# HG changeset patch
# User mvdbeek
# Date 1422891201 18000
# Node ID 2e041a1564ad510de8322ac114610a021c53a6fa
# Parent 27068162577556368c6e40a43e87bd71be98a713
Uploaded
diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies.py
--- a/mismatch_frequencies.py Wed Jan 28 13:12:56 2015 +0100
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,229 +0,0 @@
-import pysam, re, string
-import matplotlib.pyplot as plt
-import pandas as pd
-from collections import defaultdict
-from collections import OrderedDict
-import argparse
-
-class MismatchFrequencies:
- '''Iterate over a SAM/BAM alignment file, collecting reads with mismatches. One
- class instance per alignment file. The result_dict attribute will contain a
- nested dictionary with name, readlength and mismatch count.'''
- def __init__(self, result_dict={}, alignment_file=None, name="name", minimal_readlength=21, maximal_readlength=21,
- number_of_allowed_mismatches=1, ignore_5p_nucleotides=0, ignore_3p_nucleotides=0):
-
- self.result_dict = result_dict
- self.name = name
- self.minimal_readlength = minimal_readlength
- self.maximal_readlength = maximal_readlength
- self.number_of_allowed_mismatches = number_of_allowed_mismatches
- self.ignore_5p_nucleotides = ignore_5p_nucleotides
- self.ignore_3p_nucleotides = ignore_3p_nucleotides
-
- if alignment_file:
- self.pysam_alignment = pysam.Samfile(alignment_file)
- result_dict[name]=self.get_mismatches(self.pysam_alignment, minimal_readlength, maximal_readlength)
-
- def get_mismatches(self, pysam_alignment, minimal_readlength, maximal_readlength):
- mismatch_dict = defaultdict(int)
- len_dict={}
- for i in range(minimal_readlength, maximal_readlength+1):
- len_dict[i]=mismatch_dict.copy()
- for alignedread in pysam_alignment:
- if self.read_is_valid(alignedread, minimal_readlength, maximal_readlength):
- len_dict[int(alignedread.rlen)]['total valid reads'] += 1
- MD=alignedread.opt('MD')
- if self.read_has_mismatch(alignedread, self.number_of_allowed_mismatches):
- (ref_base, mismatch_base)=self.read_to_reference_mismatch(MD, alignedread.seq, alignedread.is_reverse)
- if ref_base == None:
- continue
- else:
- for i, base in enumerate(ref_base):
- len_dict[int(alignedread.rlen)][ref_base[i]+' to '+mismatch_base[i]] += 1
- return len_dict
-
- def read_is_valid(self, read, min_readlength, max_readlength):
- '''Filter out reads that are unmatched, too short or
- too long or that contian insertions'''
- if read.is_unmapped:
- return False
- if read.rlen < min_readlength:
- return False
- if read.rlen > max_readlength:
- return False
- else:
- return True
-
- def read_has_mismatch(self, read, number_of_allowed_mismatches=1):
- '''keep only reads with one mismatch. Could be simplified'''
- NM=read.opt('NM')
- if NM <1: #filter out reads with no mismatch
- return False
- if NM >number_of_allowed_mismatches: #filter out reads with more than 1 mismtach
- return False
- else:
- return True
-
- def mismatch_in_allowed_region(self, readseq, mismatch_position):
- '''
- >>> M = MismatchFrequencies()
- >>> readseq = 'AAAAAA'
- >>> mismatch_position = 2
- >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
- True
- >>> M = MismatchFrequencies(ignore_3p_nucleotides=2, ignore_5p_nucleotides=2)
- >>> readseq = 'AAAAAA'
- >>> mismatch_position = 1
- >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
- False
- >>> readseq = 'AAAAAA'
- >>> mismatch_position = 4
- >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
- False
- '''
- mismatch_position+=1 # To compensate for starting the count at 0
- five_p = self.ignore_5p_nucleotides
- three_p = self.ignore_3p_nucleotides
- if any([five_p > 0, three_p > 0]):
- if any([mismatch_position <= five_p,
- mismatch_position >= (len(readseq)+1-three_p)]): #Again compensate for starting the count at 0
- return False
- else:
- return True
- else:
- return True
-
- def read_to_reference_mismatch(self, MD, readseq, is_reverse):
- '''
- This is where the magic happens. The MD tag contains SNP and indel information,
- without looking to the genome sequence. This is a typical MD tag: 3C0G2A6.
- 3 bases of the read align to the reference, followed by a mismatch, where the
- reference base is C, followed by 10 bases aligned to the reference.
- suppose a reference 'CTTCGATAATCCTT'
- ||| || ||||||
- and a read 'CTTATATTATCCTT'.
- This situation is represented by the above MD tag.
- Given MD tag and read sequence this function returns the reference base C, G and A,
- and the mismatched base A, T, T.
- >>> M = MismatchFrequencies()
- >>> MD='3C0G2A7'
- >>> seq='CTTATATTATCCTT'
- >>> result=M.read_to_reference_mismatch(MD, seq, is_reverse=False)
- >>> result[0]=="CGA"
- True
- >>> result[1]=="ATT"
- True
- >>>
- '''
- search=re.finditer('[ATGC]',MD)
- if '^' in MD:
- print 'WARNING insertion detected, mismatch calling skipped for this read!!!'
- return (None, None)
- start_index=0 # refers to the leading integer of the MD string before an edited base
- current_position=0 # position of the mismatched nucleotide in the MD tag string
- mismatch_position=0 # position of edited base in current read
- reference_base=""
- mismatched_base=""
- for result in search:
- current_position=result.start()
- mismatch_position=mismatch_position+1+int(MD[start_index:current_position]) #converts the leading characters before an edited base into integers
- start_index=result.end()
- reference_base+=MD[result.end()-1]
- mismatched_base+=readseq[mismatch_position-1]
- if is_reverse:
- reference_base=reverseComplement(reference_base)
- mismatched_base=reverseComplement(mismatched_base)
- mismatch_position=len(readseq)-mismatch_position-1
- if mismatched_base=='N':
- return (None, None)
- if self.mismatch_in_allowed_region(readseq, mismatch_position):
- return (reference_base, mismatched_base)
- else:
- return (None, None)
-
-def reverseComplement(sequence):
- '''do a reverse complement of DNA base.
- >>> reverseComplement('ATGC')=='GCAT'
- True
- >>>
- '''
- sequence=sequence.upper()
- complement = string.maketrans('ATCGN', 'TAGCN')
- return sequence.upper().translate(complement)[::-1]
-
-def barplot(df, library, axes):
- df.plot(kind='bar', ax=axes, subplots=False,\
- stacked=False, legend='test',\
- title='Mismatch frequencies for {0}'.format(library))
-
-def result_dict_to_df(result_dict):
- mismatches = []
- libraries = []
- for mismatch, library in result_dict.iteritems():
- mismatches.append(mismatch)
- libraries.append(pd.DataFrame.from_dict(library, orient='index'))
- df=pd.concat(libraries, keys=mismatches)
- df.index.names = ['library', 'readsize']
- return df
-
-def df_to_tab(df, output):
- df.to_csv(output, sep='\t')
-
-def plot_result(result_dict, args):
- names=args.name
- nrows=len(names)/2+1
- fig = plt.figure(figsize=(16,32))
- for i,library in enumerate (names):
- axes=fig.add_subplot(nrows,2,i+1)
- library_dict=result_dict[library]
- for length in library_dict.keys():
- for mismatch in library_dict[length]:
- if mismatch == 'total valid reads':
- continue
- library_dict[length][mismatch]=library_dict[length][mismatch]/float(library_dict[length]['total valid reads'])*100
- del library_dict[length]['total valid reads']
- df=pd.DataFrame(library_dict)
- barplot(df, library, axes),
- axes.set_ylabel('Mismatch count / all valid reads * 100')
- fig.savefig(args.output_pdf, format='pdf')
-
-def setup_MismatchFrequencies(args):
- resultDict=OrderedDict()
- kw_list=[{'result_dict' : resultDict,
- 'alignment_file' :alignment_file,
- 'name' : name,
- 'minimal_readlength' : args.min,
- 'maximal_readlength' : args.max,
- 'number_of_allowed_mismatches' : args.n_mm,
- 'ignore_5p_nucleotides' : args.five_p,
- 'ignore_3p_nucleotides' : args.three_p}
- for alignment_file, name in zip(args.input, args.name)]
- return (kw_list, resultDict)
-
-def run_MismatchFrequencies(args):
- kw_list, resultDict=setup_MismatchFrequencies(args)
- [MismatchFrequencies(**kw_dict) for kw_dict in kw_list]
- return resultDict
-
-def main():
- result_dict=run_MismatchFrequencies(args)
- df=result_dict_to_df(result_dict)
- plot_result(result_dict, args)
- df_to_tab(df, args.output_tab)
-
-if __name__ == "__main__":
-
- parser = argparse.ArgumentParser(description='Produce mismatch statistics for BAM/SAM alignment files.')
- parser.add_argument('--input', nargs='*', help='Input files in SAM/BAM format')
- parser.add_argument('--name', nargs='*', help='Name for input file to display in output file. Should have same length as the number of inputs')
- parser.add_argument('--output_pdf', help='Output filename for graph')
- parser.add_argument('--output_tab', help='Output filename for table')
- parser.add_argument('--min', '--minimal_readlength', type=int, help='minimum readlength')
- parser.add_argument('--max', '--maximal_readlength', type=int, help='maximum readlength')
- parser.add_argument('--n_mm', '--number_allowed_mismatches', type=int, default=1, help='discard reads with more than n mismatches')
- parser.add_argument('--five_p', '--ignore_5p_nucleotides', type=int, default=0, help='when calculating nucleotide mismatch frequencies ignore the first N nucleotides of the read')
- parser.add_argument('--three_p', '--ignore_3p_nucleotides', type=int, default=1, help='when calculating nucleotide mismatch frequencies ignore the last N nucleotides of the read')
- #args = parser.parse_args(['--input', '3mismatches_ago2ip.bam', '2mismatch.bam', '--name', 'Siomi1', 'Siomi2' , '--five_p', '3','--three_p','3','--output_pdf', 'out.pdf', '--output_tab', 'out.tab', '--min', '21', '--max', '21'])
- args = parser.parse_args()
- main()
-
diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies.xml
--- a/mismatch_frequencies.xml Wed Jan 28 13:12:56 2015 +0100
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,70 +0,0 @@
-
- Analyze mismatch frequencies in BAM/SAM alignments
-
- pysam
- pandas
- matplotlib
-
- mismatch_frequencies.py --input
- #for i in $rep
- "$i.input_file"
- #end for
- --name
- #for i in $rep
- "$i.input_file.name"
- #end for
- --output_pdf $output_pdf --output_tab $output_tab --min $min_length --max $max_length
- --n_mm $number_of_mismatches
- --five_p $five_p
- --three_p $three_p
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-.. class:: infomark
-
-
-***What it does***
-
-This tool reconstitues for each aligned read of an alignment file in SAM/BAM format whether
-a mismatch is annotated in the MD tag, and if that is the case counts the identity of the
-mismatch relative to the reference sequence. The output is a PDF document with the calculated
-frequency for each mismatch that occured relative to the total number of valid reads and a table
-with the corresponding values. Read length can be limited to a specific read length, and 5 prime and
-3 prime-most nucleotides of a read can be ignored.
-
-----
-
-.. class:: warningmark
-
-***Warning***
-
-This tool skips all read that have insertions and has been tested only with bowtie and bowtie2
-generated alignment files.
-
-Written by Marius van den Beek, m.vandenbeek at gmail . com
-
-
diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies/.hg_archival.txt
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/mismatch_frequencies/.hg_archival.txt Mon Feb 02 10:33:21 2015 -0500
@@ -0,0 +1,5 @@
+repo: e8ebe5132737d76a94ec92eda7d1fc1059bffb88
+node: 27068162577556368c6e40a43e87bd71be98a713
+branch: default
+latesttag: null
+latesttagdistance: 6
diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies/mismatch_frequencies.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/mismatch_frequencies/mismatch_frequencies.py Mon Feb 02 10:33:21 2015 -0500
@@ -0,0 +1,229 @@
+import pysam, re, string
+import matplotlib.pyplot as plt
+import pandas as pd
+from collections import defaultdict
+from collections import OrderedDict
+import argparse
+
+class MismatchFrequencies:
+ '''Iterate over a SAM/BAM alignment file, collecting reads with mismatches. One
+ class instance per alignment file. The result_dict attribute will contain a
+ nested dictionary with name, readlength and mismatch count.'''
+ def __init__(self, result_dict={}, alignment_file=None, name="name", minimal_readlength=21, maximal_readlength=21,
+ number_of_allowed_mismatches=1, ignore_5p_nucleotides=0, ignore_3p_nucleotides=0):
+
+ self.result_dict = result_dict
+ self.name = name
+ self.minimal_readlength = minimal_readlength
+ self.maximal_readlength = maximal_readlength
+ self.number_of_allowed_mismatches = number_of_allowed_mismatches
+ self.ignore_5p_nucleotides = ignore_5p_nucleotides
+ self.ignore_3p_nucleotides = ignore_3p_nucleotides
+
+ if alignment_file:
+ self.pysam_alignment = pysam.Samfile(alignment_file)
+ result_dict[name]=self.get_mismatches(self.pysam_alignment, minimal_readlength, maximal_readlength)
+
+ def get_mismatches(self, pysam_alignment, minimal_readlength, maximal_readlength):
+ mismatch_dict = defaultdict(int)
+ len_dict={}
+ for i in range(minimal_readlength, maximal_readlength+1):
+ len_dict[i]=mismatch_dict.copy()
+ for alignedread in pysam_alignment:
+ if self.read_is_valid(alignedread, minimal_readlength, maximal_readlength):
+ len_dict[int(alignedread.rlen)]['total valid reads'] += 1
+ MD=alignedread.opt('MD')
+ if self.read_has_mismatch(alignedread, self.number_of_allowed_mismatches):
+ (ref_base, mismatch_base)=self.read_to_reference_mismatch(MD, alignedread.seq, alignedread.is_reverse)
+ if ref_base == None:
+ continue
+ else:
+ for i, base in enumerate(ref_base):
+ len_dict[int(alignedread.rlen)][ref_base[i]+' to '+mismatch_base[i]] += 1
+ return len_dict
+
+ def read_is_valid(self, read, min_readlength, max_readlength):
+ '''Filter out reads that are unmatched, too short or
+ too long or that contian insertions'''
+ if read.is_unmapped:
+ return False
+ if read.rlen < min_readlength:
+ return False
+ if read.rlen > max_readlength:
+ return False
+ else:
+ return True
+
+ def read_has_mismatch(self, read, number_of_allowed_mismatches=1):
+ '''keep only reads with one mismatch. Could be simplified'''
+ NM=read.opt('NM')
+ if NM <1: #filter out reads with no mismatch
+ return False
+ if NM >number_of_allowed_mismatches: #filter out reads with more than 1 mismtach
+ return False
+ else:
+ return True
+
+ def mismatch_in_allowed_region(self, readseq, mismatch_position):
+ '''
+ >>> M = MismatchFrequencies()
+ >>> readseq = 'AAAAAA'
+ >>> mismatch_position = 2
+ >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
+ True
+ >>> M = MismatchFrequencies(ignore_3p_nucleotides=2, ignore_5p_nucleotides=2)
+ >>> readseq = 'AAAAAA'
+ >>> mismatch_position = 1
+ >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
+ False
+ >>> readseq = 'AAAAAA'
+ >>> mismatch_position = 4
+ >>> M.mismatch_in_allowed_region(readseq, mismatch_position)
+ False
+ '''
+ mismatch_position+=1 # To compensate for starting the count at 0
+ five_p = self.ignore_5p_nucleotides
+ three_p = self.ignore_3p_nucleotides
+ if any([five_p > 0, three_p > 0]):
+ if any([mismatch_position <= five_p,
+ mismatch_position >= (len(readseq)+1-three_p)]): #Again compensate for starting the count at 0
+ return False
+ else:
+ return True
+ else:
+ return True
+
+ def read_to_reference_mismatch(self, MD, readseq, is_reverse):
+ '''
+ This is where the magic happens. The MD tag contains SNP and indel information,
+ without looking to the genome sequence. This is a typical MD tag: 3C0G2A6.
+ 3 bases of the read align to the reference, followed by a mismatch, where the
+ reference base is C, followed by 10 bases aligned to the reference.
+ suppose a reference 'CTTCGATAATCCTT'
+ ||| || ||||||
+ and a read 'CTTATATTATCCTT'.
+ This situation is represented by the above MD tag.
+ Given MD tag and read sequence this function returns the reference base C, G and A,
+ and the mismatched base A, T, T.
+ >>> M = MismatchFrequencies()
+ >>> MD='3C0G2A7'
+ >>> seq='CTTATATTATCCTT'
+ >>> result=M.read_to_reference_mismatch(MD, seq, is_reverse=False)
+ >>> result[0]=="CGA"
+ True
+ >>> result[1]=="ATT"
+ True
+ >>>
+ '''
+ search=re.finditer('[ATGC]',MD)
+ if '^' in MD:
+ print 'WARNING insertion detected, mismatch calling skipped for this read!!!'
+ return (None, None)
+ start_index=0 # refers to the leading integer of the MD string before an edited base
+ current_position=0 # position of the mismatched nucleotide in the MD tag string
+ mismatch_position=0 # position of edited base in current read
+ reference_base=""
+ mismatched_base=""
+ for result in search:
+ current_position=result.start()
+ mismatch_position=mismatch_position+1+int(MD[start_index:current_position]) #converts the leading characters before an edited base into integers
+ start_index=result.end()
+ reference_base+=MD[result.end()-1]
+ mismatched_base+=readseq[mismatch_position-1]
+ if is_reverse:
+ reference_base=reverseComplement(reference_base)
+ mismatched_base=reverseComplement(mismatched_base)
+ mismatch_position=len(readseq)-mismatch_position-1
+ if mismatched_base=='N':
+ return (None, None)
+ if self.mismatch_in_allowed_region(readseq, mismatch_position):
+ return (reference_base, mismatched_base)
+ else:
+ return (None, None)
+
+def reverseComplement(sequence):
+ '''do a reverse complement of DNA base.
+ >>> reverseComplement('ATGC')=='GCAT'
+ True
+ >>>
+ '''
+ sequence=sequence.upper()
+ complement = string.maketrans('ATCGN', 'TAGCN')
+ return sequence.upper().translate(complement)[::-1]
+
+def barplot(df, library, axes):
+ df.plot(kind='bar', ax=axes, subplots=False,\
+ stacked=False, legend='test',\
+ title='Mismatch frequencies for {0}'.format(library))
+
+def result_dict_to_df(result_dict):
+ mismatches = []
+ libraries = []
+ for mismatch, library in result_dict.iteritems():
+ mismatches.append(mismatch)
+ libraries.append(pd.DataFrame.from_dict(library, orient='index'))
+ df=pd.concat(libraries, keys=mismatches)
+ df.index.names = ['library', 'readsize']
+ return df
+
+def df_to_tab(df, output):
+ df.to_csv(output, sep='\t')
+
+def plot_result(result_dict, args):
+ names=args.name
+ nrows=len(names)/2+1
+ fig = plt.figure(figsize=(16,32))
+ for i,library in enumerate (names):
+ axes=fig.add_subplot(nrows,2,i+1)
+ library_dict=result_dict[library]
+ for length in library_dict.keys():
+ for mismatch in library_dict[length]:
+ if mismatch == 'total valid reads':
+ continue
+ library_dict[length][mismatch]=library_dict[length][mismatch]/float(library_dict[length]['total valid reads'])*100
+ del library_dict[length]['total valid reads']
+ df=pd.DataFrame(library_dict)
+ barplot(df, library, axes),
+ axes.set_ylabel('Mismatch count / all valid reads * 100')
+ fig.savefig(args.output_pdf, format='pdf')
+
+def setup_MismatchFrequencies(args):
+ resultDict=OrderedDict()
+ kw_list=[{'result_dict' : resultDict,
+ 'alignment_file' :alignment_file,
+ 'name' : name,
+ 'minimal_readlength' : args.min,
+ 'maximal_readlength' : args.max,
+ 'number_of_allowed_mismatches' : args.n_mm,
+ 'ignore_5p_nucleotides' : args.five_p,
+ 'ignore_3p_nucleotides' : args.three_p}
+ for alignment_file, name in zip(args.input, args.name)]
+ return (kw_list, resultDict)
+
+def run_MismatchFrequencies(args):
+ kw_list, resultDict=setup_MismatchFrequencies(args)
+ [MismatchFrequencies(**kw_dict) for kw_dict in kw_list]
+ return resultDict
+
+def main():
+ result_dict=run_MismatchFrequencies(args)
+ df=result_dict_to_df(result_dict)
+ plot_result(result_dict, args)
+ df_to_tab(df, args.output_tab)
+
+if __name__ == "__main__":
+
+ parser = argparse.ArgumentParser(description='Produce mismatch statistics for BAM/SAM alignment files.')
+ parser.add_argument('--input', nargs='*', help='Input files in SAM/BAM format')
+ parser.add_argument('--name', nargs='*', help='Name for input file to display in output file. Should have same length as the number of inputs')
+ parser.add_argument('--output_pdf', help='Output filename for graph')
+ parser.add_argument('--output_tab', help='Output filename for table')
+ parser.add_argument('--min', '--minimal_readlength', type=int, help='minimum readlength')
+ parser.add_argument('--max', '--maximal_readlength', type=int, help='maximum readlength')
+ parser.add_argument('--n_mm', '--number_allowed_mismatches', type=int, default=1, help='discard reads with more than n mismatches')
+ parser.add_argument('--five_p', '--ignore_5p_nucleotides', type=int, default=0, help='when calculating nucleotide mismatch frequencies ignore the first N nucleotides of the read')
+ parser.add_argument('--three_p', '--ignore_3p_nucleotides', type=int, default=1, help='when calculating nucleotide mismatch frequencies ignore the last N nucleotides of the read')
+ #args = parser.parse_args(['--input', '3mismatches_ago2ip.bam', '2mismatch.bam', '--name', 'Siomi1', 'Siomi2' , '--five_p', '3','--three_p','3','--output_pdf', 'out.pdf', '--output_tab', 'out.tab', '--min', '21', '--max', '21'])
+ args = parser.parse_args()
+ main()
+
diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies/mismatch_frequencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/mismatch_frequencies/mismatch_frequencies.xml Mon Feb 02 10:33:21 2015 -0500
@@ -0,0 +1,70 @@
+
+ Analyze mismatch frequencies in BAM/SAM alignments
+
+ pysam
+ pandas
+ matplotlib
+
+ mismatch_frequencies.py --input
+ #for i in $rep
+ "$i.input_file"
+ #end for
+ --name
+ #for i in $rep
+ "$i.input_file.name"
+ #end for
+ --output_pdf $output_pdf --output_tab $output_tab --min $min_length --max $max_length
+ --n_mm $number_of_mismatches
+ --five_p $five_p
+ --three_p $three_p
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. class:: infomark
+
+
+***What it does***
+
+This tool reconstitues for each aligned read of an alignment file in SAM/BAM format whether
+a mismatch is annotated in the MD tag, and if that is the case counts the identity of the
+mismatch relative to the reference sequence. The output is a PDF document with the calculated
+frequency for each mismatch that occured relative to the total number of valid reads and a table
+with the corresponding values. Read length can be limited to a specific read length, and 5 prime and
+3 prime-most nucleotides of a read can be ignored.
+
+----
+
+.. class:: warningmark
+
+***Warning***
+
+This tool skips all read that have insertions and has been tested only with bowtie and bowtie2
+generated alignment files.
+
+Written by Marius van den Beek, m.vandenbeek at gmail . com
+
+
diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies/test-data/3mismatches_ago2ip_ovary.bam
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diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies/test-data/3mismatches_ago2ip_s2.bam
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diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies/test-data/mismatch.pdf
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diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies/test-data/mismatch.tab
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/mismatch_frequencies/test-data/mismatch.tab Mon Feb 02 10:33:21 2015 -0500
@@ -0,0 +1,3 @@
+library readsize A to C A to G A to T C to A C to G C to T G to A G to C G to T T to A T to C T to G total valid reads
+3mismatches_ago2ip_s2.bam 21 31 5484 69 25 40 137 156 109 188 51 196 29 43881
+3mismatches_ago2ip_ovary.bam 21 293 879 411 452 231 872 845 191 473 384 818 324 138649
diff -r 270681625775 -r 2e041a1564ad mismatch_frequencies/tool_dependencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/mismatch_frequencies/tool_dependencies.xml Mon Feb 02 10:33:21 2015 -0500
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diff -r 270681625775 -r 2e041a1564ad test-data/3mismatches_ago2ip_ovary.bam
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diff -r 270681625775 -r 2e041a1564ad test-data/3mismatches_ago2ip_s2.bam
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diff -r 270681625775 -r 2e041a1564ad test-data/mismatch.pdf
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diff -r 270681625775 -r 2e041a1564ad test-data/mismatch.tab
--- a/test-data/mismatch.tab Wed Jan 28 13:12:56 2015 +0100
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,3 +0,0 @@
-library readsize A to C A to G A to T C to A C to G C to T G to A G to C G to T T to A T to C T to G total valid reads
-3mismatches_ago2ip_s2.bam 21 31 5484 69 25 40 137 156 109 188 51 196 29 43881
-3mismatches_ago2ip_ovary.bam 21 293 879 411 452 231 872 845 191 473 384 818 324 138649
diff -r 270681625775 -r 2e041a1564ad tool_dependencies.xml
--- a/tool_dependencies.xml Wed Jan 28 13:12:56 2015 +0100
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,12 +0,0 @@
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