summary module

mavis.summary.summary.annotate_dgv(bpps, dgv_regions_by_reference_name, distance=0)

given a list of bpps and a dgv reference, annotate the events that are within the set distance of both breakpoints

Parameters:

• bpps (list) – the list of BreakpointPair objects
• dgv_regions_by_reference_name (dict) – the dgv reference regions file loaded by load_masking_regions
• distance (int) – the minimum distance required to match a dgv event with a breakpoint

mavis.summary.summary.filter_by_annotations(bpp_list, best_transcripts)

Parameters:

• bpp_list (list of BreakpointPair) – list of pairs to filter
• ( (best_transcripts) – class dict of Transcript by str): the best transcripts of the annotations based on their names

mavis.summary.summary.filter_by_call_method(bpp_list)

Filters a set of breakpoint pairs to returns the call with the most evidence. Prefers contig evidence over spanning over split over flanking, etc.

mavis.summary.summary.filter_by_evidence(bpps, filter_min_remapped_reads=5, filter_min_spanning_reads=5, filter_min_flanking_reads=10, filter_min_split_reads=5, filter_min_linking_split_reads=1)

mavis.summary.summary.get_pairing_state(current_protocol, current_disease_state, other_protocol, other_disease_state, is_matched=False, inferred_is_matched=False)

given two libraries, returns the appropriate descriptor for their matched state

Parameters:

• current_protocol (PROTOCOL) – the protocol of the current library
• current_disease_state (DISEASE_STATUS) – the disease status of the current library
• other_protocol (PROTOCOL) – protocol of the library being comparing to
• other_disease_state (DISEASE_STATUS) – disease status of the library being compared to
• is_matched (bool) – True if the libraries are paired

Returns:

descriptor of the pairing of the two libraries

Return type:

(PAIRING_STATE)

mavis.summary.summary.group_by_distance(calls, distances)

groups a set of calls based on their proximity. Returns a new list of calls where close calls have been merged

mavis.summary.summary.group_events(events)

group events together and join data attributes