Name: moff-gui
Owner: Computational Omics and Systems Biology Group
Description: A graphical user interface for the Modest Feature Finding algorithm (moFF)
Created: 2016-04-11 09:29:32.0
Updated: 2017-03-02 21:58:16.0
Pushed: 2017-11-09 14:56:24.0
Size: 5847
Language: Java
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(Click on a figure to see the full size version)
moFF-gui is an intuitive graphical user interface for the moFF (modest Feature Finder) algorithm. MoFF is a fast and lightweight tool for the quantification of peptides directly from Thermo RAW files. Being programmed in the JAVA programming language, moFF-gui is able to run platform independently and was extensively tested on windows and linux. The tool can be readily be integrated in pipelines using PeptideShaker as it accepts cps files as input. Alternatively, a tab separated file (tsv) can be provided with minimal [annotation requirements](##Input Data) on the level of peptide identifications. A command-line version of moFF is also available.
Required java version :
Required python libraries :
Optional requirements : -When using PeptideShaker results as a source, a PeptideShaker installation (http://compomics.github.io/projects/peptide-shaker.html) needs to be availabe.
During processing, moFF makes use of a third party algorithm (txic or txic.exe) which allows for the parsing of the Thermo RAW data. Txic is compatible with the raw outputfiles originating from any Orbitrap or triple quadrupole Thermo machine. However, Thermo Fusion instruments are currently not supported.
moFF-GUI requires two types of input for the quantification procedure :
The MS2 identified peptides can be provided in two different ways:
(a) The tab-delimited file must contain the following information for all the peptides:
NOTE : In case the tab-delimited file provided by the user contains also other fields (i.e modifications,petides length), those will remain in the result output.
(b) When using a cpsx file, the respective spectra (MGF) and sequence database (.FASTA) that were used in the PeptideShaker processing must be provided. A detailed tutorial on PeptideShaker can be found at http://compomics.com/bioinformatics-for-proteomics/
Sample data is provided at folder . It contains the cpsx and the raw files related to 3 runs of CPTAC Study 6 (Paulovich, MCP Proteomics, 2010).
The cpsx files were generated through a pipeline consisting of SearchGUI and PeptideShaker. The used search algorithms were X!Tandem and MSGF+. The sequence database (FASTA) and spectra (MGF) for all 3 runs are also provided.
moFF can be run in two modes:
In the apex mode , the parameters are :
In the match-between-run some more parameters are requested:
The match-between-run uses all the shared peptides among the replicates to train the RT prediction models as a default option. In case the user wants to provide a specific set of peptides as training set, it is possible to specify them in a tab delimited format with two fields : peptides and mass.
The output consists of :
(a) Description of the fields added by moFF in the output file:
Parameter | Meaning — | ————– | rt_peak | retention time (in seconds) of the discovered apex peak SNR | signal-to-noise ratio of the peak intensity. log_L_R'| peak shape. 0 indicates that the peak is centered. Positive or negative values are an indicator for respectively right or left skewness intensity | MS1 intensity log_int | log 2 transformed MS1 intensity lwhm | first rt value where the intensity is at least the 50% of the apex peak intensity on the left side rwhm | first rt value where the intensity is at least the 50% of the apex peak intensity on the right side 5p_noise | 5th percentile of the intensity values contained in the XiC. This value is used for the SNR computation 10p_noise | 10th percentile of the intensity values contained in the XiC. code_unique | this field is concatenation of the peptide sequence and mass values. It is used by moFF during the match-between-runs. matched | this value indicated if the featured has been added by the match-between-run (1) or is a ms2 identified features (0)
(b) A log file is also provided containing the process output.
(c) A log file where all the information about all the trained linear model are displayed.
NOTE : The log files and the output files are in the output folder specified by the user.