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End-Motifs#

class finaletoolkit.frag.EndMotifFreqs(kmer_frequencies: Iterable[tuple[str, float]], k: int, quality_threshold: int = 20)#

Class that stores frequencies of end-motif k-mer frequencies and contains methods to manipulate this data.

Parameters:

  • kmer_frequencies (Iterable) – A Iterable of tuples, each containing a str representing a k-mer and a float representing its frequency

  • k (int) – Size of k-mers

  • quality_threshold (int, optional) – Minimum mapping quality used. Default is 30.

classmethod from_file(file_path: str | Path, quality_threshold: int, sep: str = '\t', header: int = 0) → EndMotifFreqs#

Reads kmer frequency from a two-column tab-delimited file.

Parameters:

  • file_path (str) – Path string containing path to file.

  • sep (str, optional) – Delimiter used in file.

  • header (int, optional) – Number of lines to ignore at the head of the file.

Returns:

kmer_freqs

Return type:

EndMotifFreqs

motif_diversity_score() → float#

Calculates a motif diversity score (MDS) using normalized Shannon entropy as described by Jiang et al (2020). This function is generalized for any k instead of just 4-mers.

to_tsv(output_file: str | Path, sep: str = '\t')#

Prints k-mer frequencies to a tsv

class finaletoolkit.frag.EndMotifsIntervals(intervals: Iterable[tuple[tuple, dict]], k: int, quality_threshold: int = 20)#

Class that stores frequencies of end-motif k-mers over user-specified intervals and contains methods to manipulate this data.

Parameters:

  • intervals (Iterable) – A collection of tuples, each containing a tuple representing a genomic interval (chrom, 0-based start, 1-based stop) and a dict that maps kmers to frequencies in the interval.

  • k (int) – Size of k-mers

  • quality_threshold (int, optional) – Minimum mapping quality used. Default is 30.

freq(kmer: str) → list[tuple[str, int, int, float]]#

Returns a list of intervals and associated frquency for given kmer. Results are in the form (chrom, 0-based start, 1-based stop, frequency).

classmethod from_file(file_path: str, quality_threshold: int, sep: str = ',') → EndMotifFreqs#

Reads kmer frequency from a tab-delimited file. Expected columns are contig, start, stop, name, count, *kmers. Because exporting to file includes an option to turn counts to a fraction, this doesn’t perfectly correspond to replicating the other file.

Parameters:

  • file_path (str) – Path string containing path to file.

  • quality_threshold (int) – MAPQ filter used. Only used for some calculations.

  • sep (str, optional) – Delimiter used in file.

Returns:

kmer_freqs

Return type:

EndMotifFreqs

mds_bed(output_file: str | Path, sep: str = '\t')#

Writes MDS for each interval to a bed/bedgraph file.

motif_diversity_score() → list[tuple[tuple, float]]#

Calculates a motif diversity score (MDS) for each interval using normalized Shannon entropy as described by Jiang et al (2020). This function is generalized for any k instead of just 4-mers.

to_bed(kmer: str, output_file: str | Path, calc_freq: bool = True, sep: str = '\t')#

Take frequency of specified kmer and writes to BED.

Parameters:

  • output_file (str) – File to write frequencies to.

  • calc_freq (bool, optional) – Calculates frequency of motifs if true. Otherwise, writes counts for each motif. Default is true.

  • sep (str, optional) – Separator for table. Tab-separated by default.

to_bedgraph(kmer: str, output_file: str | Path, calc_freq: bool = True, sep: str = '\t')#

Take frequency of specified kmer and writes to bedgraph.

Parameters:

  • output_file (str) – File to write frequencies to.

  • calc_freq (bool, optional) – Calculates frequency of motifs if true. Otherwise, writes counts for each motif. Default is true.

  • sep (str, optional) – Separator for table. Tab-separated by default.

to_tsv(output_file: str | Path, calc_freq: bool = True, sep: str = '\t')#

Writes all intervals and associated frquencies to file. Columns are contig, start, stop, name, count, *kmers.

Parameters:

  • output_file (str) – File to write frequencies to.

  • calc_freq (bool, optional) – Calculates frequency of motifs if true. Otherwise, writes counts for each motif. Default is true.

  • sep (str, optional) – Separator for table. Tab-separated by default.

finaletoolkit.frag.region_end_motifs(input_file: str, contig: str, start: int, stop: int, refseq_file: str | Path, k: int = 4, fraction_low: int = 10, fraction_high: int = 600, both_strands: bool = True, output_file: None | str = None, quality_threshold: int = 20, verbose: bool | int = False) → dict#

Function that reads fragments in the specified region from a BAM, SAM, or tabix indexed file and returns the 5’ k-mer (default is 4-mer) end motif counts as a dictionary. This function reproduces the methodology of Zhou et al (2023).

Parameters:

  • input_file (str) – Path of SAM, BAM, CRAM, or Frag.gz containing pair-end reads.

  • contig (str) – Name of contig or chromosome for region.

  • start (int) – 0-based start coordinate.

  • stop (int) – 1-based end coordinate.

  • refseq_file (str or Path) – 2bit file with reference sequence input_file was aligned to.

  • k (int, optional) – Length of end motif kmer. Default is 4.

  • fraction_low (int, optional) – Minimum fragment length.

  • fraction_high (int, optional) – Maximum fragment length.

  • both_strands (bool, optional) – Choose whether to use forward 5’ ends only or use 5’ ends for both ends of PE reads.

  • output_file (None or str, optional)

  • quality_threshold (int, optional)

  • verbose (bool or int, optional)

Returns:

end_motif_freq

Return type:

dict

finaletoolkit.frag.end_motifs(input_file: str, refseq_file: str | Path, k: int = 4, fraction_low: int = 10, fraction_high: int = 600, both_strands: bool = False, output_file: None | str = None, quality_threshold: int = 30, workers: int = 1, verbose: bool | int = False) → EndMotifFreqs#

Function that reads fragments from a BAM, SAM, or tabix indexed file and returns the 5’ k-mer (default is 4-mer) end motif frequencies as a dictionary. Optionally writes data to a tsv. This function reproduces the methodology of Zhou et al (2023).

Parameters:

  • input_file (str) – SAM, BAM, CRAM, or Frag.gz file with paired-end reads.

  • refseq_file (str or Path) – 2bit file with sequence of reference genome input_file is aligned to.

  • k (int, optional) – Length of end motif kmer. Default is 4.

  • output_file (None or str, optional) – File path to write results to. Either tsv or csv.

  • quality_threshold (int, optional) – Minimum MAPQ to filter.

  • workers (int, optional) – Number of worker processes.

  • verbose (bool or int, optional)

Returns:

end_motif_freq

Return type:

EndMotifFreqs

finaletoolkit.frag.interval_end_motifs(input_file: str, refseq_file: str | Path, intervals: str | Iterable[tuple[str, int, int, str]], k: int = 4, fraction_low: int = 10, fraction_high: int = 600, both_strands: bool = True, output_file: None | str = None, quality_threshold: int = 30, workers: int = 1, verbose: bool | int = False) → EndMotifsIntervals#

Function that reads fragments from a BAM, SAM, or tabix indexed file and user-specified intervals and returns the 5’ k-mer (default is 4-mer) end motif. Optionally writes data to a tsv.

Parameters:

  • input_file (str) – Path of SAM, BAM, CRAM, or Frag.gz containing pair-end reads.

  • refseq_file (str or Path) – Path of 2bit file for reference genome that reads are aligned to.

  • intervals (str or tuple) – Path of BED file containing intervals or list of tuples (chrom, start, stop, name).

  • k (int, optional) – Length of end motif kmer. Default is 4.

  • output_file (None or str, optional) – File path to write results to. Either tsv or csv.

  • quality_threshold (int, optional) – Minimum MAPQ to filter.

  • workers (int, optional) – Number of worker processes.

  • verbose (bool or int, optional)

Returns:

end_motif_freq

Return type:

EndMotifIntervals

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