ISOQuant 1.6
Help pages
Jörg Kuharev, Pedro Navarro and Stefan Tenzer
September 29, 2016
NOTE:
Some contents of this document are not
aligned with the current software version.
An update is coming soon …
Table of contents
1 Description
ISOQuant is an academically developed, integrated bioinformatics pipeline for in-depth evaluation and statistical data analysis
of data-independent acquisition (MSE and IMS-MSE) based label-free quantitative proteomics that improves data reliability
and quality by application of well-established and novel analysis methods.
1.1 About ISOQuant
One of the main bottlenecks in the evaluation of label-free quantitative proteomics experiments is the often cumbersome data
export for in-depth data evaluation and analysis. Data-independent, alternate scanning LC-MS (MSE/HDMSE/UDMSE)
peptide fragmentation data can currently only be processed by Waters PLGS software (and by recently introduced Progenesis
QI for Proteomics (Nonlineare Dynamics / Waters)).

The bioinformatics pipeline ISOQuant directly accesses xml files from the PLGS root folder and browses for relevant data from
a label-free Expression Analysis project (quantification analyses, sample descriptions, search results) for fully
automated import into a MySQL database. EMRTs are subjected to multidimensional LOWESS-based intensity
normalization and annotated by matching exact masses and aligned retention times of detected features with
highest scoring peptide identification data from associated workflows. Based on the annotated cluster table,
ISOQuant calculates absolute in-sample amounts with an integrated protein isoform quantification method,
utilizing average intensities of proteotypic peptides for the partitioning of non-unique peptide intensities between
protein isoforms. All data is stored in a local MySQL based database that can be queried directly by experienced
users.
1.2 Citing ISOQuant
ISOQuant has been developed since 2009. We introduced the basic principles of ISOQuant analysis to the community as part of
a Nature Methods articles 2014 (Distler et al. 2014).
Distler, U., Kuharev, J., Navarro, P., Levin, Y., Schild, H., & Tenzer, S. (2014). Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics. Nature Methods, 11(2), –167170. http://doi.org/10.1038/nmeth.2767
Please cite the mentioned publication, when using ISOQuant to produce publication data or referencing to ISOQuant in other
context. Use the following BibTeX code to import into the reference manager of your choice:
@article{distler_drift_2014, title = {Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics}, author = {Distler, Ute and Kuharev, Jörg and Navarro, Pedro and Levin, Yishai and Schild, Hansjörg and Tenzer, Stefan}, journal = {Nature Methods}, volume = {11}, issn = {1548-7091}, url = {http://www.nature.com/nmeth/journal/v11/n2/full/nmeth.2767.html}, doi = {10.1038/nmeth.2767}, month = feb, year = {2014}, pages = {167--170} }
1.3 ISOQuant workflow
The data analysis workflow (see fig. 1) consists of raw data preprocessing using vendor software PLGS and the downstream
analysis using ISOQuant. In our data analysis workflow, PLGS is used for the initial signal processing as well as for peptide and
protein identification. Before automatically importing PLGS results into a relational database (MySQL), ISOQuant allows to
change the structure of underlying PLGS project and in this way to redesign the label-free experiment. The ISOQuant data
analysis workflow is composed of multiple dedicated algorithms. At different stages of analysis, data is filtered on peptide and
protein level based on user defined criteria (identification score and type, sequence length, replication rate, FDR threshold, etc.)
to ensure a constant level of high data quality for all runs in the project. The retention time alignment procedure
corrects non-linear retention time distortions between LC-MS runs of the experiment (Podwojski et al. 2009).
To group corresponding features from different runs of the experiment, exact mass and retention time pairs
(EMRT) extended by ion mobility values are evaluated using the density based clustering algorithm DBSCAN
(Ester et al. 1996). Resulting feature clusters are annotated by evaluation of consent peptide identifications and
identification probabilities. The feature cluster annotation approach transfers peptide identifications between runs and
reduces missing values increasing the reproducibility of data analysis. Resolving ambiguous peptides-in-proteins
networks, the protein homology filtering algorithm reduces side effects of the protein inference problem (Nesvizhskii
and Aebersold 2005). The multi-dimensional feature intensity normalization algorithm reduces the effects of
technical variance between independently acquired LC-MS runs of the experiment and increases the reproducibility
of quantification. Finally, ISOQuant uses a derivative of the Top3 method (Silva et al. 2006) for the absolute
protein quantification and exports analysis results to multiple output formats for subsequent interpretation.
Alternatively to ISOQuant, the commercial software Progenesis QI for proteomics (Nonlinear Dynamics / Waters)
or the freely available R-package synapter (Bond et al. 2013) could also be used to analyze MSE data. In a
recent study, we tested Progenesis QIP, synapter and ISOQuant for the performance of protein identification
and the label-free quantification based on the analysis a complex metaproteome sample set (Kuharev et al.
2015).
1.4 Known problems
- On some Windows Vista or Window 7 machines ISOQuant can not write its configuration file. In this case
you have to execute ISOQuant with administrative privileges or correct file system permissions for ISOQuant
installation folder. This is not an ISOQuant issue, sometimes Windows messes up file system permissions by
using different and inconsistent user privileges at different time points.
- Analysis of high complexity datasets may take a while.
- Importing (and analyzing) large projects or runs of high complexity may cause out of memory errors, make
sure your PC has enough memory and assign more Heap-Space to Java Virtual Machine for running ISOQuant
Application.
- ISOQuant may fail to import and process data if some PLGS project files are broken.
- Running MySQL on Mac OSX machines significantly decreases the performance of ISOQuant. This is a known
problem of MySQL not of ISOQuant. Use Windows or Linux machines and/or install MariaDB instead of MySQL
for better performance.
2 Program requirements
ISOQuant will only work properly if the system for running ISOQuant meets following requirements.
- Operating System: Windows, Mac OS X or Linux
- PLGS root folder with projects containing processed MSE/HDMSE/UDMSE data is accessible (tested PLGS
versions: 2.3/2.4/2.5/3.0)
- at least 3GB RAM
- Java Runtime Environment version 1.6.0 (or newer) is installed and works properly
- MySQL Server 5.1 (or newer) is installed and running on local machine or network. (tested MySQL versions: 5.1
- 5.5)
- MySQL configuration file options for heap and temporary tables have large values as shown in following listing.
In some cases it may be useful also to increase the size of MySQL thread stack.
max_heap_table_size = 2048M tmp_table_size = 2048M thread_stack = 256K
Depending on your operating system and MySQL-Version the configuration file is named either my.ini or my.cnf and its
location may vary.
Following listing shows an example of MySQL configuration section [mysqld] working for us on MacOSX
10.6.8 Snow Leopard running MySQL Server from XAMPP 1.7.3, the configuration file is located in
/Applications/XAMPP/xamppfiles/etc/my.cnf
[mysqld] port = 3306 socket = /Applications/XAMPP/xamppfiles/var/mysql/mysql.sock skip-locking key_buffer = 128M max_allowed_packet = 16M table_cache = 128 sort_buffer_size = 32M read_buffer_size = 8M read_rnd_buffer_size = 8M net_buffer_length = 64K thread_stack = 256K myisam_sort_buffer_size = 32M tmpdir = /Applications/XAMPP/xamppfiles/temp/ max_heap_table_size = 2048M tmp_table_size = 2048M sync_frm = 0 skip-sync-frm=OFF
Do not forget to restart MySQL after editing its configuration!
Expert note:
If you get some “out of memory” errors while running ISOQuant please make sure you start the application by giving Java
Virtual Machine a chance to have enough memory space by command line options, e.g.
java -Xms256m -Xmx2G -jar ISOQuant.jar
this command will assign up to 2 GBs (parameter -Xmx2G) RAM to the virtual machine and run the ISOQuant application.
For some very complex datasets, it may be useful to increase the -Xmx value e.g. -Xmx48G to allow the virtual machine to
access 48 GBs of RAM.
3 Data requirements
3.1 Rawdata type
ISOQuant has been developed for Waters QTOF LC-MSE and Waters Synapt G2/G2-S LC-MSE/HDMSE/UDMSE
instrument data. At this time, only 1D-UPLC data is fully supported, 2D-UPLC support will be included in later
releases.
3.2 Database searches
At the moment, ISOQuant can only process Ion Accounting workflows (MSE/HDMSE/UDMSE-data). Classical DDA-Type
experiments are not yet supported.
3.3 Project design
There are two different ways to use ISOQuant either as an extension to PLGS Expression Analysis or completely replacing
it.
3.3.1 Expression analysis
You can use your experiment design given by running PLGS Expression analysis. As a prerequisite for this approach, a
complete expression analysis of multiple samples and replicates is required. In PLGS, please select autonormalization of
samples for generating EMRT and protein tables. Both EMRT and Protein tables have to be created during
the expression analysis. Each expression analysis within a PLGS project can be selected during processing in
ISOQuant. ISOQuant will create separate databases for storing the data of every single processed expression
analysis.
3.3.2 ISOQuant Project Designer
As an alternative to the PLGS Expression analysis, you can use the simple and efficient built-in Project Designer described in
section 4.6.
3.4 Peak detection/alignment/clustering
ISOQuant is based on peak detection, alignment and clustering of data performed by PLGS. We are aware of some peak
splitting/alignment/clustering issues in PLGS. Therefore, we have have spent a lot of time to develop own methods for these
tasks. You can either keep EMRT alignment/clustering results or let ISOQuant do the complete analysis. For details see future
publications.
4 GUI and control elements
4.1 Main view
Figure 2 shows the main view of ISOQuant. User interaction is applied by following control elements:
- List of projects found in PLGS root folder
- List of projects from ISOQuant database
- Button choose PLGS root folder
- Button choose database
- Button restore project from file
- Button find projects
- Button edit configuration
- Button show help window
- Button shutdown application
4.2 Project Finder
The Project Finder window as shown in figure 3 makes possible to search projects lists for projects by substrings of project
titles and regular expressions matching to them. In case your search string matches to one or multiple projects the Project
Finder will mark these projects by selecting them in both file system and database projects lists. The Project
Finder window can be accessed by clicking the button find projects from the tool bar on the main application
window.
4.3 Context menu for PLGS projects in file system
Advanced options for each project from PLGS root folder are available from a context menu like shown in figure
4:
- find in database
finds selected projects in the list of projects from database by comparing their titles and select them if such
projects exist.
- about project
shows additional information about selected projects.
- import and process
allows to select one of predefined processing queues and starts processing selected projects using selected
processing queue.
4.4 Context menu for projects in database
Advanced options for each project from database are available from a context menu like shown in figure 5
- find in file system
finds selected projects in the list of projects from file system by comparing their titles and select them if such
projects exist.
- show info
shows additional information about selected projects.
- rename project
rename selected projects.
- reprocess
reprocess a project starting from user selected processing stage. All needed subsequent processing steps are
automatically applied.
- create report
generate on of implemented report types.
- export to file
export selected projects from database to (backup) files which can be imported by other ISOQuant instances.
- remove from database
removes selected projects from database.
Note: We continue to develop and improve ISOQuant. The context menus could look different in different software releases.
The elements in context menus are subject of change, their number and order may vary.
4.5 Expression Analysis Selector
In some cases a single PLGS project contains multiple defined Expression Analyses. Some processing queues work with project
structures provided by PLGS Expression Analyses. These queues start with the selection of contained expression analyses for
each selected project. The selection of expression analyses is done within the Expression Analysis Selector window as shown in
figure 6 by activating the checkboxes from the column include for each Expression Analysis to be processed. The Expression
Analysis Selector shows each previously selected project in its own tab pane. ISOQuant generates a separate database for each
selected Expression Analysis.
4.6 Project Designer
ISOQuant allows to create user defined project structures and then to process these newly structured project data. User
defined project structures are created using the Project Designer as shown in figure 7. The Project Designer
window shows the PLGS project structure on the left and the user defined structure on the right. A new project
structure is created by drag and drop based moving of workflows, samples or groups between left and right
structure trees. Additionally to drag and drop actions, right click context menus are available on the right side
of Project Designer enabling editing and removing of selected structure elements. On the top of window you
can switch between Project Designer panes to restructure each previously selected PLGS project. Processing
of designed project can be initiated by clicking the button Ok on the bottom of window or can be aborted
by clicking the Cancel button. While processing ISOQuant generates a separate database for each designed
project.
5 ISOQuant configuration
ISOQuant stores parameters for program behavior and data processing algorithms in a single configuration file named
isoquant.ini. This configuration file is located in the folder you have installed ISOQuant to. For resetting parameters to
default values just close ISOQuant then delete or rename the configuration file (or single parameter lines) and start ISOQuant
again. If no configuration file can be found on application start a new one will be created using default parameter
values.
Do not change the configuration file unless you know what you do!
ISOQuant configuration can be edited from ISOQuant Configuration Editor accessible from graphical user interface.
Configuration Editor allows to edit parameters and also export/import configuration settings to/from files.
Two configuration files are provided with ISOQuant installation packages:
- isoquant_high_confidence.ini example configuration file for high confidence quantitative analyses
- isoquant_maxID.ini example configuration file for discovery proteomics experiments
These files can be imported into ISOQuant from Configuration Editor or manually copied to isoquant.ini
file.
6 Configuration guide
This chapter lists and describes the main set of available parameters. The number of parameters, their names and the
behavior of application caused by parameters are subjects of change because we actively work on improving
ISOQuant and underlying methods. Thus the following list of parameters may be incomplete and/or up to
date.
6.1 EMRT cluster annotation
6.1.1 Peptide identification filter
User can define minimum reliability criteria for a peptide to be used for ISOQuant processing. Note: Only peptides passing this
filter will be used for all further analysis steps!
Peptide types
Peptides identified in first pass of PLGS database search (type: PEP_FRAG_1) are generally accepted. Setting one of
following parameters to TRUE will configure ISOQuant to accept additional peptide types.
- process.identification.peptide.acceptType.IN_SOURCE=false
- process.identification.peptide.acceptType.MISSING_CLEAVAGE=false
- process.identification.peptide.acceptType.NEUTRAL_LOSS_H20=false
- process.identification.peptide.acceptType.NEUTRAL_LOSS_NH3=false
- process.identification.peptide.acceptType.PEP_FRAG_2=false
- process.identification.peptide.acceptType.PTM=false
- process.identification.peptide.acceptType.VAR_MOD=false
Following additional filtering criteria are used as thresholds to select peptides used for further processing steps.
- process.identification.peptide.minReplicationRate=2.0
minimum acceptable peptide replication rate based on absolute number of runs in which every peptide (as
sequence-modifier tuple) was identified.
- process.identification.peptide.minScore=1.0
minimum acceptable PLGS peptide identification score.
- process.identification.peptide.minOverallMaxScore=1.0
minimum acceptable value of highest PLGS identification score of a peptide reached in any run of a project. For a
peptide detected in multiple runs its maximum reached score has to hit this score to be accepted for annotation.
Increasing score will reduce the number of peptides used for EMRT cluster annotation and not necessarily the
overall protein quantification quality. Recommended values are between 0.0 and 5.0
- process.identification.peptide.minSequenceLength=6
minimum acceptable peptide sequence length. Recommended value is 6 or more.
6.1.2 Annotation mode
- process.annotation.useSharedPeptides=all
- all this is the normal case.
- unique only unique peptides are used for further processing, this option removes all shared peptides from
peptides-in-proteins relation instead of protein homology filtering solving the problem of protein inference
in a very radical way.
- razor only razor and unique peptides are used for further processing, this option removes all shared peptides
from peptides-in-proteins relation after protein homology filtering. Razor and unique peptides are highly
reliable for protein quantification because their intensity can be directly assigned to a protein.
6.1.3 Annotation conflict filter
There are cases when multiple peptide identifications map to a single EMRT cluster.
- process.annotation.peptide.sequence.maxCountPerEMRTCluster=1
acceptable number of different peptide sequences (remaining after filtering peptides) allowed to annotate a
single cluster. The annotation process will skip ambiguous clusters if this value is set to 1. For bigger values,
the annotation conflicts are resolved by annotating clusters with the peptide having the highest sum of PLGS
identification scores in this cluster.
6.1.4 Homology / isoform and FDR filtering
- process.annotation.protein.resolveHomology=true
Should proteins be filtered for (peptide sequence based) homology/isoform. Only one of detected homologue
proteins will be reported.
- process.annotation.peptide.maxFDR=0.01
maximum accepted false discovery rate level for peptides. (value 0.01 means maximum 1% FDR)
6.2 Data preprocessing
- process.peptide.deplete.PEP_FRAG_2=false
should PEP_FRAG_2 peptides be completely removed from database.
- process.peptide.deplete.CURATED_0=false
should CURATED=0 peptides be completely removed from database. If true, low-quality peptide IDs are
removed.
6.3 EMRT table creation
- process.emrt.minIntensity=1000
peaks having intensities below this limit are assumed to be noise and will not appear in EMRT table
- process.emrt.minMass=500
peaks having masses below this limit are assumed to be noise and will not appear in EMRT table
6.4 Retention time alignment
- process.emrt.rt.alignment.match.maxDeltaMass.ppm=10.0
maximum accepted mass difference between two signals to be assumed as matching for retention time alignment.
- process.emrt.rt.alignment.match.maxDeltaDriftTime=2.0
maximum accepted drift time (ion mobility) difference between two signals to be assumed as matching for
retention time alignment. This value is ignored for non-ion-mobility projects. Large value, e.g. 200 will disable
the effect of ion mobility on the time alignment.
- process.emrt.rt.alignment.minIntensity=1000
only peaks with intensity over this threshold value are considered for the retention time alignment procedure
- process.emrt.rt.alignment.minMass=800.0
only peaks with mass over this threshold value are considered for the retention time alignment procedure
- process.emrt.rt.alignment.normalizeReferenceTime=false
if true, resulting reference times are adjusted to median distortions at every time point.
- process.emrt.rt.alignment.maxProcesses=4
the maximum number of concurrent retention time alignment processes. We recommend values between 1 and
the number of available CPU cores. Default value is set to ½ of the number of CPU cores
- process.emrt.rt.alignment.maxProcessForkingDepth=4
the maximum multithreading depth for each retention time alignment process
6.5 EMRT clustering
- process.emrt.clustering.distance.unit.mass.ppm=6.0
minimum mass based distance between clusters. This is an instrument dependent parameter, e.g. 6 ppm is a
good value for Waters Synapt G2/G2-S and 10-12 ppm for Waters Q-TOF Premier and Synapt G1
- process.emrt.clustering.distance.unit.time.min=0.2
minimum retention time based distance between clusters. This is a LC gradient length and peak width dependent
parameter, good values are observed to be between 0.06 and 0.2, we recommend to try 0.08, 0.12, 0.16, 0.2; please
report which values would work for your setup at which gradient length
- process.emrt.clustering.distance.unit.drift.bin=2.0
menimum drift time based distance between clusters. This is an instrument and also project setup dependent
parameter, e.g. for pure IMS projects containing G2 or G2S data, we recommend a value of 2.0. This value is
ignored for non-ion-mobility projects. Large value, e.g. 200 will disable the effect of ion mobility on the EMRT
clustering.
- process.emrt.clustering.dbscan.minNeighborCount=2
the minimum cluster size (except of noise) and also the minimum required number of peaks inside the reachability
radius for cluster expansion. This is a DBSCAN specific parameter and should be increased for big projects. The
value of this parameter also depends on used clustering distance units.
- process.emrt.clustering.maxProcesses=8
the maximum number of concurrent clustering processes. For best performance is reached by setting this value
to the number of available CPU cores. Default value is set by the estimated number of available CPU cores.
6.6 Peak intensity normalization
6.7 Protein quantification
6.7.1 Peptide filtering
Peptides for protein quantification may be filtered by their type and minimum reached score of a peptide per EMRT cluster.
PEP_FRAG_1 peptides are always accepted. User may decide to accept additional peptide types for quantification.
Allowing additional types may result in higher number of quantified proteins but also may affect the quality of
quantification. Note: This peptide filtering step can not recover peptides not passed the peptide identification
filter.
- process.quantification.peptide.acceptType.IN_SOURCE=false
- process.quantification.peptide.acceptType.MISSING_CLEAVAGE=false
- process.quantification.peptide.acceptType.NEUTRAL_LOSS_H20=false
- process.quantification.peptide.acceptType.NEUTRAL_LOSS_NH3=false
- process.quantification.peptide.acceptType.PEP_FRAG_2=false
- process.quantification.peptide.acceptType.PTM=false
- process.quantification.peptide.acceptType.VAR_MOD=false
- process.quantification.peptide.minMaxScorePerCluster=5.0
6.7.2 Protein quantification setting
- process.quantification.absolute.standard.entry=ENO1_YEAST
entry of protein used as quantification standard
- process.quantification.absolute.standard.fmol=50.0
amount of quantification standard protein
- process.quantification.absolute.standard.used=true
is a quantification standard protein used at all?
- process.quantification.topx.degree=3
maximum number of peptides for quantifying single proteins
- process.quantification.maxProteinFDR=0.01
maximum accepted false discovery rate level for reported proteins. (value 0.01 means 1% FDR level)
- process.quantification.minPeptidesPerProtein=1
a protein is reported only if it can be quantified by using as minimum this number of peptides
6.8 Application behavior
6.8.1 User interface
- setup.ui.captureConsoleMessages=true
show Java console messages inside ISOQuant message panel
- setup.ui.location.left=560
ISOQuant window location, pixels from left
- setup.ui.location.top=360
ISOQuant window location, pixels from top
- setup.ui.size.height=480
ISOQuant window height
- setup.ui.size.width=800
ISOQuant window width
- setup.ui.promptForExit=true
ask user on closing window
- setup.ui.iconScaleFactor=1.0
scale original icon sizes by this factor, may be useful on unusually small or large screens.
6.8.2 Data source
- setup.db.autoLoad=false
should application connect database on start
- setup.db.host=localhost
the MySQL database host name or ip address. Default tcp port number for MySQL servers is 3306. If your
MySQL server is running using an other tcp port number, its host name has to be expanded by adding ‘:’ and
the correct port number, e.g. localhost:3307 for MySQL server running on local machine and listening at tcp
port 3307
- setup.db.user=root
MySQL user name
- setup.db.pass=
MySQL users password
- setup.plgs.root.showEACount=true
should number of PLGS expression analyses be determined and shown
- setup.plgs.root.showFSSize=false
should file system size of a projects folder be determined and shown
- setup.plgs.root.dir=/Volumes/RAID0/PLGS2.5/root
path of last selected PLGS root folder
- setup.plgs.root.autoLoad=false
should application read last used root folder on start
6.8.3 Report
- setup.report.dir=/Volumes/RAID0/reports
path of last selected report output folder.
- setup.report.csv.columnSeparator=‘,’
column separator string (enclosed in ’ or "), usually either ‘,’ or ‘;’
- setup.report.csv.decimalPoint=‘.’
decimal point string (enclosed in ’ or "), usually either ‘.’ or ‘,’
- setup.report.csv.textQuote=’“’
string for quoting text blocks, usually
- setup.report.mzidentml.DBNCBITaxID=
- setup.report.mzidentml.DBOrganismScientificName=
- setup.report.mzidentml.DBversion=
- setup.report.mzidentml.researcherFirstName=John
- setup.report.mzidentml.researcherLastName=Doe
- setup.report.mzidentml.researcherOrganization=Uni-Mainz
- setup.report.xls.showAbsQuantFMOLUG=true
create an extra sheet for absolute protein quantity in femtomoles per microgram.
- setup.report.xls.showAbsQuantFMOL=true
create an extra sheet for absolute protein quantity in femtomoles.
- setup.report.xls.showAbsQuantNG=true
create an extra sheet for absolute protein quantity in nanograms.
- setup.report.xls.showAbsQuantPPM=true
create an extra sheet for absolute protein quantity in parts per million.
- setup.report.xls.showAllProteins=false
create an extra sheet for some PLGS based proteins details.
- setup.report.xls.showRTAlignment=false
create an extra sheet for retention alignment results.
Generated excel sheets are limited to maximum 65536 rows when using old XLS (Excel 97/2000/2003) format due to its
technical limitations. In case of doubt, please use XLSX format for creating ISOQuant reports.
7 End-user license agreement
7.1 External components
ISOQuant relies on external components (re)distributed under different conditions. By using ISOQuant you need to
agree to terms and conditions of third party libraries and software included in ISOQuant or needed for running
ISOQuant.
ISOQuant uses following external Java libraries:
|
Library | Version | License
Type | Purpose |
|
| |
| | | |
|
JDOM | 1.1.3 | BSD
License | XML
handling |
Tagsoup | 1.2.1 | Apache
v2.0 | XML
parsing |
MySQL
Connector/J | 5.1.13 | GPLv2
with
FOSS
Exception | database
communication |
JSiX | 1.0 | BSD
License | Java
extensions |
POI | 3.8 | Apache
v2.0 | spreadsheet
file
creation |
DOM4J | 1.6.1 | BSD
License | POI
dependency |
StAX | 1.0.1 | Apache
v2.0 | POI
dependency |
|
| |
| |
| |
| |
| |
Binary versions of these libraries are repackaged into and redistributed with ISOQuant software package according to their
license conditions. Please find original licenses as part of ISOQuant package.
Furthermore, ISOQuant relies on external environmental software being not a part or a component of ISOQuant but needed to
run it:
- Operating System
- Java Virtual Machine
- MySQL 5.1 compatible database engine (e.g. MySQL Server: http://www.mysql.com/ or MariaDB:
http://mariadb.org/)
- Waters ProteinLynx Global Server
Please pay attention to terms and conditions arising from any software usage in any way related to ISOQuant.
7.2 ISOQuant license agreement
ISOQuant binaries and source code are available under conditions of BSD license as follows:
ISOQuant - integrated solution for LC-MS based label-free protein quantification Copyright (c) 2009 - 2013, JOERG KUHAREV and STEFAN TENZER All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. All advertising materials mentioning features or use of this software must display the following acknowledgement: This product includes software developed by JOERG KUHAREV and STEFAN TENZER. 4. Neither the name "ISOQuant" nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.  IN NO EVENT SHALL JOERG KUHAREV BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
7.3 Disclaimer in plain language
- ISOQuant is an academically developed freely available software implemented using only freely available
technologies
- By using ISOQuant you automatically agree to terms and conditions of third party libraries and software included
in ISOQuant or needed for running ISOQuant
- We provide ISOQuant as is, without any kind of warranty
- You are using ISOQuant at your own risk, we are not responsible for any kind of damage, errors or data loss
produced in any way related to ISOQuant.
8 About developers
ISOQuant is developed by
UNIVERSITÄTSMEDIZIN
der Johannes Gutenberg-Universität
Institut für Immunologie
Core Facility für Massenspektrometrie
Germany, Mainz, September 29, 2016
_________________________________________________
References
Bond, Nicholas J, Pavel V Shliaha, Kathryn S Lilley, and Laurent Gatto. 2013. “Improving Qualitative and Quantitative
Performance for MS E-Based Label-Free Proteomics.” Journal of Proteome Research.
Distler, Ute, Jörg Kuharev, Pedro Navarro, Yishai Levin, Hansjörg Schild, and Stefan Tenzer. 2014. “Drift Time-Specific
Collision Energies Enable Deep-Coverage Data-Independent Acquisition Proteomics.” Nature Methods 11 (2): 167–70.
doi:10.1038/nmeth.2767.
Ester, Martin, Hans P. Kriegel, Jorg Sander, and Xiaowei Xu. 1996. “A Density-Based Algorithm for Discovering
Clusters in Large Spatial Databases with Noise.” In Second International Conference on Knowledge Discovery and
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Interests
Creativity
Guitarist
Improv Actor (beginner)
Podcast host and editor
Blogger
Video game streamer
Short fictions writer
Pop culture fan
Comics (US, mangas, french)
Novels (fantasy, sci-fi…)
Movies and TV Series
Gamer
Video games
Board games
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Role-playing games
Web Development
http://www.codeheroics.com
Karakuri - Karaoke app
Mobile rhythm game
https://www.nope.wtf - HTML5 social game
Speaker @ ParisJS, TakeOff Conf


Water Bills Are About To Go Down
Adelaide household water bills are reportedly set to go down by an average of $26 a year.
According to The Advertiser:
The plan, now open for public comment, aims to cut water prices by 1.8 per cent and sewerage by 3.2 per cent — delivering a $26 cut to this financial year’s average annual bill of $1258 for a city household.
“Through our ongoing focus on smart technologies, maximising productivity of our assets, and containing the costs of an expanded capital program, we’re now leading the efficiency frontier in Australia and have made the biggest efficiency gains of our peer water utilities,” SA Water manager of customer delivery Kerry Rowlands said.
“We’re now in a position to deliver a real price decrease, while still covering all of the fundamentals that protect public health and the environment, as well as pursuing initiatives that will deliver sustainable experience improvements for our customers.”