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2021 Analysis using BEDTools

2021 Analysis using BEDTools

Jun 06, 2022

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 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

  1. chrom (required) – string naming the chromosome or other contig

  2. start (required) – the 0-based start position of the region

  3. end (required) – the 1-based end position of the region

  4. name (optional) – an arbitrary string describing the region

    • for BED files loaded as UCSC Genome Browser tracks, this text is displayed above the region

  5. 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)

  6. 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)

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 the original FASTA sequences, on a set of regions of interest.  In addition to the 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).

coverage

  • Compute genome-wide coverage of your regions

  • Generate per-base genome-wide signal trace

  • You have performed a WGS (whole genome sequencing) experiment and want to know if has resulted in the desired coverage depth.

  • Calculate what proportion of the (known) transcriptome is covered by your RNA-seq alignments. Provide the transcript regions as a BED or GFF/GTF file.

  • Produce a per-base genome-wide signal (in bedGraph format) for a ChIP-seq or ATAC-seq experiment. After conversion to binary bigWig format, such tracks can be configured in the UCSC Genome Browser as custom tracks.

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.

Remove rRNA gene regions from a merged gene annotations file before counting.

intersect

Determine the overlap between two sets of regions.

Similar to multicov, but can also report (not just count) the overlapping regions.

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 stampede2, start and idev session, then load the BioContainers bedtools module, and check its version.

Start an idev session
idev -p normal -m 120 -A UT-2015-05-18 -N 1 -n 68 # ... module load biocontainers module load bedtools bedtools --version # should be bedtools v2.27.1

Input format considerations

  • Most BEDTools functions now 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 region files must share the same set 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:

  1. sense stranded – the R1 read should be on the same strand as the gene.

  2. antisense stranded – the R1 read should be on the opposite strand as the gene.

  3. 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 (note that most RNA-seq libraries 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:

  1. seqname - The name of the chromosome or scaffold.

  2. source - Name of the program that generated this feature, or other data source (e.g. database)

  3. feature_type - Type of the feature. Examples of common feature types include:

    • Some examples of common feature types are:

      • CDS (coding sequence), exon

      • gene, transcript

      • start_codon, stop_codon

  4. start - Start position of the feature, with sequence numbering starting at 1.

  5. end - End position of the feature, with sequence numbering starting at 1.

  6. score - A numeric value. Often but not always an integer.

  7. strand - Defined as + (forward), - (reverse), or . (no relevant strand)

  8. 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.

Start an idev session
idev -p normal -m 120 -A UT-2015-05-18 -N 1 -n 68 # ... module load biocontainers module load bedtools bedtools --version # should be bedtools v2.27.1
Look at GFF annotation entries with less
mkdir -p $SCRATCH/core_ngs/bedtools cd $SCRATCH/core_ngs/bedtools cp $CORENGS/yeast_rna/sacCer_R64-1-1_20110208.gff . cp $CORENGS/yeast_rna/yeast_mrna.sort.filt.bam* . # Use the less pager to look at multiple lines less sacCer_R64-1-1_20110208.gff # Look at just the most-important Tab-separated columns cat sacCer_R64-1-1_20110208.gff | grep -v '#' | cut -f 1,3-5 | head -20 # Include the ugly 9th column where attributes are stored cat sacCer_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:

  1. We only care about a subset of feature types (here genes), and

  2. 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.)

mkdir -p $SCRATCH/core_ngs/bedtools cd $SCRATCH/core_ngs/bedtools cp $CORENGS/yeast_rna/sacCer_R64-1-1_20110208.gff .
Create a histogram of all the feature types in a GFF
cd $SCRATCH/core_ngs/bedtools cat sacCer_R64-1-1_20110208.gff | grep -v '^#' | cut -f 3 | \ sort | uniq -c | sort -k1,1nr | more

You should see something like this.

Histogram of yeast annotation features
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:

Filter GFF gene feature with awk
cat sacCer_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, /work2/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).

mkdir -p $SCRATCH/core_ngs/bedtools cd $SCRATCH/core_ngs/bedtools cp $CORENGS/yeast_rna/*.gff .
Convert GFF to BED with BioITeam script
/work2/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

Converted BED attributes
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, becuase 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.

Re-order the final BED fields
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:

Re-ordered BED attributes
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:

Examine our BED-format annotations
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.

mkdir -p $SCRATCH/core_ngs/bedtools cd $SCRATCH/core_ngs/bedtools cp $CORENGS/yeast_rna/*.gff . cp $CORENGS/yeast_rna/sc_genes* .

Exercise: How many genes in our sc_genes.bed file are in each category?

Use cut to isolate that field, sort to sort the resulting values into blocks, then uniq -c to count the members of each block.

cut -f 8 sc_genes.bed | sort | uniq -c

You should see this: