gwseq-io 0.0.16

Process BBI (bigWig/bigBed) and HiC files

Installation

pip install gwseq-io

Requires numpy and pybind11.

Usage

Open bigWig, bigBed and HiC files

reader = gwseq_io.open(path, *, parallel, zoom_correction, file_buffer_size, max_file_buffer_count)

Parameters:

• parallel Number of parallel file handles and processing threads. 24 by default.
• zoom_correction Scaling factor for automatic zoom level selection based on bin size. Only for bigWig files. 1/3 by default.
• file_buffer_size Size in bytes of each file buffer for caching file reads. Use -1 for recommended (32768 or 1048576 for URLs). -1 by default.
• max_file_buffer_count Maximum number of file buffers to keep in cache. Use -1 for recommended (128). -1 by default.

Attributes for bigWig and bigBed files:

• main_header General file formatting info.
• zoom_headers Zooms levels info (reduction level and location).
• auto_sql BED entries declaration (only in bigBed).
• total_summary Statistical summary of entire file values (coverage, sums and extremes).
• chr_sizes Chromosomes IDs and sizes.
• type Either "bigwig" or "bigbed".

Attributes for HiC files:

• header footer General file info.
• chr_sizes Chromosomes IDs and sizes.
• normalizations Available normalizations.
• units Available units.
• bin_sizes Available bin sizes.

Read bigWig and bigBed signal

values = reader.read_signal(chr_ids, starts, ends)
values = reader.read_signal(chr_ids, starts=starts, span=span)
values = reader.read_signal(chr_ids, ends=ends, span=span)
values = reader.read_signal(chr_ids, centers=centers, span=span)

Parameters:

• chr_ids starts ends centers Chromosomes ids, starts, ends and centers of locations. Both starts ends or one of starts ends centers (with span) may be specified.
• span Reading window in bp relative to locations starts ends centers. Only one reference may be specified if specified. Not by default.
• bin_size Reading bin size in bp. May vary in output if locations have variable spans or bin_count is specified. 1 by default.
• bin_count Output bin count. Inferred as max location span / bin size by default.
• bin_mode Method to aggregate bin values. Either "mean", "sum" or "count". "mean" by default.
• full_bin Extend locations ends to overlapping bins if true. Not by default.
• def_value Default value to use when no data overlap a bin. 0 by default.
• zoom BigWig zoom level to use. Use full data if -1. Auto-detect the best level if -2 by selecting the larger level whose bin size is lower than the third of bin_size (may be the full data). Full data by default.
• progress Function called during data extraction. Takes the extracted coverage and the total coverage in bp as parameters. Use default callback function if true. None by default.

Returns a numpy float32 array of shape (locations, bin count).

Quantify bigWig and bigBed signal

values = reader.quantify(chr_ids, starts, ends)

Parameters:

• chr_ids starts ends centers span bin_size full_bin def_value zoom progress Identical to read_signal method.
• reduce Method to aggregate values over span. Either "mean", "sd", "sem", "sum", "count", "min" or "max". "mean" by default.

Returns a numpy float32 array of shape (locations).

Profile bigWig and bigBed signal

values = reader.profile(chr_ids, starts, ends)

Parameters:

• chr_ids starts ends centers span bin_size bin_count bin_mode full_bin def_value zoom progress Identical to read_signal method.
• reduce Method to aggregate values over locations. Either "mean", "sd", "sem", "sum", "count", "min" or "max". "mean" by default.

Returns a numpy float32 array of shape (bin count).

Read bigBed entries

values = reader.read_entries(chr_ids, starts, ends)

Parameters:

• chr_ids starts ends centers spans progress Identical to read_signal method.

Returns a list (locations) of list of entries (dict with at least "chr", "start" and "end" keys).

Read all bigBed entries

values = reader.read_all_entries()

Parameters:

• chr_ids Only extract data from these chromosomes. All by default.

Returns a list of entries (dict with at least "chr", "start" and "end" keys).

Convert bigWig to bedGraph or WIG

reader.to_bedgraph(output_path)
reader.to_wig(output_path)

Parameters:

• output_path Path to output file.
• chr_ids Only extract data from these chromosomes. All by default.
• zoom Zoom level to use. Use full data if -1. Full data by default.
• progress Function called during data extraction. Takes the extracted coverage and the total coverage in bp as parameters. None by default.

Convert bigBed to BED

reader.to_bed(output_path)

Parameters:

• output_path chr_ids progress Identical to to_bedgraph and to_wig methods.
• col_count Only write this number of columns (eg, 3 for chr, start and end). All by default.

Write bigWig file

writer = bigwig_io.open(path, "w")
writer = bigwig_io.open(path, "w", def_value=0)
writer = bigwig_io.open(path, "w", chr_sizes={"chr1": 1234, "chr2": 1234})
writer.add_entry("chr1", start=1000, end=1010, value=0.1)
writer.add_value("chr1", start=1000, span=10, value=0.1)
writer.add_values("chr1", start=1000, span=10, values=[0.1, 0.1, 0.1, 0.1])

must be pooled by chr, and sorted by (1) start (2) end no overlap

Write bigBed file

writer = bigwig_io.open(path, "w", type="bigbed")
writer = bigwig_io.open(path, "w", type="bigbed", chr_sizes={"chr1": 1234, "chr2": 1234})
writer = bigwig_io.open(path, "w", type="bigbed", fields=["chr", "start", "end", "name"])
writer = bigwig_io.open(path, "w", type="bigbed", fields={"chr": "string", "start", "uint", "end": "uint", "name": "string"})
writer.add_entry("chr1", start=1000, end=1010)
writer.add_entry("chr1", start=1000, end=1010, fields={"name": "read#1"})

must be pooled by chr, and sorted by (1) start (2) end may be overlapping

Read HiC signal

values = reader.read_signal(chr_ids, starts, ends)

Parameters:

• chr_ids starts ends Chromosomes ids, starts and ends of the 2 locations.
• bin_size Input bin size or -1 to use the smallest. Must be available in the file. Smallest by default.
• bin_count Approximate output bin count. Takes precedence over bin_size if specified by selecting the closest bin size resulting in bin_count. Not specified by default.
• exact_bin_count Resize output to match bin_count (if specified). Not by default.
• full_bin Extend locations ends to overlapping bins if true. Not by default.
• def_value Default value to use when no data overlap a bin. 0 by default.
• triangle Skip symmetrical data if true. Not by default.
• min_distance max_distance Min and max distance in bp from diagonal for contacts to be reported. All by default.
• normalization Either "none" or any normalization available in the file, such as "kr", "vc" or "vc_sqrt". "none" by default.
• mode Either "observed" or "oe" (observed/expected). "observed" by default.
• unit Either "bp" or "frag". "bp" by default.
• save_to Save output to this .npz path (under "values" key) and return nothing. Not by default.

Returns a numpy float32 array of shape (loc 1 bins, loc 2 bins).

Read HiC sparse signal

values = reader.read_sparse_signal(chr_ids, starts, ends)

Parameters:

• chr_ids starts ends bin_size bin_count exact_bin_count full_bin def_value triangle min_distance max_distance normalization mode unit save_to Identical to read_signal method.

Returns a COO sparse matrix as a dict with keys:

• values Values as a numpy float32 array.
• row Values rows indices as a numpy uint32 array.
• col Values columns indices as a numpy uint32 array.
• shape Shape of the dense array as a tuple.

Convert in python using scipy.sparse.csr_array((x["values"], (x["row"], x["col"])), shape=x["shape"]).