moca.bedoperations package¶

Submodules¶

class moca.bedoperations.model.Bedfile(filepath, genome_table, output_dir=None)[source]¶

Bases: future.types.newobject.newobject

Class to crate a bed file object

Parameters:	filepath (string) – Absolute path to bedfile genome_table (string) – Absolute path to geonme chromosome size file output_dir (string) – Output directory

extract_fasta(bed_in, fasta_in, fasta_out=None)[source]¶

Extract fasta of bed regions

Parameters:	bed_in (string) – Path to input bed fasta_in (string) – Absolute path to location of reference fasta file fasta_out (string) – Path to write extracted fasta sequence
Returns:	fasta – Fasta sequence combined
Return type:	string

guess_bedformat()[source]¶

Method to guess bed format

Returns:	bed_format (string) – BED format Example – >>> bed_df = Bedfile(‘file.bed’) >>> print(bed_df.guess_bed_format())

slop_bed(bed_in, flank_length=50)[source]¶

Add flanking sequences to bed file

Parameters:	bed_in (string) – Path to input bed file flank_length (int) – the bed region is expanded in both direction by flank_length number of bases
Returns:	slopped_bed – Slopped bed data object
Return type:	dataframe

sort_by(columns=None, ascending=False)[source]¶

Method to sort columns of bedfiles

Parameters:	columns (list) – list of column names to sort by ascending (bool) – Sort order(Default: true)
Returns:	sorted_bed_df – dataframe with sorted columns
Return type:	dataframe

split_train_test_bed(train_peaks_count=500, test_peaks_count=500, train_suffix='train', test_suffix='test')[source]¶

Split bedfile into ranked training and testing sets

Training set consists of top train_peaks_count peaks while testing set will consist of next test_peaks_count ranked peaks

Parameters:	train_peaks_count (int) – Count of training peaks test_peaks_count (int) – Count of testing peaks

write_to_scorefile(bed_df, out_suffix='sorted')[source]¶: Write bed file as score file