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title	Reservations

Use our summer school reservation (CoreNGS-Fri) when submitting batch jobs to get higher priority on the ls6 normal queue today:

sbatch --reservation=CoreNGS-Fri <batch_file>.slurm idev -m 180 -N 1 -A OTH21164 -r CoreNGS-Fri

Table of Contents

The BED format

...

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).
genomecov	Generate per-base	coverage	Compute genome-wide coverage of your regionsGenerate signal trace	Produce a per-base genome-wide signaltrace(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	You have performed a WGS (whole genome sequencing) experiment and 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.	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 (the overlapping regions, not just count ) the overlapping regionsthem.
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.

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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 ls6, start and idev session, then load the BioContainers bedtools module, and check its version.

Code Block

language	bash
title	Start an idev session

idev -pm development120 -mN 1201 -A OTH21164 UT-2015-05-18r CoreNGS-Fri
# or
idev -m 90 -N 1 -nA 68OTH21164 --reservation=BIO_DATA_week_1
# ...p development

module load biocontainers
module load bedtools
bedtools --version   # should be bedtools v2.27.1

...

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 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.

...

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).

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seqname - The name of the chromosome or scaffoldcontig.
source - Name of the program that generated this feature, or other data source (e.g. database)
feature_type - Type of the feature. Examples of common feature types include:, for example:
- Some examples of common feature types are: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.
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 '.'

...

Expand

title	Make sure you're in an idev session

Code Block

language	bash
title	Start an idev session

idev -pm normal120 -mN 1201 -A UT-2015-05-18OTH21164 -r CoreNGS-Fri
# or
idev -m 90 -N 1 -n 68 
# ...A OTH21164 -p development

module load biocontainers
module load bedtools
bedtools --version   # should be bedtools v2.27.1

...

Code Block

language	bash
title	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_rnarnaseq/yeast_mrna.sort.filt.bam* .

# Use  
cp $CORENGS/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 | 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 piping a histogram.)

Expand

title	Setup (if needed)

Code Block

language	bash

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 .

Code Block

language	bash
title	Create a histogram of all the feature types in a GFF

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

...

Code Block

title	Filter GFF gene feature with awk

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

...

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, /work2work/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.

...

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).

Expand

title	Setup (if needed)

Code Block

language	bash

mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rna/*catchup/references/gff/sacCer3.R64-1-1_20110208.gff .

Code Block

language	bash
title	Convert GFF to BED with BioITeam script

/work2work/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

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, becuase 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

title	Setup (if needed)

Code Block

language	bash

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

Code Block

language	bash
title	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.

...

Code Block

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    -    1782   SEO1 -   Verified
chrI    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    +    1149   BDH1 +   Verified
chrI    36495BDH1       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.

Expand

title	Setup (if needed)

Code Block

language	bash

mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeastcatchup/bedtools_rnamerge/*.gff .  
cp $CORENGS/catchup/yeastbedtools_rnamerge/sc_genes* .

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

...

Expand

title	Answer

Code Block

language	bash

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

You should see this:

Code Block
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

language	bash

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

language	bash
title	Start an idev session

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

language	bash
title	Setup for BEDTools exercises

# 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

title	Hints

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

title	Answer

Code Block

language	bash

cd $SCRATCH/core_ngs/bedtools
samtools flagstat yeast_mrna.sort.filt.bam | tee yeast_mrna.flagstat.txt

Code Block

title	samtools flagstat output

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

language	bash

samtools view yeast_mrna.sort.filt.bam | cut -f 5 | sort | uniq -c

Code Block

title	distribution of mapping qualities

To compute average mapping quality:

Code Block

language	bash

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:

Expand

title	Setup (if needed)

Code Block

language	bash

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

language	bash
title	Run bedtools multicov to count BAM alignments overlapping a set of genes

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?

Expand

title	Answers

Code Block

language	bash

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?

Expand

title	Answer

Code Block

language	bash

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?

Expand

title	Answer

Code Block

language	bash

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?

Expand

title	Hint

grep -v 'Dubious'

Expand

title	Answer

Code Block

language	bash

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 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):

Expand

title	Setup (if needed)

Code Block

language	bash

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

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.

Expand

title	Answer

Code Block

language	bash

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

title	Answer

Output column 5 has the gene count.

Code Block

language	bash

cut -f 5 merged.sc_genes.txt | sort | uniq -c | sort -k2,2n

Produces this histogram:

Code Block

language	bash

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.

Expand

title	Answer

Code Block

language	bash

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

language	bash

cut -f 5 merged.good.sc_genes.txt | sort | uniq -c | sort -k2,2n

Produces this histogram:

Code Block

language	bash

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 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

language	bash

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 (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/mergemulticov.html) takes a feature file (GFF/BED/VCF) and counts how many reads from one or more input BAM files overlap those feature.

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):

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.

Code Block

language	bash
title	Start an idev session

idev -m 120 -N 1 -A OTH21164 -r CoreNGS-Fri
# or
idev -m 90 -N 1 -A OTH21164 -p development

Copy over the yeast RNA-seq files we'll need (also copy the GFF gene annotation file if you didn't make one).

mkdir -p

 $SCRATCH/core_ngs/bedtools

cd $SCRATCH/core_ngs/bedtools

_multicov
cp $CORENGS/yeast_

rna

rnaseq/

*.gff . cp $CORENGS/yeast_rna/sc_genes.bed* . cp $CORENGS/yeast_rna/

yeast_mrna.sort.filt.bam* .

Code Block

language	bash
title	Setup for BEDTools multicov

# 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

Expand

title	Setup (if needed)

Code Block

language	bash

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

title	Hints

samtools flagstat for the different read counts.

samtools view + cut + sort + uniq -c for mapping quality distribution

samtools view + awk for average mapping quality

* . module load biocontainers module load bedtoolsUse bedtools merge to collapse overlapping gene annotations

Expand

title	Answer

Code Block

language	bash

title

cd $SCRATCH/core_ngs/bedtools_multicov

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.

Expand

title	Answer

Code Block

language	bash

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

title	Answer

Output column 5 has the gene count.

Code Block

language	bash

cut -f 5 merged.sc_genes.txt | sort | uniq -c | sort -k2,2n

Produces this histogram:

Code Block

language	bash

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.

Expand

title	Answer

Code Block

language	bash

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

language	bash

cut -f 5 merged.good.sc_genes.txt | sort | uniq -c | sort -k2,2n

Produces this histogram:

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language	bash

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?

samtools flagstat yeast_mrna.sort.filt.bam | tee yeast_mrna.flagstat.txt

Code Block

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:

Code Block

language	bash

samtools view yeast_mrna.sort.filt.bam | cut -f 5 | sort | uniq -c

Code Block

title	distribution of mapping qualities

To compute average mapping quality:

Code Block

language	bash

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.

Here's how to run bedtools multicov in stranded mode, directing the standard output to a file:

Expand

title	Setup (if needed)

Code Block

language	bash

idev -m 120 -N 1 -A OTH21164 -r CoreNGSday5
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

language	bash
title	Run bedtools multicov to count BAM alignments overlapping a set of genes

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?

Expand

title	Answers

Code Block

language	bash

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 (here field 7, since the input annotation file had 6 columns).

Exercise: How many features have non-zero overlap counts?

Expand

title	Answer

Code Block

language	bash

cut -f 7 yeast_mrna_gene_counts.bed | grep -v '^0' | wc -l
# 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

title	Answer

Code Block

language	bash

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 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.

Code Block

language	bash
title	Prepare for bedtools coverage

idev -m 120 -N 1 -A OTH21164 -r CoreNGS-day5
# or
idev -m 90 -N 1 -A OTH21164 -p development

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.

Code Block

language	bash

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

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 coresponding 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

# 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 (see Tips and tricks#perlpatternsubstitution):

Code Block

language	bash

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

language	bash

cd $SCRATCH/core_ngs/bedtools_genomecov
export LC_COLLATE=C
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

language	bash

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:

cat merged.good.sc_genes.txt | awk ' BEGIN{FS=OFS="\t"} {print $1,$2,$3,$6,$5,$4}' > merged.good.sc_genes.bed

Code Block

language	bash

# 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

Expand

title	Answer

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

language	bash

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Versions Compared

Old Version 57

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Key

The BED format

Exercises

Get the RNA-seq BAM

Use bedtools multicov to count feature overlaps

Use bedtools merge to collapse overlapping annotations

Use bedtools merge to collapse overlapping annotations

Use bedtools multicov to count feature overlaps

Use bedtools genomecov to create a signal track