Pybigwig

pyBigWig

Overview

BigWig files are the standard binary format for genome-wide continuous signal (read depth, fold enrichment, log2 ratio, per-CpG methylation) — the typical output of deeptools bamCoverage/bamCompare. BigBed files store interval annotations (peaks, genes) with optional scores in the same indexed binary family. pyBigWig is a thin, fast C-extension wrapper around libBigWig that gives you random access to either format: fetch signal for an arbitrary region in milliseconds without loading the whole (often multi-GB) file into memory.

Use it whenever an analysis needs to go beyond what deeptools' CLI already covers — pulling signal at custom coordinates for a model, correlating ChIP enrichment with expression at specific genes, or building a signal-aggregation plot with your own binning logic.

Installation

uv pip install pyBigWig
# or, to skip the libcurl/zlib build requirement entirely:
conda install -c bioconda -c conda-forge pybigwig

Import as import pyBigWig (mixed-case module name, despite the lowercase PyPI/conda package listing pybigwig).

When to Use

• Extracting signal values at specific genomic coordinates for downstream ML feature vectors (e.g., ATAC-seq accessibility at a set of candidate enhancers).
• Computing summary statistics (mean/max/coverage) over a custom BED of regions — differential peaks, gene bodies, TSS windows — without going back through the deeptools CLI.
• Writing a BigWig track from a numpy array or custom per-base signal you've computed in Python.
• Streaming through a BigWig/BigBed without materializing the whole file (chromosome-by-chromosome iteration).
• Opening a BigWig directly from an HTTPS/FTP URL (e.g., a public ENCODE or UCSC track) without downloading it first.

Opening a File

import pyBigWig

bw = pyBigWig.open("sample.bw")            # read mode (default)
bw_remote = pyBigWig.open("https://.../track.bw")   # remote access works transparently
bwrite = pyBigWig.open("output.bw", "w")   # write mode — can ONLY be written to, not queried

pyBigWig.open() raises RuntimeError: Received an error during file opening! if the file doesn't exist, isn't a valid BigWig/BigBed, or is an unreachable URL — it never returns None, so wrap it in try/except RuntimeError rather than testing the handle.

Reading: Header and Chromosomes

print(bw.isBigWig(), bw.isBigBed())
print(bw.chroms())          # {'chr1': 248956422, 'chr2': 242193529, ...}
print(bw.chroms("chr1"))    # 248956422
print(bw.header())          # nBasesCovered, minVal, maxVal, sumData, sumSquared, etc.

Per-Base Values

# 0-based, half-open interval [start, end) — same convention as BED
values = bw.values("chr1", 1000, 2000)     # list of floats, one per base; nan where no data

For large regions, prefer bw.stats(..., exact=False) (bin-level, uses precomputed zoom levels — fast) over pulling every base with bw.values and reducing in Python.

Bin-Level Statistics

# Mean signal in 100 equal-width bins across the region — fast, uses zoom levels
means = bw.stats("chr1", 0, 10000, type="mean", nBins=100)

# Single summary value for the whole region
mean_val = bw.stats("chr1", 0, 10000, type="mean")[0]
max_val = bw.stats("chr1", 0, 10000, type="max")[0]
coverage = bw.stats("chr1", 0, 10000, type="coverage")[0]   # fraction of bases with data

# Exact (base-pair-precise) instead of zoom-level-approximated — slower, use for small/critical regions
exact_mean = bw.stats("chr1", 0, 10000, type="mean", exact=True)[0]

type accepts "mean", "max", "min", "std", "coverage", or "sum". Bins with no data return None in the result list — handle that explicitly (see pitfalls).

Iterating Intervals

for start, end, value in bw.intervals("chr1"):     # every stored interval on chr1
    ...

for start, end, value in bw.intervals("chr1", 1000, 5000):   # restricted to a region
    ...

BigBed Entries

bb = pyBigWig.open("peaks.bb")
for start, end, rest in bb.entries("chr1", 0, 1000000):
    # `rest` is a tab-separated string of any extra BED columns (name, score, strand, ...)
    name, score, strand = rest.split("\t")[:3]

Region-Based Signal Extraction (BED → DataFrame)

The most common real-world pattern — mean signal per region from a BED file:

import pandas as pd
import pyBigWig

bw = pyBigWig.open("H3K27ac.bw")
regions = pd.read_csv("peaks.bed", sep="\t", header=None,
                       names=["chrom", "start", "end", "name"])

def mean_signal(row):
    val = bw.stats(row.chrom, row.start, row.end, type="mean")[0]
    return val if val is not None else 0.0

regions["mean_signal"] = regions.apply(mean_signal, axis=1)
bw.close()

For thousands of regions, batching by chromosome and using bw.stats(..., nBins=1) per region is still the bottleneck-free approach — pyBigWig's stats call is C-level and fast per call, but calling it hundreds of thousands of times in a tight Python loop still dominates wall-clock time; vectorize with multiprocessing across chromosomes if this becomes a bottleneck.

Writing a BigWig File

import pyBigWig

bw = pyBigWig.open("custom_signal.bw", "w")
bw.addHeader([("chr1", 248956422), ("chr2", 242193529)])   # must be called before addEntries
bw.addEntries(
    ["chr1", "chr1", "chr1"],
    [0, 100, 200],
    ends=[100, 200, 300],
    values=[1.5, 2.7, 0.3],
)
bw.close()   # required — data isn't flushed to disk until close()

addEntries also accepts a single-chromosome + span/step form (bw.addEntries("chr1", 0, values=[...], span=10, step=10)) for fixed-interval tracks. addEntries accepts numpy arrays for chroms/starts/ends/values directly — no flag is needed — provided pyBigWig was compiled with numpy available; check with pyBigWig.numpy == 1. (There is no numpy keyword on open() or addEntries(). The numpy=True keyword is real but belongs to the read side, on values() and stats(), where it returns a numpy array instead of a Python list.)

Common Pitfalls

• bw.stats() returns None for empty bins, not 0 or nan. Code that does arithmetic on the raw result list will crash or silently propagate None; explicitly substitute 0.0 (no signal) or float("nan") (missing data) depending on what's semantically correct for your analysis.
• exact=True on huge regions is slow. It re-reads every base rather than using precomputed zoom-level summaries; only use it when you need base-pair precision (e.g., very small windows or footprinting), not for genome-wide scans.
• Files opened "w" cannot be queried. A write-mode handle can only be written to (addHeader/addEntries) — open a second, separate read-mode handle if you need to verify what you just wrote.
• Forgetting bw.close() after writing. BigWig writes are buffered; skipping close() (or crashing before it) leaves a truncated/invalid file.
• Coordinate convention mismatch. pyBigWig follows BED's 0-based, half-open convention (values("chr1", 1000, 2000) covers positions 1000-1999). If you're cross-referencing 1-based coordinates from a VCF or GFF, convert (start - 1) before querying.
• Remote file latency. Opening a BigWig over HTTPS/FTP works but each stats/values call may trigger a range request; for repeated heavy querying, download the file locally first.
• Missing libcurl/zlib when building from source. If a prebuilt wheel isn't available for your platform, pip install pyBigWig fails with a linker error unless curl-config and zlib headers are installed — switch to the conda-forge/bioconda build instead of chasing system headers.

Resources

• Docs / README: https://github.com/deeptools/pyBigWig#readme
• Source: https://github.com/deeptools/pyBigWig
• Underlying C library: https://github.com/dpryan79/libBigWig