiment.bed -b reference_file.bed
The resulting output will contain several additional columns which summarize this information:
bedtools coverage output
After each interval in B, coverageBed will report:
- The number of features in A that overlapped (by at least one base pair) the B interval.
- The number of bases in B that had non-zero coverage from features in A.
- The length of the entry in B.
- The fraction of bases in B that had non-zero coverage from features in A.
For this exercise, we'll use a bed file that summarizes the S. cerevisiae genome, version 3 (aka sacCer3). For this class, I've made a bed file out of the genome, using the file sacCer3.chrom.sizes. First go and copy the file over from my scratch directory:
cd bedtools #if you aren't already there
cp /scratch/01786/awh394/core_ngs/day4_2015/sacCer3.chrom.sizes.bed .
Use more to take a quick look at the file...
more sacCer3.chrom.sizes.bed
chrIV 1 1531933
chrXV 1 1091291
chrVII 1 1090940
chrXII 1 1078177
chrXVI 1 948066
chrXIII 1 924431
chrII 1 813184
chrXIV 1 784333
chrX 1 745751
chrXI 1 666816
chrV 1 576874
chrVIII 1 562643
chrIX 1 439888
chrIII 1 316620
chrVI 1 270161
chrI 1 230218
chrM 1 85779
The format is bed3 - just chrom, start (which is always 1) and stop, which is always the length of the chromosome, for this type of bed file.
Now use bedtools coverage to find the coverage of the file output.bed over the sacCer3 genome and examine the output coverage.
Exercise 3: Find the coverage of your bed file over the sacCer3 genome
click here to see the code for bedtools coverage
module load bedtools #again, if not already loaded
bedtools coverage -a yeast_pairedend_sort.mapped.q1.bed -b sacCer3.chrom.sizes.bed > sacCer3coverage.bed
more sacCer3coverage.bed #this file should have 17 lines, one for each chromosome
And here is what my output looks like:
more sacCer3coverage.bed
chrI 1 230218 7972 128701 230217 0.5590421
chrII 1 813184 35818 539222 813183 0.6631004
chrIII 1 316620 13701 199553 316619 0.6302623
chrIV 1 1531933 70633 1026387 1531932 0.6699951
chrIX 1 439888 15953 276571 439887 0.6287319
chrM 1 85779 3264 58599 85778 0.6831472
chrV 1 576874 26918 381078 576873 0.6605926
chrVI 1 270161 10662 167222 270160 0.6189740
chrVII 1 1090940 49762 722821 1090939 0.6625677
chrVIII 1 562643 23424 356421 562642 0.6334774
chrX 1 745751 30743 472357 745750 0.6333986
chrXI 1 666816 27950 446567 666815 0.6697015
chrXII 1 1078177 48155 658373 1078176 0.6106359
chrXIII 1 924431 40054 618798 924430 0.6693833
chrXIV 1 784333 32565 513382 784332 0.6545468
chrXV 1 1091291 47871 710376 1091290 0.6509507
chrXVI 1 948066 43531 612122 948065 0.6456540
It's worth noting that just computing coverage over the genome isn't the most useful thing, but you might compute coverage over a set of genes or regions of interest. Coverage is really useful coupled with intersect or subtract as well.
When we originally examined the bed files produced from our BAM file, we can see many reads that overlap over the same interval. While this level of detail is useful, for some analyses, we can collapse each read into a single line, and indicate how many reads occurred over that genomic interval. We can accomplish this using bedtools merge.
bedtools merge [OPTIONS] -i experiment.bed > experiment.merge.bed
Bedtools merge also directs the output to standard out, to make sure to point the output to a file or a program. While we haven't discussed the options for each bedtools function in detail, here they are very important. Many of the options define what to do with each column (-c) of the output (-o). This defines what type of operation to perform on each column, and in what order to output the columns. Standard bed6 format is chrom, start, stop, name, score, strand and controlling column operations allows you to control what to put into each column of output. The valid operations defined by the -o operation are as follows:
valid operations using the -o option
- sum, min, max, absmin, absmax,
- mean, median,
- collapse (i.e., print a delimited list (duplicates allowed)),
- distinct (i.e., print a delimited list (NO duplicates allowed)),
- count
- count_distinct (i.e., a count of the unique values in the column)
For this exercise, we'll be summing the number of reads over a region to get a score column, using distinct to choose a name, and using distinct again to keep track of the strand. For the -c options, define which columns to operate on, in the order you want the output. In this case, to keep the standard bed format, we'll list as -c 5,4,6 and -o count_distinct,sum,distinct, to keep the proper order of name, score, strand. Strandedness can also be controlled with merge, using the -s option.
Exercise 4: Use bedtools merge to merge an experiment, look at the output and see how many lines there are in the file.
Hint: make sure to remove whitespace between lists for the -c and -o options!
click here to see the bedtools merge code and output
bedtools merge -s -c 4,5,6 -o count_distinct,sum,distinct -i yeast_pairedend_sort.mapped.q1.bed > yeast_pairedend_sort.mapped.q1.merge.bed
more yeast_pairedend_sort.mapped.q1.merge.bed
wc -l yeast_pairedend_sort.mapped.q1.merge.bed
wc -l yeast_pairedend_sort.mapped.q1.merge.bed
40319 yeast_pairedend_sort.mapped.q1.merge.bed #without the -s option
76601 yeast_pairedend_sort.mapped.q1.merge.bed #with the -s option
more yeast_pairedend_sort.mapped.q1.merge.bed
chrI 219 344 2 66 +
chrI 368 469 1 29 -
chrI 684 785 1 37 -
chrI 871 955 6 174 +
chrI 971 1079 7 211 -
chrI 1216 1322 6 157 +
chrI 1347 1437 6 157 -
chrI 2892 2993 14 406 +
chrI 3010 3111 1 37 +
chrI 3013 3107 14 406 -
One useful way to compare two experiments (especially biological replicates, or similar experiments in two yeast strains/cell lines/mouse strains) is to compare where reads in one experiment overlap with reads in another experiment. Bedtools offers a simple way to do this using the intersect function.
bedtools intersect [OPTIONS] -a <FILE> \
-b <FILE1, FILE2, ..., FILEN>
The intersect function has many options that control how to report the intersection. We'll be focusing on just a few of these options, listed below.
-a and -b indicate what files to intersect. in -b, you can specify one, or several files to intersect with the file specified in -a.
bedtools intersect options
- wa: Write the original entry in A for each overlap.
- wb: Write the original entry in B for each overlap. Useful for knowing what A overlaps. Restricted by -f and -r.
- loj: Perform a “left outer join”. That is, for each feature in A report each overlap with B. If no overlaps are found, report a NULL feature for B.
- wo: Write the original A and B entries plus the number of base pairs of overlap between the two features. Only A features with overlap are reported. Restricted by -f and -r.
- wao: Write the original A and B entries plus the number of base pairs of overlap between the two features. However, A features w/o overlap are also reported with a NULL B feature and overlap = 0. Restricted by -f and -r.
- f: Minimum overlap required as a fraction of A. Default is 1E-9 (i.e. 1bp).
- v: Only report those entries in A that have no overlap in B. Restricted by -f and -r. Useful to report what doesn't overlap, the inverse of typical usage.
- names: When using multiple databases (-b), provide an alias for each that will appear instead of a file Id when also printing the DB record.
In this section, we'll intersect two human experiments - one from sequencing RNA, and one from sequencing micro RNA. Copy these files over to your directory:
cds
mkdir intersect
cd intersect
cp /scratch/02423/nsabell/core_ngs/alignment/bam/human_rnaseq_bwa.bam .
cp /scratch/02423/nsabell/core_ngs/alignment/bam/human_mirnaseq_hg19.bam .
click here to see my output on copying files
-rwxrwxr-x 1 awh394 G-801021 19M May 27 14:17 human_mirnaseq_hg19.bam
-rwxrwxr-x 1 awh394 G-801021 6.6M May 27 14:17 human_rnaseq_bwa.bam
Before we can intersect these files, we need to perform the pipeline we used in samtools to index, sort and filter the files, and bedtools to convert from BAM over to bed, then collapse down the files using merge. Below is a little workflow to help you through it on the files you just copied above.
Click here to see the samtools/bedtools workflow
My output (for length of bed files) is in the comments.
module load samtools #if you haven't loaded it up this session
#sort both files
samtools sort human_mirnaseq_hg19.bam human_mirnaseq_hg19_sort # will take 1-2 minutes
samtools sort human_rnaseq_bwa.bam human_rnaseq_bwa_sort # will take 1-2 minutes
#index the new files
samtools index human_mirnaseq_hg19_sort.bam
samtools index human_rnaseq_bwa_sort.bam
#filter the sorted files, reindex the new filtered files
samtools view -b -F 0x04 -q 1 -o human_mirnaseq_hg19_sort.mapped.q1.bam human_mirnaseq_hg19_sort.bam
samtools view -b -F 0x04 -q 1 -o human_rnaseq_bwa_sort.mapped.q1.bam human_rnaseq_bwa_sort.bam
samtools index human_mirnaseq_hg19_sort.mapped.q1.bam
samtools index human_rnaseq_bwa_sort.mapped.q1.bam
#convert filtered bam files to bed format
module load bedtools #if you haven't loaded it in for this session
bedtools bamtobed -i human_mirnaseq_hg19_sort.mapped.q1.bam > human_mirnaseq_hg19_sort.mapped.q1.bed
bedtools bamtobed -i human_rnaseq_bwa_sort.mapped.q1.bam > human_rnaseq_bwa_sort.mapped.q1.bed
#check the length of the files:
wc -l *.bed
#164806 human_mirnaseq_hg19_sort.mapped.q1.bed
# 22538 human_rnaseq_bwa_sort.mapped.q1.bed
#merge the bed files, check the length again
bedtools merge -s -c 4,5,6 -o count_distinct,sum,distinct -i human_mirnaseq_hg19_sort.mapped.q1.bed > human_mirnaseq_hg19_sort.mapped.q1.merge.bed
bedtools merge -s -c 4,5,6 -o count_distinct,sum,distinct -i human_rnaseq_bwa_sort.mapped.q1.bed > human_rnaseq_bwa_sort.mapped.q1.merge.bed
wc -l *.merge.bed
#14794 human_mirnaseq_hg19_sort.mapped.q1.merge.bed
# 7134 human_rnaseq_bwa_sort.mapped.q1.merge.bed
If we run low on time, you can copy the merged bed files over from my directory on scratch:
click to copy over the merged bed files...
cds
cd intersect
cp /scratch/01786/awh394/core_ngs/day4_2015/intersect/human_mirnaseq_hg19_sort.mapped.q1.merge.bed .
cp /scratch/01786/awh394/core_ngs/day4_2015/intersect/human_rnaseq_bwa_sort.mapped.q1.merge.bed .
Exercise 5: Intersect two experiments using intersect and examine the output
click here for the bedtools intersect code and output
My output is commented in this code block.
cd intersect
module load bedtools #if you haven't loaded it up yet this session
bedtools intersect -wo -a human_rnaseq_bwa_sort.mapped.q1.merge.bed -b human_mirnaseq_hg19_sort.mapped.q1.merge.bed > hg19_rnaseq_mirnaseq_intersect.bed
wc -l hg19_rnaseq_mirnaseq_intersect.bed
#38 hg19_rnaseq_mirnaseq_intersect.bed
more hg19_rnaseq_mirnaseq_intersect.bed
#chr1 20987370 20987471 1 37 - chr1 20987402 20987430 1 12 - 28
#chr1 25555557 25555616 1 37 + chr1 25555612 25555636 1 2 - 4
#chr1 25555557 25555617 1 37 - chr1 25555612 25555636 1 2 - 5
#chr1 28906396 28906497 1 37 + chr1 28906368 28906405 6 246 - 9
#chr1 33245783 33245884 1 37 + chr1 33245880 33245908 1 24 - 4
Using the options we've specified (-wo) the resulting file will have entries for file A, file B and the number of base pairs overlap between the feature in A and the features in B, but we'll only retain lines where there is an overlap between A and B. We could also use the -v option to only contain areas with NO intersection, or control the intersections with -f and -r options. Bedtools intersect is a powerful tool, and it's always a good idea to ask "what is this code going to do?" while you're testing analysis workflows.
The manual page for bedtools closest has a really nice image of how closest behaves with overlapping options. Bedtools closest first looks for any overlaps of B with A, if it finds an overlap, the overlap in B with the highest proportional overlap with A is reported. If there are no overlaps, then it looks for the closest genomic feature proximal to A (using distance from the start or end of A to do this).
bedtools closest [OPTIONS] -a <FILE> \
-b <FILE1, FILE2, ..., FILEN>
Much like bedtools intersect, bedtools closest takes an A file and a series of B files. So if you wanted to determine the distance of your regions of interest to several different classes of genes, bedtools closest would be a useful tool for that analysis.
bedtools closest options
- s: Require same strandedness. That is, find the closest feature in B that overlaps A on the _same_ strand. By default, overlaps are reported without respect to strand.
- S: Require opposite strandedness. That is, find the closest featurein B that overlaps A on the _opposite_ strand. By default, overlaps are reported without respect to strand.
- d: In addition to the closest feature in B, report its distance to A as an extra column. The reported distance for overlapping features will be 0.
D: Like -d, report the closest feature in B, and its distance to A as an extra column. However unlike -d, use negative distances to report upstream features.
ref Report distance with respect to the reference genome. B features with a lower (start, stop) are upstream.
a Report distance with respect to A. When A is on the - strand, “upstream” means B has a higher (start,stop).
b Report distance with respect to B. When B is on the - strand, “upstream” means A has a higher (start,stop).
- io: Ignore features in B that overlap A. That is, we want close, yet not touching features only.
- iu: Ignore features in B that are upstream of features in A. This option requires -D and follows its orientation rules for determining what is “upstream”.
id: Ignore features in B that are downstream of features in A. This option requires -D and follows its orientation rules for determining what is “downstream”
- names: When using multiple databases (-b), provide an alias for each that will appear instead of a file Id when also printing the DB record.
In this section, we'll intersect the human_rnaseq_bwa_sort.mapped.q1.merge.bed file with some protein coding genes from Gencode (hg19). First go copy a couple files from my scratch directory:
cds
mkdir closest
cd closest
cp /scratch/01786/awh394/core_ngs/day4_2015/bedtools/gencode.v19.proteincoding.genes.sort.merge.final .
cp ../intersect/human_rnaseq_bwa_sort.mapped.q1.merge.bed .
#or:
cp /scratch/01786/awh394/core_ngs/day4_2015/intersect/human_rnaseq_bwa_sort.mapped.q1.merge.bed .
click here to see my output on copying files
-rwxrwxrwx 1 awh394 G-801021 1.9M May 17 09:30 gencode.v19.genes.sort.merge.final
-rwxrwxrwx 1 awh394 G-801021 646K May 17 09:30 gencode.v19.proteincoding.genes.sort.merge.final
Exercise 6: Identify the closest protein coding genes (on the same strand) for the human rnaseq file using closest, then sort by the distance column (largest distance first).
click here for the bedtools closest code and output
My output is commented in this code block.
cd intersect
module load bedtools #if you haven't loaded it up yet this session
bedtools closest -s -d -a human_rnaseq_bwa_sort.mapped.q1.merge.bed -b gencode.v19.proteincoding.genes.sort.merge.final > hg19_rnaseq_protcode_closest.bed
wc -l hg19_rnaseq_protcode_closest.bed
#XXX hg19_rnaseq_protcode_closest.bed
more hg19_rnaseq_protcode_closest.bed
sort -k12,12nr hg19_rnaseq_protcode_closest.bed > hg19_rnaseq_protcode_closest.sort.bed
more hg19_rnaseq_protcode_closest.sort.bed
###Add commentary on the exercise here
Bedtools subtract takes an A file and a B file, then searches for features in B that overlap A. When/if these features are identified, the overlapping portion is removed from A and the remaining portion of A is reported. If a feature in B overlaps all of a feature in A, that feature will not be reported.
bedtools subtract [OPTIONS] -a <BED/GFF/VCF> -b <BED/GFF/VCF>
Note that bedtools subtract is performed on two files, and unlike some of the other utilities we've used, you can't use multiple B features here. However, you can use cat to join together features you'd like to subtract from your A file.
bedtools closest options
- f: Minimum overlap required as a fraction of A. Default is 1E-9 (i.e. 1bp).
- F: Minimum overlap required as a fraction of B. Default is 1E-9 (i.e., 1bp).
- r: Require that the fraction of overlap be reciprocal for A and B. In other words, if -f is 0.90 and -r is used, this requires that B overlap at least 90% of A and that A also overlaps at least 90% of B.
e: Require that the minimum fraction be satisfied for A _OR_ B. In other words, if -e is used with -f 0.90 and -F 0.10 this requires that either 90% of A is covered OR 10% of B is covered. Without -e, both fractions would have to be satisfied.**-s** Force “strandedness”. That is, only report hits in B that overlap A on the same strand. By default, overlaps are reported without respect to strand.
S: Require different strandedness. That is, only report hits in B that overlap A on the _opposite_ strand. By default, overlaps are reported without respect to strand
- A: Remove entire feature if any overlap. That is, by default, only subtract the portion of A that overlaps B. Here, if any overlap is found (or
-f amount), the entire feature is removed. N: Same as -A except when used with -f, the amount is the sum of all features (not any single feature)
Let's do a little set-up for the next exercise:
cds
mkdir subtract
cd subtract
cp /scratch/01786/awh394/core_ngs/day4_2015/bedtools/gencode.v19.proteincoding.genes.sort.merge.final .
cp /scratch/01786/awh394/core_ngs/day4_2015/bedtools/gencode.v19.genes.sort.merge.final .
Exercise 7: remove the protein-coding genes from a gencode list of genes using subtract, then give a count of the non-protein-coding gene entries
click here for the bedtools subtract code and output
My output is commented in this code block.
cd subtract
module load bedtools #if you haven't loaded it up yet this session
bedtools subtract -s -d -a gencode.v19.genes.sort.merge.final -b gencode.v19.proteincoding.genes.sort.merge.final > gencode.v19.not.proteincoding.genes.bed
wc -l gencode.v19.not.proteincoding.genes.bed
#XXX gencode.v19.not.proteincoding.genes.bed
more gencode.v19.not.proteincoding.genes.bed
###commentary on the output and exercise
As a final note, yesterday Nathan taught you about using a lot of unix utilities, including uniq, sort and cut. One last utility I'd like to add, that is very useful for manipulating these types of tab delimited files, is awk. Awk isn't a command, but rather a little text manipulation language in it's own right. While awk can be used to do many different things, here we'll primarily use it to sort tab delimited files based on the values present in those files. That is useful to filter your files for entries on a given chromosome, or greater than/less than a given score. If your dataset is large, this type of filtering can be invaluable! Below is an example of a simple awk script:
cat file.bed | awk 'BEGIN{FS="\t";OFS="\t";}{if ($6 == '+'){print}}' > file.plusStrand.bed
- In the first section, we open the bed file of interest. Then we pipe that filestream to the awk program.
- The section: BEGIN{FS="\t";OFS="\t";} tells awk to begin a filter, the input file is tab delimited, and the output file is also tab delimited.
- Generally, you can leave this section constant (if you are working with tab delimited files).
- The second section: {if ($6 == '+'){print}} is our selection and printing criteria.
- "$6" indicates column 6, and == indicates "equals" or "matches".
- The {print} command tells awk to print the whole line if the statement for column 6 evaluates to true.
- Thus, the output file only contains those lines which satisfy the criteria in the selection statement.
We can do this filtering on the hg19_rnaseq_mirnaseq_intersect.bed file we just created using bedtools intersect.
cat hg19_rnaseq_mirnaseq_intersect.bed | awk 'BEGIN{FS="\t";OFS="\t";}{if ($6 == "+"){print}}' | more
You could also insist on columns 6 and 12 both being the plus strand as such:
cat hg19_rnaseq_mirnaseq_intersect.bed | awk 'BEGIN{FS="\t";OFS="\t";}{if ($6 == "+" && $12 == "+"){print}}' | more