From 41296a03136b0ae5344d18783a48427c22fafe6d Mon Sep 17 00:00:00 2001
From: Floreal Cabanettes <floreal.cabanettes@inra.fr>
Date: Fri, 1 Jun 2018 11:49:04 +0200
Subject: [PATCH] Make snakecnv/lib as submodule + move files
---
.gitmodules | 3 +
snakecnv/{lib => }/align_snakemake_utils.py | 0
snakecnv/lib | 1 +
snakecnv/lib/svfilter.py | 353 --------------
snakecnv/lib/svreader/__init__.py | 274 -----------
snakecnv/lib/svreader/_version.py | 1 -
snakecnv/lib/svreader/defaults.py | 68 ---
snakecnv/lib/svreader/delly.py | 272 -----------
snakecnv/lib/svreader/genomestrip.py | 214 --------
snakecnv/lib/svreader/lumpy.py | 209 --------
snakecnv/lib/svreader/pindel.py | 389 ---------------
snakecnv/lib/svreader/resources/b37.gaps.bed | 457 ------------------
snakecnv/lib/svreader/resources/hg19.gaps.bed | 457 ------------------
snakecnv/lib/svreader/resources/template.vcf | 2 -
.../resources/template_breakdancer.vcf | 37 --
.../svreader/resources/template_cnvnator.vcf | 30 --
.../lib/svreader/resources/template_merge.vcf | 154 ------
.../svreader/resources/template_pindel.vcf | 22 -
snakecnv/lib/svreader/vcf_utils.py | 32 --
snakecnv/lib/svreader/vcfwrapper.py | 121 -----
snakecnv/lib/svrunner_utils.py | 230 ---------
snakecnv/{lib => }/svsnakemake_utils.py | 0
22 files changed, 4 insertions(+), 3322 deletions(-)
rename snakecnv/{lib => }/align_snakemake_utils.py (100%)
create mode 160000 snakecnv/lib
delete mode 100644 snakecnv/lib/svfilter.py
delete mode 100644 snakecnv/lib/svreader/__init__.py
delete mode 100644 snakecnv/lib/svreader/_version.py
delete mode 100644 snakecnv/lib/svreader/defaults.py
delete mode 100644 snakecnv/lib/svreader/delly.py
delete mode 100644 snakecnv/lib/svreader/genomestrip.py
delete mode 100644 snakecnv/lib/svreader/lumpy.py
delete mode 100644 snakecnv/lib/svreader/pindel.py
delete mode 100644 snakecnv/lib/svreader/resources/b37.gaps.bed
delete mode 100644 snakecnv/lib/svreader/resources/hg19.gaps.bed
delete mode 100644 snakecnv/lib/svreader/resources/template.vcf
delete mode 100644 snakecnv/lib/svreader/resources/template_breakdancer.vcf
delete mode 100644 snakecnv/lib/svreader/resources/template_cnvnator.vcf
delete mode 100644 snakecnv/lib/svreader/resources/template_merge.vcf
delete mode 100644 snakecnv/lib/svreader/resources/template_pindel.vcf
delete mode 100644 snakecnv/lib/svreader/vcf_utils.py
delete mode 100644 snakecnv/lib/svreader/vcfwrapper.py
delete mode 100644 snakecnv/lib/svrunner_utils.py
rename snakecnv/{lib => }/svsnakemake_utils.py (100%)
diff --git a/.gitmodules b/.gitmodules
index 0fb765c..02663c6 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -1,3 +1,6 @@
[submodule "popsim"]
path = popsim
url = git@forgemia.inra.fr:genotoul-bioinfo/popsim.git
+[submodule "snakecnv/lib"]
+ path = snakecnv/lib
+ url = git@forgemia.inra.fr:genotoul-bioinfo/svlib.git
diff --git a/snakecnv/lib/align_snakemake_utils.py b/snakecnv/align_snakemake_utils.py
similarity index 100%
rename from snakecnv/lib/align_snakemake_utils.py
rename to snakecnv/align_snakemake_utils.py
diff --git a/snakecnv/lib b/snakecnv/lib
new file mode 160000
index 0000000..765e872
--- /dev/null
+++ b/snakecnv/lib
@@ -0,0 +1 @@
+Subproject commit 765e87245fcc733e09f9c3471bfa6fd42106f941
diff --git a/snakecnv/lib/svfilter.py b/snakecnv/lib/svfilter.py
deleted file mode 100644
index 68c41af..0000000
--- a/snakecnv/lib/svfilter.py
+++ /dev/null
@@ -1,353 +0,0 @@
-
-
-from math import isnan
-import re
-
-from collections import defaultdict
-
-import numpy as np
-
-from svrunner_utils import warn, vcf_to_pybed
-
-HOM_REF = (0, 0)
-HET_VAR = (0, 1)
-HOM_VAR = (1, 1)
-
-SVTYPER_GENO_LIKELI_TAG = "GL"
-GENOMESTRIP_GENO_LIKELI_TAG = "GP"
-
-Geno_likeli_tag = SVTYPER_GENO_LIKELI_TAG
-
-
-def getNonVariantSamples(sv, variants, samples):
- return set([s for s in samples if s not in variants])
-
-
-def getVariantSamples(sv):
- if re.search(r"genomestrip", sv.id) is not None:
- return set(sv.record.info['GSSAMPLES'])
- else:
- return set(sv.record.info['VSAMPLES'])
-
-
-def passed_variant(record):
- """Did this variant pass?"""
- return (record.filter is None or
- len(record.filter) == 0 or
- "PASS" in record.filter)
-
-
-def Called(record):
- called = 0
- for sample in record.samples:
- if getGQ(record.samples[sample]) > 13:
- called += 1
- # if (record.samples[sample]["GQ"] is not None and
- # record.samples[sample]["GQ"]>13):
- # called += 1
- return called
-
-
-def getGQ(sample):
- return sample["GQ"] if sample["GQ"] is not None else 0
-
-
-def callRate(sv):
- record = sv.record
- return float(Called(record))/len(record.samples)
-
-
-def MaxGQ(record, geno):
- maxGQ = 0
- for sample in record.samples:
- if (record.samples[sample]["GT"] == geno
- and record.samples[sample]["GQ"] > maxGQ):
- maxGQ = record.samples[sample]["GQ"]
- return maxGQ
-
-
-def Polymorph(sv):
- """
- At least two different genotypes
- """
- record = sv.record
- MaxHomRefGQ = MaxGQ(record, HOM_REF)
- MaxHomVarGQ = MaxGQ(record, HOM_VAR)
- MaxHetVarGQ = MaxGQ(record, HET_VAR)
- return MaxHomRefGQ*MaxHomVarGQ + MaxHomRefGQ*MaxHetVarGQ + MaxHomVarGQ*MaxHetVarGQ
-
-
-def NumGeno(record, geno):
- numgeno = 0
- for sample in record.samples:
- if record.samples[sample]["GT"] == geno:
- numgeno += 1
- return numgeno
-
-
-def InbreedingCoef(sv):
- """
- The inbreeding coefficient calculated as 1 - #observed het/#expected het
- """
- obs_hetero = NumGeno(sv, HET_VAR)
- exp_hetero = NumGeno(sv, HOM_VAR) + NumGeno(sv, HET_VAR)/2
- return 1 - float(obs_hetero)/exp_hetero
-
-
-# Redundancy annotator
-def getlikelihoods(sample):
- # return an array of genotype loglikelihoods
- return sample[Geno_likeli_tag]
-
-
-def probas(likelihoods):
- # transform log likelihoods into likelihoods
- return np.exp(np.array(likelihoods))
-
-
-def getprobas(sample):
- # Transforming likelihods into probabilities
- return probas(getlikelihoods(sample))/np.sum(probas(getlikelihoods(sample)))
-
-
-def gstrength(u):
- # Sum of phred-like genotype qualities provides a measure of the
- # combined genotype quality of the site
- return np.sum([s['GQ'] if s['GQ'] is not None else 0 for s in u.samples.values()])
-
-
-def ondiagonal(s, u, v):
- # Probability that, for that individual, the two SVs are identically
- # genotyped P1(0/0)*P2(0/0) + ... P1(1/1)*P2(1/1)
- # in the same way :
- p = getprobas(u.samples[s])
- q = getprobas(v.samples[s])
- proba = 0
- for a, b in zip(p, q):
- proba += a*b
- # print("Proba on %3.5f" %(proba))
- return proba
-
-
-def offdiagonal(s, u, v):
- # Probability that, for that individual, the two SVs are not identically
- # in the same way, complement of the previous one
- p = getprobas(u.samples[s])
- q = getprobas(v.samples[s])
- proba = 0
- for i, a in enumerate(p):
- for j, b in enumerate(q):
- if i != j:
- proba += a*b
- # print("Proba off %3.2f" %(proba))
- return proba
-
-
-def duplicatescore(u, v):
- # For the two SVs, max discordant genotype log-ratio proba
- # same genotypes against discordant against (worse individual)
-
- # Valid samples
- valid_samples = []
- for s in u.samples:
- if (u.samples[s]['GQ'] is not None
- and v.samples[s]['GQ'] is not None):
- valid_samples.append(s)
-
- max_disc = 0
- computed = float('NaN')
- for s in valid_samples:
- # ondiago is not used just for comprehension
- # ondiago = ondiagonal(s, u, v)
- offdiago = offdiagonal(s, u, v)
- if offdiago > max_disc:
- max_disc = offdiago
- if max_disc > 0 and max_disc < 1:
- ratio = (1-max_disc)/max_disc
- computed = np.log(ratio)
- return computed
-
-
-def getduplicates(u, v):
- # select the prefered duplicate
- # returns prefered, discarded, strength of both
- if gstrength(u) > gstrength(v):
- return (u, v, gstrength(u), gstrength(v))
- else:
- return (v, u, gstrength(v), gstrength(u))
-
-
-def GenotypeQuals(u):
- # the genotype qualities as an array
- return [u.samples[s]['GQ'] if u.samples[s]['GQ'] is not None else 0
- for s in u.samples]
-
-
-def maxGQ(u):
- # maximum genotype quality
- return max(GenotypeQuals(u))
-
-
-def passed(u):
- # did the variant passed the previous filter
- if len(u.filter) == 0 or "PASS" in u.filter:
- return True
- else:
- return False
-
-
-def ordered(a, b):
- # simply ordering the two string
- return (a, b) if a < b else (b, a)
-
-
-def svlen(u):
- # the length of the SV
- # vcf format is 1-based
- return u.stop-u.pos+1
-
-
-def getoverlap(u, osize):
- # percentage overlap given the size of the overlap
- return 100*osize/svlen(u)
-
-
-def setLikeliTag(genotyper):
- global Geno_likeli_tag
-
- if genotyper == "svtyper":
- Geno_likeli_tag = SVTYPER_GENO_LIKELI_TAG
- warn("Assuming genotypes provided by svtyper software" +
- " hence tag is %s" % (Geno_likeli_tag))
-
- elif genotyper == "genomestrip":
- Geno_likeli_tag = GENOMESTRIP_GENO_LIKELI_TAG
- warn("Assuming genotypes provided by genomestrip software" +
- " hence tag is %s" % (Geno_likeli_tag))
- else:
- print("Unknown genotyping software")
- exit(1)
-
-
-def GenomeSTRIPLikeRedundancyAnnotator(SVSet, duplicatescore_threshold=-2,
- genotyper="svtyper"):
- """ Annotating duplicate candidates based on the genotype likelihoods
- - genotype likelihoods can be provided by svtyper or genomestrip
- """
-
- setLikeliTag(genotyper)
-
- variants = defaultdict()
- for sv in SVSet:
- variants[sv.id] = sv
-
- pybed_variants = vcf_to_pybed(SVSet)
- self_overlap = pybed_variants.intersect(pybed_variants, f=0.5, wo=True)
-
- seen = defaultdict(tuple)
- duplicates = defaultdict(list)
- overlapping = defaultdict(tuple)
- for o in self_overlap:
- if o[3] == o[7]:
- continue
- a, b = ordered(o[3], o[7])
- if seen[(a, b)]:
- continue
- seen[(a, b)] = True
- u = variants[a]
- v = variants[b]
- score = duplicatescore(u, v)
- if score > duplicatescore_threshold:
- ref, dupli, s1, s2 = getduplicates(u, v)
- overlap_size = int(o[-1])
- overlap = getoverlap(dupli, overlap_size)
- if maxGQ(ref) > 0 and passed(dupli):
- # are dismissed
- # - reference variant with 0 genotype quality for all markers
- # - duplicate that are already tagged as filtered out
- # dupli.id is considered as a duplicate of ref.id
- duplicates[dupli.id].append((ref.id, score, overlap))
- else:
- overlap_size = int(o[-1])
- overlap_u = getoverlap(u, overlap_size)
- overlap_v = getoverlap(v, overlap_size)
- overlapping[(u, v)] = {'dupli_score': score,
- 'overlap_left': overlap_u,
- 'overlap_right': overlap_v,
- }
-
- for u in SVSet:
- if u.id in duplicates:
- duplis = sorted([a for (a, s, o) in duplicates[u.id]])
- score = max([s for (a, s, o) in duplicates[u.id]])
- overlap = max([o for (a, s, o) in duplicates[u.id]])
- add_duplicate_info_sv(u, overlap, score, duplis)
-
- for o in overlapping:
- info = overlapping[o]
- u, v = o
- add_overlap_info_sv(u, info['overlap_left'], info['dupli_score'])
- add_overlap_info_sv(v, info['overlap_right'], info['dupli_score'])
-
-
-def add_duplicate_info_sv(sv, duplicateoverlap, duplicatescore, duplicates):
- """
- simply adding two information to the sv infos
- """
- if isnan(duplicatescore):
- sv.record.info['DUPLICATESCORE'] = float('nan')
- else:
- sv.record.info['DUPLICATESCORE'] = duplicatescore
- sv.record.info['DUPLICATEOVERLAP'] = duplicateoverlap
- sv.record.info['DUPLICATES'] = duplicates
-
-
-def add_overlap_info_sv(sv, overlap, duplicatescore):
- """
- simply adding two information to the sv infos
- """
- if isnan(duplicatescore):
- sv.record.info['DUPLICATESCORE'] = float('nan')
- else:
- sv.record.info['DUPLICATESCORE'] = duplicatescore
- sv.record.info['NONDUPLICATEOVERLAP'] = overlap
-
-
-def GenomeSTRIPLikefiltering(SVSet):
- """ Filtering the candidate CNVs according to the following criteria
- - non duplicate sites
- - variant sites
- - call rate > 0.8
- - at least one variant (homozygous or heterozygote) has a genotype
- quality > 20
- - the variant is not everywhere heterozygote or homozygote
- (use NONVARIANTSCORE in both cases)
- """
-
- for sv in SVSet:
- info = sv.record.info
- if callRate(sv) < 0.75:
- sv.filter.add("CALLRATE")
- if not Polymorph(sv):
- sv.filter.add("POLYMORPH")
- # if info['GSDUPLICATESCORE'] and info['GSDUPLICATESCORE']>0:
- # sv.filter.add("DUPLICATE")
- # DUPLICATE
- if 'NONDUPLICATEOVERLAP' in info and info['NONDUPLICATEOVERLAP'] > 0.7:
- sv.filter.add("OVERLAP")
-
- if "DUPLICATESCORE" in info is not None and info['DUPLICATESCORE'] > -2:
- sv.filter.add("DUPLICATE")
- # NONVARIANT
- if 'GSNONVARSCORE' in info and info['GSNONVARSCORE'] and info['GSNONVARSCORE'] > 13:
- sv.filter.add("NONVARIANT")
- # GTDEPTH
- if 'GSDEPTHRATIO' in info and (info['GSDEPTHRATIO'] is None or info['GSDEPTHRATIO'] > 0.8):
- sv.filter.add("DEPTH")
- if 'DEPTHRATIO' in info and (isnan(info['DEPTHRATIO']) or info['DEPTHRATIO'] > 0.8):
- sv.filter.add("DDEPTH")
- if 'DEPTHCALLTHRESHOLD' in info and info['DEPTHCALLTHRESHOLD'] > 1:
- sv.filter.add("CCOVERAGE")
- # CLUSTERSEP
- if 'GSM1' in info and (info['GSM1'] <= -0.5 or info['GSM1'] >= 2.0):
- sv.filter.add("CLUSTERSEP")
diff --git a/snakecnv/lib/svreader/__init__.py b/snakecnv/lib/svreader/__init__.py
deleted file mode 100644
index de64423..0000000
--- a/snakecnv/lib/svreader/__init__.py
+++ /dev/null
@@ -1,274 +0,0 @@
-
-
-import os
-import re
-import math
-
-import vcf
-from pybedtools import BedTool
-from pybedtools import create_interval_from_list
-from pysam import tabix_compress, tabix_index
-from svreader.vcf_utils import get_template
-from svrunner_utils import eprint, vcf_to_pybed
-
-'''
-
-A Vcf wrapper object, following the specification of VCFv4.2
- https://samtools.github.io/hts-specs/VCFv4.2.pdf
-'''
-
-GT_HOMO_REF = "0/0"
-GT_HETERO = "0/1"
-GT_HOMO_VAR = "1/1"
-
-
-class SVInter(object):
- '''
- SVInter object :
- a lightweight interval object for the purpose of constructing
- connected components of SV
- each SVRecord corresponds to a node in the graph
- '''
- def __init__(self, chrom, start, end, id):
- self.__chrom = chrom
- self.__start = start
- self.__end = end
- self.__id = id
- self.__links = set()
-
- @property
- def id(self):
- return self.__id
-
- @id.setter
- def id(self, x):
- self.__id = x
-
- @property
- def chrom(self):
- return self.__chrom
-
- @property
- def start(self):
- return self.__start
-
- @property
- def end(self):
- return self.__end
-
- @start.setter
- def start(self, x):
- self.__start = x
-
- @end.setter
- def end(self, x):
- self.__end = x
-
- def length(self):
- return self.end-self.start+1
-
- # TODO should wee keep this here or have another object for connected
- # component construction ?
- @property
- def links(self):
- return set(self.__links)
-
- def add_link(self, other):
- self.__links.add(other)
- other.__links.add(self)
-
- def __str__(self):
- return self.chrom+":"+str(self.start)+"-"+str(self.end)
-
-
-class SVRecord(SVInter):
- '''
- SVRecord object :
- a generic object for storing SV records
- Toolo specific SVrecods will inherit from this tool
- '''
- def __init__(self, tool_name, chrom, pos, end, id,
- ref=None, qual=None, filter=None):
- super(SVRecord, self).__init__(chrom, pos, end, id)
-
- # VCF specific fields
- self.__ref = ref or "N"
- self.__qual = qual or "."
- self.__filter = filter
-
- self.__tool_name = tool_name
- self._sv_type = None
-
- @property
- def tool_name(self):
- return self.__tool_name
-
- @property
- def sv_len(self):
- return self.length()
-
- @property
- def sv_type(self):
- return self._sv_type
-
- @property
- def filter(self):
- return self.__filter
-
- def to_vcf_record(self):
- return None
-
- def __str__(self):
- return self.chrom+":"+str(self.start)+"-"+str(self.end)
-
-
-def from_vcf_records_to_intervals(vcf_records):
-
- intervals = []
- for record in vcf_records:
- chrom = record.chrom
- start = record.start - 1
- end = record.end
- name = record.id
- intervals.append(create_interval_from_list(
- map(str, [chrom, start, end, name])))
- return intervals
-
-
-class SVReader(object):
- '''
- SVReader object :
- virtual object for reading sv-tool output files
- The actual reading is made by the child objects
- '''
-
- svs_supported = set(["DEL", "INS", "DUP", "INV"])
-
- def __init__(self, file_name, tool_name, reference_handle=None):
- self.file_name = file_name
- self.reference_handle = reference_handle
- self.__tool_name = tool_name
-
- def __iter__(self):
- return self
-
- def next(self):
- while True:
- raw_record = self.vcf_reader.next()
- record = SVRecord(self.tool_name,
- raw_record.CHROM,
- raw_record.POS,
- raw_record.INFO['END'],
- raw_record.ID,
- raw_record.REF,
- raw_record.QUAL,
- raw_record.FILTER)
- return record
-
- @property
- def tool_name(self):
- return self.__tool_name
-
- def filtering(self, all_vcf_records, minlen, maxlen,
- excluded_regions=None, cutoff_proximity=50):
- """
- Filter the vcfrecords according to specified criteria
- - keep only sv > mnilen and < max_len
- - specific default filtering criteria for each tool
- - sv close to gaps (less than cutoff_proximity)
- :param all_vcf_records: list of VcfRecords
- :param minlen: minimum SV length
- :param maxlen: maximum SV length
- :param excluded_regions: file with regions to exclude (e.g gaps)
- :param cutoff_proximity: maximum tolerated proximity
- with exlucded region
- :return: list of VcfRecords
- """
- # First filtering on size
- vcf_records = []
- eprint("Filtering on size (>%d and <%d)" % (minlen, maxlen))
- for record in all_vcf_records:
- if (self.GeneralFilterPass(record, minlen, maxlen)
- and self.SpecificFilterPass(record)):
- vcf_records.append(record)
-
- eprint("%d records" % (len(vcf_records)))
-
- if excluded_regions is not None:
- # Convert vcf to intervals
- eprint("Converting to bedtools intervals")
- variants = vcf_to_pybed(vcf_records)
-
- # Identify SV overlapping regions to exclude
- eprint("Loading exlusion intervals ")
- eprint(excluded_regions)
- toexclude = BedTool(excluded_regions).sort()
- excluded = dict()
- if len(toexclude):
- eprint("Computing distance with gaps: %d gaps" % len(toexclude))
- # removing cnv in a close proximity to gaps in the assembly
- # -d=True In addition to the closest feature in B,
- # report its distance to A as an extra column
- for sv in variants.closest(toexclude, d=True):
- if (sv[4] != "."
- and sv[4] != "-1"
- and float(sv[-1]) < cutoff_proximity):
- print("-".join(sv))
- excluded[sv[3]] = 1
-
- vcf_records_filtered = []
- for record in vcf_records:
- if record.id not in excluded:
- vcf_records_filtered.append(record)
- vcf_records = vcf_records_filtered
-
- return vcf_records
-
- def GeneralFilterPass(self, record, minlen, maxlen):
- return (abs(record.sv_len) >= minlen and abs(record.sv_len) <= maxlen)
-
- def SpecificFilterPass(self, record):
- return True
-
- def bnd_merge(sef, svtype, records):
- return records # nothing to do in the majority of cases
-
-
-class SVWriter(object):
- def __init__(self, file_name, tool_name, template_reader):
- self.tool_name = tool_name
- self.template_reader = template_reader
- self._setFilename(file_name)
- self._isopen = False
-
- def _setFilename(self, filename):
- if filename.endswith(".gz"):
- filename = re.sub('\.gz$', '', filename)
- self.__index = True
- else:
- self.__index = False
- self.__filename = filename
-
- @property
- def filename(self):
- return self.__filename
-
- def write(self, record):
- if not self._isopen:
- self._open()
- self._write(record)
-
- def close(self):
- self._close()
- if self.__index:
- tabix_compress(self.__filename, self.__filename+".gz", force=True)
- tabix_index(self.__filename+".gz", force=True, preset="vcf")
- if os.path.exists(self.__filename):
- os.remove(self.__filename)
-
- def _dumpemptyvcf(self):
- vcf_template_reader = get_template(self.template_reader.tool_name)
- vcf_template_reader.samples = self.template_reader.getOrderedSamples()
- self.vcf_writer = vcf.Writer(open(self.filename, 'w'),
- vcf_template_reader)
- self.vcf_writer.close()
diff --git a/snakecnv/lib/svreader/_version.py b/snakecnv/lib/svreader/_version.py
deleted file mode 100644
index 7225152..0000000
--- a/snakecnv/lib/svreader/_version.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.5.2"
diff --git a/snakecnv/lib/svreader/defaults.py b/snakecnv/lib/svreader/defaults.py
deleted file mode 100644
index 39f2d14..0000000
--- a/snakecnv/lib/svreader/defaults.py
+++ /dev/null
@@ -1,68 +0,0 @@
-MIN_SV_LENGTH = 50
-MAX_SV_LENGTH = 1000000
-OVERLAP_RATIO = 0.5
-WIGGLE = 100
-INS_WIGGLE = 100
-TX_WIGGLE = 5
-SVS_SUPPORTED = set(["DEL", "DUP", "INS", "INV", "ITX", "CTX"])
-SVS_ASSEMBLY_SUPPORTED = set(["DEL", "INS" , "INV", "DUP"])
-SVS_SOFTCLIP_SUPPORTED = set(["DEL", "INS" , "INV", "DUP"])
-MEAN_READ_LENGTH=100
-
-# For generating candidate intervals for insertion assembly
-MIN_SUPPORT_SC_ONLY = 2
-MIN_SUPPORT_INS = 15
-MIN_SUPPORT_DEL = 10
-MIN_SUPPORT_INV = 10
-MIN_SUPPORT_DUP = 10
-MIN_SUPPORT_FRAC_INS = 0.05
-MIN_SUPPORT_FRAC_DEL = 0.04
-MIN_SUPPORT_FRAC_INV = 0.015
-MIN_SUPPORT_FRAC_DUP = 0.015
-MEAN_READ_COVERAGE=50
-MIN_INS_COVERAGE_FRAC=0.5
-MAX_INS_COVERAGE_FRAC=1.5
-MAX_INTERVALS = 10000
-SC_PAD = 500
-SC_MIN_SOFT_CLIP = 20
-SC_MIN_AVG_BASE_QUAL = 20
-SC_MIN_MAPQ = 5
-SC_MAX_NM = 10
-SC_MIN_MATCHES = 50
-SC_OTHER_SCALE = 5
-
-
-ISIZE_MIN = 250
-ISIZE_MAX = 450
-ISIZE_MEAN = 350.0
-ISIZE_SD = 50.0
-
-# For assembly read-extraction
-EXTRACTION_MAX_READ_PAIRS = 10000
-EXTRACTION_MAX_NM = 5
-EXTRACTION_MAX_INTERVAL_TRUNCATION = 10000
-EXTRACTION_TRUNCATION_PAD = 4000
-
-# For running SPAdes
-ASSEMBLY_MAX_TOOLS = 1
-SPADES_TIMEOUT = 300 # in seconds
-SPADES_PAD = 500
-SPADES_MAX_INTERVAL_SIZE = 50000
-SPADES_MAX_INTERVAL_SIZE_2BP = 1000000
-
-# For running AGE
-AGE_TIMEOUT = 300 # in seconds
-AGE_MIN_CONTIG_LENGTH = 200
-AGE_PAD = 500
-AGE_MAX_REGION_LENGTH = 1000000
-AGE_MAX_INTERVAL_TRUNCATION = 10000
-AGE_TRUNCATION_PAD = 2000
-AGE_DIST_TO_BP = 400
-MIN_INV_SUBALIGN_LENGTH = 50
-MIN_DEL_SUBALIGN_LENGTH = 50
-AGE_WINDOW_SIZE = 20
-AGE_DIST_TO_EXP_BP_INS = 25
-
-# For genotyping
-GT_WINDOW = 100
-GT_NORMAL_FRAC = 0.05
\ No newline at end of file
diff --git a/snakecnv/lib/svreader/delly.py b/snakecnv/lib/svreader/delly.py
deleted file mode 100644
index 3a5ebd4..0000000
--- a/snakecnv/lib/svreader/delly.py
+++ /dev/null
@@ -1,272 +0,0 @@
-import logging
-import os
-
-
-from collections import defaultdict
-
-from pysam import VariantFile
-
-from svreader import SVRecord, SVReader, SVWriter
-from svrunner_utils import eprint, vcf_to_pybed
-
-logger = logging.getLogger(__name__)
-
-mydir = os.path.dirname(os.path.realpath(__file__))
-
-'''
-Delly output is a vcf file, fields are described in the header
-
-#using awk
-cat vcf |
-awk '/##FORMAT/{ last=index($0,","); id=index($0,"ID"); \
- desc=substr($0,match($0,"Description")+12) ; gsub(/"/, "", desc); \
- printf "%-30s %s\n", substr($0,id+3,last-id-3), \
- substr(desc,0,length(desc)-1)}'
-
-FILTER
- PASS All filters passed
- LowQual PE/SR support below 3 or mapping quality below 20.
-
-INFO
- CIEND PE confidence interval around END
- CIPOS PE confidence interval around POS
- CHR2 Chromosome for END coordinate in case of a translocation
- END End position of the structural variant
- PE Paired-end support of the structural variant
- MAPQ Median mapping quality of paired-ends
- SR Split-read supportSpecificFilterPass
- SRQ Split-read consensus alignment quality
- CONSENSUS Split-read consensus sequence
- CE Consensus sequence entropy
- CT Paired-end signature induced connection type
- IMPRECISE Imprecise structural variation
- PRECISE Precise structural variation
- SVTYPE Type of structural variant
- SVMETHOD Type of approach used to detect SV
- INSLEN Predicted length of the insertion
- HOMLEN Predicted microhomology length using a max. edit distance of 2
- RDRATIO Read-depth ratio of het. SV carrier vs. non-carrier.
-
-FORMAT
- GT Genotype
- GL Log10-scaled genotype likelihoods for RR,RA,AA genotypes
- GQ Genotype Quality
- FT Per-sample genotype filter
- RC Raw high-quality read counts for the SV
- RCL Raw high-quality read counts for the left control region
- RCR Raw high-quality read counts for the right control region
- CN Read-depth based copy-number estimate for autosomal sites
- DR high-quality reference pairs
- DV high-quality variant pairs
- RR high-quality reference junction reads
- RV high-quality variant junction reads
-
-
-'''
-
-delly_name = "delly"
-delly_source = set([delly_name])
-
-
-class DellyRecord(SVRecord):
-
- def __init__(self, record, sample_order, reference_handle=None):
-
- # Custom id
- record.id = "_".join([delly_name, record.chrom, record.id])
- super(DellyRecord, self).__init__(delly_name,
- record.chrom,
- record.pos,
- record.stop,
- record.id,
- ref=record.ref,
- qual=record.qual,
- filter=record.filter)
-
- self.__record = record
- self._sv_type = record.info["SVTYPE"]
- self.__pe = record.info["PE"]
- self.__sr = record.info["SR"] if "SR" in list(record.info.keys()) else 0
- self.__ct = record.info["CT"]
- self.__record.info['SR'] = self.__sr
-
- sv = {}
- for sample in record.samples:
- sample_name = sample.rsplit('.')[0]
- sv[sample_name] = record.samples[sample]
- self.sv = sv
- self.__record.info['VSAMPLES'] = ",".join(self.variantSamples())
-
- @property
- def record(self):
- return self.__record
-
- @property
- def start(self):
- return self.record.pos
-
- @property
- def end(self):
- return self.record.stop
-
- @property
- def pe(self):
- return self.__pe
-
- @property
- def sr(self):
- return self.__sr
-
- @property
- def ct(self):
- return self.__ct
-
- def update(self, start, end, pe_supp):
- self.record.pos = start
- self.record.stop = end
- self.record.info['PE'] = pe_supp
-
- def variantSample(self, sample):
- if self.sv[sample]["DV"] > 0 or self.sv[sample]["RV"] > 0:
- return True
- else:
- return False
-
- def variantSamples(self):
- variant_samples = []
- for sample in self.sv:
- if self.variantSample(sample):
- variant_samples.append(sample)
- return sorted(variant_samples)
-
- def MaxIndSupportingRP(self):
- ''' Maximum number of supporting PE among individuals.
- '''
- max_ind_supp = 0
- su = 0
- rep_sample = ""
- for sample in self.sv:
- dv = self.sv[sample]["DV"]
- su += dv
- if dv > max_ind_supp:
- rep_sample = sample
- max_ind_supp = dv
- if max_ind_supp == 0:
- ratio = 0
- else:
- total = self.sv[rep_sample]["DV"] + self.sv[rep_sample]["DR"]
- ratio = float(self.sv[rep_sample]["DV"])/total
- return su, max_ind_supp, ratio
-
- def to_svcf_record(self):
- return self.record
-
-
-class DellyReader(SVReader):
- svs_supported = set(["DEL", "INS", "DUP", "INV"])
-
- def __init__(self, file_name, reference_handle=None, svs_to_report=None):
- super(DellyReader, self).__init__(file_name,
- delly_name,
- reference_handle)
- self.vcf_reader = VariantFile(file_name)
- if 'VSAMPLES' not in self.vcf_reader.header.info.keys():
- self.vcf_reader.header.info.add('VSAMPLES',
- number='.',
- type='String',
- description='Samples with variant alleles')
- self.svs_supported = DellyReader.svs_supported
- if svs_to_report is not None:
- self.svs_supported &= set(svs_to_report)
-
- def __iter__(self):
- return self
-
- def __next__(self):
- while True:
- delly_record = next(self.vcf_reader)
- record = DellyRecord(delly_record, self.reference_handle)
- if record.sv_type in self.svs_supported:
- return record
-
- def getHeader(self):
- return self.vcf_reader.header
-
- def getOrderedSamples(self):
- samples = self.vcf_reader.header.samples
- sample_names = [sample.rsplit('.')[0] for sample in samples]
- return sample_names
-
- def SpecificFilterPass(self, record):
- # Filtering criteria more than 5 PE or more than SR and PE > 0
- # see http://bcb.io/2014/08/12/validated-whole-genome-structural-variation-detection-using-multiple-callers
- # TODO : should we modifiy this in order to account for individual
- # specific filtering (e.g pe > 5 at least for an individual)
- return (record.pe > 5 or (record.pe > 0 and record.sr > 0))
-
- def bnd_merge(sef, svtype, records):
- """
- Merging delly two lines inversion format according to the
- CT info field (distal connection)
- 3to3 tail-to-tail
- 5to5 head-to-head
- CT types are separated and for each 3to3, the corresponding
- 5to5 is searched for (bedtools intersect with >90% overlap)
- A single line corresponding to the intersection of both
- signature is kept
- """
- if svtype != "INV":
- return records
- # a dictionnary of records with id as key
- records_dict = defaultdict()
- records_CT = defaultdict(list)
- for r in records:
- records_dict[r.id] = r
- records_CT[r.ct].append(r)
-
- eprint("Converting to bedtools intervals")
- tail2tail = vcf_to_pybed(records_CT["3to3"])
- head2head = vcf_to_pybed(records_CT["5to5"])
-
- # TODO check if the following code really recover balanced inversions
-
- balanced = tail2tail.intersect(head2head, r=True, f=0.95, wo=True)
-
- confirmed = []
- seen = defaultdict()
- for b in balanced:
- start = int(b[5]) # tail
- end = int(b[2]) # head
- t2t = records_dict[b[3]]
- h2h = records_dict[b[7]]
- repre = t2t
- if repre in seen:
- continue
- seen[repre] = 1
- pe_supp = t2t.record.info['PE'] + h2h.record.info['PE']
- print("merging %s and %s" % (t2t, h2h))
- repre.update(start, end, pe_supp)
- confirmed.append(repre)
- return confirmed
-
-
-class DellyWriter(SVWriter):
-
- def __init__(self, file_name, reference_contigs, template_reader):
- super(DellyWriter, self).__init__(file_name,
- template_reader.tool_name,
- template_reader)
-
- def _open(self):
- self.vcf_writer = VariantFile(self.filename, 'w',
- header=self.template_reader.getHeader())
- self._isopen = True
-
- def _write(self, record):
- self.vcf_writer.write(record.record)
-
- def _close(self):
- if self._isopen:
- self.vcf_writer.close()
- else: # nothing was written
- self._dumpemptyvcf()
diff --git a/snakecnv/lib/svreader/genomestrip.py b/snakecnv/lib/svreader/genomestrip.py
deleted file mode 100644
index 8eaf2de..0000000
--- a/snakecnv/lib/svreader/genomestrip.py
+++ /dev/null
@@ -1,214 +0,0 @@
-import logging
-import sys
-import os
-
-from pysam import VariantFile
-
-from svreader import SVRecord, SVReader, SVWriter
-
-logger = logging.getLogger(__name__)
-mydir = os.path.dirname(os.path.realpath(__file__))
-
-'''
-
-An ultra lightweight vcf reader for genomeSTRIP
-
-#using awk
-cat vcf |
-
-
-awk '/##FORMAT/{ last=index($0,","); id=index($0,"ID"); \
- desc=substr($0,match($0,"Description")+12) ; \
- gsub(/"/, "", desc); printf "%-30s %s\n", \
- substr($0,id+3,last-id-3), substr(desc,0,length(desc)-1)}'
-
-
-FILTER
-
- COHERENCE GSCOHPVALUE == NA || GSCOHPVALUE <= 0.01
- COVERAGE GSDEPTHCALLTHRESHOLD == NA || GSDEPTHCALLTHRESHOLD >= 1.0
- DEPTH GSDEPTHRATIO == NA || GSDEPTHRATIO > 0.8 || (GSDEPTHRATIO > 0.63 && (GSMEMBPVALUE == NA || GSMEMBPVALUE >= 0.01))
- DEPTHPVAL GSDEPTHPVALUE == NA || GSDEPTHPVALUE >= 0.01
- LQ Low Quality Genotyping filter
- PAIRSPERSAMPLE GSNPAIRS <= 1.1 * GSNSAMPLES
-
-INFO:
- CIEND Confidence interval around END for imprecise variants
- CIPOS Confidence interval around POS for imprecise variants
- END End coordinate of this variant
- GSCOHERENCE Value of coherence statistic
- GSCOHFN Coherence statistic per pair
- GSCOHPVALUE Coherence metric (not a true p-value)
- GSCOORDS Original cluster coordinates
- GSCORA6 Correlation with array intensity from Affy6 arrays
- GSCORI1M Correlation with array intensity from Illumina 1M arrays
- GSCORNG Correlation with array intensity from NimbleGen arrays
- GSDEPTHCALLS Samples with discrepant read pairs or low read depth
- GSDEPTHCALLTHRESHOLD Read depth threshold (median read depth of samples with discrepant read pairs)
- GSDEPTHNOBSSAMPLES Number of samples with discrepant read pairs in depth test
- GSDEPTHNTOTALSAMPLES Total samples in depth test
- GSDEPTHOBSSAMPLES Samples with discrepant read pairs in depth test
- GSDEPTHPVALUE Depth p-value using chi-squared test
- GSDEPTHPVALUECOUNTS Depth test read counts (carrier inside event, carrier outside event, non-carrier inside, non-carrier outside)
- GSDEPTHRANKSUMPVALUE Depth p-value using rank-sum test
- GSDEPTHRATIO Read depth ratio test
- GSDMAX Maximum value considered for DOpt
- GSDMIN Minimum value considered for DOpt
- GSDOPT Most likely event length
- GSDSPAN Inner span length of read pair cluster
- GSELENGTH Effective length
- GSEXPMEAN Expected read depth sample mean
- GSGMMWEIGHTS Genotyping depth model cluster weights
- GSM1 Genotyping depth model parameter M1
- GSM2 Genotyping depth model parameters M2[0],M2[1]
- GSMEMBNPAIRS Number of pairs used in membership test
- GSMEMBNSAMPLES Number of samples used in membership test
- GSMEMBOBSSAMPLES Samples participating in membership test
- GSMEMBPVALUE Membership p-value
- GSMEMBSTATISTIC Value of membership statistic
- GSNDEPTHCALLS Number of samples with discrepant read pairs or low read depth
- GSNHET Number of heterozygous snp genotype calls inside the event
- GSNHOM Number of homozygous snp genotype calls inside the event
- GSNNOCALL Number of snp genotype non-calls inside the event
- GSNPAIRS Number of discrepant read pairs
- GSNSAMPLES Number of samples with discrepant read pairs
- GSNSNPS Number of snps inside the event
- GSOUTLEFT Number of outlier read pairs on left
- GSOUTLIERS Number of outlier read pairs
- GSOUTRIGHT Number of outlier read pairs on right
- GSREADGROUPS Read groups contributing discrepant read pairs
- GSREADNAMES Discrepant read pair identifiers
- GSRPORIENTATION Read pair orientation
- GSSAMPLES Samples contributing discrepant read pairs
- GSSNPHET Fraction of het snp genotype calls inside the event
- HOMLEN Length of base pair identical micro-homology at event breakpoints
- HOMSEQ Sequence of base pair identical micro-homology at event breakpoints
- IMPRECISE Imprecise structural variation
- NOVEL Indicates a novel structural variation
- SVLEN Difference in length between REF and ALT alleles
- SVTYPE Type of structural variant
-
-FORMAT:
- CN Copy number genotype for imprecise events
- CNF Estimate of fractional copy number
- CNL Copy number likelihoods with no frequency prior
- CNP Copy number likelihoods
- CNQ Copy number genotype quality for imprecise events
- FT Per-sample genotype filter
- GL Genotype likelihoods with no frequency prior
- GP Genotype likelihoods
- GQ Genotype Quality
- GSPC Number of supporting read pairs for structural variant alleles
- GT Genotype
- PL Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification
-
-'''
-
-
-genomestrip_name = "genomestrip"
-genomestrip_source = set([genomestrip_name])
-
-
-class GenomeSTRIPRecord(SVRecord):
-
- def __init__(self, record, reference_handle=None):
- record.id = "_".join([genomestrip_name, record.chrom, record.id])
- super(GenomeSTRIPRecord, self).__init__(genomestrip_name,
- record.chrom,
- record.pos,
- record.stop,
- record.id,
- ref=record.ref,
- qual=record.qual,
- filter=record.filter)
- self.__record = record
- self.__sv_type = record.info['SVTYPE']
- sv = {}
- for sample in record.samples:
- sample_name = sample.rsplit('.')[0]
- sv[sample_name] = record.samples[sample]
- self.sv = sv
-
- variant_samples = self.variantSamples()
- if len(variant_samples) > 0:
- self.__record.info['VSAMPLES'] = ",".join(variant_samples)
-
- @property
- def sv_type(self):
- return self.__sv_type
-
- @property
- def record(self):
- return self.__record
-
- def variantSample(self, sample):
- if self.sv[sample]["GSPC"] > 0:
- return True
- else:
- return False
-
- def variantSamples(self):
- variant_samples = []
- for sample in self.sv:
- if self.variantSample(sample):
- variant_samples.append(sample)
- return sorted(variant_samples)
-
-
-class GenomeSTRIPReader(SVReader):
- svs_supported = set(["DEL"])
-
- def __init__(self, file_name, reference_handle=None, svs_to_report=None):
- super(GenomeSTRIPReader, self).__init__(file_name,
- genomestrip_name,
- reference_handle)
- self.vcf_reader = VariantFile(file_name)
- self.vcf_reader.header.info.add('VSAMPLES',
- number='.',
- type='String',
- description='Samples with variant alleles')
- if svs_to_report is not None:
- self.svs_supported &= set(svs_to_report)
-
- def __iter__(self):
- return self
-
- def __next__(self):
- while True:
- genomestrip_record = next(self.vcf_reader)
- record = GenomeSTRIPRecord(genomestrip_record,
- self.reference_handle)
- if record.sv_type in self.svs_supported:
- return record
-
- def getHeader(self):
- return self.vcf_reader.header
-
- def getOrderedSamples(self):
- samples = self.vcf_reader.header.samples
- sample_names = [sample.rsplit('.')[0] for sample in samples]
- return sample_names
-
-
-class GenomeSTRIPWriter(SVWriter):
-
- def __init__(self, file_name, reference_contigs, template_reader):
- super(GenomeSTRIPWriter, self).__init__(file_name,
- template_reader.tool_name,
- template_reader)
- self.__template_reader = template_reader
-
- def _open(self):
- header = self.__template_reader.getHeader()
- self.vcf_writer = VariantFile(self.filename, 'w',
- header=header)
- self._isopen = True
-
- def _write(self, record):
- self.vcf_writer.write(record.record)
-
- def _close(self):
- if self._isopen:
- self.vcf_writer.close()
- else: # nothing was written
- self._dumpemptyvcf()
diff --git a/snakecnv/lib/svreader/lumpy.py b/snakecnv/lib/svreader/lumpy.py
deleted file mode 100644
index cbb149d..0000000
--- a/snakecnv/lib/svreader/lumpy.py
+++ /dev/null
@@ -1,209 +0,0 @@
-
-import logging
-import os
-
-import re
-
-from pysam import VariantFile
-
-from svreader import SVRecord, SVReader, SVWriter
-
-logger = logging.getLogger(__name__)
-mydir = os.path.dirname(os.path.realpath(__file__))
-
-'''
-An ultra lightweight vcf reader for lumpy
-
-INFO:
- SVTYPE Type of structural variant
- SVLEN Difference in length between REF and ALT alleles
- END End position of the variant described in this record
- STRANDS Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)
- IMPRECISE Imprecise structural variation
- CIPOS Confidence interval around POS for imprecise variants
- CIEND Confidence interval around END for imprecise variants
- CIPOS95 Confidence interval (95%) around POS for imprecise variants
- CIEND95 Confidence interval (95%) around END for imprecise variants
- MATEID ID of mate breakends
- EVENT ID of event associated to breakend
- SECONDARY Secondary breakend in a multi-line variants
- SU Number of pieces of evidence supporting the variant across all samples
- PE Number of paired-end reads supporting the variant across all samples
- SR Number of split reads supporting the variant across all samples
- EV Type of LUMPY evidence contributing to the variant call
- PRPOS LUMPY probability curve of the POS breakend
- PREND LUMPY probability curve of the END breakend
-
-FORMAT:
- GT Genotype
- SU Number of pieces of evidence supporting the variant
- PE Number of paired-end reads supporting the variant
- SR Number of split reads supporting the variant
- BD Amount of BED evidence supporting the variant
- RO Reference allele observation count, with partial observations recorded fractionally
- AO Alternate allele observations, with partial observations recorded fractionally
- QR Sum of quality of reference observations
- QA Sum of quality of alternate observations
- RS Reference allele split-read observation count, with partial observations recorded fractionally
- AS Alternate allele split-read observation count, with partial observations recorded fractionally
- RP Reference allele paired-end observation count, with partial observations recorded fractionally
- AP Alternate allele paired-end observation count, with partial observations recorded fractionally
- AB Allele balance, fraction of observations from alternate allele, QA/(QR+QA)
-
-
-'''
-
-lumpy_name = "lumpy"
-lumpy_source = set([lumpy_name])
-
-
-class LumpyRecord(SVRecord):
-
- def __init__(self, record, reference_handle=None):
- record.id = "_".join(
- [lumpy_name,
- record.chrom,
- record.info['SVTYPE'],
- record.id])
- super(LumpyRecord, self).__init__(lumpy_name,
- record.chrom,
- record.pos,
- record.stop,
- record.id,
- ref=record.ref,
- qual=record.qual,
- filter=record.filter)
- self.__record = record
-
- sv = {}
- for sample in record.samples:
- sample_name = sample.rsplit('.')[0]
- sv[sample_name] = record.samples[sample]
- self.sv = sv
- # self.__record.info["MAX_IND_SU"] = self.MaxIndSupportingRP()
- self._sv_type = self.__record.info['SVTYPE']
- self.__record.info['VSAMPLES'] = ",".join(self.variantSamples())
-
- @property
- def svlen(self):
- svlen = 0
- if self.__record.INFO["SVTYPE"] == "BND":
- chrom, end = self. getAltBND()
- svlen = end - self.start + 1
- else:
- svlen = abs(int(self.__record.INFO["SVLEN"][0]))
- return svlen
-
- @property
- def record(self):
- return self.__record
-
- def getAltBND(self):
- ''' extract second breakend chr,pos from ALT field '''
- alt = self.__record.ALT[0]
- m = re.search(r"[\[\]]([\w\.:0-9]+)[\[\]]", str(alt))
- if m:
- return m.group(1).split(":")
- else:
- return 0
-
- def variantSampleOld(self, sample):
- if (max(self.sv[sample]["AS"]) > 0
- or max(self.sv[sample]["AP"])
- or max(self.sv[sample]["ASC"]) > 0):
- return True
- else:
- return False
-
- def variantSample(self, sample):
- if self.sv[sample]["SU"] > 0:
- return True
- else:
- return False
-
- def variantSamples(self):
- variant_samples = []
- for sample in self.sv:
- if self.variantSample(sample):
- variant_samples.append(sample)
- return sorted(variant_samples)
-
- def MaxIndSupportingRP(self):
- sv = self.sv
- max_ind_supp = 0
- for sample in sv:
- max_ind_supp = max(max_ind_supp, sv[sample]['SU'])
- return max_ind_supp
-
-
-class LumpyReader(SVReader):
- svs_supported = set(["DEL", "INS", "DUP", "INV"])
-
- def __init__(self, file_name, reference_handle=None, svs_to_report=None):
- super(LumpyReader, self).__init__(file_name,
- lumpy_name,
- reference_handle)
- self.vcf_reader = VariantFile(file_name)
- self.vcf_reader.header.info.add(
- 'VSAMPLES',
- number='.',
- type='String',
- description='Samples with variant alleles')
- if svs_to_report is not None:
- self.svs_supported &= set(svs_to_report)
-
- def __iter__(self):
- return self
-
- def __next__(self):
- while True:
- lumpy_record = next(self.vcf_reader)
- # BND not supported
- if lumpy_record.info['SVTYPE'] == "BND":
- continue
- record = LumpyRecord(lumpy_record, self.reference_handle)
- if record.sv_type in self.svs_supported:
- return record
-
- def getHeader(self):
- return self.vcf_reader.header
-
- def AreSamplesSpecified(self):
- return 1
-
- def getOrderedSamples(self):
- if not hasattr(self.vcf_reader, "samples"):
- return []
- return self.vcf_reader.samples
-
- def SpecificFilterPass(self, record):
- # Filtering criteria more than 4 PE
- # see
- # http://bcb.io/2014/08/12/validated-whole-genome-structural-variation-detection-using-multiple-callers
- return (record.MaxIndSupportingRP() > 4)
-
-
-class LumpyWriter(SVWriter):
-
- def __init__(self, file_name, reference_contigs, template_reader):
- super(
- LumpyWriter,
- self).__init__(file_name,
- template_reader.tool_name,
- template_reader)
-
- def _open(self):
- self.vcf_writer = VariantFile(
- self.filename,
- 'w',
- header=self.template_reader.getHeader())
- self._isopen = True
-
- def _write(self, record):
- self.vcf_writer.write(record.record)
-
- def _close(self):
- if self._isopen:
- self.vcf_writer.close()
- else: # nothing was written
- self._dumpemptyvcf()
diff --git a/snakecnv/lib/svreader/pindel.py b/snakecnv/lib/svreader/pindel.py
deleted file mode 100644
index 8195297..0000000
--- a/snakecnv/lib/svreader/pindel.py
+++ /dev/null
@@ -1,389 +0,0 @@
-import logging
-import sys
-import os
-import gzip
-
-import vcf
-
-from svrunner_utils import smart_open
-from svreader.vcf_utils import get_template, get_template_header
-from svreader import SVRecord, SVReader, SVWriter
-
-from pysam import VariantFile, VariantHeader
-
-logger = logging.getLogger(__name__)
-
-mydir = os.path.dirname(os.path.realpath(__file__))
-
-'''
-from http://gmt.genome.wustl.edu/packages/pindel/user-manual.html
-
-There is a head line for each variant reported, followed by the alignment of supporting reads to the reference on the
-second line. The example variants are a 1671bp deletion and a 10bp insertion on chr20. The breakpoints are specified
-after "BP". Due to microhomology around the breakpoints, the breakpoint coordinates may shift both upstream and
-downstream,'BP_range' is used to indicate the range of this shift. The header line contains the following data:
-
-1) The index of the indel/SV (57 means that 57 insertions precede this insertion in the file)
-
-2) The type of indel/SV: I for insertion, D for deletion, INV for inversion, TD for tandem duplication
-
-3) The length of the SV
-
-4) "NT" (to indicate that the next number is the length of non-template sequences inserted; insertions are fully covered
- by the NT-fields, deletions can have NT bases if the deletion is not 'pure', meaning that while bases have been
- deleted, some bases have been inserted between the breakpoints)
-
-5) the length(s) of the NT fragment(s)
-
-6) the sequence(s) of the NT fragment(s)
-
-7-8) the identifier of the chromosome the read was found on
-
-9-10-11) BP: the start and end positions of the SV
-
-12-13-14) BP_range: if the exact position of the SV is unclear since bases at the edge of one read-half could equally
-well be appended to the other read-half. In the deletion example, ACA could be on any side of the gap, so the original
-deletion could have been between 1337143 and 1338815, between 1337144 and 1338816, or between 1337145 and 133817, or
-between 1337146 and 133818. BP-range is used to indicate this range.
-
-15) "Supports": announces that the total count of reads supporting the SV follow.
-
-16) The number of reads supporting the SV
-
-17) The number of unique reads supporting the SV (so not counting duplicate reads)
-
-18) +: supports from reads whose anchors are upstream of the SV
-
-19-20) total number of supporting reads and unique number of supporting reads whose anchors are upstream of the SV.
-
-21) -: supports from reads whose anchors are downstream of the SV
-
-22-23) total number of supporting reads and unique number of supporting reads whose anchors are downstream of the SV
-
-24-25) S1: a simple score, ("# +" + 1)* ("# -" + 1) ;
-
-26-27) SUM_MS: sum of mapping qualities of anchor reads, The reads with variants or unmapped are called split-read,
-whose mate is called anchor reads. We use anchor reads to narrow down the search space to speed up and increase
-sensitivity;
-
-28) the number of different samples scanned
-
-29-30-31) NumSupSamples?: the number of samples supporting the SV, as well as the number of samples having unique
-reads supporting the SV (in practice, these numbers are the same)
-
-32+) Per sample: the sample name, followed by the total number of supporting reads whose anchors are upstream, the
-total number of unique supporting reads whose anchors are upstream, the total number of supporting reads whose anchors
- are downstream, and finally the total number of unique supporting reads whose anchors are downstream.
-
-WARNING see below
-
-----
-
-The reported larger insertion (LI) record is rather different than other types of variants. Here is the format:
-1) index,
-2) type(LI),
-3) "ChrID",
-4) chrName,
-5) left breakpoint,
-6) "+"
-7) number of supporting reads for the left coordinate,
-8) right breakpoint,
-9) "-"
-10) number of supporting reads for the right coordinate.
-
-Following lines are repeated for each sample
-11) Sample name
-12) "+"
-13) upstream supporting reads
-14) "-"
-15) downstream supporting reads
-
-'''
-
-'''
-New version of pindel changes the output starting from 32+
- Check https://trac.nbic.nl/pipermail/pindel-users/2013-March/000229.html
-
-hi mike,
-
-This is the output format for the new version where the number of reads
-supporting the reference allele is also counted on the left and right
-breakpoints of the variants.
-
-TS1_3051 164 167 0 0 2 2 TS1_3054 117 118 0 0 0 0
-
-For example, 164 and 167 reads supporting the reference for sample TS1_3051
-but only 2 reads and 2 unique reads from - strand support the alternative.
-It is less likely that this is a true germline variant. with the read count
-for the reference allele, you may filter the calls in the same way as snps.
-
-
-'''
-
-pindel_name = "pindel"
-pindel_source = set([pindel_name])
-min_coverage = 10
-het_cutoff = 0.2
-hom_cutoff = 0.8
-
-GT_REF = "0/0"
-GT_HET = "0/1"
-GT_HOM = "1/1"
-
-PINDEL_TO_SV_TYPE = {"I": "INS", "D": "DEL", "LI": "INS",
- "TD": "DUP", "INV": "INV", "RPL":"RPL"}
-
-
-class PindelHeader:
- def __init__(self, fd):
- self.header_dict = {}
- self.header_fields = []
-
- start = fd.tell()
- # Parse header : read the header until the last line is
- # encoutered (^#Chr1) break before reading the next line
- while True:
- try:
- line = next(fd)
- if line.find("ChrID") >= 1:
- self.parse_firstrecord_line(line)
- break
- except StopIteration:
- break
- # We return to the start of the file
- fd.seek(start)
-
- def parse_firstrecord_line(self, line):
- fields = line.split()
- num_samples = int(fields[27])
- pindel024u_or_later = len(fields) > 31 + 5 * num_samples
- if pindel024u_or_later:
- self.samples = [fields[i] for i in range(31, len(fields), 7)]
- else:
- self.samples = [fields[i] for i in range(31, len(fields), 5)]
- logger.info("\t".join(self.samples))
-
- def getOrderedSamples(self):
- # sample order is given by the sample order in the pindel output
- if not hasattr(self, "samples"):
- return []
- return self.samples
-
- def __str__(self):
- return str(self.__dict__)
-
-
-def pysam_header(pindel_header):
- # Using the new pysam interface for creating records and header
- # not implemented in the python3.4 version of pysam (0.10.0)
- # have to wait at least for the 0.11.2.2 pysam version
- header = VariantHeader()
- vcf_header = get_template_header("pindel")
-
- with open(vcf_header) as fin:
- for line in fin:
- if (line.startswith("##INFO")
- or line.startswith("##FORMAT")
- or line.startswith("##FILTER")):
- header.add_line(line.rstrip())
- for sample in pindel_header.getOrderedSamples():
- header.add_sample(sample)
-
- return header
-
-
-class PindelRecord(SVRecord):
- counter = {"I": 0, "D": 0, "LI": 0, "TD": 0, "INV": 0, "RPL": 0}
-
- # Only DEL and DUP for the moment
- svs_supported = set(["DEL", "DUP", "INV"])
-
- def __init__(self, record_string, reference_handle=None):
- self.name = pindel_name
-
- fields = record_string.split()
- index = fields[0]
- sv_type = fields[1]
-
- if PINDEL_TO_SV_TYPE[sv_type] not in PindelRecord.svs_supported:
- print(("Unsupported structural variation " + sv_type))
- exit(1)
- sv_len = int(fields[2])
- num_nt_added = fields[4]
- # nt_added = fields[5]
- chrom = fields[7]
- start_pos = int(fields[9])
- end_pos = int(fields[10])
- bp_range = (int(fields[12]), int(fields[13]))
- read_supp = int(fields[15])
- uniq_read_supp = int(fields[16])
- up_read_supp = int(fields[18])
- up_uniq_read_supp = int(fields[19])
- down_read_supp = int(fields[21])
- down_uniq_read_supp = int(fields[22])
- # score = int(fields[24])
- # nums_samples = int(fields[27])
- num_sample_supp = int(fields[29])
- # num_sample_uniq_supp = int(fields[30])
-
- sv = {}
- samples = []
- for i in range(31, len(fields), 7):
- sv[fields[i]] = {"name": fields[i],
- "up_ref_read_supp": int(fields[i + 1]),
- "down_ref_read_supp": int(fields[i + 2]),
- "up_var_read_supp": int(fields[i + 3]),
- "up_var_uniq_read_supp": int(fields[i + 4]),
- "down_var_read_supp": int(fields[i + 5]),
- "down_var_uniq_read_supp": int(fields[i + 6])
- }
- samples.append(sv[fields[i]])
-
- self.pindel_sv_type = sv_type
- self.up_read_supp = up_read_supp
- self.down_read_supp = down_read_supp
- self.up_uniq_read_supp = up_uniq_read_supp
- self.down_uniq_read_supp = down_uniq_read_supp
- self.uniq_read_supp = uniq_read_supp
-
- # Computing CIPOS and CIEND
- pos_conf = max(1, abs(int((bp_range[0] - start_pos)/2)))
- end_conf = max(1, abs(int((bp_range[1] - end_pos)/2)))
- self.cipos = (-pos_conf, pos_conf)
- self.ciend = (-end_conf, end_conf)
-
- PindelRecord.counter[sv_type] += 1
- id = "_".join([pindel_name, chrom, sv_type,
- str(PindelRecord.counter[sv_type])])
- super(PindelRecord, self).__init__(pindel_name, chrom,
- start_pos, end_pos, id)
-
- self._sv_type = PINDEL_TO_SV_TYPE[sv_type]
- self.__sv_len = sv_len
- self.__samples = samples
- self.__sv = sv
-
- self.Modinfo = {
- "END": end_pos,
- "SU": read_supp,
- "INDEX": index,
- "NTLEN": num_nt_added,
- "CIPOS": ",".join([str(x) for x in self.cipos]),
- "CIEND": ",".join([str(x) for x in self.ciend]),
- "NUM_SUPP_SAMPLES": num_sample_supp
- }
-
- def variantSample(self, sample):
- if (self.__sv[sample]["up_var_read_supp"] > 0
- or self.__sv[sample]["down_var_read_supp"] > 0):
- return True
- else:
- return False
-
- def variantSamples(self):
- variant_samples = []
- for sample in self.__sv:
- if self.variantSample(sample):
- variant_samples.append(sample)
- return sorted(variant_samples)
-
- def getOrderedSamples(self):
- # sample order is given by the sample order in the pindel output
- return self.__samples
-
- def getSampleCalls(self):
- # TODO make sure that the order of the sample conform to
- # the order of the vcf header !!
- calls = []
- for s in self.getOrderedSamples():
- sample = s["name"]
- ad = s["up_var_read_supp"] + s["down_var_read_supp"]
- called_value = vcf.model.make_calldata_tuple("AD")(AD=ad)
- calldata = vcf.model._Call(None, sample, called_value)
- calls.append(calldata)
- return calls
-
- def MaxIndSupportingRP(self):
- max_ind_supp = 0
- for s in self.__samples:
- ad = s["up_var_read_supp"] + s["down_var_read_supp"]
- max_ind_supp = max(ad, max_ind_supp)
- return max_ind_supp
-
- def to_vcf_record(self):
- alt = [vcf.model._SV(self.sv_type)]
- info = {"SVLEN": self.sv_len, "SVTYPE": self.sv_type}
-
- info["MAX_IND_SU"] = self.MaxIndSupportingRP()
- info["VSAMPLES"] = ",".join(self.variantSamples())
-
- info.update(self.Modinfo)
-
- calls = self.getSampleCalls()
-
- vcf_record = vcf.model._Record(self.chrom,
- self.start,
- self.id,
- "N",
- alt,
- ".",
- "PASS",
- info,
- "AD",
- [0],
- calls)
- return vcf_record
-
-
-class PindelReader(SVReader):
- svs_supported = set(["DEL", "INS", "DUP", "INV"])
-
- def __init__(self, file_name, reference_handle=None, svs_to_report=None):
- super(PindelReader, self).__init__(file_name, pindel_name,
- reference_handle)
- self.file_fd = smart_open(file_name)
- self.header = PindelHeader(self.file_fd)
- self.pysamheader = pysam_header(self.header)
- self.svs_supported = PindelReader.svs_supported
- if svs_to_report is not None:
- self.svs_supported &= set(svs_to_report)
-
- def __iter__(self):
- return self
-
- def __next__(self):
- while True:
- line = next(self.file_fd)
- if line.find("ChrID") >= 1:
- record = PindelRecord(line.strip(), self.reference_handle)
- if record.sv_type in self.svs_supported:
- return record
-
- def AreSamplesSpecified(self):
- return 1
-
- def getOrderedSamples(self):
- return self.header.getOrderedSamples()
-
- def SpecificFilterPass(self, record):
- # Filtering criteria more than 4 PE
- # see
- # http://bcb.io/2014/08/12/validated-whole-genome-structural-variation-detection-using-multiple-callers
- return (record.length() > 60)
-
-
-class PindelWriter(SVWriter):
- def __init__(self, file_name, reference_contigs, template_reader):
- super(PindelWriter, self).__init__(file_name,
- template_reader.tool_name,
- template_reader)
- vcf_template_reader = get_template(template_reader.tool_name)
- vcf_template_reader.samples = template_reader.getOrderedSamples()
- self.vcf_writer = vcf.Writer(open(self.filename, 'w'),
- vcf_template_reader)
-
- def write(self, record):
- self.vcf_writer.write_record(record.to_vcf_record())
-
- def _close(self):
- self.vcf_writer.close()
diff --git a/snakecnv/lib/svreader/resources/b37.gaps.bed b/snakecnv/lib/svreader/resources/b37.gaps.bed
deleted file mode 100644
index 0f8c1f8..0000000
--- a/snakecnv/lib/svreader/resources/b37.gaps.bed
+++ /dev/null
@@ -1,457 +0,0 @@
-1 0 10000 1 N 10000 telomere no
-1 177417 227417 4 N 50000 clone yes
-1 267719 317719 6 N 50000 contig no
-1 471368 521368 8 N 50000 contig no
-1 2634220 2684220 32 N 50000 clone yes
-1 3845268 3995268 47 N 150000 contig no
-1 13052998 13102998 151 N 50000 clone yes
-1 13219912 13319912 154 N 100000 contig no
-1 13557162 13607162 157 N 50000 clone yes
-1 17125658 17175658 196 N 50000 clone yes
-1 29878082 30028082 337 N 150000 contig no
-1 103863906 103913906 1088 N 50000 clone yes
-1 120697156 120747156 1263 N 50000 clone yes
-1 120936695 121086695 1265 N 150000 contig no
-1 121485434 121535434 1269 N 50000 clone no
-1 121535434 124535434 1270 N 3000000 centromere no
-1 124535434 142535434 1271 N 18000000 heterochromatin no
-1 142731022 142781022 1273 N 50000 clone yes
-1 142967761 143117761 1275 N 150000 contig no
-1 143292816 143342816 1277 N 50000 clone yes
-1 143544525 143644525 1279 N 100000 contig no
-1 143771002 143871002 1281 N 100000 contig no
-1 144095783 144145783 1285 N 50000 contig no
-1 144224481 144274481 1287 N 50000 contig no
-1 144401744 144451744 1289 N 50000 clone yes
-1 144622413 144672413 1291 N 50000 contig no
-1 144710724 144810724 1293 N 100000 clone yes
-1 145833118 145883118 1307 N 50000 clone yes
-1 146164650 146214650 1310 N 50000 clone yes
-1 146253299 146303299 1312 N 50000 clone yes
-1 148026038 148176038 1328 N 150000 contig no
-1 148361358 148511358 1331 N 150000 contig no
-1 148684147 148734147 1333 N 50000 clone yes
-1 148954460 149004460 1337 N 50000 clone yes
-1 149459645 149509645 1342 N 50000 clone yes
-1 205922707 206072707 1875 N 150000 contig no
-1 206332221 206482221 1878 N 150000 contig no
-1 223747846 223797846 2038 N 50000 clone yes
-1 235192211 235242211 2159 N 50000 clone yes
-1 248908210 249058210 2293 N 150000 contig no
-1 249240621 249250621 2302 N 10000 telomere no
-10 0 10000 1 N 10000 telomere no
-10 10000 60000 2 N 50000 contig no
-10 17974675 18024675 161 N 50000 clone yes
-10 38818835 38868835 337 N 50000 clone yes
-10 39154935 39254935 340 N 100000 contig no
-10 39254935 42254935 341 N 3000000 centromere no
-10 42254935 42354935 342 N 100000 contig no
-10 42546687 42596687 344 N 50000 clone yes
-10 46426964 46476964 375 N 50000 contig no
-10 47429169 47529169 387 N 100000 contig no
-10 47792476 47892476 390 N 100000 contig no
-10 48055707 48105707 392 N 50000 contig no
-10 49095536 49195536 403 N 100000 contig no
-10 51137410 51187410 419 N 50000 clone yes
-10 51398845 51448845 421 N 50000 clone yes
-10 125869472 125919472 1061 N 50000 clone yes
-10 128616069 128766069 1088 N 150000 contig no
-10 133381404 133431404 1129 N 50000 clone yes
-10 133677527 133727527 1135 N 50000 clone yes
-10 135524747 135534747 1156 N 10000 telomere no
-11 0 10000 1 N 10000 telomere no
-11 10000 60000 2 N 50000 contig no
-11 1162759 1212759 14 N 50000 clone yes
-11 50783853 50833853 439 N 50000 clone no
-11 50833853 51040853 440 N 207000 heterochromatin no
-11 51040853 51090853 441 N 50000 clone no
-11 51594205 51644205 446 N 50000 clone no
-11 51644205 54644205 447 N 3000000 centromere no
-11 54644205 54694205 448 N 50000 clone no
-11 69089801 69139801 575 N 50000 clone yes
-11 69724695 69774695 583 N 50000 clone yes
-11 87688378 87738378 736 N 50000 clone yes
-11 96287584 96437584 808 N 150000 contig no
-11 134946516 134996516 1134 N 50000 contig no
-11 134996516 135006516 1135 N 10000 telomere no
-12 0 10000 1 N 10000 telomere no
-12 10000 60000 2 N 50000 contig no
-12 95739 145739 4 N 50000 clone yes
-12 7189876 7239876 66 N 50000 contig no
-12 34856694 37856694 304 N 3000000 centromere no
-12 109373470 109423470 954 N 50000 contig no
-12 122530623 122580623 1075 N 50000 contig no
-12 132706992 132806992 1171 N 100000 contig no
-12 133841895 133851895 1183 N 10000 telomere no
-13 0 10000 1 N 10000 telomere no
-13 10000 16000000 2 N 15990000 short_arm no
-13 16000000 19000000 3 N 3000000 centromere no
-13 19000000 19020000 4 N 20000 heterochromatin no
-13 86760324 86910324 617 N 150000 contig no
-13 112353994 112503994 848 N 150000 contig no
-13 114325993 114425993 866 N 100000 contig no
-13 114639948 114739948 870 N 100000 contig no
-13 115109878 115159878 875 N 50000 contig no
-13 115159878 115169878 876 N 10000 telomere no
-14 0 10000 1 N 10000 telomere no
-14 10000 16000000 2 N 15990000 short_arm no
-14 16000000 19000000 3 N 3000000 centromere no
-14 107289540 107339540 667 N 50000 contig no
-14 107339540 107349540 668 N 10000 telomere no
-15 0 10000 1 N 10000 telomere no
-15 10000 17000000 2 N 16990000 short_arm no
-15 17000000 20000000 3 N 3000000 centromere no
-15 20894633 20935075 12 N 40442 clone yes
-15 21398819 21885000 16 N 486181 clone yes
-15 22212114 22262114 19 N 50000 contig no
-15 22596193 22646193 23 N 50000 contig no
-15 23514853 23564853 31 N 50000 contig no
-15 29159443 29209443 83 N 50000 contig no
-15 82829645 82879645 518 N 50000 contig no
-15 84984473 85034473 539 N 50000 contig no
-15 102521392 102531392 690 N 10000 telomere no
-16 0 10000 1 N 10000 telomere no
-16 10000 60000 2 N 50000 contig no
-16 8636921 8686921 128 N 50000 clone yes
-16 34023150 34173150 343 N 150000 contig no
-16 35285801 35335801 353 N 50000 clone no
-16 35335801 38335801 354 N 3000000 centromere no
-16 38335801 46335801 355 N 8000000 heterochromatin no
-16 46335801 46385801 356 N 50000 clone no
-16 88389383 88439383 712 N 50000 contig no
-16 90294753 90344753 731 N 50000 contig no
-16 90344753 90354753 732 N 10000 telomere no
-17 296626 396626 4 N 100000 contig no
-17 21566608 21666608 185 N 100000 contig no
-17 22263006 25263006 192 N 3000000 centromere no
-17 34675848 34725848 278 N 50000 contig no
-17 62410760 62460760 523 N 50000 clone yes
-17 77546461 77596461 644 N 50000 clone yes
-17 79709049 79759049 665 N 50000 contig no
-17_ctg5_hap1 1256794 1306794 12 N 50000 clone yes
-17_ctg5_hap1 1588968 1638968 15 N 50000 clone yes
-18 0 10000 1 N 10000 telomere no
-18 15410898 15460898 124 N 50000 clone no
-18 15460898 18460898 125 N 3000000 centromere no
-18 18460898 18510898 126 N 50000 clone no
-18 52059136 52209136 398 N 150000 contig no
-18 72283353 72333353 555 N 50000 clone yes
-18 75721820 75771820 585 N 50000 clone yes
-18 78017248 78067248 604 N 50000 contig no
-18 78067248 78077248 605 N 10000 telomere no
-19 0 10000 1 N 10000 telomere no
-19 10000 60000 2 N 50000 contig no
-19 7346004 7396004 170 N 50000 contig no
-19 8687198 8737198 190 N 50000 contig no
-19 20523415 20573415 367 N 50000 clone yes
-19 24631782 24681782 409 N 50000 heterochromatin no
-19 24681782 27681782 410 N 3000000 centromere no
-19 27681782 27731782 411 N 50000 heterochromatin no
-19 59118983 59128983 870 N 10000 telomere no
-2 0 10000 1 N 10000 telomere no
-2 3529312 3579312 38 N 50000 contig no
-2 5018788 5118788 52 N 100000 contig no
-2 16279724 16329724 149 N 50000 contig no
-2 21153113 21178113 192 N 25000 contig no
-2 87668206 87718206 739 N 50000 clone yes
-2 89630436 89830436 755 N 200000 contig no
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-6_apd_hap1 588761 692953 11 N 104192 clone yes
-6_apd_hap1 944435 963558 16 N 19123 clone yes
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-6_apd_hap1 1173934 1198528 23 N 24594 clone yes
-6_apd_hap1 1307951 1317697 26 N 9746 clone yes
-6_apd_hap1 1341208 1344139 28 N 2931 clone yes
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-6_apd_hap1 1582549 1598989 33 N 16440 clone yes
-6_apd_hap1 1736596 1786349 37 N 49753 clone yes
-6_apd_hap1 1833811 1937682 39 N 103871 clone yes
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-6_apd_hap1 2169002 2254169 43 N 85167 clone yes
-6_apd_hap1 2359974 2761234 48 N 401260 clone yes
-6_apd_hap1 2821679 2858431 51 N 36752 clone yes
-6_apd_hap1 2894657 3037482 54 N 142825 clone yes
-6_apd_hap1 3122973 3136480 56 N 13507 clone yes
-6_apd_hap1 3180323 3355295 59 N 174972 clone yes
-6_apd_hap1 3370179 3415446 62 N 45267 clone yes
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-6_apd_hap1 3696623 3736194 68 N 39571 clone yes
-6_apd_hap1 3762870 3766192 70 N 3322 clone yes
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-6_apd_hap1 4220467 4274176 76 N 53709 clone yes
-6_apd_hap1 4390029 4597885 79 N 207856 clone yes
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-6_mcf_hap5 2241824 2251998 39 N 10174 clone yes
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diff --git a/snakecnv/lib/svreader/resources/hg19.gaps.bed b/snakecnv/lib/svreader/resources/hg19.gaps.bed
deleted file mode 100644
index d516325..0000000
--- a/snakecnv/lib/svreader/resources/hg19.gaps.bed
+++ /dev/null
@@ -1,457 +0,0 @@
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-chr17_ctg5_hap1 1588968 1638968 15 N 50000 clone yes
-chr18 0 10000 1 N 10000 telomere no
-chr18 15410898 15460898 124 N 50000 clone no
-chr18 15460898 18460898 125 N 3000000 centromere no
-chr18 18460898 18510898 126 N 50000 clone no
-chr18 52059136 52209136 398 N 150000 contig no
-chr18 72283353 72333353 555 N 50000 clone yes
-chr18 75721820 75771820 585 N 50000 clone yes
-chr18 78017248 78067248 604 N 50000 contig no
-chr18 78067248 78077248 605 N 10000 telomere no
-chr19 0 10000 1 N 10000 telomere no
-chr19 10000 60000 2 N 50000 contig no
-chr19 7346004 7396004 170 N 50000 contig no
-chr19 8687198 8737198 190 N 50000 contig no
-chr19 20523415 20573415 367 N 50000 clone yes
-chr19 24631782 24681782 409 N 50000 heterochromatin no
-chr19 24681782 27681782 410 N 3000000 centromere no
-chr19 27681782 27731782 411 N 50000 heterochromatin no
-chr19 59118983 59128983 870 N 10000 telomere no
-chr2 0 10000 1 N 10000 telomere no
-chr2 3529312 3579312 38 N 50000 contig no
-chr2 5018788 5118788 52 N 100000 contig no
-chr2 16279724 16329724 149 N 50000 contig no
-chr2 21153113 21178113 192 N 25000 contig no
-chr2 87668206 87718206 739 N 50000 clone yes
-chr2 89630436 89830436 755 N 200000 contig no
-chr2 90321525 90371525 760 N 50000 clone yes
-chr2 90545103 91545103 762 N 1000000 heterochromatin no
-chr2 91545103 91595103 763 N 50000 clone no
-chr2 92326171 95326171 770 N 3000000 centromere no
-chr2 110109337 110251337 893 N 142000 contig no
-chr2 149690582 149790582 1221 N 100000 contig no
-chr2 234003741 234053741 1942 N 50000 contig no
-chr2 239801978 239831978 1992 N 30000 contig no
-chr2 240784132 240809132 2001 N 25000 contig no
-chr2 243102476 243152476 2025 N 50000 clone yes
-chr2 243189373 243199373 2027 N 10000 telomere no
-chr20 0 10000 1 N 10000 telomere no
-chr20 10000 60000 2 N 50000 contig no
-chr20 26319569 26369569 274 N 50000 clone no
-chr20 26369569 29369569 275 N 3000000 centromere no
-chr20 29369569 29419569 276 N 50000 clone no
-chr20 29653908 29803908 279 N 150000 contig no
-chr20 34897085 34947085 336 N 50000 clone yes
-chr20 61091437 61141437 624 N 50000 clone yes
-chr20 61213369 61263369 628 N 50000 contig no
-chr20 62965520 63015520 648 N 50000 contig no
-chr20 63015520 63025520 649 N 10000 telomere no
-chr21 0 10000 1 N 10000 telomere no
-chr21 10000 5211193 2 N 5201193 short_arm no
-chr21 5211193 9411193 3 N 4200000 contig no
-chr21 9595548 9645548 5 N 50000 contig no
-chr21 9775437 9825437 7 N 50000 contig no
-chr21 10034920 10084920 10 N 50000 contig no
-chr21 10215976 10365976 12 N 150000 contig no
-chr21 10647896 10697896 15 N 50000 contig no
-chr21 11188129 11238129 20 N 50000 clone no
-chr21 11238129 11288129 21 N 50000 heterochromatin no
-chr21 11288129 14288129 22 N 3000000 centromere no
-chr21 14288129 14338129 23 N 50000 clone no
-chr21 42955559 43005559 419 N 50000 contig no
-chr21 44632664 44682664 447 N 50000 clone yes
-chr21 48119895 48129895 515 N 10000 telomere no
-chr22 0 10000 1 N 10000 telomere no
-chr22 10000 13000000 2 N 12990000 short_arm no
-chr22 13000000 16000000 3 N 3000000 centromere no
-chr22 16000000 16050000 4 N 50000 contig no
-chr22 16697850 16847850 27 N 150000 contig no
-chr22 20509431 20609431 81 N 100000 contig no
-chr22 50364777 50414777 563 N 50000 contig no
-chr22 51244566 51294566 591 N 50000 contig no
-chr22 51294566 51304566 592 N 10000 telomere no
-chr3 0 10000 1 N 10000 telomere no
-chr3 10000 60000 2 N 50000 clone no
-chr3 66170270 66270270 565 N 100000 contig no
-chr3 90504854 93504854 784 N 3000000 centromere no
-chr3 194041961 194047251 1693 N 5290 contig no
-chr3 197962430 198012430 1732 N 50000 contig no
-chr3 198012430 198022430 1733 N 10000 telomere no
-chr4 0 10000 1 N 10000 telomere no
-chr4 1423146 1478646 15 N 55500 contig no
-chr4 8799203 8818203 83 N 19000 contig no
-chr4 9274642 9324642 88 N 50000 clone yes
-chr4 31820917 31837417 283 N 16500 contig no
-chr4 32834638 32840638 294 N 6000 contig no
-chr4 40296396 40297096 365 N 700 contig no
-chr4 49338941 49488941 445 N 150000 contig no
-chr4 49660117 52660117 447 N 3000000 centromere no
-chr4 59739333 59789333 510 N 50000 contig no
-chr4 75427379 75452279 651 N 24900 contig no
-chr4 191044276 191144276 1657 N 100000 clone no
-chr4 191144276 191154276 1658 N 10000 telomere no
-chr5 0 10000 1 N 10000 telomere no
-chr5 17530657 17580657 171 N 50000 clone yes
-chr5 46405641 49405641 452 N 3000000 centromere no
-chr5 91636128 91686128 873 N 50000 contig no
-chr5 138787073 138837073 1350 N 50000 contig no
-chr5 155138727 155188727 1514 N 50000 contig no
-chr5 180905260 180915260 1775 N 10000 telomere no
-chr6 0 10000 1 N 10000 telomere no
-chr6 10000 60000 2 N 50000 contig no
-chr6 58087659 58137659 620 N 50000 clone yes
-chr6 58780166 58830166 627 N 50000 clone no
-chr6 58830166 61830166 628 N 3000000 centromere no
-chr6 61830166 61880166 629 N 50000 clone no
-chr6 62128589 62178589 633 N 50000 clone yes
-chr6 95680543 95830543 992 N 150000 contig no
-chr6 157559467 157609467 1652 N 50000 clone yes
-chr6 157641300 157691300 1654 N 50000 clone yes
-chr6 167942073 168042073 1761 N 100000 clone yes
-chr6 170279972 170329972 1787 N 50000 clone yes
-chr6 171055067 171105067 1797 N 50000 contig no
-chr6 171105067 171115067 1798 N 10000 telomere no
-chr6_apd_hap1 207807 252060 4 N 44253 clone yes
-chr6_apd_hap1 327074 356458 6 N 29384 clone yes
-chr6_apd_hap1 448574 536172 9 N 87598 clone yes
-chr6_apd_hap1 588761 692953 11 N 104192 clone yes
-chr6_apd_hap1 944435 963558 16 N 19123 clone yes
-chr6_apd_hap1 970040 1029417 18 N 59377 clone yes
-chr6_apd_hap1 1173934 1198528 23 N 24594 clone yes
-chr6_apd_hap1 1307951 1317697 26 N 9746 clone yes
-chr6_apd_hap1 1341208 1344139 28 N 2931 clone yes
-chr6_apd_hap1 1451581 1556172 31 N 104591 clone yes
-chr6_apd_hap1 1582549 1598989 33 N 16440 clone yes
-chr6_apd_hap1 1736596 1786349 37 N 49753 clone yes
-chr6_apd_hap1 1833811 1937682 39 N 103871 clone yes
-chr6_apd_hap1 2009922 2164480 41 N 154558 clone yes
-chr6_apd_hap1 2169002 2254169 43 N 85167 clone yes
-chr6_apd_hap1 2359974 2761234 48 N 401260 clone yes
-chr6_apd_hap1 2821679 2858431 51 N 36752 clone yes
-chr6_apd_hap1 2894657 3037482 54 N 142825 clone yes
-chr6_apd_hap1 3122973 3136480 56 N 13507 clone yes
-chr6_apd_hap1 3180323 3355295 59 N 174972 clone yes
-chr6_apd_hap1 3370179 3415446 62 N 45267 clone yes
-chr6_apd_hap1 3491066 3594101 64 N 103035 clone yes
-chr6_apd_hap1 3638667 3642320 66 N 3653 clone yes
-chr6_apd_hap1 3696623 3736194 68 N 39571 clone yes
-chr6_apd_hap1 3762870 3766192 70 N 3322 clone yes
-chr6_apd_hap1 3790366 3970602 72 N 180236 clone yes
-chr6_apd_hap1 4220467 4274176 76 N 53709 clone yes
-chr6_apd_hap1 4390029 4597885 79 N 207856 clone yes
-chr6_dbb_hap3 138055 245960 4 N 107905 clone yes
-chr6_dbb_hap3 622457 640380 11 N 17923 clone yes
-chr6_dbb_hap3 1337861 1344119 21 N 6258 clone yes
-chr6_dbb_hap3 1836690 1837200 29 N 510 clone yes
-chr6_dbb_hap3 2117295 2119813 33 N 2518 clone yes
-chr6_dbb_hap3 2606257 2723615 42 N 117358 clone yes
-chr6_dbb_hap3 3407000 3481304 52 N 74304 clone yes
-chr6_dbb_hap3 3736386 3772614 59 N 36228 clone yes
-chr6_dbb_hap3 4206542 4226964 66 N 20422 clone yes
-chr6_dbb_hap3 4354126 4376794 69 N 22668 clone yes
-chr6_mann_hap4 145852 184243 3 N 38391 clone yes
-chr6_mann_hap4 1388812 1438812 19 N 50000 clone yes
-chr6_mann_hap4 2047420 2056121 28 N 8701 clone yes
-chr6_mann_hap4 2252213 2277026 32 N 24813 clone yes
-chr6_mann_hap4 2312291 2324610 34 N 12319 clone yes
-chr6_mann_hap4 2517052 2540962 37 N 23910 clone yes
-chr6_mann_hap4 2772006 2810953 42 N 38947 clone yes
-chr6_mann_hap4 2921096 2929556 45 N 8460 clone yes
-chr6_mann_hap4 3062107 3151453 47 N 89346 clone yes
-chr6_mann_hap4 3174271 3227203 49 N 52932 clone yes
-chr6_mann_hap4 3261361 3374492 52 N 113131 clone yes
-chr6_mann_hap4 3412258 3450867 54 N 38609 clone yes
-chr6_mann_hap4 3553455 3557741 56 N 4286 clone yes
-chr6_mann_hap4 3895092 3926341 62 N 31249 clone yes
-chr6_mann_hap4 4236950 4245095 68 N 8145 clone yes
-chr6_mann_hap4 4441658 4480941 73 N 39283 clone yes
-chr6_mcf_hap5 135141 138043 3 N 2902 clone yes
-chr6_mcf_hap5 179429 186757 5 N 7328 clone yes
-chr6_mcf_hap5 229133 282732 7 N 53599 clone yes
-chr6_mcf_hap5 383988 404415 10 N 20427 clone yes
-chr6_mcf_hap5 491590 560421 12 N 68831 clone yes
-chr6_mcf_hap5 676599 685588 15 N 8989 clone yes
-chr6_mcf_hap5 994250 1009296 20 N 15046 clone yes
-chr6_mcf_hap5 1177759 1292220 23 N 114461 clone yes
-chr6_mcf_hap5 1562952 1726699 28 N 163747 clone yes
-chr6_mcf_hap5 1815769 1838106 30 N 22337 clone yes
-chr6_mcf_hap5 2091199 2189158 37 N 97959 clone yes
-chr6_mcf_hap5 2241824 2251998 39 N 10174 clone yes
-chr6_mcf_hap5 2300850 2305647 41 N 4797 clone yes
-chr6_mcf_hap5 2712451 2811903 49 N 99452 clone yes
-chr6_mcf_hap5 2947576 2958999 52 N 11423 clone yes
-chr6_mcf_hap5 3154498 3188719 55 N 34221 clone yes
-chr6_mcf_hap5 3329672 3355524 58 N 25852 clone yes
-chr6_mcf_hap5 3602941 3659279 65 N 56338 clone yes
-chr6_mcf_hap5 3899294 3929849 69 N 30555 clone yes
-chr6_mcf_hap5 4149579 4149626 73 N 47 clone yes
-chr6_mcf_hap5 4284353 4302274 76 N 17921 clone yes
-chr6_mcf_hap5 4422172 4594253 78 N 172081 clone yes
-chr6_qbl_hap6 521900 681210 9 N 159310 clone yes
-chr6_qbl_hap6 2680914 2732064 37 N 51150 clone yes
-chr6_qbl_hap6 3023176 3049607 42 N 26431 clone yes
-chr6_qbl_hap6 3316246 3369039 46 N 52793 clone yes
-chr6_qbl_hap6 3993031 4020006 57 N 26975 clone yes
-chr6_ssto_hap7 861800 941242 15 N 79442 clone yes
-chr6_ssto_hap7 1727717 1744408 28 N 16691 clone yes
-chr6_ssto_hap7 1836632 1838892 34 N 2260 clone yes
-chr6_ssto_hap7 1983561 1997824 37 N 14263 clone yes
-chr6_ssto_hap7 2052735 2087415 39 N 34680 clone yes
-chr6_ssto_hap7 2101474 2113875 41 N 12401 clone yes
-chr6_ssto_hap7 2138491 2303018 43 N 164527 clone yes
-chr6_ssto_hap7 2411247 2461193 48 N 49946 clone yes
-chr6_ssto_hap7 3040184 3054966 60 N 14782 clone yes
-chr6_ssto_hap7 3093568 3154699 62 N 61131 clone yes
-chr6_ssto_hap7 3191196 3214366 64 N 23170 clone yes
-chr6_ssto_hap7 3368931 3445987 68 N 77056 clone yes
-chr6_ssto_hap7 4370564 4420564 83 N 50000 clone yes
-chr6_ssto_hap7 4580410 4610218 86 N 29808 clone yes
-chr6_ssto_hap7 4639806 4661556 88 N 21750 clone yes
-chr6_ssto_hap7 4723509 4774367 90 N 50858 clone yes
-chr6_ssto_hap7 4783798 4836049 92 N 52251 clone yes
-chr7 0 10000 1 N 10000 telomere no
-chr7 232484 282484 5 N 50000 clone yes
-chr7 50370631 50410631 488 N 40000 contig no
-chr7 58054331 61054331 564 N 3000000 centromere no
-chr7 61310513 61360513 569 N 50000 heterochromatin no
-chr7 61460465 61510465 571 N 50000 clone yes
-chr7 61677020 61727020 573 N 50000 clone yes
-chr7 61917157 61967157 575 N 50000 heterochromatin no
-chr7 74715724 74765724 693 N 50000 clone yes
-chr7 100556043 100606043 952 N 50000 clone yes
-chr7 130154523 130254523 1278 N 100000 clone yes
-chr7 139379377 139404377 1368 N 25000 contig no
-chr7 142048195 142098195 1397 N 50000 clone yes
-chr7 142276197 142326197 1399 N 50000 clone yes
-chr7 143347897 143397897 1409 N 50000 clone yes
-chr7 154270634 154370634 1517 N 100000 contig no
-chr7 159128663 159138663 1563 N 10000 telomere no
-chr8 0 10000 1 N 10000 telomere no
-chr8 7474649 7524649 66 N 50000 contig no
-chr8 12091854 12141854 105 N 50000 contig no
-chr8 43838887 46838887 376 N 3000000 centromere no
-chr8 48130499 48135599 389 N 5100 contig no
-chr8 86576451 86726451 716 N 150000 contig no
-chr8 142766515 142816515 1177 N 50000 clone yes
-chr8 145332588 145432588 1209 N 100000 contig no
-chr8 146304022 146354022 1216 N 50000 contig no
-chr8 146354022 146364022 1217 N 10000 telomere no
-chr9 0 10000 1 N 10000 telomere no
-chr9 39663686 39713686 343 N 50000 clone yes
-chr9 39974796 40024796 346 N 50000 contig no
-chr9 40233029 40283029 348 N 50000 clone yes
-chr9 40425834 40475834 350 N 50000 contig no
-chr9 40940341 40990341 354 N 50000 contig no
-chr9 41143214 41193214 357 N 50000 clone yes
-chr9 41365793 41415793 359 N 50000 contig no
-chr9 42613955 42663955 370 N 50000 contig no
-chr9 43213698 43313698 375 N 100000 contig no
-chr9 43946569 43996569 381 N 50000 clone yes
-chr9 44676646 44726646 386 N 50000 clone yes
-chr9 44908293 44958293 388 N 50000 clone yes
-chr9 45250203 45350203 391 N 100000 contig no
-chr9 45815521 45865521 397 N 50000 contig no
-chr9 46216430 46266430 400 N 50000 clone yes
-chr9 46461039 46561039 403 N 100000 contig no
-chr9 47060133 47160133 408 N 100000 contig no
-chr9 47317679 47367679 410 N 50000 clone no
-chr9 47367679 50367679 411 N 3000000 centromere no
-chr9 50367679 65367679 412 N 15000000 heterochromatin no
-chr9 65367679 65467679 413 N 100000 contig no
-chr9 65918360 65968360 418 N 50000 contig no
-chr9 66192215 66242215 421 N 50000 clone yes
-chr9 66404656 66454656 423 N 50000 clone yes
-chr9 66614195 66664195 425 N 50000 clone yes
-chr9 66863343 66913343 428 N 50000 clone yes
-chr9 67107834 67207834 430 N 100000 contig no
-chr9 67366296 67516296 432 N 150000 contig no
-chr9 67987998 68137998 437 N 150000 contig no
-chr9 68514181 68664181 441 N 150000 contig no
-chr9 68838946 68988946 443 N 150000 contig no
-chr9 69278385 69328385 446 N 50000 clone yes
-chr9 70010542 70060542 452 N 50000 clone yes
-chr9 70218729 70318729 454 N 100000 contig no
-chr9 70506535 70556535 456 N 50000 contig no
-chr9 70735468 70835468 458 N 100000 contig no
-chr9 92343416 92443416 641 N 100000 clone yes
-chr9 92528796 92678796 643 N 150000 clone yes
-chr9 133073060 133223060 982 N 150000 contig no
-chr9 137041193 137091193 1020 N 50000 contig no
-chr9 139166997 139216997 1040 N 50000 contig no
-chr9 141153431 141203431 1057 N 50000 contig no
-chr9 141203431 141213431 1058 N 10000 telomere no
-chrX 0 10000 1 N 10000 telomere no
-chrX 10000 60000 2 N 50000 contig no
-chrX 94821 144821 4 N 50000 contig no
-chrX 231384 281384 8 N 50000 contig no
-chrX 1047557 1097557 16 N 50000 contig no
-chrX 1134113 1184113 18 N 50000 contig no
-chrX 1264234 1314234 21 N 50000 contig no
-chrX 2068238 2118238 31 N 50000 contig no
-chrX 7623882 7673882 82 N 50000 clone yes
-chrX 10738674 10788674 111 N 50000 clone yes
-chrX 37098256 37148256 355 N 50000 contig no
-chrX 49242997 49292997 484 N 50000 contig no
-chrX 49974173 50024173 494 N 50000 contig no
-chrX 52395914 52445914 518 N 50000 contig no
-chrX 58582012 58632012 582 N 50000 clone no
-chrX 58632012 61632012 583 N 3000000 centromere no
-chrX 61632012 61682012 584 N 50000 clone no
-chrX 76653692 76703692 724 N 50000 contig no
-chrX 113517668 113567668 1183 N 50000 contig no
-chrX 115682290 115732290 1214 N 50000 contig no
-chrX 120013235 120063235 1268 N 50000 clone yes
-chrX 143507324 143557324 1536 N 50000 contig no
-chrX 148906424 148956424 1591 N 50000 clone yes
-chrX 149032062 149082062 1594 N 50000 contig no
-chrX 152277099 152327099 1624 N 50000 clone yes
-chrX 155260560 155270560 1668 N 10000 telomere no
-chrY 0 10000 1 N 10000 telomere no
-chrY 44821 94821 3 N 50000 contig no
-chrY 181384 231384 7 N 50000 contig no
-chrY 997557 1047557 15 N 50000 contig no
-chrY 1084113 1134113 17 N 50000 contig no
-chrY 1214234 1264234 20 N 50000 contig no
-chrY 2018238 2068238 30 N 50000 contig no
-chrY 8914955 8964955 92 N 50000 contig no
-chrY 9241322 9291322 97 N 50000 contig no
-chrY 10104553 13104553 105 N 3000000 centromere no
-chrY 13143954 13193954 107 N 50000 contig no
-chrY 13748578 13798578 112 N 50000 contig no
-chrY 20143885 20193885 169 N 50000 clone yes
-chrY 22369679 22419679 190 N 50000 clone yes
-chrY 23901428 23951428 204 N 50000 contig no
-chrY 28819361 58819361 249 N 30000000 heterochromatin no
-chrY 58917656 58967656 251 N 50000 contig no
-chrY 59363566 59373566 255 N 10000 telomere no
diff --git a/snakecnv/lib/svreader/resources/template.vcf b/snakecnv/lib/svreader/resources/template.vcf
deleted file mode 100644
index 68d8147..0000000
--- a/snakecnv/lib/svreader/resources/template.vcf
+++ /dev/null
@@ -1,2 +0,0 @@
-##fileformat=VCFv4.2
-#CHROM POS ID REF ALT QUAL FILTER INFO
diff --git a/snakecnv/lib/svreader/resources/template_breakdancer.vcf b/snakecnv/lib/svreader/resources/template_breakdancer.vcf
deleted file mode 100644
index c097539..0000000
--- a/snakecnv/lib/svreader/resources/template_breakdancer.vcf
+++ /dev/null
@@ -1,37 +0,0 @@
-##fileformat=VCFv4.1
-##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
-##INFO=<ID=IMPRECISE,Number=0,Type=Flag,Description="Imprecise structural variation">
-##INFO=<ID=SVLEN,Number=.,Type=Integer,Description="Difference in length between REF and ALT alleles">
-##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">
-##INFO=<ID=NUM_READS,Number=1,Type=Integer,Description="Number of reads supporting event">
-##INFO=<ID=SCORE,Number=1,Type=Integer,Description="Score reported by tool">
-##INFO=<ID=SVMETHOD,Number=.,Type=String,Description="Type of approach used to detect SV: RP (read pair), RD (read depth), SR (split read), JT (junction) or AS (assembly)">
-##INFO=<ID=CHR2,Number=1,Type=String,Description="Chromosome for END coordinate in case of a translocation">
-##INFO=<ID=POS2,Number=1,Type=String,Description="The new position of the translocated or duplicated region. Used of ITX, CTX, and IDP SVs">
-##INFO=<ID=BD_CHR1,Number=1,Type=String,Description="BreakDancer: chr1 field">
-##INFO=<ID=BD_POS1,Number=1,Type=Integer,Description="BreakDancer: pos1 field">
-##INFO=<ID=BD_ORI1,Number=1,Type=String,Description="BreakDancer: orientation1 field">
-##INFO=<ID=BD_CHR2,Number=1,Type=String,Description="BreakDancer: chr2 field">
-##INFO=<ID=BD_POS2,Number=1,Type=Integer,Description="BreakDancer: pos2 field">
-##INFO=<ID=BD_ORI2,Number=1,Type=String,Description="BreakDancer: orientation2 field">
-##INFO=<ID=BD_SCORE,Number=1,Type=Float,Description="BreakDancer: score field">
-##INFO=<ID=BD_SU,Number=1,Type=Integer,Description="BreakDancer: num_Reads field">
-##INFO=<ID=MAX_IND_SU,Number=1,Type=Integer,Description="Maximum individual PE support of the variant">
-##INFO=<ID=VSAMPLES,Number=.,Type=String,Description="Samples with variant alleles">
-##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
-##FORMAT=<ID=DV,Number=1,Type=Integer,Description="supporting read pair for the sample">
-##FILTER=<ID=LowQual,Description="Low Quality">
-##ALT=<ID=DEL,Description="Deletion">
-##ALT=<ID=DEL:ME:ALU,Description="Deletion of ALU element">
-##ALT=<ID=DEL:ME:L1,Description="Deletion of L1 element">
-##ALT=<ID=DUP,Description="Duplication">
-##ALT=<ID=DUP:TANDEM,Description="Tandem Duplication">
-##ALT=<ID=IDP,Description="Interspersed Duplication">
-##ALT=<ID=INS,Description="Insertion of novel sequence">
-##ALT=<ID=INS:ME:ALU,Description="Insertion of ALU element">
-##ALT=<ID=INS:ME:L1,Description="Insertion of L1 element">
-##ALT=<ID=INV,Description="Inversion">
-##ALT=<ID=CNV,Description="Copy number variable region">
-##ALT=<ID=ITX,Description="Intra-chromosomal translocation">
-##ALT=<ID=CTX,Description="Inter-chromosomal translocation">
-#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT
diff --git a/snakecnv/lib/svreader/resources/template_cnvnator.vcf b/snakecnv/lib/svreader/resources/template_cnvnator.vcf
deleted file mode 100644
index 112ce67..0000000
--- a/snakecnv/lib/svreader/resources/template_cnvnator.vcf
+++ /dev/null
@@ -1,30 +0,0 @@
-##fileformat=VCFv4.1
-##INFO=<ID=CIEND,Number=2,Type=Integer,Description="Confidence interval around END for imprecise variants">
-##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
-##INFO=<ID=IMPRECISE,Number=0,Type=Flag,Description="Imprecise structural variation">
-##INFO=<ID=SVCNV,Number=0,Type=Flag,Description="Structural variation or copy number variation">
-##INFO=<ID=MEINFO,Number=4,Type=String,Description="Mobile element info of the form NAME,START,END,POLARITY">
-##INFO=<ID=SVLEN,Number=.,Type=Integer,Description="Difference in length between REF and ALT alleles">
-##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">
-##INFO=<ID=VT,Number=1,Type=String,Description="indicates what type of variant the line represents">
-##INFO=<ID=SVMETHOD,Number=.,Type=String,Description="Type of approach used to detect SV: RP (read pair), RD (read depth), SR (split read), JT (junction) or AS (assembly)">
-##INFO=<ID=CHR2,Number=1,Type=String,Description="Chromosome for END coordinate in case of a translocation">
-##INFO=<ID=POS2,Number=1,Type=String,Description="The new position of the translocated or duplicated region. Used of ITX, CTX, and IDP SVs">
-##INFO=<ID=CN_NORMALIZED_RD,Number=1,Type=Float,Description="CNVnator: Normalized RD">
-##INFO=<ID=CN_EVAL1,Number=1,Type=Float,Description="CNVnator: e-val by t-test">
-##INFO=<ID=CN_EVAL2,Number=1,Type=Float,Description="CNVnator: e-val by Gaussian tail">
-##INFO=<ID=CN_EVAL3,Number=1,Type=Float,Description="CNVnator: e-val by t-test (middle)">
-##INFO=<ID=CN_EVAL4,Number=1,Type=Float,Description="CNVnator: e-val by Gaussian tail (middle)">
-##INFO=<ID=CN_Q0,Number=1,Type=Float,Description="CNVnator: Fraction of reads with 0 mapping quality">
-##INFO=<ID=CN,Number=1,Type=Float,Description="CNVnator: genotype">
-##INFO=<ID=NUM_IND,Number=1,Type=Integer,Description="CNVnator merge: number of individuals merged in this call">
-##INFO=<ID=VSAMPLES,Number=.,Type=String,Description="Samples with variant alleles">
-##FILTER=<ID=LowQual,Description="Low Quality">
-##ALT=<ID=DEL,Description="Deletion">
-##ALT=<ID=DEL:ME:ALU,Description="Deletion of ALU element">
-##ALT=<ID=DEL:ME:L1,Description="Deletion of L1 element">
-##ALT=<ID=DUP,Description="Duplication">
-##ALT=<ID=DUP:TANDEM,Description="Tandem Duplication">
-##ALT=<ID=CNV,Description="Copy number variable region">
-##FORMAT=<ID=CN,Number=A,Type=Float,Description="Copy number">
-#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT
diff --git a/snakecnv/lib/svreader/resources/template_merge.vcf b/snakecnv/lib/svreader/resources/template_merge.vcf
deleted file mode 100644
index 1d3a706..0000000
--- a/snakecnv/lib/svreader/resources/template_merge.vcf
+++ /dev/null
@@ -1,154 +0,0 @@
-##fileformat=VCFv4.1
-##ALT=<ID=DEL,Description="Deletion">
-##ALT=<ID=DUP,Description="Duplication">
-##ALT=<ID=DUP:TANDEM,Description="Tandem Duplication">
-##ALT=<ID=INV,Description="Inversion">
-##ALT=<ID=CNV,Description="Copy number variable region">
-##FILTER=<ID=COHERENCE,Description="GSCOHPVALUE == \NA\ || GSCOHPVALUE <= 0.01">
-##FILTER=<ID=COVERAGE,Description="GSDEPTHCALLTHRESHOLD == \NA\ || GSDEPTHCALLTHRESHOLD >= 1.0">
-##FILTER=<ID=DEPTH,Description="GSDEPTHRATIO == \NA\ || GSDEPTHRATIO > 0.8 || (GSDEPTHRATIO > 0.63 && (GSMEMBPVALUE == \NA\ || GSMEMBPVALUE >= 0.01))">
-##FILTER=<ID=DEPTHPVAL,Description="GSDEPTHPVALUE == \NA\ || GSDEPTHPVALUE >= 0.01">
-##FILTER=<ID=LQ,Description="Low Quality Genotyping filter">
-##FILTER=<ID=LowQual,Description="Low Quality">
-##FILTER=<ID=PAIRSPERSAMPLE,Description="GSNPAIRS <= 1.1 * GSNSAMPLES">
-##FILTER=<ID=PASS,Description="All filters passed">
-##FILTER=<ID=POLYMORPH,Description="All samples have the same genotype">
-##FILTER=<ID=ALIGNLENGTH,Description="Site has insufficient alignable bases (default 200)">
-##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">
-##INFO=<ID=SVLEN,Number=.,Type=Integer,Description="Difference in length between REF and ALT alleles">
-##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
-##INFO=<ID=CALLERS,Number=.,Type=String,Description="Callers that made this call">
-##INFO=<ID=CNV,Number=.,Type=String,Description="CNV merged in this CNVR">
-##INFO=<ID=CIPOS,Number=2,Type=Integer,Description="Confidence interval around POS for imprecise variants">
-##INFO=<ID=CIPOS95,Number=2,Type=Integer,Description="Confidence interval (95%) around POS for imprecise variants">
-##INFO=<ID=CIEND,Number=2,Type=Integer,Description="Confidence interval around END for imprecise variants">
-##INFO=<ID=CIEND95,Number=2,Type=Integer,Description="Confidence interval (95%) around END for imprecise variants">
-##INFO=<ID=PRECISION,Number=2,Type=Integer,Description="Precision measure">
-##INFO=<ID=IMPRECISE,Number=0,Type=Flag,Description="Imprecise structural variation">
-##INFO=<ID=SVMETHOD,Number=.,Type=String,Description="Type of approach used to detect SV: RP (read pair), RD (read depth), SR (split read), JT (junction) or AS (assembly)">
-##INFO=<ID=CHR2,Number=1,Type=String,Description="Chromosome for END coordinate in case of a translocation">
-##INFO=<ID=POS2,Number=1,Type=String,Description="The new position of the translocated or duplicated region. Used of ITX, CTX, and IDP SVs">
-##INFO=<ID=BD_CHR1,Number=1,Type=String,Description="BreakDancer: chr1 field">
-##INFO=<ID=BD_POS1,Number=1,Type=Integer,Description="BreakDancer: pos1 field">
-##INFO=<ID=BD_ORI1,Number=1,Type=String,Description="BreakDancer: orientation1 field">
-##INFO=<ID=BD_CHR2,Number=1,Type=String,Description="BreakDancer: chr2 field">
-##INFO=<ID=BD_POS2,Number=1,Type=Integer,Description="BreakDancer: pos2 field">
-##INFO=<ID=BD_ORI2,Number=1,Type=String,Description="BreakDancer: orientation2 field">
-##INFO=<ID=BD_SCORE,Number=1,Type=Float,Description="BreakDancer: score field">
-##INFO=<ID=BD_SU,Number=1,Type=Integer,Description="BreakDancer: num_Reads field">
-##INFO=<ID=VSAMPLES,Number=.,Type=String,Description="Samples with variant alleles">
-##INFO=<ID=NORMAL_COUNT,Number=1,Type=Integer,Description="Number of normal reads supporting reference">
-##INFO=<ID=NUM_READS,Number=1,Type=Integer,Description="Number of reads supporting event">
-##INFO=<ID=CONSENSUS,Number=1,Type=String,Description="Split-read consensus sequence">
-##INFO=<ID=CT,Number=1,Type=String,Description="Paired-end signature induced connection type">
-##INFO=<ID=EV,Number=.,Type=String,Description="Type of LUMPY evidence contributing to the variant call">
-##INFO=<ID=EVENT,Number=1,Type=String,Description="ID of event associated to breakend">
-##INFO=<ID=GCFRACTION,Number=1,Type=Float,Description="GC content fraction">
-##INFO=<ID=GCLENGTH,Number=1,Type=Integer,Description="Denominator of GC content">
-##INFO=<ID=GSCLUSTERSEP,Number=1,Type=Float,Description="Cluster separation in depth-based genotyping model">
-##INFO=<ID=GSCLUSTERSEPWEIGHTEDMEAN,Number=1,Type=Float,Description="Weighted cluster separation mean">
-##INFO=<ID=GSCLUSTERSEPWEIGHTEDMEDIAN,Number=1,Type=Float,Description="Weighted cluster separation median">
-##INFO=<ID=GSCOHERENCE,Number=1,Type=Float,Description="Value of coherence statistic">
-##INFO=<ID=GSCOHFN,Number=1,Type=Float,Description="Coherence statistic per pair">
-##INFO=<ID=GSCOHPVALUE,Number=1,Type=Float,Description="Coherence metric (not a true p-value)">
-##INFO=<ID=GSCOORDS,Number=4,Type=Integer,Description="Original cluster coordinates">
-##INFO=<ID=GSCORA6,Number=1,Type=Float,Description="Correlation with array intensity from Affy6 arrays">
-##INFO=<ID=GSCORI1M,Number=1,Type=Float,Description="Correlation with array intensity from Illumina 1M arrays">
-##INFO=<ID=GSCORNG,Number=1,Type=Float,Description="Correlation with array intensity from NimbleGen arrays">
-##INFO=<ID=GSDEPTHCALLS,Number=.,Type=String,Description="Samples with discrepant read pairs or low read depth">
-##INFO=<ID=GSDEPTHCALLTHRESHOLD,Number=1,Type=Float,Description="Read depth threshold (median read depth of samples with discrepant read pairs)">
-##INFO=<ID=GSDEPTHNOBSSAMPLES,Number=1,Type=Integer,Description="Number of samples with discrepant read pairs in depth test">
-##INFO=<ID=GSDEPTHNTOTALSAMPLES,Number=1,Type=Integer,Description="Total samples in depth test">
-##INFO=<ID=GSDEPTHOBSSAMPLES,Number=.,Type=String,Description="Samples with discrepant read pairs in depth test">
-##INFO=<ID=GSDEPTHPVALUE,Number=1,Type=Float,Description="Depth p-value using chi-squared test">
-##INFO=<ID=GSDEPTHPVALUECOUNTS,Number=4,Type=Integer,Description="Depth test read counts (carrier inside event, carrier outside event, non-carrier inside, non-carrier outside)">
-##INFO=<ID=GSDEPTHRANKSUMPVALUE,Number=1,Type=Float,Description="Depth p-value using rank-sum test">
-##INFO=<ID=GSDEPTHRATIO,Number=1,Type=Float,Description="Read depth ratio test">
-##INFO=<ID=GSDMAX,Number=1,Type=Integer,Description="Maximum value considered for DOpt">
-##INFO=<ID=GSDMIN,Number=1,Type=Integer,Description="Minimum value considered for DOpt">
-##INFO=<ID=GSDOPT,Number=1,Type=Integer,Description="Most likely event length">
-##INFO=<ID=GSDSPAN,Number=1,Type=Integer,Description="Inner span length of read pair cluster">
-##INFO=<ID=GSDUPLICATEOVERLAP,Number=1,Type=Float,Description="Highest overlap with a duplicate event">
-##INFO=<ID=GSDUPLICATES,Number=.,Type=String,Description="List of duplicate events preferred to this one">
-##INFO=<ID=GSDUPLICATESCORE,Number=1,Type=Float,Description="LOD score that the events are distinct based on the genotypes of the most discordant sample">
-##INFO=<ID=GSELENGTH,Number=1,Type=Integer,Description="Effective length">
-##INFO=<ID=GSEXPMEAN,Number=1,Type=Float,Description="Expected read depth sample mean">
-##INFO=<ID=GSGMMWEIGHTS,Number=.,Type=Float,Description="Genotyping depth model cluster weights">
-##INFO=<ID=GSM1,Number=1,Type=Float,Description="Genotyping depth model parameter M1">
-##INFO=<ID=GSM2,Number=2,Type=Float,Description="Genotyping depth model parameters M2[0],M2[1]">
-##INFO=<ID=GSMEMBNPAIRS,Number=1,Type=Integer,Description="Number of pairs used in membership test">
-##INFO=<ID=GSMEMBNSAMPLES,Number=1,Type=Integer,Description="Number of samples used in membership test">
-##INFO=<ID=GSMEMBOBSSAMPLES,Number=.,Type=String,Description="Samples participating in membership test">
-##INFO=<ID=GSMEMBPVALUE,Number=1,Type=Float,Description="Membership p-value">
-##INFO=<ID=GSMEMBSTATISTIC,Number=1,Type=Float,Description="Value of membership statistic">
-##INFO=<ID=GSNDEPTHCALLS,Number=1,Type=Integer,Description="Number of samples with discrepant read pairs or low read depth">
-##INFO=<ID=GSNHET,Number=1,Type=Integer,Description="Number of heterozygous snp genotype calls inside the event">
-##INFO=<ID=GSNHOM,Number=1,Type=Integer,Description="Number of homozygous snp genotype calls inside the event">
-##INFO=<ID=GSNNOCALL,Number=1,Type=Integer,Description="Number of snp genotype non-calls inside the event">
-##INFO=<ID=GSNONVARSCORE,Number=1,Type=Float,Description="For homozygous reference sites, the minimum genotype quality score">
-##INFO=<ID=GSNPAIRS,Number=1,Type=Integer,Description="Number of discrepant read pairs">
-##INFO=<ID=GSNSAMPLES,Number=1,Type=Integer,Description="Number of samples with discrepant read pairs">
-##INFO=<ID=GSNSNPS,Number=1,Type=Integer,Description="Number of snps inside the event">
-##INFO=<ID=GSOUTLEFT,Number=1,Type=Integer,Description="Number of outlier read pairs on left">
-##INFO=<ID=GSOUTLIERS,Number=1,Type=Integer,Description="Number of outlier read pairs">
-##INFO=<ID=GSOUTRIGHT,Number=1,Type=Integer,Description="Number of outlier read pairs on right">
-##INFO=<ID=GSREADGROUPS,Number=.,Type=String,Description="Read groups contributing discrepant read pairs">
-##INFO=<ID=GSREADNAMES,Number=.,Type=String,Description="Discrepant read pair identifiers">
-##INFO=<ID=GSRPORIENTATION,Number=1,Type=String,Description="Read pair orientation">
-##INFO=<ID=GSSAMPLES,Number=.,Type=String,Description="Samples contributing discrepant read pairs">
-##INFO=<ID=GSSNPHET,Number=1,Type=Float,Description="Fraction of het snp genotype calls inside the event">
-##INFO=<ID=HOMLEN,Number=1,Type=Integer,Description="Predicted microhomology length using a max. edit distance of 2">
-##INFO=<ID=HOMSEQ,Number=.,Type=String,Description="Sequence of base pair identical micro-homology at event breakpoints">
-##INFO=<ID=INSLEN,Number=1,Type=Integer,Description="Predicted length of the insertion">
-##INFO=<ID=MAPQ,Number=1,Type=Integer,Description="Median mapping quality of paired-ends">
-##INFO=<ID=MATEID,Number=.,Type=String,Description="ID of mate breakends">
-##INFO=<ID=MAX_IND_SU,Number=1,Type=Integer,Description="Maximum individual PE support of the variant">
-##INFO=<ID=NOVEL,Number=0,Type=Flag,Description="Indicates a novel structural variation">
-##INFO=<ID=NUM_READS,Number=1,Type=Integer,Description="Number of reads supporting event">
-##INFO=<ID=PE,Number=1,Type=Integer,Description="Paired-end support of the structural variant">
-##INFO=<ID=POS2,Number=1,Type=String,Description="The new position of the translocated or duplicated region. Used of ITX, CTX, and IDP SVs">
-##INFO=<ID=PRECISE,Number=0,Type=Flag,Description="Precise structural variation">
-##INFO=<ID=PREND,Number=.,Type=String,Description="LUMPY probability curve of the END breakend">
-##INFO=<ID=PRPOS,Number=.,Type=String,Description="LUMPY probability curve of the POS breakend">
-##INFO=<ID=RDRATIO,Number=1,Type=Float,Description="Read-depth ratio of het. SV carrier vs. non-carrier.">
-##INFO=<ID=SCORE,Number=1,Type=Integer,Description="Score reported by tool">
-##INFO=<ID=SECONDARY,Number=0,Type=Flag,Description="Secondary breakend in a multi-line variants">
-##INFO=<ID=SR,Number=1,Type=Integer,Description="Split-read support">
-##INFO=<ID=SRQ,Number=1,Type=Float,Description="Split-read consensus alignment quality">
-##INFO=<ID=STRANDS,Number=.,Type=String,Description="Strand orientation of the adjacency in BEDPE format (DEL:+-, DUP:-+, INV:++/--)">
-##INFO=<ID=SU,Number=.,Type=Integer,Description="Number of pieces of evidence supporting the variant across all samples">
-##FORMAT=<ID=AB,Number=A,Type=Float,Description="Allele balance, fraction of observations from alternate allele, QA/(QR+QA)">
-##FORMAT=<ID=AO,Number=A,Type=Integer,Description="Alternate allele observations, with partial observations recorded fractionally">
-##FORMAT=<ID=AP,Number=A,Type=Integer,Description="Alternate allele paired-end observation count, with partial observations recorded fractionally">
-##FORMAT=<ID=AS,Number=A,Type=Integer,Description="Alternate allele split-read observation count, with partial observations recorded fractionally">
-##FORMAT=<ID=BD,Number=1,Type=Integer,Description="Amount of BED evidence supporting the variant">
-##FORMAT=<ID=CN,Number=1,Type=Integer,Description="Copy number genotype for imprecise events">
-##FORMAT=<ID=CNF,Number=1,Type=Float,Description="Estimate of fractional copy number">
-##FORMAT=<ID=CNL,Number=.,Type=Float,Description="Copy number likelihoods with no frequency prior">
-##FORMAT=<ID=CNP,Number=.,Type=Float,Description="Copy number likelihoods">
-##FORMAT=<ID=CNQ,Number=1,Type=Float,Description="Copy number genotype quality for imprecise events">
-##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read depth">
-##FORMAT=<ID=DR,Number=1,Type=Integer,Description="# high-quality reference pairs">
-##FORMAT=<ID=DV,Number=1,Type=Integer,Description="supporting read pair for the sample">
-##FORMAT=<ID=FT,Number=1,Type=String,Description="Per-sample genotype filter">
-##FORMAT=<ID=GL,Number=G,Type=Float,Description="Genotype likelihoods with no frequency prior">
-##FORMAT=<ID=GP,Number=G,Type=Float,Description="Genotype likelihoods">
-##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
-##FORMAT=<ID=GSPC,Number=1,Type=Integer,Description="Number of supporting read pairs for structural variant alleles">
-##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
-##FORMAT=<ID=PE,Number=1,Type=Integer,Description="Number of paired-end reads supporting the variant">
-##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
-##FORMAT=<ID=QA,Number=A,Type=Integer,Description="Sum of quality of alternate observations">
-##FORMAT=<ID=QR,Number=1,Type=Integer,Description="Sum of quality of reference observations">
-##FORMAT=<ID=RC,Number=1,Type=Integer,Description="Raw high-quality read counts for the SV">
-##FORMAT=<ID=RCL,Number=1,Type=Integer,Description="Raw high-quality read counts for the left control region">
-##FORMAT=<ID=RCR,Number=1,Type=Integer,Description="Raw high-quality read counts for the right control region">
-##FORMAT=<ID=RO,Number=1,Type=Integer,Description="Reference allele observation count, with partial observations recorded fractionally">
-##FORMAT=<ID=RP,Number=1,Type=Integer,Description="Reference allele paired-end observation count, with partial observations recorded fractionally">
-##FORMAT=<ID=RR,Number=1,Type=Integer,Description="# high-quality reference junction reads">
-##FORMAT=<ID=RS,Number=1,Type=Integer,Description="Reference allele split-read observation count, with partial observations recorded fractionally">
-##FORMAT=<ID=RV,Number=1,Type=Integer,Description="# high-quality variant junction reads">
-##FORMAT=<ID=SQ,Number=1,Type=Float,Description="Phred-scaled probability that this site is variant (non-reference in this sample">
-##FORMAT=<ID=SR,Number=1,Type=Integer,Description="Number of split reads supporting the variant">
-##FORMAT=<ID=SU,Number=1,Type=Integer,Description="Number of pieces of evidence supporting the variant">
-#CHROM POS ID REF ALT QUAL FILTER INFO
diff --git a/snakecnv/lib/svreader/resources/template_pindel.vcf b/snakecnv/lib/svreader/resources/template_pindel.vcf
deleted file mode 100644
index e4645d2..0000000
--- a/snakecnv/lib/svreader/resources/template_pindel.vcf
+++ /dev/null
@@ -1,22 +0,0 @@
-##fileformat=VCFv4.1
-##fileDate=2015-9-16
-##source=pindel
-##INFO=<ID=END,Number=1,Type=Integer,Description="End position of the variant described in this record">
-##INFO=<ID=HOMLEN,Number=1,Type=Integer,Description="Length of base pair identical micro-homology at event breakpoints">
-##INFO=<ID=PF,Number=1,Type=Integer,Description="The number of samples carry the variant">
-##INFO=<ID=HOMSEQ,Number=.,Type=String,Description="Sequence of base pair identical micro-homology at event breakpoints">
-##INFO=<ID=SVLEN,Number=1,Type=Integer,Description="Difference in length between REF and ALT alleles">
-##INFO=<ID=SVTYPE,Number=1,Type=String,Description="Type of structural variant">
-##INFO=<ID=NTLEN,Number=.,Type=Integer,Description="Number of bases inserted in place of deleted code">
-##INFO=<ID=NUM_SUPP_SAMPLES,Number=.,Type=Integer,Description="Number of samples with supporting reads">
-##INFO=<ID=SU,Number=.,Type=Integer,Description="The number of unique reads supporting SV (not count duplicate reads)">
-##INFO=<ID=INDEX,Number=1,Type=Integer,Description="The index of the indel/SV">
-##INFO=<ID=MAX_IND_SU,Number=1,Type=Integer,Description="The mamximum number (among all individuals) of supporting PE">
-##INFO=<ID=CIPOS,Number=2,Type=Integer,Description="Confidence interval around POS for imprecise variants">
-##INFO=<ID=CIEND,Number=2,Type=Integer,Description="Confidence interval around END for imprecise variants">
-##INFO=<ID=VSAMPLES,Number=.,Type=String,Description="Samples with variant alleles">
-##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
-##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
-##FORMAT=<ID=RD,Number=1,Type=Integer,Description="Reference depth, how many reads support the reference">
-##FORMAT=<ID=AD,Number=2,Type=Integer,Description="Allele depth, how many reads support this allele">
-#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT
diff --git a/snakecnv/lib/svreader/vcf_utils.py b/snakecnv/lib/svreader/vcf_utils.py
deleted file mode 100644
index 6b53046..0000000
--- a/snakecnv/lib/svreader/vcf_utils.py
+++ /dev/null
@@ -1,32 +0,0 @@
-
-import os
-import vcf
-
-
-def get_template_header(toolname):
- """
- Add contig infos to vcf
- :param toolname: the name of the tool
- :type toolname: string
- :return: file (path) with vcf header with toll-specific fields
- """
- thismoduledir = os.path.dirname(os.path.realpath(__file__))
- templateRessources = os.path.join(thismoduledir, "resources")
- if os.path.exists(os.path.join(templateRessources,
- "template_" + toolname + ".vcf")):
- template = os.path.join(templateRessources,
- "template_" + toolname + ".vcf")
- else:
- template = os.path.join(templateRessources, "template.vcf")
- return template
-
-
-def get_template(toolname):
- """
- Add contig infos to vcf
- :param toolname: the name of the tool
- :type toolname: string
- :return: a vcf.Reader object with tool specific vcf header
- """
- template = get_template_header(toolname)
- return vcf.Reader(open(template))
diff --git a/snakecnv/lib/svreader/vcfwrapper.py b/snakecnv/lib/svreader/vcfwrapper.py
deleted file mode 100644
index 94dedde..0000000
--- a/snakecnv/lib/svreader/vcfwrapper.py
+++ /dev/null
@@ -1,121 +0,0 @@
-
-from svreader import SVRecord, SVReader, SVWriter
-
-from pysam import VariantFile
-
-generic_name = "generic"
-generic_source = set([generic_name])
-
-
-class VCFRecord(SVRecord):
- """
-
- """
- def __init__(self, record):
- """ The required vcf record values and adittional optional values
- required: chrom, pos, sropt, id,
- optional: reference, quality
-
- """
- super(VCFRecord, self).__init__(generic_name,
- record.chrom,
- record.pos,
- record.stop,
- record.id,
- ref=record.ref,
- qual=record.qual,
- filter=record.filter)
- self.__record = record
- if "SVTYPE" in record.info.keys():
- self._sv_type = record.info["SVTYPE"]
-
- @property
- def record(self):
- return self.__record
-
- @property
- def id(self):
- return self.record.id
-
- @property
- def pos(self):
- return self.record.pos
-
- @property
- def chrom(self):
- return self.record.chrom
-
- @property
- def stop(self):
- return self.record.stop
-
- @property
- def filter(self):
- return self.record.filter
-
- @property
- def samples(self):
- return self.record.samples
-
-
-class VCFReader(SVReader):
-
- def __init__(self, file_name, reference_handle=None, svs_to_report=None):
- super(VCFReader, self).__init__(file_name,
- generic_name,
- reference_handle)
- self.vcf_reader = VariantFile(file_name)
-
- def __iter__(self):
- return self
-
- def __next__(self):
- while True:
- raw_record = next(self.vcf_reader)
- record = VCFRecord(raw_record)
- return record
-
- def getHeader(self):
- return self.vcf_reader.header
-
- def addInfo(self, name, number, type, description):
- self.vcf_reader.header.info.add(id=name,
- number=number,
- type=type,
- description=description)
-
- def addFilter(self, name, description):
- self.vcf_reader.header.filters.add(id=name,
- number=None,
- type=None,
- description=description)
-
- def getOrderedSamples(self):
- samples = self.vcf_reader.header.samples
- sample_names = [sample.rsplit('.')[0] for sample in samples]
- return sample_names
-
- def numSamples(self):
- return len(self.vcf_reader.header.samples)
-
-
-class VCFWriter(SVWriter):
-
- def __init__(self, file_name, template_reader, index=True):
- super(VCFWriter, self).__init__(file_name,
- template_reader.tool_name,
- template_reader)
-
- def _open(self):
- self.vcf_writer = VariantFile(self.filename, 'w',
- header=self.template_reader.getHeader())
- self._isopen = True
-
- def _write(self, record):
- self.vcf_writer.write(record.record)
-
- def _close(self):
- if self._isopen:
- self.vcf_writer.close()
- else: # nothing was written
- self._dumpemptyvcf()
diff --git a/snakecnv/lib/svrunner_utils.py b/snakecnv/lib/svrunner_utils.py
deleted file mode 100644
index 6215b95..0000000
--- a/snakecnv/lib/svrunner_utils.py
+++ /dev/null
@@ -1,230 +0,0 @@
-
-import sys
-import os
-import collections
-import gzip
-from subprocess import call
-import re
-
-from collections import defaultdict
-
-from pybedtools import BedTool, create_interval_from_list
-from pysam import AlignmentFile
-import numpy as np
-
-VERBOSE = True
-
-def get_insert_size(bam):
- """
- Compute mean insert size
- :param bam: bam file
- :type bam: str
- :return: mean insert size
- :rtype: int
- """
- fbam = AlignmentFile(bam, "rb")
- count = 0
- isizes_raw = []
- for read in fbam:
- if read.is_proper_pair:
- isizes_raw.append(np.abs(read.template_length))
- count += 1
- if count == 50000:
- break
- n99 = np.percentile(isizes_raw, 99)
- n1 = np.percentile(isizes_raw, 1)
- isizes = []
- for isize in isizes_raw:
- if n1 < isize < n99:
- isizes.append(isize)
- fbam.close()
- return int(np.round(np.mean(np.round(isizes))))
-
-
-def getChromLength(reference, chrom):
- """
- Retrieve the length of chromosome chrom from .fai file
- :param reference: the genome fasta file
- :param chrom: the chromosome of interest
- :type reference: file
- :type chrom: str
- :return: chromosome length
- :rtype: int
- """
- reference_fai = str(reference) + ".fai"
- chroms = {}
- if reference is not None and os.path.isfile(reference_fai):
- with open(reference_fai) as fai_file:
- for line in fai_file:
- line_items = line.strip().split("\t")
- name, length = line_items[0:2]
- name = name.split(" ")[0]
- chroms[name] = int(length)
- if chrom not in chroms:
- raise KeyError('the chromosome '+chrom+' is not in the *.fai file....')
- return chroms[chrom]
-
-
-def fetchId(bamfile):
- """
- Fetch sample id in a bam file
- :param bamfile: the bam file
- :type bamfile: file
- :return: sample name
- :rtype: str
- """
- bamfile_fin = AlignmentFile(bamfile, 'rb')
- name = bamfile_fin.header['RG'][0]['SM']
- bamfile_fin.close()
- return name
-
-
-_Contig = collections.namedtuple('Contig', ['name', 'length'])
-
-
-def get_contigs(reference):
- """
- Retrieve the contigs (chromosomes) from the genome fasta file
- :param reference: the genome fasta file
- :type reference: fasta filefile
- :return: list of contigs in the format specified by PyCvf
- :rtype: list
- """
- contigs = []
- reference_fai = str(reference) + ".fai"
- if reference is not None and os.path.isfile(reference_fai):
- with open(reference_fai) as fai_file:
- for line in fai_file:
- line_items = line.strip().split("\t")
- name, length = line_items[0:2]
- name = name.split(" ")[0]
- contigs.append(_Contig(name, int(length)))
- return contigs
-
-
-def chromNum(chrom):
- m = re.search(r"[cC]hr(?P<num>[\dXY]+)", chrom)
- if m is not None:
- if m.group('num') == 'X':
- num = 100
- elif m.group('num') == 'Y':
- num = 101
- else:
- num = int(m.group('num'))
- return num
- else:
- return chrom
-
-
-def addContigInfosTovcf(vcffile, outvcffile, reference):
- """
- Add contig infos to vcf
- :param vcffile: input vcffile
- :param outvcffile: output vcffile
- :param reference: the reference genome fasta filefile
- :type vcffile: file
- :type outvcffile: vcffile
- :type reference: fasta filefile
- :return: nothing
- """
- # compute contig information
- contigs = get_contigs(reference)
- filename, extension = os.path.splitext(outvcffile)
- if extension == ".gz":
- outputfile = filename
- gzip = True
- else:
- outputfile = outvcffile
- gzip = False
- with open(outputfile, "w") as fout:
- with smart_open(vcffile) as fd:
- for line in fd:
- if re.search(r"^##contig", line) is not None:
- continue
- if re.search(r"^#CHROM", line):
- for ctg in sorted(contigs, key=lambda x: chromNum(x.name)):
- fout.write('##contig=<ID=%s,length=%d>\n'
- % (ctg.name, ctg.length))
- fout.write(line)
- # bgzip file
- if gzip:
- cmd = "bgzip -f "+outputfile
- call(cmd, shell=True)
- cmd = "tabix -p vcf " + outvcffile
- call(cmd, shell=True)
-
-
-def sorting(records, reference_contigs):
- if reference_contigs:
- contigs_order_dict = defaultdict()
- for (index, contig) in enumerate(reference_contigs):
- contigs_order_dict[contig.name] = index
- records.sort(
- key=lambda x: (contigs_order_dict[x.chrom], x.start))
- else:
- records.sort(key=lambda x: (x.chrom, x.start))
- return records
-
-
-def vcf_to_pybed(vcf_records):
- """
- Convert vcf records to bed records
- :param vcf_records: the vcf records
- :type vcfrecords: a list of vcf records
- :return: the corresponding betools records
- :rtype: a bedtools object
- """
- intervals = []
- for record in vcf_records:
- chrom = record.chrom
- start = record.start - 1
- end = record.end
- name = record.id
- interval_list = list(map(str, [chrom, start, end, name]))
- intervals.append(create_interval_from_list(interval_list))
- return BedTool(intervals).sort()
-
-
-# A small wrapper to print to stderr
-def eprint(*args, **kwargs):
- if VERBOSE:
- print(*args, file=sys.stderr, **kwargs)
-
-
-def warn(*args, **kwargs):
- print(*args, file=sys.stderr, **kwargs)
-
-
-def log(objs):
- print("-- log -- ", objs, file=sys.stderr)
-
-
-def warning(*objs):
- print("WARNING: ", *objs, file=sys.stderr)
-
-
-def smart_open(file_name):
- """
- Opening compressed or uncompressed file according to the extension (.gz)
- :param file_name: the file to open
- :type file_name: str
- :return: the corresponding file handle
- :rtype: a file handle
- """
- if file_name is not None:
- if file_name.endswith(".gz"):
- file_fd = gzip.open(file_name, "rt")
- else:
- file_fd = open(file_name)
- else:
- file_fd = sys.stdin
- return file_fd
-
-
-def flatten(l):
- for el in l:
- if isinstance(el, collections.Iterable) and not isinstance(el, str):
- for sub in flatten(el):
- yield sub
- else:
- yield el
diff --git a/snakecnv/lib/svsnakemake_utils.py b/snakecnv/svsnakemake_utils.py
similarity index 100%
rename from snakecnv/lib/svsnakemake_utils.py
rename to snakecnv/svsnakemake_utils.py
--
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