Bio Bedgraph Handling

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 -b1 sample1.bw -b2 sample2.bw -o diff.bw --ratio subtract

Filtering and Subsetting

Filter by Value

awk '$4 >= 1.0' sample.bedgraph > high_signal.bedgraph
awk '$4 > 0' sample.bedgraph > nonzero.bedgraph

Extract Regions

bedtools intersect -a sample.bedgraph -b regions.bed > subset.bedgraph

Remove Specific Chromosomes

grep -v "^chrM" sample.bedgraph | grep -v "_random" > filtered.bedgraph
awk '$1 ~ /^chr[0-9XY]+$/' sample.bedgraph > standard_chroms.bedgraph

Aggregate to Bins

Fixed-Size Bins

bedtools makewindows -g chrom.sizes -w 1000 > bins.bed
bedtools map -a bins.bed -b sample.bedgraph -c 4 -o mean > binned.bedgraph

Gene Bodies

bedtools map -a genes.bed -b sample.bedgraph -c 4 -o mean > gene_signal.bed

Quality Control

Check for Overlapping Intervals

bedtools merge -i sample.bedgraph -c 4 -o collapse | \
    awk 'index($4,",") > 0' | head

Verify Sorted Order

sort -c -k1,1 -k2,2n sample.bedgraph && echo "Sorted" || echo "Not sorted"

Check Value Range

awk 'NR==1 {min=$4; max=$4} {if($4<min) min=$4; if($4>max) max=$4}
     END {print "Min:", min, "Max:", max}' sample.bedgraph

Complete Pipeline

#!/bin/bash
BAM=$1
NAME=$(basename $BAM .bam)
CHROM_SIZES=$2

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

bedtools genomecov -ibam $BAM -bg -scale $scale > ${NAME}.bedgraph

sort -k1,1 -k2,2n ${NAME}.bedgraph > ${NAME}.sorted.bedgraph

bedClip ${NAME}.sorted.bedgraph $CHROM_SIZES ${NAME}.clipped.bedgraph

bedGraphToBigWig ${NAME}.clipped.bedgraph $CHROM_SIZES ${NAME}.bw

rm ${NAME}.bedgraph ${NAME}.sorted.bedgraph ${NAME}.clipped.bedgraph

echo "Created ${NAME}.bw (CPM normalized)"

Track Header for UCSC

echo 'track type=bedGraph name="Sample" description="CPM normalized" visibility=full color=0,0,255 altColor=255,0,0 autoScale=on graphType=bar' > track.bedgraph
cat sample.bedgraph >> track.bedgraph

Related Skills

• coverage-analysis - Generate coverage from alignments
• bigwig-tracks - Work with bigWig format
• chip-seq/chipseq-visualization - Visualize signal tracks
• alignment-files/sam-bam-basics - BAM file processing