Commit 1694ccba authored by Edlira Nano's avatar Edlira Nano
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removed translation + changed doc

git-svn-id: https://subversion.renater.fr/masschroq/trunk@2097 e4b6dbb4-9209-464b-83f7-6257456c460c
parent 986052fe
......@@ -519,45 +519,7 @@ under CC-BY-SA on the same site at any time before August 1, 2009,
provided the MMC is eligible for relicensing.
\begin{center}
{\Large\bf ADDENDUM: How to use this License for your documents\par}
\phantomsection
\addcontentsline{toc}{section}{ADDENDUM: How to use this License for your documents}
\end{center}
To use this License in a document you have written, include a copy of
the License in the document and put the following copyright and
license notices just after the title page:
\bigskip
\begin{quote}
Copyright \copyright{} YEAR YOUR NAME.
Permission is granted to copy, distribute and/or modify this document
under the terms of the GNU Free Documentation License, Version 1.3
or any later version published by the Free Software Foundation;
with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
A copy of the license is included in the section entitled ``GNU
Free Documentation License''.
\end{quote}
\bigskip
If you have Invariant Sections, Front-Cover Texts and Back-Cover Texts,
replace the ``with \dots\ Texts.'' line with this:
\bigskip
\begin{quote}
with the Invariant Sections being LIST THEIR TITLES, with the
Front-Cover Texts being LIST, and with the Back-Cover Texts being LIST.
\end{quote}
\bigskip
If you have Invariant Sections without Cover Texts, or some other
combination of the three, merge those two alternatives to suit the
situation.
If your document contains nontrivial examples of program code, we
recommend releasing these examples in parallel under your choice of
free software license, such as the GNU General Public License,
to permit their use in free software.
%---------------------------------------------------------------------
......@@ -188,13 +188,13 @@ every analysis step is detailed and the most important algorithms (peak
detection, alignment) are explained step-by-step.
In chapter \ref{xml-sec} the input xml file to {\M} is explained using
an example file which we detail line by line.
an example file which we explain line by line.
In chapter \ref{pep-sec} you will find the complete specification of the
identified peptides text files format.
identified peptides CSV file format.
In chapter \ref{cheat-sheet} you will find a cheat-sheet of all the
parameters in {\M} with corresponding recommended or advised values.
parameters in {\M} with corresponding recommended values.
Finally in the appendixes you will find the complete files used in
this manual.
......@@ -279,11 +279,13 @@ To achieve this {\M} can combine and perform the following features :
\end{itemize}
{\M} uses the notion of \emph{groups} of LC-MS data according to their
technical similarities. Grouping affects alignments (all runs from the
same group will be aligned with the same method to the same reference)
and quantification (peak detection and quantification will be
technical similarities. Grouping affects alignments: all runs from the
same group will be aligned with the same method;
and quantification: peak detection and quantification will be
performed in all runs of the same group for peptides that were
identified in at least one run of this group). Groups also give the user the
identified in at least one run of this group.
Groups also give the user the
possibility to perform specialized analysis on several different sets of
data in one shot.
For example, in a peptide SCX separation experiment,
......@@ -291,22 +293,20 @@ only the samples of the same LC fraction should be aligned to each
other. In that case, we form a group of these samples in
MassChroQ and we assign the desired alignment method to it.
We can do the same with quantification methods : suppose we have a set of runs
obtained with an HR Orbitrap spectrometer which is known for producing
artifact spikes on the signal and another set of runs obtained with
an LR LTQ spectrometer which produces a certain baseline noise but no
spikes. We can group the Orbitrap runs together and put the LTQ ones in another
We can do the same with quantification methods: suppose we have a set of runs
obtained with an HR Orbitrap spectrometer (which is known for producing
artifact signal spikes) and another set of runs obtained with
an LR LTQ spectrometer (which produces a certain baseline noise but no
spikes). We can group the Orbitrap runs together and put the LTQ ones in another
group. We then apply a quantification method containing an anti-spike
XIC filter to the first group, and another quantification method containing
a background filter to the latter and tell masschroq to perform
analysis in both groups in one shot. The quantification results will be
summarized in a single file, sorted by group and sample, allowing
comparisons to be performed easily.
analysis in both groups in one shot.
{\M} accepts mzXML and mzML LC-MS data formats.
{\M} accepts mzXML as well as mzML LC-MS data formats.
To include the identified peptides in \M's analysis there are two
possibilities :
To include the identified peptides/isotopes in a {\M} analysis there
are two possibilities :
\begin{itemize}
\item by using the freely available open-source tool
\href{http://pappso.inra.fr/bioinfo/xtandempipeline/}{\Xp}
......@@ -316,7 +316,16 @@ possibilities :
(see section \ref{pep-sec} for details on the peptides files format);
\end{itemize}
{\M} offers two alignment methods: the
\href{http://obi-warp.sourceforge.net/}{OBI-Warp} alignment method
which uses MS level one retention times only and
the MS2 alignment method which uses MS level 2 retention times.
The quantification results in {\M} are
summarized in a single file, sorted by group and sample, allowing
comparisons to be performed easily. Several results file formats are
available: gnumeric, TSV, xhtml and XML (masschroqML XML format).
\section{Help}
On the {\M} homepage (\sitemasschroq) you
......@@ -343,7 +352,7 @@ On the {\M} project page hosted on
\section{Installation}
\subsection{Linux platforms}
\subsection{Linux platforms (32 and 64 bytes)}
\begin{itemize}
\item Ubuntu: {\M} is available from the repository :
......@@ -367,9 +376,8 @@ On the {\M} project page hosted on
the -dev package for Qt in addition to any other libraries.
\item Cmake 2.6 or higher.
\ei
An archive named
\ttt{masschroq-1.0.tar.gz} containing the source code and
precompiled Linux binaries for release version 1.0 is available from
Archives for 32 and 64 bytes systems containing the source code and
precompiled Linux binaries for {\M} release version 1.0 are available from
\url{\sitemasschroq}. Decompress the
archive and if necessary rebuild the binaries with the commands :
\begin{verbatim}
......@@ -384,24 +392,13 @@ in your system.
\end{itemize}
\subsection{Windows platforms}
A win32 archive named \ttt{masschroq-1.0.zip} is available for download at:
\url{\sitemasschroq}. Decompressing this archive
creates a directory named \ttt{masschroq-1.0} containing the following files:
\begin{itemize}
\item \ttt{masschroq.exe}: the win32 executable of MassChroQ;
\item \ttt{masschroq.xsd}: the masschroqML schema that the executable
uses (this file should always be placed in the same directory as the
executable and should not be renamed);
\item \ttt{QtCore4.dll}, \ttt{QtXml4.dll}, \ttt{QtXmlPatterns4.dll}
and \ttt{QtNetwork4.dll}: the Qt libraries that the executable uses
(these files should always be placed in the same directory as the
executable and should not be renamed).
\end{itemize}
You can place the above files in a directory of your choice, you
should just make sure that the schema and the Qt libraries are always
in the same directory that the executable \ttt{masschroq.exe}.
A win32 setup installer named \ttt{masschroq\_setup.exe} is available for download at:
\url{\sitemasschroq}. This setup installs in the chosen directory of
your system {\M} and a set of example files that you can immediatly
try by double-clicking on the masschroqML files they contain.
To run masschroq on Windows :
To run masschroq on Windows you can double-click directly on yout
input \emph{masschroqML} files or, for a more advanced use:
\begin{itemize}
\item click on $start \rightarrow Run$;
\item type \ttt{cmd} on the Run window that appeared, and then press \ttt{OK}.
......@@ -411,7 +408,14 @@ A console command-line window appears. To run masschroq, you type in it:
masschroq path_to_input_file\input_file.masschroqML
\end{verbatim}
where \ttt{input\_file.masschroqML} is the
input file of your analysis.
input file of your analysis. To use the masschroq options type:
\begin{verbatim}
masschroq --help
\end{verbatim}
on the command-line console.
Please refer to the \ttt{masschroq\_readme.pdf} file that comes with your
Windows installation for further details.
\subsection{SVN repository}
The subversion repository located at
......@@ -421,7 +425,7 @@ the public at large. Assuming you have at least version 1.0.0 of
checkout {\M} sources, including latest developments from \ttt{trunk}
by using the following command :
\begin{verbatim}
svn checkout http://subversion.cru.fr/masschroq/ some_local_directory
svn checkout https://subversion.cru.fr/masschroq/ some_local_directory
\end{verbatim}
\section{Running {\M}}
......@@ -439,7 +443,7 @@ program.
The most important input parameter for {\mexe} is an XML file. This is
where the user defines the LC-MS data to be analyzed and
all the different treatments and parameters to be performed on them.
This input XML file follows the \emph{masschroqML} format, whom
This input XML file follows the \emph{masschroqML} format, whose
annotated schema can be found on the {\M} \href{\sitemasschroq}{homepage}
and also in the \ttt{doc/schema/} directory of your installation directory.
......@@ -576,30 +580,21 @@ The user can choose one or several of the above items. {\M} will
extract the corresponding XICs in every sample data being analyzed and
will perform peak detection and quantification on each of them.
\subsection{Peptides}
\subsection{Identified peptides}
A peptide in {\M} is defined as :
\begin{itemize}
\item a unique amino-acid sequence, where all the $Leu$ amino-acids have
been replaced by an $Ile$ amino-acid;
\item a unique amino-acid sequence;
\item a unique MH value : mass of the peptide plus mass of an $H^+$ ion).
\end{itemize}
\textdbend In {\M} the $Leu$ amino-acids are
always replaced by an $Ile$ amino-acid in all the peptides from the beginning of
the analysis, until the final results. Hence, two peptides differing
only by a $Leu \leftrightarrow Ile$ are considered the same in {\M}
and will appear so in the results file.
\subsection{Identified peptides}
{\M} does not perform identification of peptides/proteins. They have
{\M} does not perform identification of peptides/proteins. This has
to be performed upstream using the identification tool of your
choice. It is up to the user to provide the identified peptides (in
case he wants to operate on them) in TSV or CSV (tab, comma or
semi-colon separated values) format files (for details on this format
see section ref{pep-sec}. {\M} parses
these files and puts the information they contain in its input XML
see section \ref{pep-sec}). {\M} parses
these files and puts the information they contain in its input masschroqML
file.
Most of the identification tools (Mascot, X!Tandem, Phenyx) offer the
......@@ -607,31 +602,44 @@ possibility to export identification results in TSV files. Our {\Xp} offers the
possibility to directly export X!Tandem identification results in a
masschroq XML input file (no parsing is necessary).
\textdbend As indicated previously, remember that the $Leu$ amino-acids of your
peptides are replaced by the $Ile$ amino-acid in {\M}, during
analysis and also in the final result files.
\subsection{Identified isotopes}
If isotopic labelling has been performed and the user needs to
quantify all the identified isotopes, he simply describes the
different isotopic labelings performed on the different data
being analysed in the masschroqML file. During analysis {\M}
automatically computes isotopic masses (using these descriptions)
and quantifies the desired isotopes.
For a precise explanation of how to describe and quantify isotopes see
section \ref{iso-labels}.
\section{Parsing of LC-MS/MS files in mzXML or mzML formats}\label{mzxml}
{\M} can parse mzXML as well as mzML LC-MS files. Due to the simpler,
far more robust and stable nature of the mzXML format, we highly
recommend its use in {\M} instead of the mzML one.
{\M} can parse mzXML as well as mzML LC-MS files. In both of these
formats, it parses all the MS levels it finds (1, 2 and greater). If
{\M} does not validate mzXML and mzML files against their respective
schema (it is not its goal), the user has to provide valid
files. Usually, most of the proteomic pipeline tools that convert raw
data to mzXML/mzML format produce valid files.
In both formats, {\M} parses all the MS levels it finds (1, 2 and greater). If
the items to be quantified are the identified peptides, LC-MS run
files should contain MS levels 1 and 2 in order for {\M} to
work. Indeed, {\M} uses the MS/MS information to compute the real
observed retention times of these peptides in each run.
This retention time will be used later during peak matching to assign the computed
quantitative value to the right corresponding observed peptide and to
avoid false assignments.
A given peptide can be observed at several different retention times
in a given run, with different intensities. During parsing {\M} will
get from the LC-MS run file all the observed retention times and corresponding
intensities of this peptide and will retain only the retention time
corresponding to the most intense occurrence of this peptide. This
will be the retention time of this peptide for this run during the rest
of the analysis. We refer to this method as the \emph {best RT}
method.
This retention time will be used later during peak matching to
assign the computed quantitative value to the right corresponding
observed peptide and to avoid false assignments.
{\M} automatically decodes the base64 encoded spectra in both mzXML
and mzML formats, in both 32 or 64 bytes precision.
{\M} processes neither compressed spectra, nor compressed
mzXML/mzML files. If this feature is important to you, please let us
know and we will try to implement it sooner than scheduled.
\section{Grouping of LC-MS runs}
......@@ -644,23 +652,21 @@ resolution spectrometer, etc.).
The user can define several different
groups in the same analysis. He can then define different alignment
methods and different quantification methods for each of these
groups. This allows him to run analysis on several different set of samples
in one shot.
groups. This allows him to run specialized analysis on several
different set of samples in one shot.
Groups do not only offer flexibility but also some extra possibilities:
\bi
%??? bizarre demander Benot
\item XICs for a given identified
Groups do not only offer flexibility, they are also helpfull with some
extra possibilities that {\M} implements:
\begin{description}
\item[Efficient XIC extraction:] XICs for a given identified
peptide will only be extracted in groups where the MS/MS allowed its
identification, no unneccessary extractions will be performed.
\item peptides identified in at least one run of the group, will be
quantified after alignment in every run of the group. Suppose that a
given peptide has been identified in just one run of the group at a
given retention time but not in other runs, because of the LC
instrument retention time deviations. After alignment, {\M} will
quantify this peptide in every msrun of this group, not only the one
in which it was identified.
\ei
\item[Smart quantification:] peptides identified in at least one run of
the group, will be quantified in every run of this group, including
those where they have not been identified. See section
\ref{smart_quanti} for more details on how this is done in {\M}.
\end{description}
The final quantification results in {\M} are sorted by group and by
run, associating to each identified peptide (or other chosen entity)
......@@ -751,34 +757,35 @@ been widely replaced by the Zivy one in practice). This method proceeds as follo
\subsection{The Zivy peak detection algorithm}
\textdbend The Zivy peak detection method is a peak localization
method : its unique purpose is to determine the peak
positions in the signal. Peak intensities, boundaries and area are
computed on the original unaltered signal.
The Zivy peak detection method has widely replaced the Moulon one in
practice in our laboratory, giving much more accurate and precise
results.
This method uses morphological opening and closure signal transforms
with flat structural elements (also known as respectively max/min and
min/max transforms). Mathematical morphology was born in 1964 from the
collaborative work of Georges Matheron and John Serra at the \emph{Ecole des
Mines de Paris} (see \cite{serra}). Since then, morphological
transforms have been widely used in image processing to remove noise
and to detect peaks or edges showing their efficiency in particular in
noisy signals (see \cite{handbook} and \cite{edge}).
\textdbend The Zivy peak detection method is a peak localization
method: its purpose is to determine the peak
positions and the peak boundaries on the signal. Peak intensities an
peak area are then computed on the original unaltered signal.
This method uses morphological opening and closing signal transforms
with small flat linear structural elements (also known as respectively
max/min and min/max transforms). Mathematical morphology was born in
1964 from the collaborative work of Georges Matheron and John Serra at
the \emph{Ecole des Mines de Paris} (see \cite{serra}). Since then,
morphological transforms have been widely used in image processing to
remove noise and to detect peaks or edges showing their efficiency in
particular in noisy signals (see \cite{handbook} and \cite{edge}).
\textdbend On a one-dimensional signal, the open (resp. close) transform
with a flat structural element (i.e. a segment) of size R is equivalent to
replacing the signal values at every point by the maximum of the
minimum (resp. minimum of the maximum) of all the points in a neighborhood
of radius R (see \cite{leymarie} and \cite{kluwer}). This is what we do in {\M}.
with a flat linear structural element (i.e. a segment) of size R is
equivalent to replacing the signal values at every point by the
maximum of the minimum (resp. minimum of the maximum) of all the
points in a neighborhood of radius R (see \cite{leymarie} and
\cite{kluwer}). This is what we do in {\M}.
\newpage
Schematically, as illustrated in the figure below, opening
and closing transforms with flat structural elements both smooth and
and closing transforms with flat linear structural elements both smooth and
simplify the original signal: opening removes small peaks by
flattening them from the top and closing fills small holes by filling
them from below.
......@@ -806,29 +813,32 @@ Here is how the Zivy peak detection algorithm works:
\item These maxima are then filtered using two intensity thresholds;
only the ones that overpass the thresholds are retained.
\item Threshold on the open signal : If a local maxima position
\begin{description}
\item[Threshold on the open signal:] If a local maxima position
reported on the open signal does not exceed in intensity the open
threshold it is eliminated. This allows us to eliminate background
noise peaks which usually are very thin (hence flattened by the open
transform).
threshold it is eliminated. This eliminates intense and thin noise peaks
(almost spikes) that are flattened by the open
transform given their thinness.
\item Threshold on the closed signal : If a local maxima position
\item[Threshold on the closed signal:] If a local maxima position
reported on the close signal does not exceed in intensity the
threshold on the close signal, it is eliminated. This allows us to
eliminate very wide noise peaks (hence not flattened by the open
transform) but not very intense.
\item The final retained local maxima are the peak position in
{\M}. We use this positions to compute the real maximum intensity,
peak boundaries and peak area on the original signal. So it is very
important to keep in mind that the morphological transforms in {\M}
threshold on the close signal, it is eliminated. This eliminates
very wide noise peaks (hence not flattened by the open
transform) but not intense.
\end{description}
\item The final retained local maxima are the peak positions in
{\M}. We use this positions to compute peak boundaries on the closed
signal and also peak intensity,
and peak area on the original signal. Indeed, morphological
transforms in {\M}
are solely used to detect peak positions: the peak intensity
and peak area are computed on the original signal using these positions.
\ei
\textdbend In the figure above one can see different interesting
cases: for example the very intense peak on the left of the unique detected peak
cases: for example the very intense peak on the left of the unique
detected peak
is eliminated because its intensity in the open signal does not exceed
the open threshold (too thin to be a relevant peak). This peak is
indeed a noisy pulsing spectrometer effect.
......@@ -868,11 +878,10 @@ Here follows a precise description of the Zivy peak detection algorithm:
\item[Line 2 and 3:] compute the open and close transforms of the
signal;
\item[Line 4:] perform a local maximum detection on the intensities of the
closed signal (by simply browsing the $\mathscr{C}_X$ points and
retaining the ones surrounded by smaller ones); we obtain
closed signal; we obtain
\ttt{\{local-max-points\}}, a preliminary set of (retention time,
intensity) potential peak positions.
\item[Lines 5-10:] for every such (retention time, intensity) point,
\item[Lines 5-10:] for every such (retention time, intensity) point:
\bi
\item check that the corresponding retention time point in the opened signal
$\mathscr{O}_X$ has a greater intensity than the open threshold
......@@ -881,7 +890,7 @@ Here follows a precise description of the Zivy peak detection algorithm:
$\mathscr{C}_X$ has a greater intensity than the close threshold
$detection\_threshold\_on\_max$;
\ei
The final peaks are the points that verify both of these conditions.
The final peak positions are the points that verify both of these conditions.
\item[Afterwards:] In reality, the peak boundaries and peak area
(final quantification value) are also
computed during the detection Zivy algorithm. Indeed, after having
......@@ -900,15 +909,14 @@ The Zivy peak detection method is inspired in part by the morphological
\section{Alignment}
As the LC-MS instruments do not trigger MS/MS at exactly the same
retention time in every sample, retention time distortions can often occur
between LC-MS runs. Hence, RTs must be aligned in every sample
before peak matching (assignment of the quantitative values computed in different
LC-MS runs to the same correponding peptide).
LC-MS instruments do not trigger MS/MS at exactly the same
retention time in every sample, hence retention time distortions can often occur
between runs. Thus, RTs must be aligned in every sample
before peak matching.
{\M} performs alignment of samples of the same group.
For each group of runs to be aligned the user chooses a
\emph{reference alignment run} : the reference run against whom
\emph{reference alignment run} : the reference run against which
all the other runs of the group will be aligned.
It stands to reason that the user should choose a
representative run as reference alignment run, otherwise all the runs
......@@ -944,18 +952,20 @@ This method uses MS/MS identifications as landmarks to evaluate
time deviation along the chromatography. More precisely, suppose we
want to align two runs. We first calculate the retention time
deviation of the MS/MS identified peptides these two runs have in
common. Then by linear interpolation
common. Then, by linear interpolation,
we use this deviation curve to calculate a tendency deviation curve of
the MS level one retention times. Of course this deviation curve is
accurate only if there are enough points that allowed its shape,
i.e. if there are enough common identified peptides in the two
runs. For each alignment {\M} will output the number of common
peptides. Also, he will output a .trace file for each run aligned,
containing all necessary information for checking.
runs. For each alignment {\M} will output the number of shared
peptides. Also, he will output a .trace file for each run
aligned. These files contain the traces of every alignment step and
can be used for precise checking og the alignment procedure ans
parameter adjustment.
Here follows a precise explanation step by step of the MS/MS alignment
algorithm. Let $Run_1$ be the run whom MS level one retention times
algorithm. Let $Run_1$ be the run whose MS level one retention times
are going to be aligned towards the MS level one retention times of
the reference run $Run_{ref}$. We will use the following notation conventions :
\bi
......@@ -1025,21 +1035,19 @@ and correct it if necessary;
\begin{description}
\item[Step 1 (lines 1-4):]
here we get the retention times of all the common peptides
identified in both runs. As identification is made possible by MS/MS
acquisition we call these values the MS level 2 retention times. The
identified in both runs. The
computation of peptide retention times follows the \emph{best Rt}
method explained in section \ref{mzxml} above.
method explained in section \ref{bestRT}.
\item [Step 2 (lines 2-6):] to each peptide MS2 retention time in $Run_1$ we
associate its deviation from the peptide's MS2 retention time in
$Run_{ref}$.
\item [Step 3 (lines 7-8):]
we smooth this deviation curve if the user has defined positive
non-zero MS2 smoothing parameters : we
smoothing of this deviation curve: we
first apply a moving median filter followed by a moving average
filter. The half-window sizes for these filters are parameters defined by
the user. They can be set to zero if no filtering is needed.
\item [Step 4 (lines 9-11):] compute the first and last $rt_{ms2} and
\Delta rt_{ms2}(Run_1)$ elements by linear extrapolation on the
\item [Step 4 (lines 9-11):] compute the first and last $rt_{ms2}$ and
$\Delta rt_{ms2}(Run_1)$ elements by linear extrapolation on the
first and last $rt_{ms1}(Run_1)$ points.
\item [Step 5 (lines 12-15):] for each $rt_{ms1}$ level one retention time in
$Run_1$ to be aligned, we compute a corresponding $\Delta rt_{ms1}$
......@@ -1056,13 +1064,13 @@ the user. They can be set to zero if no filtering is needed.
\item [Step 8 (line 20):] the such computed aligned MS1 retention
times must be in an ascending order (as the original retention times
are). Despite the smoothings, sometimes (rarely observed) this is not
necessarily the case at this point. Indeed, since the computed value
the case at this point. Indeed, since the computed value
is a deviation in time, once we apply this deviation to the original
time to obtain the aligned one, there is no guaranty that the
ascending order of aligned time values is preserved. Hence, the
algorithm always checks the ascending order of the aligned values and
automatically applies a correction to ensure it if needed. This correction is
computed as follows:
automatically applies a correction to ensure it if needed.
The correction is performed as follows:
\bi
\item compute the correction parameter as the slope of the original
retention time curve ($rt_{ms1}(Run_1)$); divide this parameter by
......@@ -1101,19 +1109,55 @@ alignment files.
\subsubsection*{Cascade alignments}
\section{Peak matching}
\section{Peak matching}\label{peak_match}
After alignment peak matching in {\M} is performed as follows: the
After alignment, xic extraction and peak detection, {\M} performs peak
matching: the detected peaks are assigned to the peptides or other
entities being quantified. Peak matching in {\M} is based on retention
times, it is performed as follows: the
quantitative value of a peak (i.e. the peak area)
is assigned to a peptide if and only if the MS/MS RT of this peptide
is assigned to a peptide if and only if the RT of this peptide
is within the boundaries of this peak.
\newpage
What is the RT of a peptide?
In a given run a peptide can be identified or not. In case it has been
identified, it can be identified at several retention times, this is
why its RT is computed with the \emph{best RT} method; in case it has
not been identified, its RT is computed with the \emph{smart
quantification} method which allows quantification of peptides even
in runs they have not been identified, provided they have been
identified in another run of the same group.
\subsection{The best RT method}\label{bestRT}
A given peptide can be observed/identified at several different
retention times in a given run, with different intensities or charge
states. During parsing {\M} will get from the LC-MS run mzXML or mzML
file all the retention times the peptide has been identified in and
the corresponding precursor intensities. He will then retain only the
retention time corresponding to the most intense occurrence of this
peptide. This will be the retention time of this peptide for this run
during the rest of the analysis. We refer to this method as the \emph
{best RT} method.
\subsection{Smart Quantification}\label{smart_quanti}
If a given peptide has been identified in at least one sample of the
group, during peak matching in the samples where this peptide has not
been identified {\M} will nevertheless check for peaks corresponding
to this peptide.
Indeed {\M} computes the mean of this peptide's retention times in
the samples it has been identified in (more precisely it computes the
mean of all its best RTs). In the sample the peptide has
not been identified in, {\M} checks whether this mean RT belongs to a
detected peak area or not; if it does the peak and its quantification
value are assigned to this peptide.