Use our summer school reservation (CoreNGSday5) when submitting batch jobs to get higher priority on the ls6 normal queue today:

sbatch --reservation=CoreNGSday5 <batch_file>.slurm
idev -m 180 -N 1 -A OTH21164 -r CoreNGSday5

The BED format

BED (Browser Extensible Data) format is a simple text format for location-oriented data (genomic regions) developed to support UCSC Genome Browser tracks. Standard BED files have 3 to 6 Tab-separated columns, although up to 12 columns are defined. (Read more about the UCSC Genome Browser's official BED format.)

These 6 BED fields are so important that you should memorize them. Keep repeating "chrom, start, end, name, score, strand" until the words trip off your tongue (smile)

  1. chrom (required) – string naming the chromosome or other contig
  2. start (required) – the 0-based start position of the region
  3. end (required) – the 1-based end position of the region
  4. name (optional) – an arbitrary string describing the region
  5. score (optional) – an integer score for the region
  6. strand (optional) - a single character describing the region's strand

Important rules for BED format:

Note that the UCSC Genome Browser also defines many BED-like data formats (e.g. bedGraph, narrowPeak, tagAlign and various RNA element formats). See supported UCSC Genome Browser data formats for more information and examples.

In addition to standard-format BED files, one can create custom BED files that have at least 3 of the standard fields (chrom, start, end), followed by any number of custom fields. For example:

As we will see, BEDTools functions require BED3+ input files, or BED6+ if strand-specific operations are requested.

BEDTools overview

The BEDTools suite is a set of utilities for manipulating BED and BAM files. We call it the "Swiss army knife" for genomic region analyses because its sub-commands are so numerous and versatile. Some of the most common bedtools operations perform set-theory functions on regions: intersection (intersect), union (merge), set difference (subtract) – but there are many others. The table below lists some of the most useful sub-commands along with applicable use cases.

Sub-commandDescriptionUse case(s)
bamtobedConvert 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.
bamtofastqExtract 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.
getfastaGet 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 genome-wide signal trace
  • Produce a per-base genome-wide signal (in bedGraph format), for example for a ChIP-seq or ATAC-seq experiment. After conversion to binary bigWig format, such tracks can be visualized in the Broad's IGV (Integrative Genome Browser) application, or configured in the UCSC Genome Browser as custom tracks.
coverage
  • Compute coverage of your regions
  • 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.
multicovCount 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.
mergeCombine 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.
subtractRemove unwanted regions.Remove rRNA gene regions from a merged gene annotations file before counting.
intersectDetermine the overlap between two sets of regions.Similar to multicov, but can also report (not just count) the overlapping regions.
closestFind the genomic features nearest to a set of regions.For a set of significant ChIP-seq transcription factor (TF) binding regions ("peaks") that have been identified, determine nearby genes that may be targets of TF regulation.

We will explore a few of these functions in our exercises.

BEDTools versions

BEDTools is under active development and is always being refined and extended. Unfortunately, sometimes changes are made that are incompatible with previous BEDTools versions. For example, a major change to the way bedtool merge functions was made after bedtools v2.17.0.

So it is important to know which version of BEDTools you are using, and read the documentation carefully to see if changes have been made since your version.

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

idev -m 120 -N 1 -A OTH21164 -r CoreNGSday5 
# ...
module load biocontainers
module load bedtools
bedtools --version   # should be bedtools v2.27.1

Input format considerations

The most important thing to remember about comparing regions using BEDTools, is that all input files must share the same set contig names and be based on the same reference! For example, if an alignment was performed against a human GRCh38 reference genome from Gencode, use annotations from the corresponding GFF/GTF annotations.

About strandedness

By default many bedtools utilities that perform overlapping, consider reads overlapping the feature on either strand, but can be made strand-specific with the -s or -S option. This strandedness options for bedtools utilities refers the orientation of the R1 read with respect to the feature's (gene's) strand.

RNA-seq libraries can be constructed with 3 types of strandedness:

  1. sense stranded – the R1 read should be on the same strand as the gene.
  2. antisense stranded – the R1 read should be on the opposite strand as the gene.
  3. unstranded – the R1 could be on either strand

Which type of RNA-seq library you have depends on the library preparation method – so ask your sequencing center! Our yeast RNA-seq library is sense stranded (note that most RNA-seq libraries prepared by GSAF are antisense stranded).

If you have a stranded RNA-seq library, you should use either -s or -S to avoid false counting against a gene on the wrong strand.

About GFF/GTF annotation files

Annotation files that you retrieve from public databases are often in GTF (Gene Transfer Format) or one of the in GFF (General Feature Format) formats (usually GFF3 these days).

Unfortunately, both formats are obscure and hard to work with directly. While bedtools does accept annotation files in GFF/GTF format, you will not like the results. This is because the most useful information in a GFF/GTF file is in a loosely-structured attributes field.

Also unfortunately, there are a number of variations of both annotation formats However both GTF and GFF share the first 8 Tab-separated fields:

  1. seqname - The name of the chromosome or scaffold.
  2. source - Name of the program that generated this feature, or other data source (e.g. database)
  3. feature_type - Type of the feature. Examples of common feature types include:
  4. start - Start position of the feature, with sequence numbering starting at 1.
  5. end - End position of the feature, with sequence numbering starting at 1.
  6. score - A numeric value. Often but not always an integer.
  7. strand - Defined as + (forward), - (reverse), or . (no relevant strand)
  8. frame - For a CDS, one of 0, 1 or 2, specifying the reading frame of the first base; otherwise '.'

The Tab-separated columns will care about are (1) seqname, (3) feature_type and (4,5) start, end. The reason we care is that when working with annotations, we usually only want to look at annotations of a particular type, most commonly gene, but also transcript or exon.

So where is the real annotation information, such as the unique gene ID or gene name? Both formats also have a 9th field, which is usually populated by a set of name/value pair attributes, and that's where the useful information is (e.g. the unique feature identifier, name, and so forth).

Take a quick look at a yeast annotation file, sacCer_R64-1-1_20110208.gff using less.


idev -m 120 -N 1 -A OTH21164 -r CoreNGSday5
# ...
module load biocontainers
module load bedtools
bedtools --version   # should be bedtools v2.27.1



mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools 
cp $CORENGS/yeast_rnaseq/sacCer3.R64-1-1_20110208.gff . 
cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* .

# Use the less pager to look at multiple lines
less sacCer3.R64-1-1_20110208.gff

# Look at just the most-important Tab-separated columns
cat sacCer3.R64-1-1_20110208.gff | grep -v '#' | cut -f 1,3-5 | head -20

# Include the ugly 9th column where attributes are stored
cat sacCer3.R64-1-1_20110208.gff | grep -v '#' | cut -f 1,3,9 | head

In addition to comment lines (starting with #), you can see the chrI contig names in column 1 and various feature types in column 3. You see also see tags like Name=YAL067C;gene=SEO1; among the attributes on some records, but in general the attributes column information is really ugly.

To summarize, we have two problems to solve:

  1. We only care about a subset of feature types (here genes), and
  2. We want the important annotation information – gene names and IDs – to appear as regular columns instead of weird name/value pairs.

Filter annotations based on desired feature type

One of the first things you want to know about your annotation file is what gene features it contains. Here's how to find that: (Read more about what's going on here at Piping a histogram.)


mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rnaseq/sacCer3.R64-1-1_20110208.gff .



cd $SCRATCH/core_ngs/bedtools
cat sacCer3.R64-1-1_20110208.gff | grep -v '^#' | cut -f 3 | \
  sort | uniq -c | sort -k1,1nr | more

You should see something like this.

  7077 CDS
  6607 gene
   480 noncoding_exon
   383 long_terminal_repeat
   376 intron
   337 ARS
   299 tRNA
   190 region
   129 repeat_region
   102 nucleotide_match
    89 transposable_element_gene
    77 snoRNA
    50 LTR_retrotransposon
    32 telomere
    31 binding_site
    27 rRNA
    24 five_prime_UTR_intron
    21 pseudogene
    17 chromosome
    16 centromere
    15 ncRNA
     8 external_transcribed_spacer_region
     8 internal_transcribed_spacer_region
     6 snRNA
     3 gene_cassette
     2 insertion

Let's create a file that contains only the 6607 gene entries:

cat sacCer3.R64-1-1_20110208.gff | grep -v '#' | \
  awk 'BEGIN{FS=OFS="\t"}{ if($3=="gene"){print} }' \
  > sc_genes.gff
wc -l sc_genes.gff

The line count of sc_genes.gff should be 6607 – one for each gene entry.

Convert GFF/GTF format to BED with ID in the name field

Our sc_genes.gff annotation subset now contains only the 6607 genes in the Saccharomyces cerevisiae genome. This addresses our first problem, but entries in this file still have the important information – the gene ID and name – in the loosely-structured 9th attributes field.

If we want to associate reads with features, we need to have the feature names where they are easy to extract!

What most folks to is find some way to convert their GFF/GTF file to a BED file, parsing out some (or all) of the name/value attribute pairs into BED file columns after the standard 6. You can find such conversion programs on the web – or write one yourself. Or you could use the BioITeam conversion script, /work/projects/BioITeam/common/script/gtf_to_bed.pl. While it will not work 100% of the time, it manages to do a decent job on most GFF/GTF files. And it's pretty easy to run.

If this script doesn't work on your annotation file, please let Anna know. She is always looking for cases where the conversion fails, and will try to fix it.

Here we just give the script the GFF file to convert, plus a 1 that tells it to URL decode weird looking text (e.g. our Note attribute values).


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



/work/projects/BioITeam/common/script/gtf_to_bed.pl sc_genes.gff 1 \
  > sc_genes.converted.bed

The program reads the input file twice – once to gather all the attribute names, and then a second time to write the attribute values in well-defined columns. You'll see output like this:

----------------------------------------
Gathering all attribute names for GTF 'sc_genes.gff'...
  urlDecode = 1, tagAttr = tag
Done!
  6607 lines read
  6607 locus entries
  8 attributes found:
(Alias ID Name Note Ontology_term dbxref gene orf_classification)
----------------------------------------
Writing BED output for GTF 'sc_genes.gff'...
Done! Wrote 6607 locus entries from 6607 lines

To find out what the resulting columns are, look at the header line out the output BED file:

head -1 sc_genes.converted.bed 

For me the resulting 16 attributes are as follows (they may have a different order for you). I've numbered them below for convenience.

 1. chrom          2. start   3. end     4. featureType  5. length  6. strand
 7. source         8. frame   9. Alias  10. ID          11. Name   12. Note
13. Ontology_term 14. dbxref 15. gene   16. orf_classification

The final transformation is to do a bit of re-ordering, dropping some fields. We'll do this with awk, because cut can't re-order fields. While this is not strictly required, it can be helpful to have the critical fields (including the gene ID) in the 1st 6 columns. We do this separately for the header line and the rest of the file so that the BED file we give bedtools does not have a header (but we know what those fields are). We would normally preserve valuable annotation information such as Ontology_term, dbxref and Note, but drop them here for simplicity.


mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rnaseq/*.gff .
cp $CORENGS/yeast_rnaseq/sc_genes.converted.bed .



head -1 sc_genes.converted.bed | sed 's/\r//' | awk '
 BEGIN{FS=OFS="\t"}{print $1,$2,$3,$10,$5,$6,$15,$16}
 ' > sc_genes.bed.hdr

tail -n +2 sc_genes.converted.bed | sed 's/\r//' | awk '
 BEGIN{FS=OFS="\t"}
 { if($15 == "") {$15 = $10} # make sure gene name is populated
   print $1,$2,$3,$10,$5,$6,$15,$16}
 ' > sc_genes.bed

One final detail. Annotation files you download may have non-Unix (linefeed-only) line endings. Specifically, they may use Windows line endings (carriage return + linefeed). (Read about Line ending nightmares.) The expression sed 's/\r//' uses the sed (substitution editor) tool to replace carriage return characters ( \r ) with nothing, removing them from the output.

Finally, the 8 re-ordered attributes are:

 1. chrom  2. start  3. end  4. ID  5. length  6. strand
 7. gene   8. orf_classification

**Whew**! That was a lot of work. Welcome to the world of annotation wrangling – it's never pretty! But at least the result is much nicer looking. Examine the results using more or less or head:

cat sc_genes.bed | head -20

Doesn't this look better? (I've tidied up the output a bit below.)

chrI    334     649     YAL069W         315     +       YAL069W    Dubious
chrI    537     792     YAL068W-A       255     +       YAL068W-A  Dubious
chrI    1806    2169    YAL068C         363     -       PAU8       Verified
chrI    2479    2707    YAL067W-A       228     +       YAL067W-A  Uncharacterized
chrI    7234    9016    YAL067C         1782    -       SEO1       Verified
chrI    10090   10399   YAL066W         309     +       YAL066W    Dubious
chrI    11564   11951   YAL065C         387     -       YAL065C    Uncharacterized
chrI    12045   12426   YAL064W-B       381     +       YAL064W-B  Uncharacterized
chrI    13362   13743   YAL064C-A       381     -       YAL064C-A  Uncharacterized
chrI    21565   21850   YAL064W         285     +       YAL064W    Verified
chrI    22394   22685   YAL063C-A       291     -       YAL063C-A  Uncharacterized
chrI    23999   27968   YAL063C         3969    -       FLO9       Verified
chrI    31566   32940   YAL062W         1374    +       GDH3       Verified
chrI    33447   34701   YAL061W         1254    +       BDH2       Uncharacterized
chrI    35154   36303   YAL060W         1149    +       BDH1       Verified
chrI    36495   36918   YAL059C-A       423     -       YAL059C-A  Dubious
chrI    36508   37147   YAL059W         639     +       ECM1       Verified
chrI    37463   38972   YAL058W         1509    +       CNE1       Verified
chrI    38695   39046   YAL056C-A       351     -       YAL056C-A  Dubious
chrI    39258   41901   YAL056W         2643    +       GPB2       Verified

Note that value in the 8th column. In the yeast annotations from SGD there are 3 gene classifications: Verified, Uncharacterized and Dubious. The Dubious ones have no experimental evidence so are generally excluded.


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


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

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



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

You should see this:

    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:

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:

   4896 Verified
    897 Uncharacterized
    809 Dubious
      4 Verified|silenced_gene


Exercises

Use bedtools merge to collapse overlapping annotations

One issue that often arises when dealing with BED regions is that they can overlap one another. For example, on the yeast genome, which has very few non-coding areas, there are some overlapping ORFs (Open Reading Frames), especially Dubious ORFs that overlap Verified or Uncharacterized ones. When bedtools looks for overlaps, it will count a read that overlaps any of those overlapping ORFs – so some reads can be counted twice.

One way to avoid this double-counting is to collapse the overlapping regions into a merged set of non-overlapping regions – and that's what the bedtools merge utility does (http://bedtools.readthedocs.io/en/latest/content/tools/merge.html).

Here we're going to use bedtools merge to collapse our gene annotations into a non-overlapping set, first for all genes, then for only non-Dubious genes.

The output from bedtools merge always starts with 3 columns: chrom, start and end of the merged region only.

Using the -c (column) and -o (operation) options, you can 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:

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):


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



cd $SCRATCH/core_ngs/bedtools
sort -k1,1 -k2,2n sc_genes.bed > sc_genes.sorted.bed
bedtools merge -i sc_genes.sorted.bed -s -c 6,4,4 -o distinct,count,collapse > merged.sc_genes.txt

The first few lines of the merged.sc_genes.txt file look like this (I've tidied it up a bit):

2-micron        251     1523    +       1       R0010W
2-micron        1886    3008    -       1       R0020C
2-micron        3270    3816    +       1       R0030W
2-micron        5307    6198    -       1       R0040C
chrI            334     792     +       2       YAL069W,YAL068W-A
chrI            1806    2169    -       1       YAL068C
chrI            2479    2707    +       1       YAL067W-A
chrI            7234    9016    -       1       YAL067C
chrI            10090   10399   +       1       YAL066W
chrI            11564   11951   -       1       YAL065C

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.


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?

Output column 5 has the gene count.

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

Produces this histogram:

   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.


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.

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

Produces this histogram:

   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 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:

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

Use bedtools multicov to count feature overlaps

We're now (finally!) actually going to do some gene-based analyses of a yeast RNA-seq dataset using bedtools and the BED-formatted, merged yeast gene annotation file we created above.

In this section we'll use bedtools multicov to count RNA-seq reads that overlap our gene features. The bedtools multicov command (http://bedtools.readthedocs.io/en/latest/content/tools/multicov.html) takes a feature file (GFF/BED/VCF) and counts how many reads from one or more input BAM files overlap those feature. The input BAM file(s) must be position-sorted and indexed.

Make sure you're in an idev session, since we will be doing some significant computation, and make bedtools and samtools available.

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

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

# Get the merged yeast genes bed file if you didn't create one
mkdir -p $SCRATCH/core_ngs/bedtools_multicov
cd $SCRATCH/core_ngs/bedtools_multicov
cp $CORENGS/catchup/bedtools_merge/merged*bed .

# Copy the BAM file
cd $SCRATCH/core_ngs/bedtools_multicov
cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* .

Exercises: How many reads are represented in the yeast_mrna.sort.filt.bam file? How many mapped? How many proper pairs? How many duplicates? What is the distribution of mapping qualities? What is the average mapping quality?

samtools flagstat for the different read counts.

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

samtools view + awk for average mapping quality



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


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:

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


    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:

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:


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
cp $CORENGS/catchup/bedtools_merge/merged*bed      $SCRATCH/core_ngs/bedtools_multicov/
cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* $SCRATCH/core_ngs/bedtools_multicov/



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?


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?


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?


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 (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/).

The bedtools genomecov function (https://bedtools.readthedocs.io/en/latest/content/tools/coverage.html), with the -bg (bedgraph) option produces output in bedGraph format. Here we'll analyze the per-base coverage of yeast RNAseq reads in our merged yeast gene regions.

Make sure you're in an idev session, then prepare a directory for this exercise.

idev -m 120 -N 1 -A OTH21164 -r CoreNGSday5
module load biocontainers
module load bedtools

mkdir -p $SCRATCH/core_ngs/bedtools_genomecov
cp $CORENGS/catchup/bedtools_merge/merged*bed      $SCRATCH/core_ngs/bedtools_genomecov/
cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* $SCRATCH/core_ngs/bedtools_genomecov/

Then calling bedtools genomecov is easy. The -bg option says to report the depth in bedGraph format.

cd $SCRATCH/core_ngs/bedtools_genomecov
bedtools genomecov -bg -ibam yeast_mrna.sort.filt.bam > yeast_mrna.genomecov.bedGraph

wc -l yeast_mrna.genomecov.bedGraph # 1519274 lines

The bedGraph (BED3+) format has only 4 columns: chrom start end value and does not need to include positions with 0 reads. Here the count is the number of reads covering each base in the region given by chrom start end, as you can see looking at the first few lines with head:

chrI    4348    4390    2
chrI    4390    4391    1
chrI    4745    4798    2
chrI    4798    4799    1
chrI    4949    4957    2
chrI    4957    4984    4
chrI    4984    4997    6
chrI    4997    4998    5
chrI    4998    5005    4
chrI    5005    5044    2
chrI    5044    5045    1
chrI    6211    6268    2
chrI    6268    6269    1
chrI    7250    7257    3
chrI    7257    7271    4
chrI    7271    7274    6
chrI    7274    7278    7
chrI    7278    7310    8
chrI    7310    7315    6
chrI    7315    7317    5 

Because this bedGraph file is for the small-ish (12Mb) yeast genome, and for reads that cover only part of that genome, it is not too big – only ~34M. But depending on the species and read depth, bedGraph files can get very large, so there is a 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:

# 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):

module load ucsc_tools

cd $SCRATCH/core_ngs/bedtools_genomecov
bedGraphToBigWig  # look at its usage

# create the needed chromosome sizes file from our BAM header
module load samtools
samtools view -H yeast_mrna.sort.filt.bam | grep -P 'SN[:]' | \
  perl -pe 's/.*SN[:]//' | perl -pe 's/LN[:]//' > sc_chrom_sizes.txt

cat sc_chrom_sizes.txt

# displays:
chrI    230218
chrII   813184
chrIII  316620
chrIV   1531933
chrV    576874
chrVI   270161
chrVII  1090940
chrVIII 562643
chrIX   439888
chrX    745751
chrXI   666816
chrXII  1078177
chrXIII 924431
chrXIV  784333
chrXV   1091291
chrXVI  948066
chrM    85779

Finally, call bedGraphToBigWig after sorting the bedGraph file again using the sort format bedGraphToBigWig likes. (You can try calling bedGraphToBigWig without sorting to see the error).

cd $SCRATCH/core_ngs/bedtools_genomecov
export LC_COLLATE=C; sort -k1,1 -k2,2n -k3,3n yeast_mrna.genomecov.bedGraph > yeast_mrna.genomecov.sorted.bedGraph
bedGraphToBigWig yeast_mrna.genomecov.bedGraph sc_chrom_sizes.txt yeast_mrna.genomecov.bw

Just like 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. Unfortunately, the ucsc-bigwigtobedgraph BioContainer seems to be broken, so we'll use a version in the BioITeam area instead:

cd $SCRATCH/core_ngs/bedtools_genomecov
# see usage for bigWigToBedGraph:
/work/projects/BioITeam/common/opt/UCSC_utils.2019_08/bigWigToBedGraph

# use the program to view a few lines of the binary bigWig file
/work/projects/BioITeam/common/opt/UCSC_utils.2019_08/bigWigToBedGraph \
  yeast_mrna.genomecov.bw stdout | head