init

COPYRIGHT.txt

+
+Copyright 2006-2010
+  
+LipidXplorer is free software; you can redistribute it and/or modify it under the
+terms of the GNU General Public License as published by the Free Software
+Foundation; either version 2 of the License, or (at your option) any later
+version.
+ 
+LipidXplorer is distributed in the hope that it will be useful, but WITHOUT ANY
+WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
+A PARTICULAR PURPOSE. See the GNU General Public License for more details.
+  
+You should have received a copy of the GNU General Public License along with
+LipidXplorer; if not, write to the Free Software Foundation, Inc., 51 Franklin St,
+Fifth Floor, Boston, MA 02110-1301 USA or find it under http://www.gnu.org/licenses/gpl.html

LICENSES-third-party.txt

+ELEMENTTREE is released under the terms of the Python (MIT style) license:
+http://pypi.python.org/pypi/elementtree/
+
+LXML is released under the terms of the BSD license:
+http://lxml.de/
+
+PLY is released under the terms of the BSD license:
+http://www.dabeaz.com/ply/
+
+mzAPI is part of the multiplerz project and released under 
+the terms of the GNU Lesser General Public License:
+http://blais.dfci.harvard.edu/index.php?id=63

LipidXplorer.py

+from lx.gui import lpdxGUI
+import wx
+
+class MyApp(wx.App):
+
+	def OnInit(self):
+		self.frame = lpdxGUI.LpdxFrame(None, -1, "",
+				rawimport = False,
+				lipidxplorer = True,
+				version = "1.2.7")
+		self.frame.Show(True)
+		self.frame.Center()
+		self.SetTopWindow(self.frame)
+		return True
+
+def main():
+
+	app = MyApp(0)
+	app.MainLoop()
+	## end of the software
+
+if __name__ == "__main__":
+	main()

LipidXplorer.pyw

+from lx.gui import lpdxGUI
+import wx
+
+class MyApp(wx.App):
+
+	def OnInit(self):
+		self.frame = lpdxGUI.LpdxFrame(None, -1, "",
+				rawimport = False,
+				lipidxplorer = True,
+				version = "1.2.6")
+		self.frame.Show(True)
+		self.frame.Center()
+		self.SetTopWindow(self.frame)
+		return True
+
+def main():
+
+	app = MyApp(0)
+	app.MainLoop()
+	## end of the software
+
+if __name__ == "__main__":
+	main()

LipidXplorer.spec

+# -*- mode: python -*-
+
+block_cipher = None
+
+
+a = Analysis(['LipidXplorer.py'],
+             pathex=['D:\\tmp\\LipidXplorer-1.2.7'],
+             binaries=[],
+             datas=[('lx\\stuff\*','lx\\stuff')],
+             hiddenimports=[],
+             hookspath=[],
+             runtime_hooks=[],
+             excludes=[],
+             win_no_prefer_redirects=False,
+             win_private_assemblies=False,
+             cipher=block_cipher)
+pyz = PYZ(a.pure, a.zipped_data,
+             cipher=block_cipher)
+exe = EXE(pyz,
+          a.scripts,
+          exclude_binaries=True,
+          name='LipidXplorer',
+          debug=False,
+          strip=False,
+          upx=False,
+          console=False )
+coll = COLLECT(exe,
+               a.binaries,
+               a.zipfiles,
+               a.datas,
+               strip=False,
+               upx=True,
+               name='LipidXplorer')

PKG-INFO

+Metadata-Version: 1.0
+Name: LipidXplorer
+Version: 1.2.7
+Summary: UNKNOWN
+Home-page: https://wiki.mpi-cbg.de/wiki/lipidx/index.php/Main_Page
+Author: Ronny Herzog
+Author-email: herzog@mpi-cbg.de
+License: UNKNOWN
+Description: ...
+Platform: UNKNOWN

README

+12.08.2008
+
+- new function isStandard(<variable>, "<sample>", "<scope>"). Place it in SUCHTHAT section. 
+  A scan is started to find <variable> in <sample>. Once found it is declared as the standard and the
+  intensities of all other peaks in <scope> are recalculated as ratio. 
+  Example: isStandard(varStandard, "hilde01", "MS1+")
+
+- new function for addressing groups of samples with the help of placeholders:
+
+    Patterns are Unix shell style:
+    
+    *       matches everything
+    ?       matches any single character
+    [seq]   matches any character in seq
+    [!seq]  matches any char not in seq
+
+  Example: FA1.intensity["*hilde0[1-9]*"] > FA1.intensity["*hilde1[0-9]*"]
+
+- also possible: column(FA1, "*hilde0[1-9]*")
+
+13.08.2008
+
+- semi-new function: isStandard(<variable>, "<scope>"). With this syntax the standard is
+  calculated for every single sample.
+
+14.08.2008
+
+- every peak (either MS or MS/MS) has now additionally the following information:
+    . peak mean
+    . peak median
+    . peak variance
+    . peak standard deviation
+  all this info will be put out in the dump
+
+22.08.2008
+
+- isStandard() function works now for MS1+/- (! no MS2+/- !). The function should be placed in the SUCHTHAT
+  section and has as attributes 1) a variable containing the marked standard and 2) the scope of
+  the standard (MS1+, MS1-, MS2+ or MS2-). Next an example of an MFQL script identifying
+  PC with calculating the standard:
+
+	QUERYNAME = Phosphatidylcholine;
+	DEFINE PR = 'C[36..50] H[30..200] N[1] O[8] P[1]' WITH DBR = (1.5,7.5), CHG = 1;
+	DEFINE DietherPC = 'C44 H93 O6 N1 P1' WITH CHG = 1;
+	DEFINE DietherPE = 'C45 H95 O6 N1 P1' WITH CHG = 1;
+
+	IDENTIFY Phosphatidylcholine WHERE
+		
+		# marking
+		PR IN MS1+ WITH TOLERANCE = 4 ppm OR
+		DietherPC IN MS1+
+	  
+	SUCHTHAT
+		isEven(PR.chemsc[C]) AND
+		isStandard(DietherPC, "MS1+")
+	  
+	REPORT 
+		MASS = "%4.4f" % "(PR.mass)";
+		NAME = "PC [%d:%d]" % "((PR.chemsc)[C] - 8, (PR.chemsc)[db] - 1.5)";
+		PRECURINTENS = PR.intensity;;
+
+2.09.2008 
+
+- de-isotoping was implemented for MS mode. The algorithm uses the sum compositions
+  which where calculated with the used MFQL scripts. So, only molecular species of interest
+  are considered for the de-isotoping. The algorithm is the following:
+  1) sort MS spectrum increasingly.
+  2) for every mass m which has a chemical sum composition assigned:
+    3) look, if there is are masses i1, i2, i3 or i4 which could be isotopes, i.e.
+      is there a mass i1 = m + 1.0033,
+      is there a mass i2 = m + 2 * (1.0033), and so on ...
+    4) calcuate the isotopic distribution of m for only 13C. Isotopes of other elements
+       are so little for lipids, that we leave them beside. The distribution is binomial
+       with a probability that a 13C occurs of 0.01082 <insert citation here>.
+    5) substract calculated isotopic percentage from i1-4
+
+- de-isotoping of MS/MS was revisited. The algorithm is the following:
+  1) generate artificial PIS spectra P1, ..., Pn for fragments f1, ..., fn by collecting 
+     precursor masses which have f1 (f2, ..., fn) in thier MS/MS spectrum.
+  2) for every PIS spectrum Pi:
+    3) for every mass m in Pi:
+      4) look, if there is are masses i1, i2, i3 or i4 which could be isotopes
+      5) calculate the isotopica distribution of the neutral loss of m accoding to
+        <insert citation here>
+      6) subtract the calculated isotopic percentage from i1-4 
+
+1.10.2008
+
+- implemented a debugging window in the GUI.
+
+- GUI looks a bit more compact now
+
+- update of the merging algorithm (for *.mzXML import). Average masses are now calculated 
+  by intensity weighted average. This yields more accurate spectra.
+
+9.10.2008
+
+- switched off the cleaning procedure for *.mzXML imported files
+
+- if more than one sum composition is found for a precursor mass, it will be reported
+  ordered by identification error.
+
+- Bug with *.mzXML file, where only MS/MS spectra are given is fixed.
+
+23.10.2008
+
+- new Function: complementMasterScan. Switch it on with a checkbox on the
+  Run-Panel. It will produce the "complement MasterScan" of the current
+  query, i.e. a MasterScan with all peaks which where not identified in
+  the current run. It will be saved as <original MasterScan name>-complement.sc.
+
+- the purpose is do blind queries for unknown or not excpected sum compositions.
+
+18.11.2008
+
+- new Function: DEFINE allows now to define a list of same variables with
+  different names in one line. The user just writes a list:
+    old:
+      DEFINE FA1 = 'C[14..22] H[20..60] O[2] N[1]' WITH DBR = (0.0,6.0), CHG = 0;
+      DEFINE FA2 = 'C[14..22] H[20..60] O[2] N[1]' WITH DBR = (0.0,6.0), CHG = 0;
+      DEFINE FA3 = 'C[14..22] H[20..60] O[2] N[1]' WITH DBR = (0.0,6.0), CHG = 0;
+    new:
+      DEFINE (FA1, FA2, FA3) = 'C[14..22] H[20..60] O[2] N[1]' WITH DBR = (0.0,6.0), CHG = 0;
+    
+- new Function: DEFINE allow the definition of a list of fragments. For example:
+    DEFINE FA = ('C14 H27 O2 N1',
+                 'C14 H29 O2 N1',
+                 'C14 H31 O2 N1',
+                 'C15 H21 O2 N1',
+                 'C15 H31 O2 N1');
+
+2009-03-06
+
+- adding a new Variable to TypeMFQL: currVars, which carries the current variables
+  and is accessible for all functions
+- adding a new function: avg(), which calculates the average of the attribute (which
+  should be an intensity vector by now)
+
+2009-07-02
+
+- new function in the import which allows grouping the samples by letting the user put
+  them into group folders. This does only work for *.mzXML until now.
+
+2009-12-01
+
+* fixed massrange error when using masses in DEFINE
+* fixed problem with order of the *.mfql scripts in the Run Panel
+
+2009-12-03
+
+* implemented multiple charge recognition !NOTE!: it is to be tested!
+  Lipids with charge unequal to 1, 0 or -1 are not able to account to
+  isotopic correction.
+* upgrade of the Exception handling.
+
+

lpdxImportSettings_tutorial.ini

+[FAS_LTQ_30000MS1]
+msmsminoccupation = 0.5
+msresolutiondelta = -14
+msmsresolution = 300
+msmscalibration = 
+mscalibration = 
+msmassrange = (450,1200)
+mstolerance = 20 ppm
+timerange = (0,1500)
+msresolution = 30000
+msmstolerance = 0.3 Da
+selectionwindow = 0.5
+forcesinglecharge = 1
+msminoccupation = 0.5
+msmsthreshold = 1
+msmsmassrange = (50,2000)
+msthreshold = 6000
+msmsresolutiondelta = 1
+
+[FAS_MS1_100000_MS2_15000]
+msmsminoccupation = 0.5
+msresolutiondelta = -53.4
+msmscalibration = 
+mscalibration = 
+msmassrange = (450,1200)
+mstolerance = 5 ppm
+timerange = (0,3600)
+msresolution = 110000
+msmstolerance = 0.005 Da
+msthreshold = 400
+precursormassshift = 0
+selectionwindow = 0.5
+msmsresolution = 25000
+msminoccupation = 0.5
+msmsthreshold = 75
+msmsmassrange = (50,2000)
+forcesinglecharge = 1
+msmsresolutiondelta = -20
+
+[FAS_LTQ_7500MS1]
+msmsminoccupation = 0
+msresolutiondelta = -3
+msmsresolution = 7500
+msmscalibration = 
+mscalibration = 
+msmassrange = (450,1200)
+mstolerance = 75 ppm
+timerange = (0,1500)
+msresolution = 7500
+msmsmassrange = (50,2000)
+msmstolerance = 0.3 Da
+precursormassshift = 0
+selectionwindow = 0.5
+forcesinglecharge = 1
+msminoccupation = 0.5
+msmsthreshold = 1
+precursormassshiftorbi = 0
+msthreshold = 1
+msmsresolutiondelta = -3
+
+[FAS_LTQ_100000MS1]
+mscalibration = 718.5392, 852.5396
+timerange = (0,1500)
+msmsresolution = 300
+forcesinglecharge = 1
+msthreshold = 2500
+msmsresolutiondelta = 1
+msmsminoccupation = 0.5
+msresolutiondelta = -53.4
+msmsthresholdtype = absolute
+selectionwindow = 0.5
+msmsthreshold = 1
+msmsmassrange = (50,2000)
+msthresholdtype = absolute
+msmscalibration = 
+msmassrange = (450,1000)
+mstolerance = 5 ppm
+msresolution = 110000
+precursormassshift = 0
+msmstolerance = 0.3 Da
+msminoccupation = 0.5
+precursormassshiftorbi = 0
+
+[FAS_LTQ]
+msmsminoccupation = 0.5
+msresolutiondelta = 1
+msmsresolution = 300
+msmscalibration = 
+mscalibration = 
+msmassrange = (450,1200)
+mstolerance = 0.3 Da
+timerange = (0,1500)
+msresolution = 700
+msmsmassrange = (50,2000)
+msmstolerance = 0.3 Da
+precursormassshift = 0
+selectionwindow = 0.5
+forcesinglecharge = 1
+msminoccupation = 0.5
+msmsthreshold = 5
+precursormassshiftorbi = 0
+msthreshold = 100
+msmsresolutiondelta = 1
+
+[HCD_7500_7500]
+msmsminoccupation = 0
+msresolutiondelta = 0
+msmscalibration = 
+mscalibration = 
+msmassrange = (450,1200)
+mstolerance = 75 ppm
+timerange = (0,120)
+msresolution = 7500
+precursormassshiftorbi = 0
+msmstolerance = 75 ppm
+msthreshold = 1
+precursormassshift = 0
+selectionwindow = 0.5
+msmsresolution = 7500
+msminoccupation = 0
+msmsthreshold = 1
+msmsmassrange = (50,2000)
+forcesinglecharge = 1
+msmsresolutiondelta = 0
+

lpdxSCC.py

+#!/usr/bin/python
+
+import os, sys, math
+from optparse import OptionParser
+
+sys.path.append('..' + os.sep + 'lib')
+
+from lpdxDataStr import *
+from lpdxSCTools import *
+from lpdxTools import *
+from lpdxUITools import *
+from lpdxParser import *
+
+#  -c / --csv          : the comma separated file for output
+#  -d / --dump         : the scan dump file
+
+def main():
+
+	optParser = OptionParser(usage="Usage: lpdxSCC.py <subcommand> [options] [args]\n\n\
+Availible subcommands:\n\
+\t msc (mass to sum composition) [mass] [sf-constraint]\n\
+\t scm (sum composition to mass) [sum composition]\n\
+\t sfsc (sf-constraint to sum composition) [sum composition]\n\
+\t corrDP (compare 2 mass spectra with the dot-product correlation) [*.csv1] [*.csv2] [tolerance in ppm]\n\
+\t corrPC (compare 2 mass spectra with the Pearson correlation) [*.csv1] [*.csv2] [tolerance in ppm]\n")
+
+	optParser.add_option("-a", "--accuracy", dest="accuracy",
+                  help="Set accuracy for sum composition calculation. Default is 5 ppm")
+
+	(options, args) = optParser.parse_args()
+
+	# open input mfql file
+	if len(args) > 0:
+
+		if options.accuracy:
+				accuracy = 1000000 / float(options.accuracy)
+		else:
+			accuracy = 1000000 / 5
+
+		if not args[0] or not args[1]:
+			print "You forgot the arguments/subcommands!"
+		else:
+			if args[0] == "msc":
+				elscp = parseElemSeq(args[2])
+				rslt = lpdxSCTools.calcSFbyMass(float(args[1]), elscp, accuracy)
+				rsltlist = []
+				for i in rslt:
+					rsltlist.append((i.getWeight(), i))
+				#	rsltlist = sorted(rsltlist)
+				if rsltlist == []:
+					print "No sum composition found for %s with m/z %2.f" % (elscp, float(args[1]))
+				for mass, scp in rsltlist:
+					print "%.4f" % mass, scp, "error: %.4f" % (float(args[1]) - scp.getWeight())
+
+			elif args[0] == "scm":
+				print "Check, if you did not forget to add the charge!"
+				elscp = parseElemSeq(args[1])
+				rslt = elscp.getWeight()
+				print "Weight is:", rslt, "; Double Bounds are:", elscp.get_DB()
+
+			elif args[0] == "sfsc":
+				elscp = parseElemSeq(args[1])
+				for i in elscp.get_norangeElemSeq():
+					print i, i.getWeight()
+
+			elif args[0] == "corrDP":
+
+				res = 1000000 / float(args[3])
+
+				# open the spectra files
+				s1 = open(args[1], 'r')
+				spec1 = []
+				for line in s1.readlines():
+					spec1.append([float(line.split(',')[0]), float(line.split(',')[1])])
+				s1.close()
+				s2 = open(args[2], 'r')
+				spec2 = []
+				for line in s2.readlines():
+					spec2.append([float(line.split(',')[0]), float(line.split(',')[1])])
+				s2.close()
+
+				# match both spectra. The result are vectors VSpec1/2 with the same dimension
+				spec1.sort(cmp = lambda x, y: cmp(x[0], y[0]), reverse = False)
+				spec2.sort(cmp = lambda x, y: cmp(x[0], y[0]), reverse = False)
+
+				vSpec1 = []
+				vSpec2 = []
+				
+				sum = 0
+				peak = 0
+				while peak < max(len(spec1), len(spec2)) - 1:
+					
+					t = spec1[peak][0] / res
+
+					if spec2[peak][0] - t < spec1[peak][0] and spec1[peak][0] < spec2[peak][0] + t:
+						if peak < len(spec1) - 1 and peak < len(spec2) - 1:
+							if not (spec2[peak][0] - t < spec1[peak + 1][0] and spec1[peak + 1][0] < spec2[peak][0] + t):
+								vSpec1.append(spec1[peak])
+								vSpec2.append(spec2[peak])
+
+							else:
+								if abs(spec1[peak][0] - spec2[peak][0]) > abs(spec1[peak + 1][0] - spec2[peak][0]):
+									vSpec1.append(spec1[peak])
+									vSpec2.append([0.0, 0.0])
+									spec2.insert(peak, [0.0, 0.0])
+
+								else:
+									vSpec1.append(spec1[peak])
+									vSpec2.append(spec2[peak])
+
+					elif spec1[peak][0] < spec2[peak][0]:
+						vSpec1.append(spec1[peak])
+						vSpec2.append([0.0, 0.0])
+						spec2.insert(peak, [0.0, 0.0])
+
+					elif spec1[peak][0] > spec2[peak][0]:
+						vSpec1.append([0.0, 0.0])
+						vSpec2.append(spec2[peak])
+						spec1.insert(peak, [0.0, 0.0])
+
+					sum += abs(spec1[peak][0] - spec2[peak][0])
+					peak += 1
+
+				# calc mean of vectors vSpec1/2 (which is the expectation value)
+				sumInt = 0.0
+				for p in vSpec1:
+					sumInt += p[1]
+				meanVSpec1 = sumInt / len(vSpec1)
+
+				sumInt = 0.0
+				for p in vSpec2:
+					sumInt += p[1]
+				meanVSpec2 = sumInt / len(vSpec2)
+
+				# substract mean from the intensities of vSpec1/2 to center the 2 vectors
+				for p in vSpec1:
+					p[1] = p[1] - meanVSpec1
+
+				for p in vSpec2:
+					p[1] = p[1] - meanVSpec2
+				
+				# calc geometrical length of vectors vSpec1/2
+				sum = 0.0
+				for p in vSpec1:
+					sum += p[1] * p[1]
+				lenghtVSpec1 = math.sqrt(sum)
+
+				sum = 0.0
+				for p in vSpec2:
+					sum += p[1] * p[1]
+				lenghtVSpec2 = math.sqrt(sum)
+
+				# calc the dot product
+				sum = 0.0
+				for index in range(len(vSpec1)):
+					sum += vSpec1[index][1] * vSpec2[index][1]
+
+				# calc the ankle
+				phi = math.acos(sum / (lenghtVSpec1 * lenghtVSpec2))
+				
+				print 'dot product: %.4f, similarity: %.2f %%' % (sum, 100 - (phi * 100) / math.pi)
+
+			elif a