local-background-estimation-multiple-scales

local-background-estimation-multiple-scales

Summary

Estimate local background noise in ChIP-Seq data by computing bias tracks at multiple genomic scales (fragment length d, 1 kb slocal, and 10 kb llocal windows) from control samples, then combining them to generate a normalized local lambda track. This multi-scale approach accounts for distance-dependent sequencing bias and improves the specificity of peak calling.

When to use

When performing ChIP-Seq peak calling with MACS3, after duplicate filtering and fragment length prediction (d), to construct the background model that will be compared against ChIP signal. This is essential for any ChIP-Seq analysis where control samples are available and accurate peak calling requires accounting for regional sequencing biases beyond genome-wide background.

When NOT to use

• When no control sample is available — genome-wide background only should be used instead.
• When input is single-end reads with fragment length d not yet predicted — predictd must be run first.
• When working with paired-end data in BEDPE format without converting to single-end fragment representation — pileup assumes single-end BED input.

Inputs

• Filtered control BED file (deduplicated reads)
• Predicted fragment length d (in base pairs)
• Genome size (integer, e.g., 'hs' for human)
• ChIP and control read counts (for sequencing depth ratio calculation)

Outputs

• d-background bedGraph track (fragment-length-scale local bias)
• slocal-background bedGraph track (1 kb window local bias)
• llocal-background bedGraph track (10 kb window local bias)
• Combined maximum background bedGraph track
• Normalized local lambda bedGraph track (final background model)

How to apply

First, generate three control-derived background tracks using macs3 pileup with -B flag at three complementary scales: d-background at extension size d/2 (half the predicted fragment length), slocal background at 500 bp (or 1 kb default window), and llocal background at 5000 bp (or 10 kb default window). Normalize slocal and llocal backgrounds using macs3 bdgopt multiply with factors 0.254 and 0.0254 respectively (ratios of extension size to original scale). Combine the three background tracks using macs3 bdgcmp max operation to determine the maximum bias at each genomic position. Add the genome-wide background constant (calculated as number_of_control_reads × fragment_length / genome_size) using macs3 bdgopt. Finally, scale the combined background by the ChIP-to-control sequencing depth ratio using macs3 bdgopt multiply to create the final local lambda track. This multi-scale combination ensures that local enrichment is measured relative to the most relevant background at each genomic location.