| Tip |
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summer school CoreNGSday5core-ngs-class-0606) when submitting batch jobs to get higher priority on the ls6 normal queue sbatch --reservation=CoreNGSday5 <batch_file>.slurm
idev -m 180 -N 1 -A OTH21164 -r CoreNGSday5
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The BED format
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. | Code Block |
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| language | bash |
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| title | Request an interactive (idev) node |
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| # Request a 180 minute idev node on the normal queue w/our reservation
idev -m 120 -N 1 -A OTH21164 -r core-ngs-class-0606
# Request a 120 minute interactive node on the development queue
idev -m 120 -N 1 -A OTH21164 -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.)
...
- 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)
...
- 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 one of the single greatest source of errors dealing with BED files!
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- A BED3+ file contains the 3 required BED fields, followed by some number of user-defined columns ( with
- having the same number 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
...
- all records having the same number number columns
As we will see, BEDTools functions require BED3+ input files, or BED6+ if strand-specific operations are requested.
...
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 |
the original | FASTA sequences, on a set of regions of interest. |
the Compute genome-wide coverage of your regions| 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). |
coverage | | genomecov | 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 example for a ChIP-seq or ATAC-seq experiment. |
After configured in the UCSC Genome Browser as custom tracks.Combine a set of 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 | - 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.
In either case, regions (e.g. chromosomes or transcripts) are provided as a BED or GFF/GTF file. |
| 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 | | intersect | Determine the overlap between two sets of regions. | Similar to multicov, but can also report the overlapping regions, not just count them. |
| 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. For example, to create non-overlapping transcipt regions before counting RNA-seq reads (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/tRNA gene regions
|
from a merged gene annotations file .intersect | Determine the overlap between two sets of regions. | Similar to multicov, but can also report (not just count) the overlapping regions- for RNA-seq.
- Remove low-complexity genomic regions before peak calling for ChIP-seq or ATAC-seq.
|
| closest | Find the genomic features nearest to a set of regions. | For a set of significant ChIP-seq |
transcription factor | 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.
...
Login to ls6, start and idev session, then load the BioContainers bedtools module, and then check its version.
| Code Block |
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| language | bash |
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| title | Start an idev session |
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idev -m 120 -N 1 -A OTH21164 -r CoreNGSday5core-ngs-class-0606
# ...or, without a reservation
idev -m 120 -N 1 -A OTH21164 -p development
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/GFFGFF3, 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 GFFGTF/GTFGFF3 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).
...
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 more often 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.
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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 GTF/GFF/GTF format, you will not like the results. This is because the most useful information in a GTF/GFF/GTF file is in a loosely-structured attributes field.
Also unfortunately, there are a number of variations of both annotation formats However ; however both GTF and GFF share the first 8 Tab-separated fields:
- seqname - The name of the chromosome or scaffoldcontig.
- source - Name of the program that generated this feature, or other data source (e.g. public database)
- feature_type - Type of the feature. Examples of common feature types include:Some examples of common feature types are:, 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 and (7) strand. 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.
...
| Expand |
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| title | Make sure you're in an idev session |
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| Code Block |
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| language | bash |
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| title | Start an idev session |
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| idev -m 120 -N 1 -A OTH21164 -r CoreNGSday5
# ...core-ngs-class-0606
# or, without a reservation:
idev -m 120 -N 1 -A OTH21164 -p development
module load biocontainers
module load bedtools
bedtools --version # should be bedtools v2.27.1 |
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...
| Code Block |
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| language | bash |
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| title | Look at GFF annotation entries with less |
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mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rnarnaseq/sacCer_R64-1-1_20110208.gffyeast_mrna.sort.filt.bam* .
cp $CORENGS/yeast_rna/yeast_mrna.sort.filt.bam* .catchup/references/gff/sacCer3.R64-1-1_20110208.gff .
# Use the less pager to look at multiple lines
less sacCer_sacCer3.R64-1-1_20110208.gff
# Look at just the most-important Tab-separated columns
cat sacCer_sacCer3.R64-1-1_20110208.gff | grep -v '#' | cut -f 1,3-5,7 | head -20
# Include the ugly 9th column where attributes are stored
cat sacCer_sacCer3.R64-1-1_20110208.gff | grep -v '#' | cut -f 1,3,9 | head |
...
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.)
| Expand |
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| Code Block |
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| mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rna/sacCer_/catchup/references/gff/sacCer3.R64-1-1_20110208.gff . |
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Read more about what's going on here at piping a histogram.
| Code Block |
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| language | bash |
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| title | Create a histogram of all the feature types in a GFF |
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cd $SCRATCH/core_ngs/bedtools
cat sacCer_sacCer3.R64-1-1_20110208.gff | grep -v '^#' | cut -f 3 | \
sort | uniq -c | sort -k1,1nr | more |
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| Code Block |
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| title | Filter GFF gene feature with awk |
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cat sacCer_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 |
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What most folks to is find some way to convert their GTF/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 GFFGTF/GTF filesGFF3 files. And it's pretty easy to run.
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| Expand |
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| Code Block |
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| mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rna/*catchup/references/gff/sacCer3.R64-1-1_20110208.gff . |
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| Code Block |
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| language | bash |
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| title | Convert GFF to BED with BioITeam script |
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/work/projects/BioITeam/common/script/gtf_to_bed.pl sc_genes.gff 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.
| Code Block |
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| title | 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, 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.
| Expand |
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| Code Block |
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| mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/catchup/bedtools_merge/*.gff .
cp $CORENGS/catchup/bedtools_merge/sc_genes.converted.bed |
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| Code Block |
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| language | bash |
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| title | Re-order the final BED fields |
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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"}
{ # make sure gene name is populated
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 if they are present.
Finally, the 8 re-ordered attributes are:
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| Code Block |
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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 - YAL056CYAL056C-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.
| Expand |
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| Code Block |
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| mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeastcatchup/bedtools_rnamerge/*.gff .
cp $CORENGS/yeastcatchup/bedtools_rnamerge/sc_genes* . |
|
Exercise: How many genes in our sc_genes.bed file are in each category?
| Expand |
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|
Use cut to isolate that field (8), sort to sort the resulting values into blocks, then uniq -c to count the members of each block. |
...
| Expand |
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| Code Block |
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| cut -f 8 sc_genes.bed | sort | uniq -c |
You should see this: | Code Block |
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810 Dubious
897 Uncharacterized
4896 Verified
4 Verified|silenced_gene |
If you want to further order this output listing the most abundant category first, add another sort statement: | Code Block |
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| cut -f 8 sc_genes.bed | sort | uniq -c | sort -k1,1nr |
The -k 1,1nr options says to sort on the 1st field (whitespace delimited) of input, using numeric sorting, in reverse order (i.e., largest first). Which produces: | Code Block |
|---|
4896 Verified
897 Uncharacterized
809 Dubious
4 Verified|silenced_gene |
|
Exercises
We're now (finally!) actually going to do some gene-based analyses of a yeast RNA-seq dataset using bedtools and the BED-formatted yeast gene annotation file we created above.
Get the RNA-seq BAM
Make sure you're in an idev session, since we will be doing some significant computation, and make bedtools and samtools available.
| Code Block |
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| language | bash |
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| title | Start an idev session |
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idev -p development -m 120 -A UT-2015-05-18 -N 1 -n 24 --reservation=BIO_DATA_week_1 |
Copy over the yeast RNA-seq files we'll need (also copy the GFF gene annotation file if you didn't make one).
| Code Block |
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| language | bash |
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| title | Setup for BEDTools exercises |
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# To catch up...
mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rna/sc_genes.bed* .
cp $CORENGS/yeast_rna/*.gff .
# Copy the BAM file
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rna/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?
| Expand |
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samtools flagstat for the different read counts. samtools view + cut + sort + uniq -c for mapping quality distribution samtools view + awk for average mapping quality |
...
| Code Block |
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cd $SCRATCH/core_ngs/bedtools
samtools flagstat yeast_mrna.sort.filt.bam | tee yeast_mrna.flagstat.txt |
| Code Block |
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| title | samtools flagstat output |
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|
3323242 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 secondary
0 + 0 supplementary
922114 + 0 duplicates
3323242 + 0 mapped (100.00% : N/A)
3323242 + 0 paired in sequencing
1661699 + 0 read1
1661543 + 0 read2
3323242 + 0 properly paired (100.00% : N/A)
3323242 + 0 with itself and mate mapped
0 + 0 singletons (0.00% : N/A)
0 + 0 with mate mapped to a different chr
0 + 0 with mate mapped to a different chr (mapQ>=5) |
There are 3323242 total reads, all mapped and all properly paired. So this must be a quality-filtered BAM. There are 922114 duplicates, or about 28%.
To get the distribution of mapping qualities:
| Code Block |
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samtools view yeast_mrna.sort.filt.bam | cut -f 5 | sort | uniq -c |
| Code Block |
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| title | distribution of mapping qualities |
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|
453 20
6260 21
889 22
326 23
971 24
2698 25
376 26
12769 27
268 28
337 29
933 30
1229 31
345 32
5977 33
256 34
249 35
1103 36
887 37
292 38
4648 39
5706 40
426 41
1946 42
1547 43
1761 44
6138 45
1751 46
3019 47
3710 48
3236 49
4467 50
15691 51
25370 52
16636 53
18081 54
7084 55
2701 56
59851 57
2836 58
2118 59
3097901 60 |
To compute average mapping quality:
| Code Block |
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samtools view yeast_mrna.sort.filt.bam | awk '
BEGIN{FS="\t"; sum=0; tot=0}
{sum = sum + $5; tot = tot + 1}
END{printf("mapping quality average: %.1f for %d reads\n", sum/tot,tot) }' |
Mapping qualities range from 20 to 60 – excellent quality! Because the majority reads have mapping quality 60, the average is 59. So again, there must have been quality filtering performed on upstream alignment records.
Use bedtools multicov to count feature overlaps
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.
Here's how to run bedtools multicov, directing the standard output to a file:
...
| Code Block |
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idev -p development -m 120 -A UT-2015-05-18 -N 1 -n 68 --reservation=BIO_DATA_week_1
module load biocontainers
module load samtools
module load bedtools
mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rna/*.gff .
cp $CORENGS/yeast_rna/sc_genes.bed* .
cp $CORENGS/yeast_rna/yeast_mrna.sort.filt.bam* . |
| Code Block |
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| language | bash |
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| title | Run bedtools multicov to count BAM alignments overlapping a set of genes |
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cd $SCRATCH/core_ngs/bedtools
bedtools multicov -s -bams yeast_mrna.sort.filt.bam \
-bed 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?
...
| Code Block |
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wc -l yeast_mrna_gene_counts.bed |
6607 records were written, one for each feature in the sc_genes.bed file.
The overlap count was added as the last field in each output record (here field 9, since the input annotation file had 8 columns).
Exercise: How many features have non-zero overlap counts?
...
| Code Block |
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cut -f 9 yeast_mrna_gene_counts.bed | grep -v '^0' | wc -l
# or
cat yeast_mrna_gene_counts.bed | \
awk '{if ($9 > 0) print $9}' | wc -l |
Most of the genes (6235/6607) have non-zero read overlap counts.
Exercise: What is the total count of reads mapping to gene features?
...
| Code Block |
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cat yeast_mrna_gene_counts.bed | awk '
BEGIN{FS="\t";sum=0;tot=0}
{if($9 > 0) { sum = sum + $9; tot = tot + 1 }}
END{printf("%d overlapping reads in %d genes\n", sum, tot) }' |
There are 1144990 overlapping reads in 6235 gene annotations.
Recall that in the yeast annotations from SGD there are 3 gene classifications: Verified, Uncharacterized and Dubious, and the Dubious ones have no experimental evidence.
Exercise: What is the total count of reads mapping to gene features other than Dubious?
...
| Code Block |
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grep -v 'Dubious' yeast_mrna_gene_counts.bed | awk '
BEGIN{FS="\t";sum=0;tot=0}
{if($9 > 0) { sum = sum + $9; tot = tot + 1 }}
END{printf("%d overlapping reads in %d non-Dubious genes\n", sum, tot) }' |
There are 1093140 overlapping reads in 5578 non-Dubious genes
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.
...
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):
| Expand |
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|
| Code Block |
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| mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rnaseq/*.gff .
cp $CORENGS/yeast_rnaseq/sc_genes.bed* .
cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* .
module load biocontainers
module load bedtools |
|
| Code Block |
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| language | bash |
|---|
| title | Use bedtools merge to collapse overlapping gene annotations |
|---|
|
cd $SCRATCH/core_ngs/bedtools
sort -k1,1 -k2,2n sc_genes.bed > sc_genes.sorted.bed
bedtools merge -i sc_genes.sorted.bed -s \
-c 6,4,4 -o distinct,count,collapse > merged.sc_genes.txt |
The first few lines of the merged.sc_genes.txt file look like this (I've tidied it up a bit):
| Code Block |
|---|
2-micron 251 1523 + 1 R0010W
2-micron 1886 3008 - 1 R0020C
2-micron 3270 3816 + 1 R0030W
2-micron 5307 6198 - 1 R0040C
chrI 334 792 + 2 YAL069W,YAL068W-A
chrI 1806 2169 - 1 YAL068C
chrI 2479 2707 + 1 YAL067W-A
chrI 7234 9016 - 1 YAL067C
chrI 10090 10399 + 1 YAL066W
chrI 11564 11951 - 1 YAL065C |
As we specified:
- Output column 4 has the region's strand.
- Column 5 is the count of merged regions
- Column 6 is a collapsed, comma-separated list of the merged gene names
Exercise: Compare the number of regions in the merged and before-merge gene files.
| Expand |
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|
| Code Block |
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| wc -l sc_genes.bed merged.sc_genes.txt |
There were 6607 genes before merging and 6485 after. |
Exercise: How many regions represent only 1 gene, 2 genes, or more?
| Expand |
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|
Output column 5 has the gene count. | Code Block |
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| cut -f 5 merged.sc_genes.txt | sort | uniq -c | sort -k2,2n |
Produces this histogram: | Code Block |
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| 6374 1
105 2
4 3
1 4
1 7 |
There are 111 regions (105 + 4 + 1 + 1) where more than one gene contributed. Or being fancy: | Code Block |
|---|
| cut -f 5 merged.sc_genes.txt | sort | uniq -c | sort -k2,2n | \
grep -v ' 1$' | awk 'BEGIN{ct=0}{ct=ct+$1}END{print ct}' |
|
Exercise: Repeat the steps above, but first create a good.sc_genes.bed file that does not include Dubious ORFs.
| Expand |
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|
| Code Block |
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| cd $SCRATCH/core_ngs/bedtools
grep -v 'Dubious' sc_genes.bed > good.sc_genes.bed
sort -k1,1 -k2,2n good.sc_genes.bed > good.sc_genes.sorted.bed
bedtools merge -i good.sc_genes.sorted.bed -s \
-c 6,4,4 -o distinct,count,collapse > merged.good.sc_genes.txt
wc -l good.sc_genes.bed merged.good.sc_genes.txt |
There were 5797 "good" (non-Dubious) genes before merging and 5770 after. | Code Block |
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| cut -f 5 merged.good.sc_genes.txt | sort | uniq -c | sort -k2,2n |
Produces this histogram: | Code Block |
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| 5750 1
18 2
1 4
1 7 |
Now there are only 20 regions where more than one gene was collapsed. Clearly eliminating the Dubious ORFs helped. |
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:
| Code Block |
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|
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/mergemulticov.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 have information added 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 should result from collapsing distinct (unique) values of gene file column 6 (the strand, + 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):
...
...
...
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.
| Expand |
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| title | Make sure you're in an idev session |
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|
| Code Block |
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| language | bash |
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| title | Start an idev session |
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| idev -m 120 -N 1 -A OTH21164 -r core-ngs-class-0606
# or, without a reservation
idev -m 120 -N 1 -A OTH21164 -p development
module load biocontainers
module load bedtools
bedtools --version # should be bedtools v2.27.1 |
|
Copy over the yeast RNA-seq files we'll need (also copy the GFF gene annotation file if you didn't make one).
| Code Block |
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| language | bash |
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| title | Setup for BEDTools multicov |
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|
# 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
cd $SCRATCH/core_ngs/bedtools_multicov
cp $CORENGS/ yeastcatchup/bedtools_ rnamerge/merged* .gff .
cp $CORENGS/yeast_rna/sc_genes.bed* .
bed .
# Copy the BAM file
cd $SCRATCH/core_ngs/bedtools_multicov
cp $CORENGS/yeast_ rnarnaseq/yeast_mrna.sort.filt.bam* .
module load biocontainers
module load bedtools |
| Code Block |
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| language | bash |
|---|
| title | Use bedtools merge to collapse overlapping gene annotations |
|---|
|
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):
| Code Block |
|---|
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 |
Output column 4 has the region's strand. Column 5 is the count of merged regions, and column 6 is a comma-separated list of the merged gene names.
Exercise: Compare the number of regions in the merged and before-merge gene files.
...
| Code Block |
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|
wc -l sc_genes.bed merged.sc_genes.txt |
There were 6607 genes before merging and 6485 after.
Exercise: How many regions represent only 1 gene, 2 genes, or more?
| Expand |
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|
Output column 5 has the gene count. | Code Block |
|---|
| cut -f 5 merged.sc_genes.txt | sort | uniq -c | sort -k2,2n |
Produces this histogram: | Code Block |
|---|
| 6374 1
105 2
4 3
1 4
1 7 |
There are 111 regions (105 + 4 + 1 + 1) where more than one gene contributed. |
Exercise: Repeat the steps above, but first create a good.sc_genes.bed file that does not include Dubious ORFs.
...
| Code Block |
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|
cd $SCRATCH/core_ngs/bedtools
grep -v 'Dubious' sc_genes.bed > good.sc_genes.bed
sort -k1,1 -k2,2n good.sc_genes.bed > good.sc_genes.sorted.bed
bedtools merge -i good.sc_genes.sorted.bed -s \
-c 6,4,4 -o distinct,count,collapse > merged.good.sc_genes.txt
wc -l good.sc_genes.bed merged.good.sc_genes.txt |
There were 5797 "good" (non-Dubious) genes before merging and 5770 after.
| Code Block |
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cut -f 5 merged.good.sc_genes.txt | sort | uniq -c | sort -k2,2n |
Produces this histogram:
| Code Block |
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|
5750 1
18 2
1 4
1 7 |
Now there are only 20 regions where more than one gene was collapsed. Clearly eliminating the Dubious ORFs helped.
Exercise: Why did we name the merged file with the extension .txt instead of .bed? What would we need to do to convert it to a proper BED6 file?
| Expand |
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|
The output does not follow the BED6 specification: "chrom, start, end, name, score, strand" The first 3 output columns comply with the BED3 standard (chrom, start, end), but if strand is to be included, it 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: | Code Block |
|---|
|
|
cat merged.good.sc_genes.txt | awk '
BEGIN{FS=OFS="\t"}
{print $1,$2,$3,$6,$5,$4}' > merged.good.sc_genes.bed
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?
| Expand |
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|
samtools flagstat for the different read counts. samtools view + cut + sort + uniq -c for mapping quality distribution samtools view + awk for average mapping quality |
| Expand |
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| Code Block |
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| module load samtools
cd $SCRATCH/core_ngs/bedtools_multicov
samtools flagstat yeast_mrna.sort.filt.bam | tee yeast_mrna.flagstat.txt |
| Code Block |
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| title | samtools flagstat output |
|---|
| 3347559 + 0 in total (QC-passed reads + QC-failed reads)
24317 + 0 secondary
0 + 0 supplementary
922114 + 0 duplicates
3347559 + 0 mapped (100.00% : N/A)
3323242 + 0 paired in sequencing
1661699 + 0 read1
1661543 + 0 read2
3323242 + 0 properly paired (100.00% : N/A)
3323242 + 0 with itself and mate mapped
0 + 0 singletons (0.00% : N/A)
0 + 0 with mate mapped to a different chr
0 + 0 with mate mapped to a different chr (mapQ>=5) |
There are 3323242 total reads, all mapped and all properly paired. So this must be a quality-filtered BAM. There are 922114 duplicates, or about 28%. To get the distribution of mapping qualities (BAM field 5) | Code Block |
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| samtools view yeast_mrna.sort.filt.bam | cut -f 5 | sort | uniq -c |
| Code Block |
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| title | distribution of mapping qualities |
|---|
| 498 20
6504 21
1012 22
355 23
1054 24
2800 25
495 26
14133 27
282 28
358 29
954 30
1244 31
358 32
6143 33
256 34
265 35
1112 36
905 37
309 38
4845 39
5706 40
427 41
1946 42
1552 43
1771 44
6140 45
1771 46
3049 47
3881 48
3264 49
4475 50
15692 51
25378 52
16659 53
18305 54
7108 55
2705 56
59867 57
2884 58
2392 59
3118705 60 |
To compute average mapping quality: | Code Block |
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| samtools view yeast_mrna.sort.filt.bam | awk '
BEGIN{FS="\t"; sum=0; tot=0}
{sum = sum + $5; tot = tot + 1}
END{printf("mapping quality average: %.1f for %d reads\n", sum/tot,tot) }' |
Mapping qualities range from 20 to 60 – excellent quality! Because the majority reads have mapping quality 60, the average is 59.2. So again, there must have been quality filtering performed on upstream alignment records. |
Here's how to run bedtools multicov in stranded mode, directing the standard output to a file:
| Expand |
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|
| Code Block |
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| idev -m 120 -N 1 -A OTH21164 -r core-ngs-class-0606
# or, without a reservation:
idev -m 120 -N 1 -A OTH21164 -p development
module load biocontainers
module load samtools
module load bedtools
mkdir -p $SCRATCH/core_ngs/bedtools_multicov
cd $SCRATCH/core_ngs/bedtools_multicov
cp $CORENGS/catchup/bedtools_merge/merged*bed .
cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* . |
|
| Code Block |
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| language | bash |
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| title | Run bedtools multicov to count BAM alignments overlapping a set of genes |
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|
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 |
Exercises:
- How may records of output were written?
- Where is the count of overlaps per output record?
| Expand |
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|
| Code Block |
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| wc -l yeast_mrna_gene_counts.bed |
6485 records were written, one for each feature in the merged.sc_genes.bed file. The overlap count was added as the last field in each output record. So here it is field 7 since the input annotation file had 6 columns. |
Exercise: How many features have non-zero overlap counts?
| Expand |
|---|
|
| Code Block |
|---|
| cut -f 7 yeast_mrna_gene_counts.bed | grep -v '^0' | wc -l
# or
cut -f 7 yeast_mrna_gene_counts.bed | grep -v -c '^0'
# or
cat yeast_mrna_gene_counts.bed | \
awk '{if ($7 > 0) print $7}' | wc -l |
Most of the genes (6141/6485) have non-zero read overlap counts. |
Exercise: What is the total count of reads mapping to gene features?
| Expand |
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|
| Code Block |
|---|
| cat yeast_mrna_gene_counts.bed | awk '
BEGIN{FS="\t";sum=0;tot=0}
{if($7 > 0) { sum = sum + $7; tot = tot + 1 }}
END{printf("%d overlapping reads in %d genes\n", sum, tot) }' |
There are 1,152,831 overlapping reads in 6,141 non-0 gene annotations. |
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 100 Vert. Cons track is a signal track
- the x-axis is the genome position
- the y-axis represents the base-wise conservation among vertebrates
- customize the 100 Vert. Cons track
- right-click on "100 Vert. Cons" text in the left margin,
- select "Configure 100 Vert. Cons" from the menu
- in the 100 Vert. Cons Track Settings dialog:
- change "Track height" to 100
- change "Data view scaling" to "auto-scale to data view"
- click "OK"
- 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.
| Expand |
|---|
| title | Make sure you're in an idev session |
|---|
|
| Code Block |
|---|
| language | bash |
|---|
| title | Start an idev session |
|---|
| idev -m 120 -N 1 -A OTH21164 -r core-ngs-class-0606
# or, without a reservation:
idev -m 120 -N 1 -A OTH21164 -p development
module load biocontainers
module load bedtools
bedtools --version # should be bedtools v2.27.1 |
|
| Code Block |
|---|
| language | bash |
|---|
| title | Prepare for bedtools coverage |
|---|
|
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.
| Code Block |
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|
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:
| Code Block |
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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:
| Code Block |
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# 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:
| Code Block |
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|
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).
| Code Block |
|---|
|
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).
| Code Block |
|---|
|
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:
| Code Block |
|---|
|
# 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 |