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LCMS analysis with envipy Uwe Schmitt Scientific IT Services ETH - - PowerPoint PPT Presentation
LCMS analysis with envipy Uwe Schmitt Scientific IT Services ETH - - PowerPoint PPT Presentation
LCMS analysis with envipy Uwe Schmitt Scientific IT Services ETH Zrich | 28.09.16 | 1 Uwe Schmitt envipy graphical user interface | 28.09.16 | 2 Uwe Schmitt envipy graphical user interface principles The graphical user interface:
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The graphical user interface: § configures and controls workflow execution § observes running workflow § allows interactive inspection of intermediate and final results § allows export of intermediate results to csv
envipy graphical user interface principles
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envipy sample inspector
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envipy peaklist inspection
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§ Multi core execution § Always runs workflow from the beginning
§ Caching of intermediate results
§ Decoupled from user interface
§ Headless execution on servers
§ Every step attaches new information to peaks
envipy workflow manager
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§ enviPick: fast peak picker for high resolution data § Handles .raw, .mzML and .mzXML files § Tested for Orbitrap and TOF data § Assigns MS/MS data from data dependent measurements
envipy workflow: peak picking
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§ enviPat : fast and machine dependent calculation of isotope patterns § Computes patterns of:
§ Internal standards § Targets § Suspects
envipy workflow: isotope pattern computations
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§ Reuses code from enviMass § m/z recalibration § rt alignment § Requires internal standards
envipy workflow: recalibration / alignment
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§ Asymmetric and symmetric peak shape models § Raw area computation
envipy workflow: peak shape fitting
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§ Screening routine from enviMass § Matches precalculated isotope patterns to picked peaks § Relaxed rt tolerances for suspect screening
envipy workflow: screening
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§ Based on nontarget R package § Assigns component id based on isotope, adduct and homologue relations § Plus: m0 computation
envipy workflow: component assignment
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§ groups peaks over samples
§ „greedy“ algorithm for speed + reassignment
§ from enviMass: scoring of interesting time series § Clustering of time series § Event detection in time series
envipy workflow: time series analysis
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§ based on affinity propagation algorithm § No prior knowledge about number of expected clusters § Similarity: correlation without mean normalization
envipy time series clustering
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§ smoothing + detection of local maxima produce „seeds“ § Fit to gaussian profiles
envipy workflow: event detection
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envipy workflow: MS/MS inspection
- The envipy peak picker extracts MS2 spectra from data
dependent MS2 scans
- MS2 spectra can be exported as well as inspected in the
envipy graphical user interface
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§ Selected peaks with assigned MS/MS spectra can be identified by querying http://msbi.ipb-halle.de/MetFrag/ webservice
envipy MS/MS based identification
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§ Installer for Windows (includes R + Python)
§ Requires 64bit Windows 7 or newer § Faster with SSD drives
§ Short release cycle, expect updates. § Runs on Linux + Mac OS X (contact me for details)
envipy: availabilty
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§ Mostly Python 2.7 § Reuses existing R code
§ Starts R in separate process § Communication over pipes
§ Some speed optimizations in C (from Cython) § Many functionalities from emzed (http://emzed.ethz.ch) § Tables partly based on HDF5 § PyQt4 for the graphical user interface
envipy: technical details
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Heinz Singer (EAWAG) heinz.singer@eawag.ch Uwe Schmitt (ETH Zürich) uwe.schmitt@id.ethz.ch
Contact us
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