Segregation_SV

A tool to flexibly merge Copy Number Variants and other Structural Variants from multiple callers in multiple samples, annotate with various external data sources, and perform family segregation

Requirements

any python2

Arguments

usage: segregation.py [-h] -i INPUTDATA -p PEDFILE [-r RUNNAME]
                      [-a CUSTOMANNODATA] [-o OUTFILE] [-cfg CFGFILE] [-eda]
                      [-min MINSIZEFILTER] [-max MAXSIZEFILTER]
                      [-e EMITSTATUS] [-v]

run segregation analysis on structural variants dataset

optional arguments:
  -h, --help            show this help message and exit
  -r RUNNAME, --run_name RUNNAME
                        run name prefix for all output files
                        ["Segregation_SV"]
  -a CUSTOMANNODATA, --custom_annotation_file CUSTOMANNODATA
                        /path/to/custom_annotation_file:anno_type:minimum_reci
                        procal_overlap_fraction ; (can be specified multiple
                        times; file must be .bed or .bedpe format)
  -o OUTFILE, --out_file OUTFILE
                        /path/to/output/file; where outputs will be written
                        [/dev/stdout]
  -cfg CFGFILE, --config_file CFGFILE
                        path/to/cfg_file (used to modify run parameters)
  -eda, --exclude_default_annotations
                        exclude default annotations
  -min MINSIZEFILTER, --min_size_filter MINSIZEFILTER
                        minimal size of raw variant to keep (smaller will be
                        excluded from any processing)
  -max MAXSIZEFILTER, --max_size_filter MAXSIZEFILTER
                        maximal size of raw variant to keep (larger will be
                        excluded from any processing
  -e EMITSTATUS, --emit_vars_from_samples_with_status EMITSTATUS
                        emit variants from samples with clinical status of
                        [affected,nonaffected,all]
  -v, --verbose         verbose output

mandatory arguments:
  -i INPUTDATA, --input_data INPUTDATA
                        /path/to/input_data_file:input_type:[batch_name] (can
                        be specified multiple times; accepted values are
                        "lumpy", "popsv", "conifer", "codex", "exomedepth",
                        "cnmops", "xhmm", "generic_bed" and "generic_bedpe")
  -p PEDFILE, --ped_file PEDFILE
                        /path/to/ped_file

Principle

Segregation_SV works by first converting individual per-sample CNV calls from multiple callers into “consensus CNVs” based on the principle of reciprocal overlap (RO). This merging is done in 3 successive steps:

• Step 1: calls by the same caller in the same sample are merged if there is any overlap between them (>0% RO), to avoid call fragmentation.
• Step 2: calls by different callers in the same sample are merged if there is >=50% RO.
• Step 3: Once this has been done for all samples, calls are then merged between multiple samples if there is >80% RO between them.

Finally for each consensus CNV, sample counts are broken down into affected vs nonaffected and family vs non-family, for each family as defined by an input pedigree file

Notes:

• At each step, CNV merging proceeds iteratively from smallest to largest, to ensure that small CNVs are not accidentally excluded.
• Pedigree file format is described here
• The config file (specified with command argument -cfg|--config_file) can be used to very precisely tailor the variant merging rules. Different RO values can be set for each variant merging step (steps 1-3 above), as well as for different variant types (deletion, duplication, inversion and translocation) within each merging step.
• Variant merging for translocations is not supported. Any such variants are simply passed along intact through all merging steps into the final outputs, though if >1 sample has a called translocation with exactly the same boundaries, this will be reflected in the final sample counts for that variant.
• Annotations included by default are: DGV_inclusive, DGV_stringent, 1000 Genomes SV, Segmental Duplications (UCSC), Micro Exons, RepeatMaster (UCSC), RefSeq Genes, RefSeq Genes exons. Default annotations are loaded as a python pickle. GRCh37 ONLY
• Python pickle-based annotation not tested on many different systems; may cause fatal errors in some configurations. Should this occur, annotation can simply be skipped using the -eda|--exclude_default_annotations command switch. Alternately, users can supply their own custom annotations (in .bed or .bedpe format) using the -a|--custom_annotation_file argument.