[med-svn] [Git][med-team/mcaller][master] 5 commits: Upstream does not provide tag for latest changes
Steffen Möller (@moeller)
gitlab at salsa.debian.org
Sat Jun 19 23:14:29 BST 2021
Steffen Möller pushed to branch master at Debian Med / mcaller
Commits:
6d2245ff by Steffen Möller at 2021-06-19T23:58:58+02:00
Upstream does not provide tag for latest changes
- - - - -
1d4c1c12 by Steffen Möller at 2021-06-19T23:59:32+02:00
routine-update: New upstream version
- - - - -
7a1180cd by Steffen Möller at 2021-06-19T23:59:33+02:00
New upstream version 1.0.3+git20210331.f7a616a
- - - - -
ce5ecfb1 by Steffen Möller at 2021-06-19T23:59:47+02:00
Update upstream source from tag 'upstream/1.0.3+git20210331.f7a616a'
Update to upstream version '1.0.3+git20210331.f7a616a'
with Debian dir 0bbab8e6783ee0f035b6819bb2a72434ac3b7383
- - - - -
20ee86e6 by Steffen Möller at 2021-06-19T23:59:56+02:00
Remove unnecessary Team Upload line in changelog.
Changes-By: lintian-brush
- - - - -
19 changed files:
- README.md
- compare_genomes.py
- debian/changelog
- debian/watch
- extract_contexts.py
- + get_positions.py
- mCaller.py
- make_bed.py
- testdata/masonread1.eventalign.diffs.6
- + testdata/masonread1.methylation.summary.bed
- − testdata/r95_test_read.eventalign.diffs.6
- − testdata/r95_test_read.eventalign.tsv
- − testdata/r95_test_read.fast5
- − testdata/r95_test_read.fastq
- − testdata/r95_test_read.fastq.fast5.fofn
- − testdata/r95_test_read.sam
- − testdata/r95_test_read.sorted.bam
- − testdata/r95_test_read.sorted.bam.bai
- train_model.py
Changes:
=====================================
README.md
=====================================
@@ -7,6 +7,7 @@
This program is designed to call m6A from nanopore data using the differences between measured and expected currents.
## Dependencies/requirements
+> - python3 (as of mCaller version 1.0)
> - nanopolish (https://github.com/jts/nanopolish)
> - an aligner to create a bam file (has been tested with graphmap and bwa mem)
python packages
@@ -84,17 +85,13 @@ arguments:
## Pipeline for methylation detection from R9 data
-1. extract template strand reads from fast5 files using a method that saves the file path in the fastq header, eg.
-```
-nanopolish extract -q -t template <fast5 directory> -o <filename>.fastq
+1. extract template strand reads from fast5 files. Follow the most up-to-date guidelines from nanopolish (https://github.com/jts/nanopolish). As of April 2019:
+```
+nanopolish index -d <fast5 directory> -s sequencing_summary.txt <filename>.fastq
```
or
```
poretools fastq --type fwd <fast5 directory> > <filename>.fastq
-```
- for albacore version > 2.0, follow the most up-to-date guidelines from nanopolish (https://github.com/jts/nanopolish). As of April 2019:
-```
-nanopolish index -d <fast5 directory> -s sequencing_summary.txt <filename>.fastq
```
2. align fastq reads to reference assembly (we have used both GraphMap and bwa mem, with comparable results):
```
@@ -128,7 +125,7 @@ Results and analysis scripts for the E. coli datasets are provided in the bioRxi
## Test data
-Reference fasta, PacBio calls for m6A and a subset of A positions, and eventalign tsv + fastq are provided for a single read for testing purposes in the "testdata" folder. The testdata_from_fast5.sh bash script is also included and can be modified with paths to external programs as appropriate to test generation of output from a fast5 file.
+Reference fasta, PacBio calls for m6A and a subset of A positions, and eventalign tsv + fastq are provided for a single read for testing purposes in the "testdata" folder.
1. To run mCaller on the testdata, use:
```
=====================================
compare_genomes.py
=====================================
@@ -28,7 +28,8 @@ def compare_by_position(bed1,bed2,xmfa):
t,pval3 = 'none','missing df'
d,pval4 = ks_2samp(pos_dict[0][pos][1],pos_dict[0][pos][1])
if pval4 < 0.9:
- print pos, pos_dict[0][pos][0], pos_dict[1][pos][0], pval, pval2, pval3, pval4
+ print(pos, pos_dict[0][pos][0], pos_dict[1][pos][0], pval, pval2, pval3, pval4)
+ #print pos, pos_dict[0][pos][0], pos_dict[1][pos][0], pval, pval2, pval3, pval4
@@ -43,7 +44,7 @@ def main():
args = parser.parse_args()
if args.version:
- print 'mCallerNP 0.1'
+ print('mCallerNP 0.3')
sys.exit(0)
assert os.path.isfile(args.bed1), 'file not found at '+args.bed1
=====================================
debian/changelog
=====================================
@@ -1,3 +1,9 @@
+mcaller (1.0.3+git20210331.f7a616a-1) UNRELEASED; urgency=medium
+
+ * New upstream version
+
+ -- Steffen Möller <moeller at debian.org> Sat, 19 Jun 2021 23:59:32 +0200
+
mcaller (1.0-1) unstable; urgency=medium
* Initial release (Closes: #969558)
=====================================
debian/watch
=====================================
@@ -1,4 +1,4 @@
version=4
-#opts="mode=git,pretty=0.3+git%cd.%h" https://github.com/al-mcintyre/mCaller.git HEAD
-opts="filenamemangle=s%(?:.*?)?v?(\d[\d.]*)\.tar\.gz%mcaller-$1.tar.gz%" \
- https://github.com/al-mcintyre/mCaller/tags (?:.*?/)?v?(\d[\d.]*)\.tar\.gz
+opts="mode=git,pretty=1.0.3+git%cd.%h" https://github.com/al-mcintyre/mCaller.git HEAD
+#opts="filenamemangle=s%(?:.*?)?v?(\d[\d.]*)\.tar\.gz%mcaller-$1.tar.gz%" \
+# https://github.com/al-mcintyre/mCaller/tags (?:.*?/)?v?(\d[\d.]*)\.tar\.gz
=====================================
extract_contexts.py
=====================================
@@ -2,8 +2,8 @@ from collections import defaultdict
from Bio import SeqIO
import scipy.stats as ss
import numpy as np
-import cPickle
-#import _pickle as cPickle
+#import cPickle
+import pickle #_pickle as cPickle
import sys
import re
import os
@@ -121,7 +121,7 @@ def extract_features(tsv_input,fasta_input,read2qual,k,skip_thresh,qual_thresh,m
if not train:
tsv_output = '.'.join(tsv_input.split('.')[:-1])+'.diffs.'+str(k)+'.tmp'+str(startline)
modfi = open(modelfile,'rb')
- model = cPickle.load(modfi)
+ model = pickle.load(modfi,encoding='latin')
modfi.close()
if type(model) != dict:
model = {'general':model} #for compatibility with previously trained model
@@ -139,11 +139,18 @@ def extract_features(tsv_input,fasta_input,read2qual,k,skip_thresh,qual_thresh,m
#tsv format: ecoli 805 CGCCAT cc1da58e-3db3-4a4b-93c2-c78e1dbe6aba:1D_000:template t 1 102.16 0.963 0.00175 CGCCAT 102.23 1.93 -0.03 101.973,100.037,102.403,101.758,104.338,102.618,101.973
with open(tsv_input,'r') as tsv:
tsv.seek(max(startline-500,0))
- tsv.readline() #to start new line
- while tsv.tell() <= endline-500: #TODO: why 500?
- lines = tsv.readlines(8000000) #TODO: why 8M?
+ linepos = max(startline-500,0)
+ #startline, endline, and linepos are in characters -- previously used tsv.tell(), but incompatible with python3
+ while linepos <= endline-500:
+ #print('current position',linepos)
+ lines = tsv.readlines(8000000) #TODO: why 8M? reasonable size for memory consumption, but could change
for line in lines:
- chrom, read_pos, read_kmer, read_name, x, read_ind, event_current, event_sd, y, ref_kmer, model_current, ref_sd = line.split()[:12]
+ linepos += len(line)
+ try:
+ chrom, read_pos, read_kmer, read_name, x, read_ind, event_current, event_sd, y, ref_kmer, model_current, ref_sd = line.split()[:12]
+ except ValueError:
+ continue
+
if chrom != last_contig:
try:
meth_fwd,meth_rev = find_and_methylate(fasta_input,chrom,base,motif,positions_list)
@@ -167,9 +174,6 @@ def extract_features(tsv_input,fasta_input,read2qual,k,skip_thresh,qual_thresh,m
meth_ref = meth_rev
read_pos = int(read_pos)
reference_kmer = meth_ref[read_pos:read_pos+k]
- #if read_name == last_read and (reference_kmer != revcomp('M'.join(ref_kmer.split('A')),rev) and reference_kmer != revcomp('M'.join(ref_kmer.split('T')),rev)) : #true at pseudomethylated positions (to check) TODO: remove this
- # print 'issue with reference kmer in line', line, reference_kmer, rev, revcomp('M'.join(ref_kmer.split('A')),rev)
- # sys.exit(0)
#if finished context for previous potentially modified position, save and reset
if mpos and ((read_pos >= mpos+1 and read_name == last_read) or (read_name != last_read)):
@@ -212,15 +216,15 @@ def extract_features(tsv_input,fasta_input,read2qual,k,skip_thresh,qual_thresh,m
towrite.append([chrom,last_read,str(mpos),context,','.join([str(diff) for diff in diffs]),strand(last_rev),label])
last_info = last_read+'\t'+str(mpos)+'\t'+context+'\t'+','.join([str(diff) for diff in diffs])+'\t'+strand(last_rev)
except (IndexError,KeyError) as e:
- print last_read+'\t'+str(mpos)+'\t'+context+'\t'+','.join([str(diff) for diff in diffs])+'\t'+strand(last_rev),'- Index or Key Error'
- print model.keys(), base_model.keys(), context[int(len(context)/2):int(len(context)/2)+2]
- print e
- print model[twobase_model].predict_proba([diffs])
+ print(last_read+'\t'+str(mpos)+'\t'+context+'\t'+','.join([str(diff) for diff in diffs])+'\t'+strand(last_rev),'- Index or Key Error')
+ print(model.keys(), base_model.keys(), context[int(len(context)/2):int(len(context)/2)+2])
+ print(e)
+ print(model[twobase_model].predict_proba([diffs]))
sys.exit(0)
else:
- print last_read+'\t'+str(mpos)+'\t'+context+'\t'+','.join([str(diff) for diff in diffs])+'\t'+strand(last_rev)
- print read_name, rev, last_read, last_rev, last_first
- print read_kmer,reference_kmer, ref_kmer, last_pos_in_kmer, mspacing, pos_in_kmer
+ print(last_read+'\t'+str(mpos)+'\t'+context+'\t'+','.join([str(diff) for diff in diffs])+'\t'+strand(last_rev))
+ print(read_name, rev, last_read, last_rev, last_first)
+ print(read_kmer,reference_kmer, ref_kmer, last_pos_in_kmer, mspacing, pos_in_kmer)
sys.exit(0)
num_observations += 1
if num_observations%5000 == 0:
@@ -252,12 +256,12 @@ def extract_features(tsv_input,fasta_input,read2qual,k,skip_thresh,qual_thresh,m
diff_col = diffs
if len(diff_col) != k:
try:
- print last_info,'- n diffs off'
+ print(last_info,'- n diffs off')
except:
pass
#GGCGCM 613883 613878 False 2289b392-746e-4fa0-8226-d3ac661c9620_Basecall_2D_template 2289b392-746e-4fa0-8226-d3ac661c9620_Basecall_2D_template [[], [], [], [], [], [], []] 7
- print reference_kmer,last_mpos,mpos,mspacing,read_pos,read_pos-last_mpos,read_name,last_read,diff_col,mspacing, last_diff_col, last_diff_col[:-mspacing]
+ print(reference_kmer,last_mpos,mpos,mspacing,read_pos,read_pos-last_mpos,read_name,last_read,diff_col,mspacing, last_diff_col, last_diff_col[:-mspacing])
diff_col = [[] for i in range(k)]
sys.exit(0)
=====================================
get_positions.py
=====================================
@@ -0,0 +1,93 @@
+#!/usr/bin/env python
+import sys
+import numpy as np
+from Bio import SeqIO
+from scipy import stats
+from scipy.cluster.hierarchy import fcluster,linkage
+import scipy.spatial.distance as ssd
+from extract_contexts import revcomp
+from plotlib import plot_w_labels
+import os
+import pandas as pd
+
+def make_pos_set(pos_list):
+ pos_set = set()
+ with open(pos_list,'r') as fi:
+ for line in fi:
+ if len(line) > 3:
+ pos_set.add(tuple(line.strip().split('\t')[:4]))
+ return pos_set
+
+def aggregate_by_pos(meth_fi,aggfi,depth_thresh,mod_thresh,pos_list,control,verbose_results,gff,ref,plot,plotdir,plotsummary):
+ pos_dict = {}
+ if verbose_results:
+ pos_dict_verbose = {}
+
+ if pos_list:
+ pos_set = make_pos_set(pos_list)
+
+ values_dict = {}
+ for line in open(meth_fi,'r'):
+ #try:
+ #print line
+ try:
+ csome,read,pos,context,values,strand,label,prob = tuple(line.split('\t'))
+ except: #for backwards compatibility; does not work with verbose results
+ csome,read,pos,context,values,strand,label = tuple(line.split('\t'))
+ nextpos = str(int(pos)+1)
+ if pos_list and (csome,pos,nextpos,strand) not in pos_set:
+ continue
+ if (csome,pos,nextpos,context,strand) not in pos_dict:
+ pos_dict[(csome,pos,nextpos,context,strand)] = []
+ values_dict[(csome,pos,nextpos,context,strand)] = []
+ if verbose_results:
+ pos_dict_verbose[(csome,pos,nextpos,context,strand)] = []
+ if (pos_list and (csome,pos,nextpos,strand) in pos_set) or (not pos_list and plot):
+ values_dict[(csome,pos,nextpos,context,strand)].append([float(v) for v in values.split(',')][:-1])
+ if label[0] == 'm':
+ pos_dict[(csome,pos,nextpos,context,strand)].append(1)
+ else:
+ pos_dict[(csome,pos,nextpos,context,strand)].append(0)
+
+def main():
+ #parse command line options
+ from argparse import ArgumentParser
+ parser = ArgumentParser(description='Produce bed file of methylated positions based on mCaller output')
+ parser.add_argument('-d','--min_read_depth',type=int,required=False,help='minimum coverage of position to determine methylation (default = 15)',default=15)
+ parser.add_argument('-t','--mod_threshold',type=float,required=False,help='minimum %% of observations at a position to include in report (default = 0.5)',default=0.5)
+ parser.add_argument('-f','--mCaller_file',type=str,required=True,help='the output file from mCaller to summarize')
+ parser.add_argument('-p','--positions',type=str,required=False,help='~bed file of positions for which to calculate % methylated (chromosome,start,end,strand); ignores other thresholds')
+ parser.add_argument('--control',action='store_true',required=False,help='take unmethylated positions as a control for motif detection')
+ parser.add_argument('--gff',action='store_true',required=False,help='output PacBio-style gff instead of bed ("identificationQv" score will be average probability of methylation)')
+ parser.add_argument('--ref',type=str,required=False,help='use reference fasta to output longer contexts surrounding a base, from -20 to +20')
+ parser.add_argument('--plot',action='store_true',required=False,help='plot currents deviations at the positions included (not recommended for many positions)')
+ parser.add_argument('--plotsummary',action='store_true',required=False,help='plot currents deviations summarized across the positions included')
+ parser.add_argument('--plotdir',type=str,required=False,default='mCaller_position_plots',help='output directory for plots, default=mCaller_position_plots')
+ parser.add_argument('--vo',action='store_true',required=False,help='verbose output including probabilities for each position')
+ parser.add_argument('-v','--version',action='store_true',required=False,help='print version')
+ args = parser.parse_args()
+
+ if args.version:
+ print 'mCallerNP 0.3'
+ sys.exit(0)
+
+ assert os.path.isfile(args.mCaller_file), 'file not found at '+args.mCaller_file
+ if args.positions:
+ output_file = args.mCaller_file.split('.')[0]+'.methylation.positions.summary'
+ elif not args.control:
+ output_file = args.mCaller_file.split('.')[0]+'.methylation.summary'
+ else:
+ output_file = args.mCaller_file.split('.')[0]+'.methylation.control.summary'
+ if args.gff:
+ output_file = output_file+'.gff'
+ else:
+ output_file = output_file+'.bed'
+ if not os.path.isdir(args.plotdir):
+ os.mkdir(args.plotdir)
+
+ print args.mCaller_file
+
+ aggregate_by_pos(args.mCaller_file,output_file,args.min_read_depth,args.mod_threshold,args.positions,args.control,args.vo,args.gff,args.ref,args.plot,args.plotdir,args.plotsummary)
+
+if __name__ == "__main__":
+ main()
=====================================
mCaller.py
=====================================
@@ -3,9 +3,8 @@
#Alexa McIntyre, 2016-2018
import sys
-assert sys.version_info >= (2,7) and sys.version_info < (3,0), 'please use python 2.7'
-import cPickle
-#import _pickle as cPickle
+assert sys.version_info >= (3,0), 'please use python3'
+import pickle
from collections import defaultdict
import numpy as np
#import time
@@ -138,7 +137,7 @@ def main():
parser.add_argument('-c','--classifier',type=str,required=False,help='use alternative classifier: options = NN (default), RF, LR, or NBC (non-default may significantly increase runtime)',default='NN')
parser.add_argument('--plot_training',action='store_true',required=False,help='plot probabilities distributions for training positions (requires labels in positions file and --train)',default=False)
#parser.add_argument('--plot_violin',action='store_true',required=False,help='train a new model (requires labels in positions file)',default=False)
- parser.add_argument('-v','--version',action='version',help='print version',version='%(prog)s v0.3')
+ parser.add_argument('-v','--version',action='version',help='print version',version='%(prog)s v1.0')
args = parser.parse_args()
if args.base == 'A':
=====================================
make_bed.py
=====================================
@@ -81,7 +81,7 @@ def aggregate_by_pos(meth_fi,aggfi,depth_thresh,mod_thresh,pos_list,control,verb
except: #for backwards compatibility; does not work with verbose results
csome,read,pos,context,values,strand,label = tuple(line.split('\t'))
nextpos = str(int(pos)+1)
- if (pos_list and (csome,pos,nextpos,strand) not in pos_set) or (context[len(context)/2] != 'M'):
+ if (pos_list and (csome,pos,nextpos,strand) not in pos_set) or (context[int(len(context)/2)] != 'M'):
continue
if (csome,pos,nextpos,context,strand) not in pos_dict:
pos_dict[(csome,pos,nextpos,context,strand)] = []
@@ -98,6 +98,7 @@ def aggregate_by_pos(meth_fi,aggfi,depth_thresh,mod_thresh,pos_list,control,verb
pos_dict_verbose[(csome,pos,nextpos,context,strand)].append(prob.strip())
#except:
# pass
+ print(values_dict)
if plotsummary:
print('plotting all current deviations...')
num2lab = {0:'A',1:'m6A'}
@@ -149,6 +150,7 @@ def aggregate_by_pos(meth_fi,aggfi,depth_thresh,mod_thresh,pos_list,control,verb
gff_info = (locus[0],locus[2],locus[4],deets)
write_gff(outfi,gff_info)
else:
+ print(aggfi)
out_line = '\t'.join(list(locus)[:-1]+[str(np.mean(pos_dict[locus]))]+[locus[-1]]+[str(len(pos_dict[locus]))]) #+[str(x) for x in values_dict[locus]])
if pos_list:
out_line = out_line + '\t' + '\t'.join([str(x) for x in values_dict[locus]])
@@ -157,9 +159,9 @@ def aggregate_by_pos(meth_fi,aggfi,depth_thresh,mod_thresh,pos_list,control,verb
outfi.write(out_line+'\n')
if not pos_list:
if not control:
- print count, 'methylated loci found with min depth', depth_thresh, 'reads'
+ print(count, 'methylated loci found with min depth', depth_thresh, 'reads')
else:
- print count, 'unmethylated loci found with min depth', depth_thresh, 'reads'
+ print(count, 'unmethylated loci found with min depth', depth_thresh, 'reads')
def main():
#parse command line options
@@ -176,13 +178,9 @@ def main():
parser.add_argument('--plotsummary',action='store_true',required=False,help='plot currents deviations summarized across the positions included')
parser.add_argument('--plotdir',type=str,required=False,default='mCaller_position_plots',help='output directory for plots, default=mCaller_position_plots')
parser.add_argument('--vo',action='store_true',required=False,help='verbose output including probabilities for each position')
- parser.add_argument('-v','--version',action='store_true',required=False,help='print version')
+ parser.add_argument('-v','--version',action='version',help='print version',version='%(prog)s v1.0')
args = parser.parse_args()
- if args.version:
- print 'mCallerNP 0.3'
- sys.exit(0)
-
assert os.path.isfile(args.mCaller_file), 'file not found at '+args.mCaller_file
if args.positions:
output_file = args.mCaller_file.split('.')[0]+'.methylation.positions.summary'
@@ -197,7 +195,7 @@ def main():
if not os.path.isdir(args.plotdir):
os.mkdir(args.plotdir)
- print args.mCaller_file
+ print(args.mCaller_file)
aggregate_by_pos(args.mCaller_file,output_file,args.min_read_depth,args.mod_threshold,args.positions,args.control,args.vo,args.gff,args.ref,args.plot,args.plotdir,args.plotsummary)
=====================================
testdata/masonread1.eventalign.diffs.6
=====================================
@@ -1,35 +1,9 @@
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 13673 CATTAMCAATC 1.1,-0.236666666667,0.28,1.17,-0.02,0.67,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 13861 GCCCGMTATAT -0.53,-2.56,-0.46,-2.09,-5.4,1.05,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 14331 TTTTGMGATCC 0.06,-1.6,-3.48,1.365,1.06,3.94,7.05526534938 - m6A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 14352 TCGTGMATGCA 2.98,1.47,-2.14,5.63,1.07,5.135,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 14444 TAAAAMAGAGA -0.68,-0.74,-0.585,-0.48,-0.815,-1.08,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 14528 ATCAAMCCGAG 4.025,-0.115,-1.02,-0.48,0.173333333333,1.19,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 14836 GCTTTMGCAAA -1.37333333333,0.18,-0.613333333333,-1.43,-0.005,-4.32,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 15536 TGCGTMTTCAA -0.1,2.23,-2.058,2.78,-0.68,0.51,7.05526534938 - m6A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 15630 CTTCCMGCCAC -2.72,-1.03,-0.14,2.625,1.0375,-1.075,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 16154 AGATAMTAAAT -2.38,-1.03,-2.88333333333,-0.59,-4.31,-4.94,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 16209 TCTTAMTAACA -0.3075,4.23,0.88,0.326666666667,-4.165,0.93,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 16397 GTATTMTCAAT -0.63,-2.97,-0.736666666667,-2.71,-1.12,-3.25111111111,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 16979 AAATGMGATTT -1.44,-5.03,1.445,0.4,-0.588,-0.55,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 17243 AGTAGMTTGAT -1.44333333333,-0.02,-0.0366666666667,-0.55,0.64,-2.71,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 17283 ACTTAMCCGGT -1.23,-1.635,-0.24,1.10333333333,-2.215,-2.93,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 17486 AGCATMTTTAT -0.21,0.075,-1.30833333333,4.15,-0.3,-0.215,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 17796 GTTATMTGCAA -1.355,-0.34,-1.86333333333,-1.87,-1.265,-1.22,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 17923 GGTAAMAATTT -5.64,-3.6,-5.13,-0.255,0.4,0.73,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 18402 GTGACMGTTTT -1.14,0.94,-4.55,2.0,-0.34,-2.71,7.05526534938 - m6A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 18488 GGCTTMTTTTA 1.02,-2.38,0.915,0.05,1.75,0.8875,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 18575 GGATGMAAGAA -0.56,-3.505,1.02,-1.55,-0.714,-0.4825,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 18804 ATATTMTGCAT 3.49,1.3,-1.495,-2.3,-1.2,-0.62,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 18828 TCGTGMAAAAT -1.405,-2.96,-3.1,0.57,-0.433333333333,-1.44,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 20255 AGGCCMTTCAA -2.15,1.885,2.58,-0.36,4.26,-2.4625,7.05526534938 - m6A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 20591 AATCAMAAGAA 1.485,4.095,-1.12,-0.91,-0.62,1.17,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 21448 TACTTMCCGCA 1.095,0.78,-2.38,1.47,-1.715,-0.828333333333,7.05526534938 - m6A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 22359 AACTCMAGGTA -3.59,1.85666666667,-0.96,0.0671428571429,0.573333333333,1.21,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 22558 GAAGAMTCAAC 1.005,3.05,3.07,0.4625,0.32,-0.603333333333,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 23402 GGATTMTTTCA -0.19,-1.68,-2.3,0.56,-1.41,1.48,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 24474 CTGGAMAAAGC -1.13,-1.67,-1.1,-0.5775,0.42,1.0225,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 24629 GGGCCMTTACC 0.005,-0.295,-0.54,3.96,1.47,0.065,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 24715 GGTGGMTGAAT 3.255,3.925,0.04,-0.156,-1.01,1.35333333333,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 24770 GGTTAMCGCGT -0.08,-1.36666666667,0.84,-0.545,-3.22,-1.53,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 25340 TGTGGMGAAAC -1.74,-2.56,-2.0775,1.84,-0.1,-0.3,7.05526534938 - A
-26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 25919 AGAACMCTTTC 1.92,2.75,1.59,1.64333333333,1.59,1.27,7.05526534938 - A
+ecoli 26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 13791 GTAAGMTCCCT -0.4066666666666667,1.6099999999999999,-1.6866666666666665,6.67,-4.55,1.775,7.055265349382997 - m6A 0.71
+ecoli 26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 14329 TTGAGMTCCGC -3.48,1.365,1.06,3.94,1.6,0.98,7.055265349382997 - A 0.45
+ecoli 26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 14827 AACAGMTCTCC -2.38,-2.11,-1.45,-1.4900000000000002,-1.98,0.53,7.055265349382997 - A 0.1
+ecoli 26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 15249 GCTTGMTCTCT 0.65,2.7,-3.18,1.41,0.76,3.43,7.055265349382997 - m6A 0.67
+ecoli 26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 19419 CACAGMTCTAC 1.41,-0.9933333333333332,-2.08,-3.42,-1.5650000000000002,-2.332,7.055265349382997 - A 0.12
+ecoli 26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 21759 TGGCGMTCCCT -1.502,-2.01,-2.26,0.5433333333333333,-7.57,-3.11,7.055265349382997 - A 0.3
+ecoli 26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 22161 CAGCGMTCCGG 2.13,0.7219999999999999,-0.44,-0.08499999999999996,3.9250000000000003,2.86,7.055265349382997 - m6A 0.71
+ecoli 26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 23875 GTCCGMTCCGA 6.19,-2.07,0.695,2.71,3.575,3.1566666666666667,7.055265349382997 - m6A 0.56
+ecoli 26dd376e-9d82-41fc-921e-71e559c8e8d1_Basecall_2D_template 24559 GCTCAMCGGCT -0.53,1.9366666666666665,-1.86,0.58,4.6450000000000005,1.17,7.055265349382997 - m6A 0.85
=====================================
testdata/masonread1.methylation.summary.bed
=====================================
@@ -0,0 +1,5 @@
+ecoli 13791 13792 GTAAGMTCCCT 1.0 - 1
+ecoli 15249 15250 GCTTGMTCTCT 1.0 - 1
+ecoli 22161 22162 CAGCGMTCCGG 1.0 - 1
+ecoli 23875 23876 GTCCGMTCCGA 1.0 - 1
+ecoli 24559 24560 GCTCAMCGGCT 1.0 - 1
=====================================
testdata/r95_test_read.eventalign.diffs.6 deleted
=====================================
@@ -1,20 +0,0 @@
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3647365 TCAGGMCCCGT 0.916666666667,-2.664,-2.25,-0.42,0.135,-1.362,18.3588624525 - A 0.34
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3650835 AATTGMCCGTG 2.48666666667,-0.33,-3.24,-0.69,0.61,-0.955,18.3588624525 - A 0.43
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3647734 AAGCGMCCGTC -0.015,-0.42,-0.96,2.38,0.625,-1.29,18.3588624525 - m6A 0.65
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3651075 GCCGGMCCATC 1.81,0.54,0.52,0.902,-0.51,1.91,18.3588624525 - A 0.09
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3647914 TGGTGMCCGCG 1.69666666667,1.305,1.24,1.3,0.545,0.025,18.3588624525 - A 0.24
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3647968 CGCTGMCCATG 0.6775,-1.24,-3.245,9.47390314347e-15,0.035,-1.57,18.3588624525 - A 0.47
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3651343 TCATGMCCGCC 0.0125,0.19,-1.58333333333,-1.01,-0.35,-2.33,18.3588624525 - A 0.21
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3648099 TTTCGMCCTGT -1.795,-0.255,0.56,1.19666666667,1.69333333333,4.44,18.3588624525 - A 0.13
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3651484 CTCTGMCCGTT 1.285,-0.0842857142857,2.365,4.36,3.12,2.715,18.3588624525 - m6A 0.82
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3648591 GGTCGMCCAGG 0.235,-0.54,0.225,0.53,-0.753333333333,0.885,18.3588624525 - A 0.05
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3651823 CTGCGMCCGGT -0.336,-0.09,-2.95,-3.43,-0.68,-1.455,18.3588624525 - A 0.19
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3651901 AACTGMCCGCC 0.8325,-2.36,-0.705,-1.10111111111,-0.645,-0.703333333333,18.3588624525 - A 0.1
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3651937 CGCTGMCCTTA -0.505,0.67,-3.07,-0.38,-1.215,-2.92,18.3588624525 - m6A 0.5
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3651954 TACTGMCCGGC -0.195,-0.66,-2.6875,-1.09,-0.976,-3.37444444444,18.3588624525 - A 0.32
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3651972 TGCCGMCCTGA -0.77,-1.61,-2.04,-1.2,0.55,-0.0233333333333,18.3588624525 - A 0.07
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3649911 TAGCGMCCCGC 1.57,-0.94,0.1675,-0.406666666667,0.37,0.68,18.3588624525 - A 0.08
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3650080 TTATGMCCGAA 3.865,-2.09,-1.09333333333,0.55,-1.2625,-3.34,18.3588624525 - A 0.5
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3653093 CCAAGMCCGCA -2.0525,-0.275,-1.91,-0.605,0.62,0.37,18.3588624525 - A 0.05
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3653162 AATTGMCCAAC -1.015,1.06,0.25,-1.05,0.56,0.09,18.3588624525 - A 0.05
-ecoli f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 3653422 CGATGMCCGTG -1.21,0.04,0.853333333333,2.175,0.44,1.44,18.3588624525 - A 0.11
=====================================
testdata/r95_test_read.eventalign.tsv deleted
=====================================
The diff for this file was not included because it is too large.
=====================================
testdata/r95_test_read.fast5 deleted
=====================================
Binary files a/testdata/r95_test_read.fast5 and /dev/null differ
=====================================
testdata/r95_test_read.fastq deleted
=====================================
@@ -1,4 +0,0 @@
- at f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template r95_test_read testdata/r95_test_read.fast5
-TCTCGTTTTTCCGGTAAAGATAATTAGAGAAAATATCGTTAAAAAATTTTACCAAATGTAGTATTTGACGAATATTGTCGGCCTCGCTGTTTCTTTTTCGAGTATATCCCCGTTAATCATGGATTCACGTTTGCCGATTGATTAGGATATTTTTATTTTACTGTCCAGAAACTGGTCAAAGTAAAGAGCCTTTTTTGTGGCTGGTTGCGTGGCCAACAATCGCGCTTCGACAGTTTGTTCACTGCTTTGGCAGCGATATGGGTATGTTGGTACTGGTTTCTGGCTGAAAAAGAAAGCGCCACGGCGGAACGGAAGCGTATCGTGATTATTGGGTCTGAGTCATGCTATTAACTGCGGTGGTATTAAACCAGCTGGAATTCAGGCGGTAATTCCTGTAAAACAGGCCAGCCAACGGTGACACCTGATAATGTACTTCGCGTCAGCAGAAACTGCTCTCTGTTCCCCGAAGATACCTCACACGAGATAGCTTTCCCGCTGTTCAGCATGATGCACCTTGTTTTCCGGCATTCATGTGGCGTTATTTCGTAAAGTTACGAGCCTTATCGCCCTTATTATTTTGTGGTTTTGCATTTCCCCTGAGTATGATTGGCGCACACTGGTTTACTGACATCATTGTCAGGTTCGATGACCGTGAAGTATTGATCGGTTTGCCACAGGTGTTGCTGACGCCATTGAAGTGATCAGATTAATCACCTTTTGAATAAATCACTACCGGGGAAGAAAACAAACATTTCCAAAACAAATAACTCACAGTAATTAACATCATCGGGTTACGTACATAGTAAGGGATAATCCTGATAGTGCACTACGTACTTTCATCTATCGAACGCAAAATGTTCTAAATTAAAATGAATGGATTGCATGCGTTTCACTCGTGTACTTTAATTGACCAACCCAGCAGTAACTTTCTGTCACTTAGCATAAAATCATGTAAAACCGCTCGCCAAGACCGCGCAATCGGGTAATCTCGAACTCGTTTTGCCTCGGCGGTGGAGATTATCTCACAGCATATAATTTTGTGCGTTGGTCACAGATTTGGCCTTAGGTGTTTCAACATACCGGGTAAGTGGTCTCGATGATAAGCGGCGTTTTTTTATAACGATGTTGTCGGTTAAGGACTTCAAAGGAAAACAAACAACATGGTCAAATCTCAACCGATTTTTGAGATATATCTTGCGCGGGATTCCCACGATTTTGATAGCAGTTCTGCTTTTCTGCATGTAGTACAAATAACACCGCAAAGAATATGCATCTGAGACGTGCAGTGGGTAGTGAAACATCATCACAACTTCTCGGATGAATTCTTTGGTTCGTAATGTCGGCAAATCGCGAATTATGGATCAGTATGCTGACTGGAAGGCGTACGTTATCGTCTGGGCGGCAGCACTAAAGGTATCGGTGTTCTGGTTTCGACAGCGTACATTCCGTGAGCAATTTGTGGCTTAGAACTTCCCGCGCGTTCGACTTACTTACGAACAGCAGGGAAATGGGTAAATCTGTTTCCGCGGTAATTTGCGTACGGGTGATTTAGTTCTGTTCCGTGCCAGTTCAACGGGACGCCATGTCGGCTGTTATATCGGCAACAATCAGTTTGTCAATGCTTCCACCAGCAGTGGTGTTATTATTTCAGCATGAATGAACCGTACTGGAAGAAGCGTGCCAAACGCCGGGTTCTCAGCCACTTGCAGCCCCAATAAACCTGTTGGATGCAATCCTTGGCTGACAGGTTAACTGAAAGCACTGCTTAGGCGGTGCTTTTTTTGTTCATTCATCCCAGAGAAAATGATGTTTCCGCGTCTTGATCGGGCTATAGTCCGATGTTATTATCTTTTAAATGTTGTATTAATTTCGGAAATTACGGGAATCATGTTCATACGCGCTCAATTTTGGACGTAAGCTCCTGCTTACCTGCATTGTGCAGGCGTAATGATTACGATACTGGTGAGTTGCCTTCAGTTTTAGTGGCGGCATAAAGCACGAAGTCAAATACGACACTGATTACCGGCGTACAAAAGTATCTCGATACCTATTTTTTGCCGACCGAAATCCACTACTGACCGGCTCAGCCGCTGACCTTAGATGCCGCCAGCAAGTAACTTCAGACGACCGCCCGCGCAGCTGAATGTCGAACGTTTGTGCTGGTGAAAGATAAAAAAACATTCTGTTCATCTGCGACCGGTGAGATGGACATTCCACTCAATAAGATTAGTTCGGCGCTCGACATTAATAAAACGTCGATATGGCGATCTTACCCGGCACGCCGATGGTGCCGAACAAACCCGCAGTATCGTCATCTGGTATCGCAGCCCTTTGCTGAAAAATAGCGGCGTCTTTGCCGCTCCTCCAGATCTCAACCCTGACGCCTTCACTCTTTTTTACATAGTTCACGGCGGGAAGATTACGATGGCGTCGCCTCATTGTGGCAATACTGCGCTATCACCTTTTCTTCGTTTGATGAACGTTAACGATTAACCGACGCTGATCCGTGAAACTAAAATTGCGGGCATTCCTCTGACCGTTCGGCTTTATACAAGATGACTGGACATGGAACGATATTAATGTTACATTTTACTGGGCGGCACATGAGTGGAACTGTCGTTGGCCTGCTCTGCTTTATTACCTGATGAGGCGTGCGTCAGTATGCACCCGGCAGGAAATCATGACCGCCATCAAGCGCAATTTCAATTTTACGTGGCGTATCAGCCGGTGGTGGATACAAAACTGCGAGTAACGGGCTGGAGATACTGCCTACGCTGGCGGCATCCTGTCACGGGAGAAATTCCCCGGATGCCTTCACAACTTTGCCGAATCGCAAAAGATGATTGTGCCATGACTCTCAGCACCTGTTTGAGGTTAATTGCCCACTTGATGCCATCAGAATTAGAAAAGTGCTGCTGTTGGGCGTCATTGAGTAATGTACTTGGACCATCTACTGTAAAGCTTTAAAACAGATATCAGAAACTGCTCACTTCCCTGCCCTTTACCAGTTCAGATTGTGCTGGAAATTACCAGGCGAGCCTGATATGTTGAAGAGCAGAAATACAACTCTTCGCTGGCTGCACTCGGTCAGCGTAGAAATTGCTATTGATGACTTCGGCACCGGGCACAGCGCGCTTATCTATCTTATTGAGCGTTTTACGCTCGATTATCCTGAAAATTGACCGTGGTTGTACAGCGCCATCGGTACAGGTGATCACTTCCCCGTACTTGACGCGGTGCTGACGCTGGCGAAACGCCTCAATATGCTGACTGGTTGCTGAAGGGTCGAAACGCCGGAACAGGCGCGATGGCTAACAGACTGACGTTAATTTCATGCAGGCTACTGGATTAACACGTTACCGCTGAGACGATTTTGTTCGCTGGCTAAAGAACTGTATACGCCGCAGTGGTGCAGGTGTGCTTACGTCCCTTATTATTCATGGTGAAAACATGCCGGATTGCGGCATTTAATGATGAGTAAAGGAAATCCGTTGCAGATGATTGGTACGCATACTGCTGCTGTTTATCGCTCTGTTCTGCTGGTGTGCAGGCGCAGGCTATCAAGGGAAAGCTATGCCTTTGCCGTGCTGGGCGAACCGGTACGCGTTAATTTCAACCGGTACCGGTATCGCTGAACCGCCACTAAAGGTGGTGAATAACGTTATCAATATCGGCACCTTCGATAATTTCAACCATATGCGCTGCGCGGCAACCCAGGCTACACCTGAGCAACTGTACGACATATTTGCGACTTCGATGACGAACCGGGGCGGTTATTACCCGCTGATTGCTGAAAGGCGCACGCTATGCTGACGTTATTCTGGTGAGTCCGCACTATTAATCCCGTTTTCATGATGGTTCGCCGTACTGCCCGCGATGTAGAGTTTACTTTTCAAAACACCGACCGAGGCGTGCCGCAATTTCGTCTGGTCACCACAAGGCACCACCGTCAAACCATTGCACCGTTAACCGTGCGCATTGGTTAGCTAAACCCGTGAAGATATGCTGAGTCTGTTTTCGCTGCCGGTATTTCCAGAAGTACTGGAAGGATCACAAACTTAGCGACCCGCTCGCCACGCGCCTCGCTTGCCAGTGGTCCATGCCGCGTTACGATCACGGAAGATGGAATGGCCGAATATTGTCTGTTCCGTGTGAGATTGCTGGGCAACAAACAAACTTACCGGTAAACCGTGGACGCTGGAATTTCCGACACCATTCGCTACGATTATTACCTCGATGATATGTCGCCTTTGAAGCGTTTAAAGCAGGTGCCTTTGATTTGCGTATGGAAAACGACGCCAAAACTGGGCCACGCGTTATACCGGTAAAATTTCGATAAAAAAGCTGTCATCAAAGATGACAAAGACAAGATCAGCCCGGGATGATACGCGTTGGCTGGCGTTTAATATCAACGTCCGGTATTCAGCGATCGCGCCGGGTCCGGGGAAGCTATCACTCTCGCGCCTGCTTTGACTTTGAATGGATGAACAAGGCGTTGTTTACATGCACGGAGTCACTGAACAGTTATTTTCAGAATACCGAATACGCGGCCAGAAATTACCCCAGCGCCGCGGAGCTGGTGCTTCTGGCACCAATGAAAAGATCTACCGTCAAGTCTTCACACCAAATCCACCAGCCGCCGGTATCAAAGGCGATGGCTACGATCGGCGAACAACCTACAGCCGACAAACTTCTCAACGAAGCGGGCTGGGTGCTGAAGGGTCAGCATGCGTTAATGCCACAACGGGTCAGCCACTCAGCTTTGGTGTACTTCCCGCAAGCAGCAATAGTCAGTGGGTATGCCGTTCCTTTGCAACCTGCAACGGCTGGTATCCAGCGCTTATGGACATTCGCAAGGTGGATAACTCGCCGTCACTAACCGCATGCGCAGTCGCGTATGACATGATGCCGCGTGGCGGGCGATGCCGTGGCCAGTTCCGGTTACAGTTAGATTTCTGGTCATCAGAATATATCAGATTCCACTTATAATGCCCCCGGCGTTACTGAAGCCCGGTTATCGACTCGCTGATCAACCAAATTATTGCGCGCAGGAATAGAAAAATTACTTTTGCCGTTGGGAGCGACACTGGATCGCGTAACGTGGAATATTACATGCTGCCAATGTGGTACATGGCGAGAAGATAGGCTCGCACAGGTGGGATAAATTCTCCCAGCCGGCCGTGCACCCATCTATAGCCTCGTATCGATACACGGTGGTATGACGTCAATAAAACGGCAAACTGCCGTCCGCCAGCAAACAGGGAGAGTAGATATGGGCGCTTACCTGATTCGCCGTCTGTTGCTGGTGATCCCAACATTATGGGCGATTATCGCCATCAACTTTCATCGCCAAATTACTGGCGGTCGGTCGACCAGTCTCGCCACGCCATTGAGTTTGGTAATGCCGGGTATTGCCCGGCGCGAACGGTAGAAGGTGTTCGATGCCAGCCATGCGCAAAACGGGGTGTCGGCAATATCAGCGACAGTATTACCGGCTGGCGGACGCCAGTTAGATCCAGAAGTGATCGCTGAGATCACTCATATCCTTTACGGTTTTTGATAAGCCGATCCGAACGTTTAAACCAAAATGCTCTGGGACTACATCCGCTTTGATTTTGGCGATAGCCTGTTTCGCGCGCCTCGGTGCTGACGCGATTAAAGACAGTCACCGCGGTTTCCATCACCCTCGGATTGGAACTTTGCTGATTATCTATCTGGTGTCGATTCAGATTTAGGCATTTCATAAAGCTGTTTATAATAGAACGCTTTGACGTCTGGGTAGCGCATTTATCATCATCGGCTACGCCATTCCGGCCTTTTGTTTGCCATCTACGGTGTCTTCTTCGCGGGCGGCAGCTATTTCGACCTGTTCCCTCTACAGCTGGTTTGCGCCTTTGATTCGCTGCCGTGACATCAGAAAATCACCAGATTATCAGCGGCATATCCGCGCTGCCGGTGCTGGCGACAGTGATTGGTGGCTTTGCGGCGCTGACCATGCTGACAAAAACTCATTCCTTGATGAGTGCATGACAATACATTGGTGACCGCGCGTGCGAAAGGGGTAAGTGAAAAATATACCAAACATGTGTTTCCGCAACGCCATGCTGCTGGTGATTGCTGCCGGGTTTTCCGGCGACTTTATCAGCATGTTTTTACCGGCTCGCTGCTGATTGAGGTGATGTTTTCGCGCTCAATGGTCTGGAGCTTACTGGACTGCAGAGCGACCGTCTCGCGCGGTGTCACGCACTGCACTCAATATTTTCACTGGTTCAATGCTGCTGAATATTGTCGGTGATATCGGCTATACGCTGTGGTTGATCGTGATGATTTTGAGGGACGCGTTAATGTCACGACTCAGCCCCGTCAATCAGGCCGTTGGGCGCGTTTCGTCATAACCATCGCGGCTACTGGTCGTTATGGATTTTCCTCGTCTTGTTTGGTTTTGAGTTGTTCTGAACTTATCGCCAACGATAAACCGTTGCTGAATTCAGATTATGACGGCAGTTGGTATTTCCCGTTATTGAAAAACTACAGCGAAAGCGATTTTGGCGGCCCGCTGGCAAGGTCAGGCTGATTATCGGGACCCGTGGCTGGAAACAACGGCTGGAAAATAACGGTGGCTGGAGTACTGTGGGCACCGAACCGCTTTGGTGCTACCAGTAGTAACTTTGCTACCAATGACCCTTCCTTCTCCACCCTCCCCGGCAAAACTGGCGCAGGAACGGATGCCAACGGCGGCGATGTGCTGGCACACGTTATTCTCTATGGCACGCGGATCTCGGTTCTGTTGTACAAGTGCTGACTCCTGTTCCAGCGTGATATGGGCGTGCTGGCGGGGGGCGCTACAAGGCTATTACGGCGGTAAAGTCGATCTCTGGGGTTAACTGTGAAGTATGGTCCGGGGATGCCGACGCTGTTTTGATTATTTTGCTTTCCAGCGTCGTACAGCCTAACTTCTGGTGGCTGGCAATTACTGTCTTGTTTGGCTGGATGAGTCTGGTCGGCGTGGATTGGGCGGAGTTTTTTACGTACTCGTAATTTCGACTACATTCGTGCGGCACAGGCGCTTGGCGTCAGCGATCCGCGGTATCGTCCTGCGTCAT
-+
-&$%%$-7CB20(--.58-,.../1.,*.,2:1)''(&*1+33BCCB?/.*%.*=D,+*%)()'*.,)((+-A/((+-3/-,.(0.+.+,6;2,/==0(*'(&'&*+,0=E/.4A6789.5.+-A04.1-):5168.-2--*1)&-.+(%=BCG;7>ED?-++'*++))3>:91/'(%.+*'%.70)*,,,,5@=6.8+*6567/<,(((,,+66>>@@=>766/112(*(,*17=/06=245501.225>6)775//+*(7.034,3>-,1&+(,+&*-&''/,445;=1.&/-+),(()%*0+++0(*-**,0'1.-*(1+B<026.2.7/')*+,).122526+'('*/033,)(,)/,*0.>ECB55/2200().+*%,(+*)*7>948*'%',-212*.&$'+'-++(*)+/*)+(2*%*23436.-((('),:9;9/23)'04223/.-01568>79/)&'-*+0/.11,-*0-25,5-.2/22A678+,-./120,.67:>44-/%(++,)(**/4*+'),;>967;8<<@:>6*-4))*',3.,41*-,1%>=(*>BD664,*>;>C6ADC at 8AA2EEF049.AGD199)(--,,--0-4;1;78::96:>@6501-228201236B@>>?'20 at 26122655:(3&-*+**-((,58-56B/(&%'(%4(**-**),,-/1C at 2A0(+*)+/253(((3>6F<=+)'*+++,-+*35-1?482-31+8<45*(29<;2 at F?C-7E;69,DD?D at A<;062/0340>@B.?,+)./98-+**.(0*.-'+(*()((()(-22/+3-,/:32,7*+5(-&(('*+.+6;DE1C5:--18++67+.2;72..0++,2/*)'&(+,98;ABCB71=/3+6*678C,,,10/'++,,26 at 5;-B/-/.)/&=4(+)3/&2.<*<A=/3'.-)&,((%.()-31)3221/.7>C>>D67A7*+&6-45<?/3(:,C2>3@;1299=;<108<?3-BE=-:8:383>=9B?080+05089;20)((+,.7569<@EC=9<1<-?,-()(&))1(552-4015/-.67'6)('<-0,-'2*1165:3(9+51@=7/-701.0-)*7=9.>?ADC at .'*3<65).'=E47A<)1,81,40@?:<5?/3;1+19-3E8A?+@>CAB:6+/((*BB9,AC97?GFF.9445549:=6@>6976+?;6:89</*..*720$&&+0)4'+=/,++-3AC8>@>3**'+/))*)1<41)(%./=.33B/*2896;:;811'('++.+-/7+1.19,.+4457=849<=?42')(+(,4*41//++(.*1*&%%'),:7=362835(**)'2:/-/8>3=6B6??ACCCC97?>BBCCB<DEE3:0/*/+265.3/+1C??>9001,).+),**.*55,&(3,48;*9-,9?@AEDE at A))*&&,0.+129E>?1<0-.-8=3:3++/365;/:;A=@4>D**250;9DB<<<*%?2<53463093130:<+67-+3987:C856<C?G-A0+,.89DDAFC=@57>@9>;==?AG:-95>04;CD=8-+)/..0<FC>41*0+(+$&'.13:4*.%)(.//08??5B at CA;AF8D//3C));;75-'+,069.1=3 at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at -&),/120)+%-<-((8D8;2:9 at CD@?:*.1.,-<FFC?=20,0124).53562<4%04...-'0(0,444@:435500?<EF=BE2(*('+-8678=5DC?D?>=AD:25'72.6A;9>87<CBC6:54>-;<@=3BG=8?E?@68+)',))<8800*+6<--8<A;<CCCA156883EAD-,,'*+27D=4<:@>(A582167E6<;<<BB at B<417=/7;41*+('*+)(%)(+869=?3/+048FC at 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=====================================
testdata/r95_test_read.fastq.fast5.fofn deleted
=====================================
@@ -1 +0,0 @@
-f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template testdata/r95_test_read.fast5
=====================================
testdata/r95_test_read.sam deleted
=====================================
@@ -1,4 +0,0 @@
- at HD VN:1.0 SO:unknown
- at SQ SN:ecoli LN:4734145
- at PG ID:graphmap PN:graphmap CL:graphmap -r testdata/pb_ecoli_polished_assembly.fasta -d testdata/r95_test_read.fastq -o testdata/r95_test_read.sam VN:v0.3.0 compiled on Jun 16 2016 at 10:59:07
-f88c6bba-5f92-4766-b8cf-21b1af4cc0b1_Basecall_1D_template:1D_000:template 16 ecoli 3646849 40 20M1I56M1I13M1D2M1I42M1D1M2D46M1D20M1I11M3D3M2D45M1I17M2I15M1D9M1I1M1D5M1D32M3I36M2I1M1D12M1I14M1D7M1D17M1D1M1I37M1I4M3I23M1I28M1I72M1I1M1I3M1I33M1D1M1D2M1D5M1I56M1D13M1D33M2I14M1D2M1D3M1D6M1I1M1I38M1I2M2D5M2D10M1D3M1D2M5D7M1D1M1I2M1D19M1D2M1I14M1I12M2I34M1D17M1D12M1I2M1I1M2I27M1I11M3D1M1D1M2D38M1I8M1I3M2D4M1D15M1D60M1I19M1I34M3D10M1D1M1D1M1D44M1D1M1D5M1D10M1D42M1D14M1D1M1D22M1I7M1I2M1I28M1I3M1D3M1D23M1I17M1D18M1D56M1I7M2D13M1I9M3I36M1I1M1D11M2I6M1D23M1I4M1I17M1I11M1I4M1I2M1D14M1D23M2I5M2D12M1D8M3D8M1D6M2D70M2I35M1D25M1D1M1I9M1D13M1D36M1I14M1I29M1D8M1D1M2D14M1D4M1I9M3I11M2D2M1D2M1D7M1D39M1I2M1I26M1I19M1D4M2I1M2I4M1D10M1D19M2D1M2D17M2D24M1D30M4I1M1I6M1D14M1D1M1D3M2I9M1D31M3D2M1I1M1D37M1D48M1D3M3D6M1I2M1I21M1D22M1I10M1D1M1D12M2D35M1I1M1D46M1I2M2D6M1D1M1I3M1D3M1D34M5I16M1I9M1I1M1I23M1D23M2I1M1I12M1D2M1D4M1I4M1D13M1I2M1D14M1D26M1D62M1D34M1I30M4I18M2D6M1D20M3I9M1D1M1I2M1I28M1D2M1I1M1I41M2D42M4D15M1D27M1D17M1I1M1I2M1D24M1D6M1I2M3D33M1D1M1D19M6D2M1D9M2I2M1I2M1D2M1D2M1D3M1D6M1D17M1I27M1I14M1D13M3D16M1I3M2D2M1D25M1D25M1D15M1D1M1D1M1I5M1D3M2D6M2D6M1I1M1I2M1D3M1I15M1D10M2D31M1I25M2D36M1I22M1D13M2I1M1I52M1I20M1D26M1I11M3D16M1D17M1D2M1D2M1I1M1D30M1D13M1I47M1D12M3D3M1I15M1D2M1I2M1D16M1I22M3I72M1D14M3D3M1D5M1D11M2I1M2I2M1I2M1D20M1D8M1D26M1D20M1D5M2D7M4D1M1D3M1D1M1D2M1I1M1I3M1I1M1D2M2D19M1D13M1I7M2I12M1I14M2I6M1D33M1D12M1D36M1I6M1I1M1D4M1D13M2D2M1D39M3I2M1D24M1D8M1I5M1I1M1I7M1I13M2I30M2I11M1D5M2D6M2I24M1I50M1I1M1D3M2D9M1D19M2D11M1D17M1D7M1D34M2I1M2I18M1I9M1I1M1I2M1I55M2I52M1D20M1D3M1I1M1D3M1I76M1D8M8D15M1D1M1D2M1I7M1D9M1D20M1D22M1D7M2I38M2D21M1I7M2D6M1D43M1D35M2D20M1I3M1D8M1D23M3I6M1D1M3D12M1D30M2I12M1D34M1I2M1D3M5D6M1D11M1D3M1I8M2I6M4I17M2D2M1D1M2D1M1D31M1D55M1D4M1I64M1D20M1I10M4I10M2I3M1I12M1I1M1I26M1D12M1D9M3D33M1D32M1D3M2D16M1I3M6D9M1D6M1D2M3D27M2D6M1D39M1I15M1I1M1D33M1I51M1I4M1D22M2D2M1I2M1I2M1I14M1D14M1D1M2D2M1D15M1D28M1D8M2I36M1D29M2D6M2D1M1D7M2D13M2D23M1D1M1D26M1I7M1D5M1D2M1D25M1D2M1I5M1I1M1D11M1I20M1D47M1I2M1I14M1I3M2D15M1I8M1I16M1D4M1I16M2I16M1I35M1D5M1I10M1D8M1D21M1D2M1I8M1D25M1D19M1D1M1D5M1I1M1D15M2I10M1D4M1D15M1I3M1I9M2I1M1D2M1I1M1I4M1I3M4D9M1D31M1I1M1I38M1I4M1I17M2I1M1I2M1I6M1D6M1I3M1I16M1D18M4I1M1I3M2I10M1I10M1D13M1D16M1I8M1I1M1I7M1D11M1I2M1D2M1I2M1D4M1D22M1I30M1I1M1I1M1D10M2D21M1I6M2I5M1D6M1I7M1D1M1I20M1I4M1I8M1I1M * 0 0 ATGACGCAGGACGATACCGCGGATCGCTGACGCCAAGCGCCTGTGCCGCACGAATGTAGTCGAAATTACGAGTACGTAAAAAACTCCGCCCAATCCACGCCGACCAGACTCATCCAGCCAAACAAGACAGTAATTGCCAGCCACCAGAAGTTAGGCTGTACGACGCTGGAAAGCAAAATAATCAAAACAGCGTCGGCATCCCCGGACCATACTTCACAGTTAACCCCAGAGATCGACTTTACCGCCGTAATAGCCTTGTAGCGCCCCCCGCCAGCACGCCCATATCACGCTGGAACAGGAGTCAGCACTTGTACAACAGAACCGAGATCCGCGTGCCATAGAGAATAACGTGTGCCAGCACATCGCCGCCGTTGGCATCCGTTCCTGCGCCAGTTTTGCCGGGGAGGGTGGAGAAGGAAGGGTCATTGGTAGCAAAGTTACTACTGGTAGCACCAAAGCGGTTCGGTGCCCACAGTACTCCAGCCACCGTTATTTTCCAGCCGTTGTTTCCAGCCACGGGTCCCGATAATCAGCCTGACCTTGCCAGCGGGCCGCCAAAATCGCTTTCGCTGTAGTTTTTCAATAACGGGAAATACCAACTGCCGTCATAATCTGAATTCAGCAACGGTTTATCGTTGGCGATAAGTTCAGAACAACTCAAAACCAAACAAGACGAGGAAAATCCATAACGACCAGTAGCCGCGATGGTTATGACGAAACGCGCCCAACGGCCTGATTGACGGGGCTGAGTCGTGACATTAACGCGTCCCTCAAAATCATCACGATCAACCACAGCGTATAGCCGATATCACCGACAATATTCAGCAGCATTGAACCAGTGAAAATATTGAGTGCAGTGCGTGACACCGCGCGAGACGGTCGCTCTGCAGTCCAGTAAGCTCCAGACCATTGAGCGCGAAAACATCACCTCAATCAGCAGCGAGCCGGTAAAAACATGCTGATAAAGTCGCCGGAAAACCCGGCAGCAATCACCAGCAGCATGGCGTTGCGGAAACACATGTTTGGTATATTTTTCACTTACCCCTTTCGCACGCGCGGTCACCAATGTATTGTCATGCACTCATCAAGGAATGAGTTTTTGTCAGCATGGTCAGCGCCGCAAAGCCACCAATCACTGTCGCCAGCACCGGCAGCGCGGATATGCCGCTGATAATCTGGTGATTTTCTGATGTCACGGCAGCGAATCAAAGGCGCAAACCAGCTGTAGAGGGAACAGGTCGAAATAGCTGCCGCCCGCGAAGAAGACACCGTAGATGGCAAACAAAAGGCCGGAATGGCGTAGCCGATGATGATAAATGCGCTACCCAGACGTCAAAGCGTTCTATTATAAACAGCTTTATGAAATGCCTAAATCTGAATCGACACCAGATAGATAATCAGCAAAGTTCCAATCCGAGGGTGATGGAAACCGCGGTGACTGTCTTTAATCGCGTCAGCACCGAGGCGCGCGAAACAGGCTATCGCCAAAATCAAAGCGGATGTAGTCCCAGAGCATTTTGGTTTAAACGTTCGGATCGGCTTATCAAAAACCGTAAAGGATATGAGTGATCTCAGCGATCACTTCTGGATCTAACTGGCGTCCGCCAGCCGGTAATACTGTCGCTGATATTGCCGACACCCCGTTTTGCGCATGGCTGGCATCGAACACCTTCTACCGTTCGCGCCGGGCAATACCCGGCATTACCAAACTCAATGGCGTGGCGAGACTGGTCGACCGACCGCCAGTAATTTGGCGATGAAAGTTGATGGCGATAATCGCCCATAATGTTGGGATCACCAGCAACAGACGGCGAATCAGGTAAGCGCCCATATCTACTCTCCCTGTTTGCTGGCGGACGGCAGTTTGCCGTTTTATTGACGTCATACCACCGTGTATCGATACGAGGCTATAGATGGGTGCACGGCCGGCTGGGAGAATTTATCCCACCTGTGCGAGCCTATCTTCTCGCCATGTACCACATTGGCAGCATGTAATATTCCACGTTACGCGATCCAGTGTCGCTCCCAACGGCAAAAGTAATTTTTCTATTCCTGCGCGCAATAATTTGGTTGATCAGCGAGTCGATAACCGGGCTTCAGTAACGCCGGGGGCATTATAAGTGGAATCTGATATATTCTGATGACCAGAAATCTAACTGTAACCGGAACTGGCCACGGCATCGCCCGCCACGCGGCATCATGTCATACGCGACTGCGCATGCGGTTAGTGACGGCGAGTTATCCACCTTGCGAATGTCCATAAGCGCTGGATACCAGCCGTTGCAGGTTGCAAAGGAACGGCATACCCACTGACTATTGCTGCTTGCGGGAAGTACACCAAAGCTGAGTGGCTGACCCGTTGTGGCATTAACGCATGCTGACCCTTCAGCACCCAGCCCGCTTCGTTGAGAAGTTTGTCGGCTGTAGGTTGTTCGCCGATCGTAGCCATCGCCTTTGATACCGGCGGCTGGTGGATTTGGTGTGAAGACTTGACGGTAGATCTTTTCATTGGTGCCAGAAGCACCAGCTCCGCGGCGCTGGGGTAATTTCTGGCCGCGTATTCGGTATTCTGAAAATAACTGTTCAGTGACTCCGTGCATGTAAACAACGCCTTGTTCATCCATTCAAAGTCAAAGCAGGCGCGAGAGTGATAGCTTCCCCGGACCCGGCGCGATCGCTGAATACCGGACGTTGATATTAAACGCCAGCCAACGCGTATCATCCCGGGCTGATCTTGTCTTTGTCATCTTTGATGACAGCTTTTTTATCGAAATTTTACCGGTATAACGCGTGGCCCAGTTTTGGCGTCGTTTTCCATACGCAAATCAAAGGCACCTGCTTTAAACGCTTCAAAGGCGACATATCATCGAGGTAATAATCGTAGCGAATGGTGTCGGAAATTCCAGCGTCCACGGTTTACCGGTAAGTTTGTTTGTTGCCCAGCAATCTCACACGGAACAGACAATATTCGGCCATTCCATCTTCCGTGATCGTAACGCGGCATGGACCACTGGCAAGCGAGGCGCGTGGCGAGCGGGTCGCTAAGTTTGTGATCCTTCCAGTACTTCTGGAAATACCGGCAGCGAAAACAGACTCAGCATATCTTCACGGGTTTAGCTAACCAATGCGCACGGTTAACGGTGCAATGGTTTGACGGTGGTGCCTTGTGGTGACCAGACGAAATTGCGGCACGCCTCGGTCGGTGTTTTGAAAAGTAAACTCTACATCGCGGGCAGTACGGCGAACCATCATGAAAACGGGATTAATAGTGCGGACTCACCAGAATAACGTCAGCATAGCGTGCGCCTTTCAGCAATCAGCGGGTAATAACCGCCCCGGTTCGTCATCGAAGTCGCAAATATGTCGTACAGTTGCTCAGGTGTAGCCTGGGTTGCCGCGCAGCGCATATGGTTGAAATTATCGAAGGTGCCGATATTGATAACGTTATTCACCACCTTTAGTGGCGGTTCAGCGATACCGGTACCGGTTGAAATTAACGCGTACCGGTTCGCCCAGCACGGCAAAGGCATAGCTTTCCCTTGATAGCCTGCGCCTGCACACCAGCAGAACAGAGCGATAAACAGCAGCAGTATGCGTACCAATCATCTGCAACGGATTTCCTTTACTCATCATTAAATGCCGCAATCCGGCATGTTTTCACCATGAATAATAAGGGACGTAAGCACACCTGCACCACTGCGGCGTATACAGTTCTTTAGCCAGCGAACAAAATCGTCTCAGCGGTAACGTGTTAATCCAGTAGCCTGCATGAAATTAACGTCAGTCTGTTAGCCATCGCGCCTGTTCCGGCGTTTCGACCCTTCAGCAACCAGTCAGCATATTGAGGCGTTTCGCCAGCGTCAGCACCGCGTCAAGTACGGGGAAGTGATCACCTGTACCGATGGCGCTGTACAACCACGGTCAATTTTCAGGATAATCGAGCGTAAAACGCTCAATAAGATAGATAAGCGCGCTGTGCCCGGTGCCGAAGTCATCAATAGCAATTTCTACGCTGACCGAGTGCAGCCAGCGAAGAGTTGTATTTCTGCTCTTCAACATATCAGGCTCGCCTGGTAATTTCCAGCACAATCTGAACTGGTAAAGGGCAGGGAAGTGAGCAGTTTCTGATATCTGTTTTAAAGCTTTACAGTAGATGGTCCAAGTACATTACTCAATGACGCCCAACAGCAGCACTTTTCTAATTCTGATGGCATCAAGTGGGCAATTAACCTCAAACAGGTGCTGAGAGTCATGGCACAATCATCTTTTGCGATTCGGCAAAGTTGTGAAGGCATCCGGGGAATTTCTCCCGTGACAGGATGCCGCCAGCGTAGGCAGTATCTCCAGCCCGTTACTCGCAGTTTTGTATCCACCACCGGCTGATACGCCACGTAAAATTGAAATTGCGCTTGATGGCGGTCATGATTTCCTGCCGGGTGCATACTGACGCACGCCTCATCAGGTAATAAAGCAGAGCAGGCCAACGACAGTTCCACTCATGTGCCGCCCAGTAAAATGTAACATTAATATCGTTCCATGTCCAGTCATCTTGTATAAAGCCGAACGGTCAGAGGAATGCCCGCAATTTTAGTTTCACGGATCAGCGTCGGTTAATCGTTAACGTTCATCAAACGAAGAAAAGGTGATAGCGCAGTATTGCCACAATGAGGCGACGCCATCGTAATCTTCCCGCCGTGAACTATGTAAAAAAGAGTGAAGGCGTCAGGGTTGAGATCTGGAGGAGCGGCAAAGACGCCGCTATTTTTCAGCAAAGGGCTGCGATACCAGATGACGATACTGCGGGTTTGTTCGGCACCATCGGCGTGCCGGGTAAGATCGCCATATCGACGTTTTATTAATGTCGAGCGCCGAACTAATCTTATTGAGTGGAATGTCCATCTCACCGGTCGCAGATGAACAGAATGTTTTTTTATCTTTCACCAGCACAAACGTTCGACATTCAGCTGCGCGGGCGGTCGTCTGAAGTTACTTGCTGGCGGCATCTAAGGTCAGCGGCTGAGCCGGTCAGTAGTGGATTTCGGTCGGCAAAAAATAGGTATCGAGATACTTTTGTACGCCGGTAATCAGTGTCGTATTTGACTTCGTGCTTTATGCCGCCACTAAAACTGAAGGCAACTCACCAGTATCGTAATCATTACGCCTGCACAATGCAGGTAAGCAGGAGCTTACGTCCAAAATTGAGCGCGTATGAACATGATTCCCGTAATTTCCGAAATTAATACAACATTTAAAAGATAATAACATCGGACTATAGCCCGATCAAGACGCGGAAACATCATTTTCTCTGGGATGAATGAACAAAAAAAGCACCGCCTAAGCAGTGCTTTCAGTTAACCTGTCAGCCAAGGATTGCATCCAACAGGTTTATTGGGGCTGCAAGTGGCTGAGAACCCGGCGTTTGGCACGCTTCTTCCAGTACGGTTCATTCATGCTGAAATAATAACACCACTGCTGGTGGAAGCATTGACAAACTGATTGTTGCCGATATAACAGCCGACATGGCGTCCCGTTGAACTGGCACGGAACAGAACTAAATCACCCGTACGCAAATTACCGCGGAAACAGATTTACCCATTTCCCTGCTGTTCGTAAGTAAGTCGAACGCGCGGGAAGTTCTAAGCCACAAATTGCTCACGGAATGTACGCTGTCGAAACCAGAACACCGATACCTTTAGTGCTGCCGCCCAGACGATAACGTACGCCTTCCAGTCAGCATACTGATCCATAATTCGCGATTTGCCGACATTACGAACCAAAGAATTCATCCGAGAAGTTGTGATGATGTTTCACTACCCACTGCACGTCTCAGATGCATATTCTTTGCGGTGTTATTTGTACTACATGCAGAAAAGCAGAACTGCTATCAAAATCGTGGGAATCCCGCGCAAGATATATCTCAAAAATCGGTTGAGATTTGACCATGTTGTTTGTTTTCCTTTGAAGTCCTTAACCGACAACATCGTTATAAAAAAACGCCGCTTATCATCGAGACCACTTACCCGGTATGTTGAAACACCTAAGGCCAAATCTGTGACCAACGCACAAAATTATATGCTGTGAGATAATCTCCACCGCCGAGGCAAAACGAGTTCGAGATTACCCGATTGCGCGGTCTTGGCGAGCGGTTTTACATGATTTTATGCTAAGTGACAGAAAGTTACTGCTGGGTTGGTCAATTAAAGTACACGAGTGAAACGCATGCAATCCATTCATTTTAATTTAGAACATTTTGCGTTCGATAGATGAAAGTACGTAGTGCACTATCAGGATTATCCCTTACTATGTACGTAACCCGATGATGTTAATTACTGTGAGTTATTTGTTTTGGAAATGTTTGTTTTCTTCCCCGGTAGTGATTTATTCAAAAGGTGATTAATCTGATCACTTCAATGGCGTCAGCAACACCTGTGGCAAACCGATCAATACTTCACGGTCATCGAACCTGACAATGATGTCAGTAAACCAGTGTGCGCCAATCATACTCAGGGGAAATGCAAAACCACAAAATAATAAGGGCGATAAGGCTCGTAACTTTACGAAATAACGCCACATGAATGCCGGAAAACAAGGTGCATCATGCTGAACAGCGGGAAAGCTATCTCGTGTGAGGTATCTTCGGGGAACAGAGAGCAGTTTCTGCTGACGCGAAGTACATTATCAGGTGTCACCGTTGGCTGGCCTGTTTTACAGGAATTACCGCCTGAATTCCAGCTGGTTTAATACCACCGCAGTTAATAGCATGACTCAGACCCAATAATCACGATACGCTTCCGTTCCGCCGTGGCGCTTTCTTTTTCAGCCAGAAACCAGTACCAACATACCCATATCGCTGCCAAAGCAGTGAACAAACTGTCGAAGCGCGATTGTTGGCCACGCAACCAGCCACAAAAAAGGCTCTTTACTTTGACCAGTTTCTGGACAGTAAAATAAAAATATCCTAATCAATCGGCAAACGTGAATCCATGATTAACGGGGATATACTCGAAAAAGAAACAGCGAGGCCGACAATATTCGTCAAATACTACATTTGGTAAAATTTTTTAACGATATTTTCTCTAATTATCTTTACCGGAAAAACGAGA ,*/11:989 at B,1+,,+15.12+*,/)08340D/</64?E3,00/2-:6=/BG@<CDE?>55=@2071159<=BBD:5FD59;+):5?<20).)?>?;=@7 at DC<9<;91>BCF==:D:DG=170+(,-*(*0)&*))&(&(++-)/-+(*()0/-0</53 at 7982./53849.?DE96<;8.>BE=;E@@EDE at F<F at 9D@<)?69??93,()+4'+*((/,*.-.-233105<A1;@500-49.6.>B=>21-/,0//99>,?DC0+;9<?ACEB=?91*'+,12=A,++6.6-*.6--*-*1+,'**+**(.4-17DB at 2=>>AAE<;<?@>D:;7?DD:<2+3/-3)3323/4:3?/)'./&032(<7901'11@:;9;18++&**=;;6=F;8505E at 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NM:i:936 AS:i:26682 H0:i:1 ZE:f:0 ZF:f:0.567683 ZQ:i:7103 ZR:i:4734145
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testdata/r95_test_read.sorted.bam deleted
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testdata/r95_test_read.sorted.bam.bai deleted
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train_model.py
=====================================
@@ -1,7 +1,6 @@
-import cPickle
-#import _pickle as cPickle
+import pickle
import numpy as np
-from plotlib import *
+#from plotlib import *
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
@@ -86,9 +85,9 @@ def train_classifier(signals,groups,modelfile,classifier='NN',plot=False): #TODO
sigs = sigs + signals[twobase_model][label][:num_examples]
grps = grps + groups[twobase_model][label][:num_examples]
- print labs[:10]
- print sigs[:10]
- print grps[:10]
+ print(labs[:10])
+ print(sigs[:10])
+ print(grps[:10])
scores = cross_val_score(model,sigs,labs,cv=gfk,groups=grps)
print("%s %s model scores: %s" %(classifier,twobase_model,','.join([str(s) for s in scores])))
@@ -109,7 +108,7 @@ def train_classifier(signals,groups,modelfile,classifier='NN',plot=False): #TODO
plot_training_probabilities(prob_scores,twobase_model)
modfi = open(modelfile,'wb')
- cPickle.dump(models,modfi)
+ pickle.dump(models,modfi)
modfi.close()
return models
View it on GitLab: https://salsa.debian.org/med-team/mcaller/-/compare/ecb0a82fb9d37058ece6e50d3e32aff5e68f4986...20ee86e6cad7216386777daa4da1f0fc87213e96
--
View it on GitLab: https://salsa.debian.org/med-team/mcaller/-/compare/ecb0a82fb9d37058ece6e50d3e32aff5e68f4986...20ee86e6cad7216386777daa4da1f0fc87213e96
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