gwseq-io-pp 0.0.2

Process BBI (bigWig/bigBed) and HiC files

Installation

pip install gwseq-io-pp

Requires numpy.

Usage

Open bigWig and bigBed files

reader = gwseq_io.open(path, *, zoom_correction)

Parameters:

• zoom_correction Scaling factor for automatic zoom level selection based on bin size. Only for bigWig files. 1/3 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".

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.

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 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 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 Identical to read_signal method.

Returns a list (locations) of 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.

Convert bigBed to BED

reader.to_bed(output_path)

Parameters:

• output_path chr_ids 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.