- Introduction
- Minimum Requirements
- Input Data
- Sample Data
- Absence of Peak Sample Sata
- Match between runs
- Apex Intensity
- Entire workflow
- Post Translation Modification file
- Docker
- Output Data
moFF is an OS independent tool designed to extract apex MS1 intensity using a set of identified MS2 peptides. It currently uses a Thermo 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 modules :
- moff_mbr.py : match between run (mbr)
- moff.py: apex intensity
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:
conda install -c bioconda moff
This automatically installs all dependencies. Note that bioconda only supports 64-bit macOS and Linux.
- Argentini et al. Nature Methods. 2016 12(13):964–966.
- If you use moFF as part of a publication, please include this reference.
Required python libraries :
- Python 3.6+
- pandas > 0.23
- numpy > 1.15.0
- argparse > 1.2.1
- scipy 1.1.0
- scikit-learn > 0.19
- pymzML > 2.0.3
- brain-isotopic-distribution > 1.3.2
- pyteomics > 3.5
Required linux library:
- Mono version 4.2.1
Required windows library:
- .NET Framework 4.6.2
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 :
- Thermo RAW file or mzML file
- MS2 identified peptide information
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:
- 'peptide' : peptide-spectrum-match sequence
- 'prot' : protein ID
- 'mod_peptide' : peptide-spectrum-match sequence that contains also possible modification (i.e
NH2-M<Mox>LTKFESK-COOH) - 'rt': peptide-spectrum-match retention time (i.e the retention time contained in the mgf file; The retention time must be specified in second)
- 'mz' : mass over charge
- 'mass' : mass of the peptide
- 'charge' : charge of the ionized peptide
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 result set 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.
To evaluate the filtering of the matched peak, we provide a data set composed by 4 runs :
| File name | iRT | yeast |
|---|---|---|
| B002413_Ap_22cm_Yeast_171215184201 | x | |
| B002417_Ap_22cm_iRT_PRC-Hans_equimolar_100fmol | x | |
| B002419_Ap_22cm_iRT_PRC-Hans_equimolar_100fmol_inYeast | x | x |
| B002421_Ap_22cm_iRT_PRC-Hans_equimolar_100fmoll | x |
Run B002413 does not contain iRT peptides and it works as controll. No iRT peptides are expected after the matching-between-runs across all four runs.
To test the filter of the matched peak, you can follow the steps:
- clone the moFF repository
- download the .zip file that contains all Thermo raw file from here
- unzip it inside the folder absence_peak_data
- check the input/output paths in the coinfiguration_iRT.ini
then you an run moFF using:
python moff_all.py --config_file absense_peak_data/config_iRT.ini
to run mbr + apex and filtering function
use : python moff_all.py -mbr only
--loc_in the folder where the input files are located
--sample reg exp to filter the input file names (only with --loc_in input option-
--ext file extention of the input file. Default .txt)
--log_label filename for the mbr log file. Default moFF_mbr
--w_filt width value for outlier filtering. Default 3
--out_flag if set, outliers for rt time allignment are filtered. Default value: True
--w_comb if set, RT model combination is weighted using traing model errors: Default value: False
python moff_mbr.py --loc_in sample_folder/ --mbr only
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 moff_mbr.py --loc_in 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 moff_mbr.py --loc_in sample_folder/ --sample *_6A (This can be combined with the aforementioned syntax)
You can set all the parameters values in a file and load them using --config_file. For an example see example_parameter_file.ini
use python moff_all.py -mbr off
--loc_in the folder containing all input files
--raw_repo the folder containing all the raw files
--tsv_list the input file with for MS2 peptides
--raw_list pecify directly the raw file
--tol mass tollerance (ppm)
--xic_length rt windows for xic (minutes). Default value is 3 min
--rt_peak_win time windows used to get the apex for the ms2 peptide/feature (minutes). Default value is 1
--rt_peak_win_match time windows used to get the apex for machted features (minutes). Default value is 1.2
--peptide_summary flag that allows have as output the peptided summary intensity file. Default is disable(0)
--tag_pepsum tag string that will be part of the peptided summary intensity file name. Default is moFF_run
--loc_out output folder
--tag_pepsum a tag that is used in the peptide summary file name
--match_filter If set, filtering on the matched peak is activated. Default value: False
--ptm_file modification json ptm file. Default file ptm_setting.json
--quantile_thr_filtering quantile value used to computed the filtering threshold for the matched peak . Default is 0.75
--sample_size percentage of MS2 identified peptides used to estimated the threshold
You can run the apex module in two ways:
python moff_all.py --mbr off --tsv_list sample_folder/20080311_CPTAC6_07_6A005.txt --raw_list sample_folder/20080311_CPTAC6_07_6A005.RAW --tol 1O --loc_out output_moff --peptide_summary
in this case you specify more than a file separated by a blanck space
In case you want to run the apex module on all the files in a folder (all so the raw files shold located in a foder)
python moff_all.py --mbr on --loc_in sample_folder/sample_data/ --raw_repo sample_folder/sample_data/your_raw_folder --tol 1O --loc_out output_moff --peptide_summary
You can activate the filtering of the matching peptides setting --match_filter. In order to do the filtering:
--ptm_fileMUST be specified and input files MUST contain a matched field.
This option is useful in the case you have run the mbr module alone and later you want to run the apex module separately.
WARNING : in case of --loc_in and --raw_repo raw file names MUST be the same of the input file otherwise the script gives you an error !
WARNING 1 : you can not mixed the two input ways ( --loc_in / --raw_repo and --tsv_list / --raw_list ) otherwise the script gives you an error !
WARNING 2: mzML raw file MUST be only specified using --tsv_list | --raw_list. The --raw_repo option is not available for mzML files.
NOTE: all the parameters related to the the time windows (xic_lentgh,rt_peak_win, rt_peak_win_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_peak_win value equal or slighly greater to the dynamic exclusion duration set in your machine. We suggest also to set the rt_peak_win_match always slightly bigger than tha values used for rt_peak_win
use python moff_all.py -mbr on
--config_file specify a moFF parameter file
--loc_in the folder containing all input files
--raw_repo the folder containing all the raw files
--tsv_list the input file with for MS2 peptides
--raw_list pecify directly the raw file
--sample reg exp to filter the input file names (only with --loc_in input option-
--ext file extention of the input file. Default .txt)
--log_label filename for the mbr log file. Default moFF_mbr
--w_filt width value for outlier filtering. Default 3
--out_flag if set, outliers for rt time allignment are filtered. Default value: True
--w_comb if set, RT model combination is weighted using traing model errors: Default value: False
--tol mass tollerance (ppm)
--xic_length rt windows for xic (minutes). Default value is 3 min
--rt_peak_win time windows used to get the apex for the ms2 peptide/feature (minutes). Default value is 1
--rt_peak_win_match time windows used to get the apex for machted features (minutes). Default value is 1.2
--peptide_summary if set, export a peptide intesity summary tab-delited file. Default value: False
--tag_pepsum tag string that will be part of the peptided summary intensity file name. Default value is moFF_run
--loc_out output folder default is the input folder, raw_repo)
--tag_pepsum a tag that is used in the peptide summary file name
--match_filter If set, filtering on the matched peak is activated. Default value: False
--ptm_file modification json ptm file. Default file ptm_setting.json
--quantile_thr_filtering quantile value used to computed the filtering threshold for the matched peak . Default is 0.75
--sample_size percentage of MS2 identified peptides used to estimated the threshold
Like for the apex module, you input you input data specifing the folder :
python moff_all.py --mbr all --loc_in sample_folder/ --raw_repo sample_folder/ --tol 10 --loc_out output_moff --peptide_summary
OR, specifing a list of input and raw files using:
python moff_all.py --mbr all --tsv_list sample_folder/input_file1.txt sample_folder/input_file2.txt --raw_list sample_folder/input_file1.raw sample_folder/input_file2.raw --tol 10 --loc_out output_moff --peptide_summary
The options are identical 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.
In case you activate the filtering of the mached peptides you have to specify with --ptm_file a valid json file that describes the modificatiuon used in your experiment. See section
You can set all the parameters values in a file and load them using --config_file. For an example see example_parameter_file.ini
WARNING: Using --tsv_list | --raw_list you can not filterted the input file using --sample --ext like in the case with --loc_in | --raw_repo
WARNING: **mzML raw file MUST be specified using --tsv_list | --raw_list. The --raw_repo option is not available for mzML files.
NOTE: The consideration of retention time window parameters (xic_length,rt_peak_win,rt_peak_win_match) mentioned for apex module are stil valid also for the entire workflow
The Post Translation Modificatio must be indicated in json file with the following structure :
{
"tagModification": {"deltaChem":[H atom, C atom, N atom ,O atom],"desc":"name unimod : unimod_id"},
}
"tagModification": the tag used in modified sequence for the modification"deltaChem":[H atom, C atom, N atom ,O atom]: the delta of chemical composition if the modification. The order of the elements is fixed, so pay attention when you add your modificationdesc: name of the modification and its unimod id.
For example a ptm file (ptm_setting_ps.json) with Carboxyamidomethylation of Cysteine and Oxidation for PeptideShaker output looks like:
{
"<cmm>": {"deltaChem":[3,2,1,1],"desc":"Carboxyamidomethylation C unimod:4"},
"<ox>": {"deltaChem":[0,0,0,1],"desc":"oxidation oxidation unimod:35" }
}
One you have cloned or downloaded moFF repository, inside the moFF folder you can build docker with the the command
docker build . -t moff
Inside the docker you can run moFF with all commands showed above. Run example with the apex module:
docker run -v /home/user/data:/data_input -i -t moff python moff_all.py --tsv_list /data_input/input_file.tab --raw_list /data_input/input_file.raw --tol 10 -rt_win_peak 1 --xic_length 3 --loc_out /data_input/output folder --mbr off
The output consists of :
- a tab delimited file (with the same name of the input raw file) containing the apex intensity values and additional information (a)
- a log file specific to the apex module (b) or the MBR module (c)
- peptide summary intensity file (when peptide summary option is enabled) (d)
(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.