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Tip
titleReservations

Use our

summer school

today's reservation (

CoreNGSday5

core-ngs-class-0606) 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

Table of Contents

The BED format

...

.

Code Block
languagebash
titleRequest an interactive (idev) node
# 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


Table of Contents

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.)

...

  1. chrom (required) – string naming the chromosome or other contig
  2. start (required) – the 0-based start position of the region
  3. end (required) – the 1-based end position of the region
  4. name (optional) – an arbitrary string describing the region
    • for BED files loaded as UCSC Genome Browser tracks, this text is displayed above the region
  5. score (optional) – an integer score for the region
    • for BED files to be loaded as UCSC Genome Browser tracks, this should be a number between 0 and 1000, higher = "better"
    • for non-GenBrowse BED files, this can be any integer value (e.g. the length of the region)
  6. strand (optional) - a single character describing the region's strand
    • + –   plus strand (Watson strand) region
    • - –    minus strand (Crick strand) region
    • . –    no strand – the region is not associated with a strand (e.g. a transcription factor binding region)

...

  • 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!

...

  • A BED3+ file contains the 3 required BED fields, followed by some number of user-defined columns (
    • all records
    with
    • having the same number number
    )
    • of columns
  • 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-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
    • 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 

      After conversion to binary bigWig format, such tracks can be

      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

      1. You have performed a WGS (Whole Genome Sequencing) experiment and want to know if has resulted in the desired coverage depth.
      2. 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.

      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.

      merge
      intersectDetermine the overlap between two sets of regions.Similar to multicov, but can also report the overlapping regions, not just count them.
      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.

      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.

      subtractRemove unwanted regions.
      1. Remove rRNA/tRNA gene regions
      from a merged gene annotations file
      1. before counting
      .intersectDetermine the overlap between two sets of regions.Similar to multicov, but can also report (not just count) the overlapping regions
      1. for RNA-seq.
      2. Remove low-complexity genomic regions before peak calling for ChIP-seq or ATAC-seq.
      closestFind 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
    languagebash
    titleStart an idev session
    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.

    ...

    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:

    1. seqname - The name of the chromosome or scaffoldcontig.
    2. source - Name of the program that generated this feature, or other data source (e.g. public database)
    3. 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
    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. Meaning differs and not usually important.
    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 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
    titleMake sure you're in an idev session


    Code Block
    languagebash
    titleStart an idev session
    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


    ...

    Code Block
    languagebash
    titleLook at GFF annotation entries with less
    mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools 
cp $CORENGS/yeast_rnaseq/sacCer3.R64-1-1_20110208.gff .yeast_mrna.sort.filt.bam* .  
cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* .catchup/references/gff/sacCer3.R64-1-1_20110208.gff . 

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

# Look at just the most-important Tab-separated columns
cat sacCer3.R64-1-1_20110208.gff | grep -v '#' | cut -f 1,3-5,7 | 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

    ...

    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
    titleSetup (if needed)


    Code Block
    languagebash
    mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/yeast_rnaseq/catchup/references/gff/sacCer3.R64-1-1_20110208.gff .


    Read more about what's going on here at piping a histogram.

    Code Block
    languagebash
    titleCreate a histogram of all the feature types in a 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

    ...

    Code Block
    titleFilter GFF gene feature with awk
    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

    ...

    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.

    ...

    Expand
    titleSetup (if needed)


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



    Code Block
    languagebash
    titleConvert GFF to BED with BioITeam script
    /work/projects/BioITeam/common/script/gtf_to_bed.pl sc_genes.gff 1 \
  > sc_genes.converted.bed

    ...

    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
    titleSetup (if needed)


    Code Block
    languagebash
    mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/catchup/yeastbedtools_rnaseqmerge/*.gff .  
cp $CORENGS/catchup/yeastbedtools_rnaseqmerge/sc_genes.converted.bed .



    Code Block
    languagebash
    titleRe-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 == ""# 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:

    ...

    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    -       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   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.

    Expand
    titleSetup (if needed)


    Code Block
    languagebash
    mkdir -p $SCRATCH/core_ngs/bedtools
cd $SCRATCH/core_ngs/bedtools
cp $CORENGS/catchup/yeastbedtools_rnaseqmerge/*.gff .  
cp $CORENGS/catchup/yeastbedtools_rnaseqmerge/sc_genes* .


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

    Expand
    titleHint

    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.

    ...

    Using the -c (column) and -o (operation) options, you can have add 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.

    ...

    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):

    ...

    Code Block
    languagebash
    titleUse 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    32703816    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
    titleAnswer

    Output column 5 has the gene count.

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

    Produces this histogram:

    Code Block
    languagebash
       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
    languagebash
    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.

    ...

    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).

    ...

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

    Expand
    titleMake sure you're in an idev session


    Code Block
    languagebash
    titleStart an idev session
    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


    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
    languagebash
    titleSetup 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 $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?
    Expand
    titleHints

    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
    titleAnswer


    Code Block
    languagebash
    module load samtools

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


    Code Block
    titlesamtools 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
    languagebash
    samtools view yeast_mrna.sort.filt.bam | cut -f 5 | sort | uniq -c


    Code Block
    titledistribution 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
    languagebash
    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
    titleSetup (if needed)


    Code Block
    languagebash
    idev -m 120 -N 1 -A OTH21164 -r CoreNGSday5
module 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      $SCRATCH/core_ngs/bedtools_multicov/.
cp $CORENGS/yeast_rnaseq/yeast_mrna.sort.filt.bam* $SCRATCH/core_ngs/bedtools_multicov/.



    Code Block
    languagebash
    titleRun 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

    ExerciseExercises:

    • How may records of output were written?
    • Where is the count of overlaps per output record?
    Expand
    titleAnswers


    Code Block
    languagebash
    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
    titleAnswer


    Code Block
    languagebash
    cut -f 7 yeast_mrna_gene_counts.bed | grep -v '^0' | wc -l
# or
catcut -f 7 yeast_mrna_gene_counts.bed | \grep -v -c awk '^0'{if 
# 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.

    ...

    Expand
    titleAnswer


    Code Block
    languagebash
    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
    titleMake sure you're in an idev session


    Code Block
    languagebash
    titleStart 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
    languagebash
    titlePrepare 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
    languagebash
    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 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
    # 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
    languagebash
    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
    languagebash
    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
    languagebash
    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
    languagebash
    # 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