Analysis using BEDTools
- Daechan Park
- Anna Battenhouse
- Daryl Barth
Reservations
Use our summer school reservation (CoreNGS) when submitting batch jobs to get higher priority on the ls6 normal queue.
# Request a 180 minute interactive node on the normal queue using our reservation idev -m 120 -N 1 -A OTH21164 -r CoreNGS idev -m 120 -N 1 -A TRA23004 -r CoreNGS # Request a 120 minute idev node on the development queue idev -m 120 -N 1 -A OTH21164 -p development idev -m 120 -N 1 -A TRA23004 -p development
The BED format
BED (Browser Extensible Data) format is a simple text format for location-oriented data (genomic regions) developed to support UCSC Genome Browser (GenBrowse) tracks. Standard BED files have 3 to 6 Tab-separated columns, although up to 12 columns are defined. (Read more about the UCSC Genome Browser's official BED format.)
Memorize the 6 main BED fields
These 6 BED fields are so important that you should memorize them. Keep repeating "chrom, start, end, name, score, strand" until the words trip off your tongue 
- chrom (required) – string naming the chromosome or other contig
- start (required) – the 0-based start position of the region
- end (required) – the 1-based end position of the region
- name (optional) – an arbitrary string describing the region
- for BED files loaded as UCSC Genome Browser tracks, this text is displayed above the region
- score (optional) – an integer score for the region
- for BED files to be loaded as UCSC Genome Browser tracks, this should be a number between 0 and 1000, higher = "better"
- for non-GenBrowse BED files, this can be any integer value (e.g. the length of the region)
- strand (optional) - a single character describing the region's strand
- + – plus strand (Watson strand) region
- - – minus strand (Crick strand) region
- . – no strand – the region is not associated with a strand (e.g. a transcription factor binding region)
Important rules for BED format:
- The number of fields per line must be consistent throughout any single BED file
- e.g. they must all have 3 fields or all have 6 fields
- The first base on a contig is numbered 0
- versus 1 for BAM file positions
- so the a BED start of 99 is actually the 100th base on the contig
- but end positions are 1-based
- so a BED end of 200 is the 200th base on the contig
- the length of a BED region is end - start
- not end - start + 1, as it would be if both coordinates with 0-based or both 1-based
- this difference is the single greatest source of errors dealing with BED files!
Note that the UCSC Genome Browser also defines many BED-like data formats (e.g. bedGraph, narrowPeak, tagAlign and various RNA element formats). See supported UCSC Genome Browser data formats for more information and examples.
In addition to standard-format BED files, one can create custom BED files that have at least 3 of the standard fields (chrom, start, end), followed by any number of custom fields. For example:
- A BED3+ file contains the 3 required BED fields, followed by some number of user-defined columns (all records with the same number)
- A BED6+ file contains the 3 required BED fields, 3 additional standard BED fields (name, score, strand), followed by some number of user-defined columns
As we will see, BEDTools functions require BED3+ input files, or BED6+ if strand-specific operations are requested.
BEDTools overview
The BEDTools suite is a set of utilities for manipulating BED and BAM files. We call it the "Swiss army knife" for genomic region analyses because its sub-commands are so numerous and versatile. Some of the most common bedtools operations perform set-theory functions on regions: intersection (intersect), union (merge), set difference (subtract) – but there are many others. The table below lists some of the most useful sub-commands along with applicable use cases.
| Sub-command | Description | Use case(s) |
|---|---|---|
| bamtobed | Convert BAM files to BED format. | You want to have the contig, start, end, and strand information for each mapped alignment record in separate fields. Recall that the strand is encoded in a BAM flag (0x10) and the exact end coordinate requires parsing the CIGAR string. |
| bamtofastq | Extract FASTQ sequences from BAM alignment records. | You have downloaded a BAM file from a public database, but it was not aligned against the reference version you want to use (e.g. it is hg19 and you want an hg38 alignment). To re-process, you need to start with the original FASTQ sequences. |
| getfasta | Get FASTA entries corresponding to regions. | You want to run motif analysis, which requires FASTA sequences, on a set of regions of interest. In addition to a BED or BAM file, you must provide FASTA file(s) for the genome/reference used for alignment (e.g. the FASTA file used to build the aligner index). |
| genomecov | Generate per-base genome-wide signal trace | Produce a per-base genome-wide signal (in bedGraph format), for example for a ChIP-seq or ATAC-seq experiment. After conversion to binary bigWig format, such tracks can be visualized in the Broad's IGV (Integrative Genome Browser) application, or configured in the UCSC Genome Browser as custom tracks. |
| coverage | Compute coverage of your regions |
|
| multicov | Count overlaps between one or more BAM files and a set of regions of interest. | Count RNA-seq alignments that overlap a set of genes of interest. While this task is usually done with a specialized RNA-seq quantification tool (e.g. featureCounts or HTSeq), bedtools multicov can provide a quick estimate, e.g. for QC purposes. |
| merge | Combine a set of possibly-overlapping regions into a single set of non-overlapping regions. | Collapse overlapping gene annotations into per-strand non-overlapping regions before counting (e.g with featureCounts or HTSeq). If this is not done, the source regions will potentially be counted multiple times, once for each (overlapping) target region it intersects. |
| subtract | Remove unwanted regions. |
|
| intersect | Determine the overlap between two sets of regions. | Similar to multicov, but can also report the overlapping regions, not just count them. |
| closest | Find the genomic features nearest to a set of regions. | For a set of significant ChIP-seq Transcription Factor (TF) binding regions ("peaks") that have been identified, determine nearby genes that may be targets of TF regulation. |
We will explore a few of these functions in our exercises.
BEDTools versions
BEDTools is under active development and is always being refined and extended. Unfortunately, sometimes changes are made that are incompatible with previous BEDTools versions. For example, a major change to the way bedtool merge functions was made after bedtools v2.17.0.
So it is important to know which version of BEDTools you are using, and read the documentation carefully to see if changes have been made since your version.
Login to ls6, start and idev session, load the BioContainers bedtools module, then check its version.
idev -m 120 -N 1 -A OTH21164 -r CoreNGS # or -A TRA23004 # or idev -m 90 -N 1 -A OTH21164 -p development # or -A TRA23004 module load biocontainers module load bedtools bedtools --version # should be bedtools v2.27.1
Input format considerations
- Most BEDTools functions accept either BAM or BED files as input.
- BED format files must be BED3+, or BED6+ if strand-specific operations are requested.
- When comparing against a set of regions, those regions are usually supplied in either BED or GTF/GFF.
- All text-format input files (BED, GTF/GFF, VCF) should use Unix line endings (linefeed only).
The most important thing to remember about comparing regions using BEDTools, is that all input files must share the same set of contig names and be based on the same reference! For example, if an alignment was performed against a human GRCh38 reference genome from Gencode, use annotations from the corresponding GFF/GTF annotations.
About strandedness
By default many bedtools utilities that perform overlapping, consider reads overlapping the feature on either strand, but can be made strand-specific with the -s or -S option. This strandedness options for bedtools utilities refers the orientation of the R1 read with respect to the feature's (gene's) strand.
- -s says the R1 read is sense stranded (on the same strand as the gene).
- -S says the R1 read is antisense stranded (the opposite strand as the gene).
RNA-seq libraries can be constructed with 3 types of strandedness:
- sense stranded – the R1 read should be on the same strand as the gene.
- antisense stranded – the R1 read should be on the opposite strand as the gene.
- unstranded – the R1 could be on either strand
Which type of RNA-seq library you have depends on the library preparation method – so ask your sequencing center! Our yeast RNA-seq library is sense stranded; however note that most RNA-seq libraries these days, including ones prepared by GSAF, are antisense stranded.
If you have a stranded RNA-seq library, you should use either -s or -S to avoid false counting against a gene on the wrong strand.
About GFF/GTF annotation files
Annotation files that you retrieve from public databases are often in GTF (Gene Transfer Format) or one of the in GFF (General Feature Format) formats (usually GFF3 these days).
Unfortunately, both formats are obscure and hard to work with directly. While bedtools does accept annotation files in GFF/GTF format, you will not like the results. This is because the most useful information in a GFF/GTF file is in a loosely-structured attributes field.
Also unfortunately, there are a number of variations of both annotation formats However both GTF and GFF share the first 8 Tab-separated fields:
- seqname - The name of the chromosome or contig.
- source - Name of the program that generated this feature, or other data source (e.g. public database)
- feature_type - Type of the feature, for example:
- chromosome
- CDS (coding sequence), exon
- gene, transcript
- start_codon, stop_codon
- start - Start position of the feature, with sequence numbering starting at 1.
- end - End position of the feature, with sequence numbering starting at 1.
- score - A numeric value. Often but not always an integer. Meaning differs and not usually important.
- strand - Defined as + (forward), - (reverse), or . (no relevant strand)
- frame - For a CDS, one of 0, 1 or 2, specifying the reading frame of the first base; otherwise '.'
The Tab-separated columns will care about are (1) seqname, (3) feature_type and (4,5) start, end. The reason we care is that when working with annotations, we usually only want to look at annotations of a particular type, most commonly gene, but also transcript or exon.
So where is the real annotation information, such as the unique gene ID or gene name? Both formats also have a 9th field, which is usually populated by a set of name/value pair attributes, and that's where the useful information is (e.g. the unique feature identifier, name, and so forth).
Take a quick look at a yeast annotation file, sacCer_R64-1-1_20110208.gff using less.
mkdir -p $SCRATCH/core_ngs/bedtools cd $SCRATCH/core_ngs/bedtools cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* . cp $CORENGS/catchup/references/gff/sacCer3.R64-1-1_20110208.gff . # Use the less pager to look at multiple lines less sacCer3.R64-1-1_20110208.gff # Look at just the most-important Tab-separated columns cat sacCer3.R64-1-1_20110208.gff | grep -v '#' | cut -f 1,3-5 | head -20 # Include the ugly 9th column where attributes are stored cat sacCer3.R64-1-1_20110208.gff | grep -v '#' | cut -f 1,3,9 | head
In addition to comment lines (starting with #), you can see the chrI contig names in column 1 and various feature types in column 3. You see also see tags like Name=YAL067C;gene=SEO1; among the attributes on some records, but in general the attributes column information is really ugly.
To summarize, we have two problems to solve:
- We only care about a subset of feature types (here genes), and
- We want the important annotation information – gene names and IDs – to appear as regular columns instead of weird name/value pairs.
Filter annotations based on desired feature type
One of the first things you want to know about your annotation file is what gene features it contains. Here's how to find that:
Read more about what's going on here at piping a histogram.
cd $SCRATCH/core_ngs/bedtools cat sacCer3.R64-1-1_20110208.gff | grep -v '^#' | cut -f 3 | \ sort | uniq -c | sort -k1,1nr | more
You should see something like this.
7077 CDS
6607 gene
480 noncoding_exon
383 long_terminal_repeat
376 intron
337 ARS
299 tRNA
190 region
129 repeat_region
102 nucleotide_match
89 transposable_element_gene
77 snoRNA
50 LTR_retrotransposon
32 telomere
31 binding_site
27 rRNA
24 five_prime_UTR_intron
21 pseudogene
17 chromosome
16 centromere
15 ncRNA
8 external_transcribed_spacer_region
8 internal_transcribed_spacer_region
6 snRNA
3 gene_cassette
2 insertion
Let's create a file that contains only the 6607 gene entries:
cat sacCer3.R64-1-1_20110208.gff | grep -v '#' | \
awk 'BEGIN{FS=OFS="\t"}{ if($3=="gene"){print} }' \
> sc_genes.gff
wc -l sc_genes.gff
The line count of sc_genes.gff should be 6607 – one for each gene entry.
Convert GFF/GTF format to BED with ID in the name field
Our sc_genes.gff annotation subset now contains only the 6607 genes in the Saccharomyces cerevisiae genome. This addresses our first problem, but entries in this file still have the important information – the gene ID and name – in the loosely-structured 9th attributes field.
If we want to associate reads with features, we need to have the feature names where they are easy to extract!
What most folks to is find some way to convert their GFF/GTF file to a BED file, parsing out some (or all) of the name/value attribute pairs into BED file columns after the standard 6. You can find such conversion programs on the web – or write one yourself. Or you could use the BioITeam conversion script, /work/projects/BioITeam/common/script/gtf_to_bed.pl. While it will not work 100% of the time, it manages to do a decent job on most GFF/GTF files. And it's pretty easy to run.
Let Anna know if you run into problems
If this script doesn't work on your annotation file, please let Anna know. She is always looking for cases where the conversion fails, and will try to fix it.
Here we just give the script the GFF file to convert, plus a 1 that tells it to URL decode weird looking text (e.g. our Note attribute values).
/work/projects/BioITeam/common/script/gtf_to_bed.pl sc_genes.gff 1 \ > sc_genes.converted.bed
The program reads the input file twice – once to gather all the attribute names, and then a second time to write the attribute values in well-defined columns. You'll see output like this:
---------------------------------------- Gathering all attribute names for GTF 'sc_genes.gff'... urlDecode = 1, tagAttr = tag Done! 6607 lines read 6607 locus entries 8 attributes found: (Alias ID Name Note Ontology_term dbxref gene orf_classification) ---------------------------------------- Writing BED output for GTF 'sc_genes.gff'... Done! Wrote 6607 locus entries from 6607 lines
To find out what the resulting columns are, look at the header line out the output BED file:
head -1 sc_genes.converted.bed
For me the resulting 16 attributes are as follows (they may have a different order for you). I've numbered them below for convenience.
1. chrom 2. start 3. end 4. featureType 5. length 6. strand 7. source 8. frame 9. Alias 10. ID 11. Name 12. Note 13. Ontology_term 14. dbxref 15. gene 16. orf_classification
The final transformation is to do a bit of re-ordering, dropping some fields. We'll do this with awk, because cut can't re-order fields. While this is not strictly required, it can be helpful to have the critical fields (including the gene ID) in the 1st 6 columns. We do this separately for the header line and the rest of the file so that the BED file we give bedtools does not have a header (but we know what those fields are). We would normally preserve valuable annotation information such as Ontology_term, dbxref and Note, but drop them here for simplicity.
head -1 sc_genes.converted.bed | sed 's/\r//' | awk '
BEGIN{FS=OFS="\t"}{print $1,$2,$3,$10,$5,$6,$15,$16}
' > sc_genes.bed.hdr
tail -n +2 sc_genes.converted.bed | sed 's/\r//' | awk '
BEGIN{FS=OFS="\t"}
{ if($15 == "") {$15 = $10} # make sure gene name is populated
print $1,$2,$3,$10,$5,$6,$15,$16}
' > sc_genes.bed
One final detail. Annotation files you download may have non-Unix (linefeed-only) line endings. Specifically, they may use Windows line endings (carriage return + linefeed). (Read about Line ending nightmares.) The expression sed 's/\r//' uses the sed (substitution editor) tool to replace carriage return characters ( \r ) with nothing, removing them from the output.
Finally, the 8 re-ordered attributes are:
1. chrom 2. start 3. end 4. ID 5. length 6. strand 7. gene 8. orf_classification
**Whew**! That was a lot of work. Welcome to the world of annotation wrangling – it's never pretty! But at least the result is much nicer looking. Examine the results using more or less or head:
cat sc_genes.bed | head -20
Doesn't this look better? (I've tidied up the output a bit below.)
chrI 334 649 YAL069W 315 + YAL069W Dubious chrI 537 792 YAL068W-A 255 + YAL068W-A Dubious chrI 1806 2169 YAL068C 363 - PAU8 Verified chrI 2479 2707 YAL067W-A 228 + YAL067W-A Uncharacterized chrI 7234 9016 YAL067C 1782 - SEO1 Verified chrI 10090 10399 YAL066W 309 + YAL066W Dubious chrI 11564 11951 YAL065C 387 - YAL065C Uncharacterized chrI 12045 12426 YAL064W-B 381 + YAL064W-B Uncharacterized chrI 13362 13743 YAL064C-A 381 - YAL064C-A Uncharacterized chrI 21565 21850 YAL064W 285 + YAL064W Verified chrI 22394 22685 YAL063C-A 291 - YAL063C-A Uncharacterized chrI 23999 27968 YAL063C 3969 - FLO9 Verified chrI 31566 32940 YAL062W 1374 + GDH3 Verified chrI 33447 34701 YAL061W 1254 + BDH2 Uncharacterized chrI 35154 36303 YAL060W 1149 + BDH1 Verified chrI 36495 36918 YAL059C-A 423 - YAL059C-A Dubious chrI 36508 37147 YAL059W 639 + ECM1 Verified chrI 37463 38972 YAL058W 1509 + CNE1 Verified chrI 38695 39046 YAL056C-A 351 - YAL056C-A Dubious chrI 39258 41901 YAL056W 2643 + GPB2 Verified
Note that value in the 8th column. In the yeast annotations from SGD there are 3 gene classifications: Verified, Uncharacterized and Dubious. The Dubious ones have no experimental evidence so are generally excluded.
Exercise: How many genes in our sc_genes.bed file are in each category?
Exercises
Use bedtools merge to collapse overlapping annotations
One issue that often arises when dealing with BED regions is that they can overlap one another. For example, on the yeast genome, which has very few non-coding areas, there are some overlapping ORFs (Open Reading Frames), especially Dubious ORFs that overlap Verified or Uncharacterized ones. When bedtools looks for overlaps, it will count a read that overlaps any of those overlapping ORFs – so some reads can be counted twice.
One way to avoid this double-counting is to collapse the overlapping regions into a merged set of non-overlapping regions – and that's what the bedtools merge utility does (http://bedtools.readthedocs.io/en/latest/content/tools/merge.html).
Here we're going to use bedtools merge to collapse our gene annotations into a non-overlapping set, first for all genes, then for only non-Dubious genes.
The output from bedtools merge always starts with 3 columns: chrom, start and end of the merged region only.
Using the -c (column) and -o (operation) options, you can add information in subsequent fields. Each comma-separated column number following -c specifies a column to operate on, and the corresponding comma-separated function name following the -o specifies the operation to perform on that column in order to produce an additional output field.
For example, our sc_genes.bed file has a gene name in column 4, and for each (possibly merged) gene region, we want to know the number of gene regions that were collapsed into the region, and also which gene names were collapsed.
We can do this with -c 6,4,4 -o distinct,count,collapse, which says that three custom output columns should be added:
- the 1st custom column (output column 4) should result from collapsing distinct (unique) values of gene file column 6 (the strand column, + or -)
- since we will ask for stranded merging, the merged regions will always be on the same strand, so this value will always be + or -
- the 2nd custom output column should result from counting the gene names in column 4 for all genes that were merged, and
- the 3rd custom output should be a comma-separated collapsed list of those same column 4 gene names.
bedtools merge also requires that the input BED file be sorted by locus (chrom + start), so we do that first, then we request a strand-specific merge (-s):
cd $SCRATCH/core_ngs/bedtools sort -k1,1 -k2,2n sc_genes.bed > sc_genes.sorted.bed bedtools merge -i sc_genes.sorted.bed -s -c 6,4,4 -o distinct,count,collapse > merged.sc_genes.txt
The first few lines of the merged.sc_genes.txt file look like this (I've tidied it up a bit):
2-micron 251 1523 + 1 R0010W 2-micron 1886 3008 - 1 R0020C 2-micron 3270 3816 + 1 R0030W 2-micron 5307 6198 - 1 R0040C chrI 334 792 + 2 YAL069W,YAL068W-A chrI 1806 2169 - 1 YAL068C chrI 2479 2707 + 1 YAL067W-A chrI 7234 9016 - 1 YAL067C chrI 10090 10399 + 1 YAL066W chrI 11564 11951 - 1 YAL065C
As we specified:
- Output column 4 has the region's strand.
- Column 5 is the count of merged regions
- Column 6 is a collapsed, comma-separated list of the merged gene names
Exercise: Compare the number of regions in the merged and before-merge gene files.
Exercise: How many regions represent only 1 gene, 2 genes, or more?
Exercise: Repeat the steps above, but first create a good.sc_genes.bed file that does not include Dubious ORFs.
So there's one more thing we need to do to create a valid BED format file. Our merged.good.sc_genes.txt columns are chrom, start, end, strand, merged_region_count, merged_region(s), but the BED6 specification is: chrom, start, end, name, score, strand.
To make a valid BED6 file, we'll include the first 3 output columns of merged.good.sc_genes.txt (chrom, start, end), but strand should be in column 6. Column 4 should be name (we'll put the collapsed gene name list there), and column 5 a score (we'll put the region count there).
We can use awk to re-order the fields:
cat merged.good.sc_genes.txt | awk '
BEGIN{FS=OFS="\t"}
{print $1,$2,$3,$6,$5,$4}' > merged.good.sc_genes.bed
Use bedtools multicov to count feature overlaps
We're now (finally!) actually going to do some gene-based analyses of a yeast RNA-seq dataset using bedtools and the BED-formatted, merged yeast gene annotation file we created above.
In this section we'll use bedtools multicov to count RNA-seq reads that overlap our gene features. The bedtools multicov command (http://bedtools.readthedocs.io/en/latest/content/tools/multicov.html) takes a feature file (GFF/BED/VCF) and counts how many reads from one or more input BAM files overlap those feature. The input BAM file(s) must be position-sorted and indexed.
Make sure you're in an idev session, since we will be doing some significant computation, and make bedtools and samtools available.
idev -m 120 -N 1 -A OTH21164 -r CoreNGS # or -A TRA23004 # or idev -m 90 -N 1 -A OTH21164 -p development # or -A TRA23004
Copy over the yeast RNA-seq files we'll need (also copy the GFF gene annotation file if you didn't make one).
# Get the merged yeast genes bed file if you didn't create one mkdir -p $SCRATCH/core_ngs/bedtools_multicov cd $SCRATCH/core_ngs/bedtools_multicov cp $CORENGS/catchup/bedtools_merge/merged*bed . # Copy the BAM file cd $SCRATCH/core_ngs/bedtools_multicov cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* .
Exercises: How many reads are represented in the yeast_mrna.sort.filt.bam file? How many mapped? How many proper pairs? How many duplicates? What is the distribution of mapping qualities? What is the average mapping quality?
Here's how to run bedtools multicov in stranded mode, directing the standard output to a file:
cd $SCRATCH/core_ngs/bedtools_multicov bedtools multicov -s -bams yeast_mrna.sort.filt.bam \ -bed merged.good.sc_genes.bed > yeast_mrna_gene_counts.bed
Exercise: How may records of output were written? Where is the count of overlaps per output record?
Exercise: How many features have non-zero overlap counts?
Exercise: What is the total count of reads mapping to gene features?
Use bedtools genomecov to create a signal track
A signal track is a bedGraph (BED3+) file with an entry for every base in a defined set of regions that shows the count of overlapping bases for the regions (see https://genome.ucsc.edu/goldenpath/help/bedgraph.html). bedGraph files can be visualized in the Broad's IGV (Integrative Genomics Viewer) application (https://software.broadinstitute.org/software/igv/download) or in the UCSC Genome Browser (https://genome.ucsc.edu/).
- Go to the UCSC Genome Browser: https://genome.ucsc.edu/
- Select Genomes from the top menu bar
- Select Human from POPULAR SPECIES
- under Human Assembly select Feb 2009 (GrCh37/hg19)
- select GO
- In the hg19 browser page, the Layered H3K27Ac track is a signal track
- the x-axis is the genome position
- the y-axis represents the count of ChIP-seq reads that overlap each position
- where the ChIP'd protein is H3K27AC (histone H3, acetylated on the Lysine at amino acid position 27)
The bedtools genomecov function (https://bedtools.readthedocs.io/en/latest/content/tools/coverage.html), with the -bg (bedgraph) option produces output in bedGraph format. Here we'll analyze the per-base coverage of yeast RNAseq reads in our merged yeast gene regions.
Make sure you're in an idev session, then prepare a directory for this exercise.
idev -m 120 -N 1 -A OTH21164 -r CoreNGS # or -A TRA23004 # or idev -m 90 -N 1 -A OTH21164 -p development # or -A TRA23004 module load biocontainers module load bedtools mkdir -p $SCRATCH/core_ngs/bedtools_genomecov cd $SCRATCH/core_ngs/bedtools_genomecov cp $CORENGS/catchup/bedtools_merge/merged*bed . cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* .
Then calling bedtools genomecov is easy. The -bg option says to report the depth in bedGraph format.
cd $SCRATCH/core_ngs/bedtools_genomecov bedtools genomecov -bg -ibam yeast_mrna.sort.filt.bam > yeast_mrna.genomecov.bedGraph wc -l yeast_mrna.genomecov.bedGraph # 1519274 lines
The bedGraph (BED3+) format has only 4 columns: chrom start end value and does not need to include positions with 0 reads. Here the count is the number of reads covering each base in the region given by chrom start end, as you can see looking at the first few lines with head:
chrI 4348 4390 2 chrI 4390 4391 1 chrI 4745 4798 2 chrI 4798 4799 1 chrI 4949 4957 2 chrI 4957 4984 4 chrI 4984 4997 6 chrI 4997 4998 5 chrI 4998 5005 4 chrI 5005 5044 2 chrI 5044 5045 1 chrI 6211 6268 2 chrI 6268 6269 1 chrI 7250 7257 3 chrI 7257 7271 4 chrI 7271 7274 6 chrI 7274 7278 7 chrI 7278 7310 8 chrI 7310 7315 6 chrI 7315 7317 5
Because this bedGraph file is for the small-ish (12Mb) yeast genome, and for reads that cover only part of that genome, it is not too big – only ~34M. But depending on the species and read depth, bedGraph files can get very large, so there is a corresponding binary format called bigWig (see https://genome.ucsc.edu/goldenpath/help/bigWig.html). The program to covert a bedGraph file to bigWig format is part of the UCSC Tools suite of programs. Look for it with module spider, and note that you can get information about all the tools in it using module spider with a specific container version:
# look for the ucsc tools package module spider ucsc # specifying a specific container version will show more information about the package module spider ucsc_tools/ctr-357--0 # displays information including the programs in the package: - bedGraphToBigWig - bedToBigBed - faToTwoBit - liftOver - my_print_defaults - mysql_config - nibFrag - perror - twoBitToFa - wigToBigWig
Looking at the help for bedGraphToBigWig, we'll need a file of chromosome sizes. We can create one from our BAM header, using a Perl substitution script, which I prefer to sed:
module load ucsc_tools cd $SCRATCH/core_ngs/bedtools_genomecov bedGraphToBigWig # look at its usage # create the needed chromosome sizes file from our BAM header module load samtools samtools view -H yeast_mrna.sort.filt.bam | grep -P 'SN[:]' | \ perl -pe 's/.*SN[:]//' | perl -pe 's/LN[:]//' > sc_chrom_sizes.txt cat sc_chrom_sizes.txt # displays: chrI 230218 chrII 813184 chrIII 316620 chrIV 1531933 chrV 576874 chrVI 270161 chrVII 1090940 chrVIII 562643 chrIX 439888 chrX 745751 chrXI 666816 chrXII 1078177 chrXIII 924431 chrXIV 784333 chrXV 1091291 chrXVI 948066 chrM 85779
Finally, call bedGraphToBigWig after sorting the bedGraph file again using the sort format bedGraphToBigWig likes. (You can try calling bedGraphToBigWig without sorting to see the error).
cd $SCRATCH/core_ngs/bedtools_genomecov export LC_COLLATE=C # may or may not need this... sort -k1,1 -k2,2n yeast_mrna.genomecov.bedGraph > yeast_mrna.genomecov.sorted.bedGraph bedGraphToBigWig yeast_mrna.genomecov.sorted.bedGraph sc_chrom_sizes.txt yeast_mrna.genomecov.bw
See the size difference between the bedGraph and the bigWig files. The bigWig (9.7M) is less that 1/3 the size of the bedGraph (34M).
cd $SCRATCH/core_ngs/bedtools_genomecov ls -lh yeast_mrna.genome*
Since the bigWig file is binary, not text, you can't use commands like cat, head, tail on them directly and get meaningful output. Instead, just as zcat converts gzip'd files to text, and samtools view convets binary BAM files to text, the bigWigToBedGraph program can convert binary bigWig format to text. That's a different BioContainers module (ucsc-bigwigtobedgraph) and the default container version doesn't work, so we'll specifically load one that does:
# The default version of is broken, so load this specific biocontainers version module load ucsc-bigwigtobedgraph/ctr-357--1 # see usage for bigWigToBedGraph: bigWigToBedGraph cd $SCRATCH/core_ngs/bedtools_genomecov # use the program to view a few lines of the binary bigWig file bigWigToBedGraph yeast_mrna.genomecov.bw stdout | head
Welcome to the University Wiki Service! Please use your IID (yourEID@eid.utexas.edu) when prompted for your email address during login or click here to enter your EID. If you are experiencing any issues loading content on pages, please try these steps to clear your browser cache.