breakpoint module

class mavis.breakpoint.Breakpoint(chr, start, end=None, orient='?', strand='?', seq=None)

Bases: mavis.interval.Interval

class for storing information about a SV breakpoint coordinates are given as 1-indexed

Parameters:

• chr (str) – the chromosome
• start (int) – the genomic position of the breakpoint
• end (int) – if the breakpoint is uncertain (a range) then specify the end of the range here
• strand (STRAND) – the strand
• orient (ORIENT) – the orientation (which side is retained at the break)

Examples

>>> Breakpoint('1', 1, 2)
>>> Breakpoint('1', 1)
>>> Breakpoint('1', 1, 2, '+', 'R')
>>> Breakpoint('1', 1, orient='R')

key

to_dict()

class mavis.breakpoint.BreakpointPair(b1, b2, stranded=False, opposing_strands=None, untemplated_seq=None, data={})

Bases: object

Parameters:

• b1 (Breakpoint) – the first breakpoint
• b2 (Breakpoint) – the second breakpoint
• stranded (bool) – if not stranded then +/- is equivalent to -/+
• opposing_strands (bool) – are the strands at the breakpoint opposite? i.e. +/- instead of +/+
• untemplated_seq (str) – seq between the breakpoints that is not part of either breakpoint
• data (dict) – optional dictionary of attributes associated with this pair

Note

untemplated_seq should always be given wrt to the positive/forward reference strand

Example

>>> BreakpointPair(Breakpoint('1', 1), Breakpoint('1', 9999), opposing_strands=True)
>>> BreakpointPair(Breakpoint('1', 1, strand='+'), Breakpoint('1', 9999, strand='-'))

breakpoint_sequence_homology(REFERENCE_GENOME)

for a given set of breakpoints matches the sequence opposite the partner breakpoint this sequence comparison is done with reference to a reference genome and does not use novel or untemplated sequence in the comparison. For this reason, insertions will never return any homologous sequence

small duplication event CTT => CTTCTT

GATACATTTCTTCTTGAAAA reference
---------<========== first breakpoint
===========>-------- second breakpoint
---------CT-CT------ first break homology
-------TT-TT-------- second break homology

Parameters:REFERENCE_GENOME (dict of Bio.SeqRecord by str) – dict of reference sequence by template/chr name Returns:

• str - homologous sequence at the first breakpoint
• str - homologous sequence at the second breakpoint

Return type:tuple Raises:AttributeError – for non specific breakpoints

classmethod call_breakpoint_pair(read1, read2=None, REFERENCE_GENOME=None)

calls a set of breakpoints from a single or a pair of pysam style read(s)

Parameters:

• read1 (pysam.AlignedSegment) – the first read
• read2 (pysam.AlignedSegment) – the second read

Returns:

the newly called breakpoint pair from the contig

Return type:

BreakpointPair

Todo

return multiple events not just the major event

classmethod classify(pair)

uses the chr, orientations and strands to determine the possible structural_variant types that this pair could support

Parameters:pair (BreakpointPair) – the pair to classify Returns:a list of possible SVTYPE Return type:list of SVTYPE

Example

>>> bpp = BreakpointPair(Breakpoint('1', 1), Breakpoint('1', 9999), opposing_strands=True)
>>> BreakpointPair.classify(bpp)
['inversion']
>>> bpp = BreakpointPair(Breakpoint('1', 1, orient='L'), Breakpoint('1', 9999, orient='R'), opposing_strands=False)
>>> BreakpointPair.classify(bpp)
['deletion', 'insertion']

see related theory documentation

copy()

flatten()

returns the key-value self for the breakpoint self information as can be written directly as a TSV row

get_bed_repesentation()

interchromosomal

bool – True if the breakpoints are on different chromosomes, False otherwise

mavis.breakpoint.read_bpp_from_input_file(filename, expand_ns=True, force_stranded=False, **kwargs)

reads a file using the TSV module. Each row is converted to a breakpoint pair and other column data is stored in the data attribute

Parameters:filename (str) – path to the input file Returns:a list of pairs Return type:list of BreakpointPair

Example

>>> read_bpp_from_input_file('filename')
[BreakpointPair(), BreakpointPair(), ...]

One can also validate other expected columns that will go in the data attribute using the usual arguments to the TSV.read_file function

>>> read_bpp_from_input_file('filename', cast={'index': int})
[BreakpointPair(), BreakpointPair(), ...]