/bio-bedgraph-handling

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--- name: bio-bedgraph-handling description: Create, manipulate, and convert bedGraph files for genome browser visualization. Covers bedGraph format, conversion to/from bigWig, normalization, and signal processing. Use when handling coverage and signal tracks from ChIP-seq, ATAC-seq, or RNA-seq. tool_type: mixed primary_tool: pyBigWig measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

---

bedGraph Handling

bedGraph is a text format for displaying continuous-valued data on genome browsers. Common for coverage, signal intensity, and scores.

bedGraph Format

track type=bedGraph name="Sample" description="Coverage"
chr1    0       100     1.5
chr1    100     200     2.3
chr1    200     300     0.8

Four columns: chrom, start, end, value (0-based, half-open)

Create bedGraph from BAM

Using bedtools genomecov

bedtools genomecov -ibam sample.bam -bg > sample.bedgraph
bedtools genomecov -ibam sample.bam -bg -split > sample.bedgraph
bedtools genomecov -ibam sample.bam -bg -scale 1.5 > sample.scaled.bedgraph

Strand-Specific

bedtools genomecov -ibam sample.bam -bg -strand + > sample.plus.bedgraph
bedtools genomecov -ibam sample.bam -bg -strand - > sample.minus.bedgraph

5' End Coverage (ChIP-seq)

bedtools genomecov -ibam sample.bam -bg -5 > sample.5prime.bedgraph

Normalize by Library Size (CPM)

total_reads=$(samtools view -c -F 260 sample.bam)
scale=$(echo "scale=10; 1000000 / $total_reads" | bc)

bedtools genomecov -ibam sample.bam -bg -scale $scale > sample.cpm.bedgraph

Sort bedGraph

bedGraph must be sorted for conversion to bigWig.

sort -k1,1 -k2,2n sample.bedgraph > sample.sorted.bedgraph
LC_ALL=C sort -k1,1 -k2,2n sample.bedgraph > sample.sorted.bedgraph

Convert bedGraph to bigWig

Using UCSC bedGraphToBigWig

bedGraphToBigWig sample.sorted.bedgraph chrom.sizes sample.bw
fetchChromSizes hg38 > hg38.chrom.sizes
bedGraphToBigWig sample.sorted.bedgraph hg38.chrom.sizes sample.bw

Generate chrom.sizes

samtools faidx reference.fa
cut -f1,2 reference.fa.fai > chrom.sizes
fetchChromSizes hg38 > hg38.chrom.sizes
mysql --user=genome --host=genome-mysql.soe.ucsc.edu -A -e \
    "select chrom, size from hg38.chromInfo" > hg38.chrom.sizes

Clip to Chromosome Boundaries

bedClip sample.bedgraph chrom.sizes sample.clipped.bedgraph
bedGraphToBigWig sample.clipped.bedgraph chrom.sizes sample.bw

Convert bigWig to bedGraph

bigWigToBedGraph sample.bw sample.bedgraph
bigWigToBedGraph sample.bw sample.chr1.bedgraph -chrom=chr1
bigWigToBedGraph sample.bw sample.region.bedgraph -chrom=chr1 -start=1000 -end=2000

Merge bedGraph Files

Using bedtools unionbedg

bedtools unionbedg -i sample1.bedgraph sample2.bedgraph sample3.bedgraph \
    -header -names sample1 sample2 sample3 > merged.bedgraph

Average Across Samples

bedtools unionbedg -i sample1.bedgraph sample2.bedgraph sample3.bedgraph | \
    awk '{sum=0; for(i=4;i<=NF;i++) sum+=$i; print $1,$2,$3,sum/(NF-3)}' OFS='\t' \
    > average.bedgraph

Mathematical Operations

bedtools map for Region Statistics

bedtools map -a regions.bed -b sample.bedgraph -c 4 -o mean > region_means.bed
bedtools map -a regions.bed -b sample.bedgraph -c 4 -o sum > region_sums.bed
bedtools map -a regions.bed -b sample.bedgraph -c 4 -o max > region_max.bed

Subtract Background

bedtools unionbedg -i treatment.bedgraph input.bedgraph | \
    awk '{diff=$4-$5; if(diff<0) diff=0; print $1,$2,$3,diff}' OFS='\t' \
    > subtracted.bedgraph

Log Transform

awk '{print $1,$2,$3,log($4+1)/log(2)}' OFS='\t' sample.bedgraph > sample.log2.bedgraph

Smooth Signal

bedtools slop -i sample.bedgraph -g chrom.sizes -b 50 | \
    bedtools merge -i - -c 4 -o mean > smoothed.bedgraph

Python with pyBigWig

Write bedGraph

import pyBigWig

bw = pyBigWig.open('output.bedgraph', 'w')
bw.addHeader([('chr1', 248956422), ('chr2', 242193529)])

chroms = ['chr1', 'chr1', 'chr1']
starts = [0, 100, 200]
ends = [100, 200, 300]
values = [1.5, 2.3, 0.8]
bw.addEntries(chroms, starts, ends=ends, values=values)
bw.close()

Read bigWig to bedGraph Format

import pyBigWig

bw = pyBigWig.open('sample.bw')

for chrom, size in bw.chroms().items():
    intervals = bw.intervals(chrom)
    if intervals:
        for start, end, value in intervals:
            print(f'{chrom}\t{start}\t{end}\t{value}')

bw.close()

Convert bigWig Region to bedGraph

import pyBigWig

bw = pyBigWig.open('sample.bw')
intervals = bw.intervals('chr1', 1000000, 2000000)

with open('region.bedgraph', 'w') as f:
    for start, end, value in intervals:
        f.write(f'chr1\t{start}\t{end}\t{value}\n')

bw.close()

deepTools for Normalization

bamCoverage (BAM to bedGraph/bigWig)

bamCoverage -b sample.bam -o sample.bw --normalizeUsing RPKM
bamCoverage -b sample.bam -o sample.bw --normalizeUsing CPM
bamCoverage -b sample.bam -o sample.bw --normalizeUsing BPM
bamCoverage -b sample.bam -o sample.bedgraph --outFileFormat bedgraph --normalizeUsing CPM

bamCompare (Treatment vs Control)

bamCompare -b1 treatment.bam -b2 input.bam -o log2ratio.bw --scaleFactorsMethod readCount
bamCompare -b1 treatment.bam -b2 input.bam -o subtracted.bw --ratio subtract

bigwigCompare

bigwigCompare -b1 treatment.bw -b2 input.bw -o ratio.bw --ratio log2
bigwigCompare -b