Name: moFF
Owner: Computational Omics and Systems Biology Group
Description: A modest Feature Finder (moFF) to extract MS1 intensities from Thermo raw file
Created: 2014-09-24 14:37:50.0
Updated: 2017-11-21 17:42:01.0
Pushed: 2017-12-15 08:53:33.0
Size: 3966
Language: Python
GitHub Committers
| User | Most Recent Commit | # Commits |
|---|---|---|
| Check your git settings! | 2017-11-29 13:25:45.0 | 49 |
| Björn Grüning | 2016-06-12 14:27:20.0 | 12 |
| Maux82 | 2018-01-18 09:40:48.0 | 200 |
| Michael A. Freitas, PhD | 2017-06-21 23:27:29.0 | 3 |
| Johannes Griss | 2017-06-12 18:54:37.0 | 3 |
| kverhegg | 2016-06-09 14:26:27.0 | 2 |
| Caleb Easterly | 2018-01-08 22:37:12.0 | 11 |
Other Committers
| User | Most Recent Commit | # Commits | |
|---|---|---|---|
| Compomics User | compomics@codeb.ugent.be | 2016-05-31 11:09:30.0 | 54 |
moFF is an OS independent tool designed to extract apex MS1 intensity using a set of identified MS2 peptides. It currently uses a Go library to directly extract data from Thermo Raw spectrum files, eliminating the need for conversions from other formats. Moreover, moFF also allows to work directly with mzML files.
moFF is built up from two standalone modules :
NOTE : Please use moff_all.py script to run the entire pipeline with both MBR and apex strategies.
The version presented here is a commandline tool that can easily be adapted to a cluster environment. A graphical user interface can be found here. The latter is designed to be able to use PeptideShaker results as an input format. Please refer to the moff-GUI manual for more information on how to do this.
moFF is also available on bioconda. To install with conda, use the following command:
a install -c bioconda moff
This automatically installs all dependencies. Note that bioconda only supports 64-bit macOS and Linux.
Required java version :
Required python libraries :
Required linux library:
Required windows library:
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_json.exe) which allows for the parsing of the Thermo RAW data.
moFF requires two types of input for the quantification procedure :
The MS2 identified peptides can be presented as a tab-delimited file containing mimimal (mandatory) annotation for each peptide (a)
(a) The tab-delimited file must contain the following information for all the peptides:
NH2-M<Mox>LTKFESK-COOH )NOTE 1 : In case the tab-delimited file provided by the user contains fields that are not mentioned here (i.e petides length, search engines score) the algorithm will retain these in the final output. The peptide-spectrum-match sequence with its modications and the protein id and informations are used only in the match-between-run module.
NOTE 2 : Users can also provide the default PSM export provided by PeptideShaker as source material for moFF.
The sample_folder contains a resultset for 3 runs of the CPTAC study 6 (Paulovich, MCP Proteomics, 2010). These MS2 peptides are identified by X!Tandem and MSGF+ using SearchGUI and then processed by PeptidesShaker . The raw files for this study are required to apply moFF to the sample data.
use : python moff_mbr.py -h
--inputF the folder where the input files are located
--sample filter based on regular expression to define the considered replicates
--ext file extention of the input file
--log_file_name filename for the log file
--filt_width width value for outlier filtering
--out_filt filtering (on/off) of the outlier in the training set
--weight_comb combination weighting : 0 for no weight 1 for a weighted schema
python moff_mbr.py --inputF sample_folder/
This command runs the MBR modules. The output will be stored in a subfolder ('mbr_output') inside the specified input folder. The MBR module will consider all the .txt files present in the specified input folder as replicates (to select specific files or different extension, please refer to the example below). The files in sample_folder/mbr_output will be identical to the input files, but they will have an additional field ('matched') that specifies which peptides have match (1) or not (0). The MBR algorithm also produces a log file in the provided input directory.
In case of a different extension (.list, etc), please use :
python --inputF sample_folder/ --ext list (Provide the extension without the period ('.'))
In case of using only specific input files within the provided directory, please use a regular expression:
python --inputF sample_folder/ --sample *_6A (This can be combined with the aforementioned syntax)
use python moff.py -h
inputtsv the input file with for MS2 peptides
inputraw specify directly the raw file
tol the mass tollerance (ppm)
rt_w the rt windows for xic (minutes). Default value is 3 min
rt_p the time windows used to get the apex for the ms2 peptide/feature (minutes). Default value is 1
rt_p_match the time windows used to get the apex for machted features (minutes). Default value is 1.5
raw_repo the folder containing the raw files
peptide_summary flag that allows have as output the peptided summary intensity file. Default is disable (0)
tag_pep_sum_file tag string that will be part of the peptided summary intensity file name. Default value is moFF_run
output_folder the target folder for the output (default is the input folder, raw_repo)
For example :
python moff.py --inputtsv sample_folder/20080311_CPTAC6_07_6A005.txt --raw_repo sample_folder/ --tol 1O --output_folder output_moff --peptide_summary 1
WARNING : the raw file names MUST be the same of the input file otherwise the script gives you an error !
NOTE: all the parameters related to the the time windows (rt_w,rt_p, rt_p_match) are basicaly the half of the entire time windows where the apex peak is searched or the XiC is retrieved. For a correct rt windows, we suggest to set the rt_p value equal or slighly greater to the dynamic exclusion duration set in your machine. We suggest also to set the rt_p_match always slightly bigger than tha values used for rt_p
You can also specify directly the raw file using:
python moff.py --inputtsv sample_folder/20080311_CPTAC6_07_6A005.txt --inputraw sample_folder/20080311_CPTAC6_07_6A005.raw --tol 1O --output_folder output_moff
WARNING: if the user need to use Thermo RAW file can specify them using --inputraw or --raw_rep. In case of mzML file the user can ONLY specify them using --inputraw
use python moff_all.py -h
--inputF the folder containing input files
--inputtsv specify the input file as a list separated by a space
--inputraw specify the raw file as a list separated by space
--sample filter based on regular expression to define the considered replicates
--ext file extension for the input files
--log_file_name a label name to use for the log file
--filt_width width value for the outlier filtering
--out_filt filtering (on/off) of the outlier in the training set
--weight_comb combination weighting : 0 for no weight 1 for a weighted schema
--tol the mass tollerance (ppm)
--rt_w the rt windows for xic (minutes). Default value is 3 min
--rt_p the time windows for the ms2 peptide/feature in apex (minutes). Default value is 1
--rt_p_match the time windows for the matched features in apex ( minutes). Default value is 1.5
--peptide_summary flag that allows have as output the peptided summary intensity file. Default is disable (0)
--tag_pep_sum_file tag string that will be part of the peptided summary intensity file name. Default value is moFF_run
--raw_repo the folder containing the raw files
python moff_all.py --inputF sample_folder/ --raw_repo sample_folder/ --tol 10 --output_folder output_moff --peptide_summary 1
The options are identifcal for both apex and MBR modules. The output for the latter (MBR) is stored in the folder sample_folder/mbr_output, while the former (apex) generates files in the specified output_moff folder. Log files for both algorithms are generated in the respective folders.
You can also specify a list of input and raw files using:
python moff_all.py --inputtsv sample_folder/input_file1.txt sample_folder/input_file2.txt --inputraw sample_folder/input_file1.raw sample_folder/input_file2.raw --tol 10 --output_folder output_moff --peptide_summary 1
Using --inputtsv | --inputraw you can not filterted the input file using --sample --ext like in the case with --inputF | --raw_repo
mzML raw file MUST be specified using --inputtsv | --inputraw. The --raw_repo option is not available for mzML files.
NOTE: The consideration of retention time window parameters (rt_w,rt_p_rt_p) mentioned for apex module are stil valid also for the entire workflow
The output consists of :
(a) Description of the fields added by moFF in the output file:
Parameter | Meaning — | ————– | rt_peak | retention time (in seconds) for 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 | log2 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.
(d) The peptide summary intensity is a tab delimited file where for each peptide sequence MS1 intensities are summed for all the occurences in each run (aggregated by charge states and modification).
In case you run the entire workflow on an a settings that contains N runs, the size of the file (rows and columns) will be M x (N+2), where M is number of peptides (across all the runs) and N are summed intensity columns plus the peptide sequence and the protein ids. In case of running only the apex module, the size of the file will be on M x 3 (only one replicate is considered).
If a peptide is shared across several proteins, the protein column will also contains all the shared protein ids usually separed by ; or ,. In case a peptide is not quantified it has 0 as intensities. The peptide summary intensity could be used for downstream statistical analysis such as in MsQRob
NOTE : The log files and the output files are in the output folder specified by the user.