Installation¶

Git Repository¶

Docker deployment¶

Login¶

Access to myProMS data requires a login and password. Contact your local myProMS administrator(s) to request an account. You will then be able to login to the server by clicking on the Start Session button from the home page. During login you can choose between tab- or full screen- display modes.

Server Architecture¶

Once logged to myProMS, your login, user class (see the Users management chapter below for more information) and connection date are displayed at the top of the browser window. Depending of your user class, either myProMS main window (Massist and bioinformatician) or the Project selection window (biologist) will be displayed as shown below. Users (including biologists) can access the main window at any time by selecting Main Window from the Project selection window.

The main window displays links to the 6 areas of myProMS:

Users management;

Projects access and management;

Protein annotation data management;

Settings management;

Data mining section*;

Server management

*Not yet available.

Each of these sections is described in a dedicated chapter (see below). Some sections might not be accessible to users depending on their access privileges. Typically, most end users will have access only to their account and projects. See User classes and access privileges for more information.

Users management¶

User classes and access privileges¶

Data access in myProMS is tightly controlled at the user-level to insure data privacy and integrity. Multiple classes of users are defined based on expertise required to perform the different data/users management and processing tasks available in myProMS. In addition, within certain classes, users can be granted additional privileges if their expertise MS data processing justifies it.

There are 4 classes of users defined in myProMS ordered by decreasing access privileges: bioinformaticians, massists, data managers and biologists.

Bioinformaticians

This class of users is intended for server administration and annotation data management. Although bioinformaticians have full access to all functionalities of myProMS, they should not be used to perform routine data processing such as MS data validation has they might not have the necessary expertise. We recommend to keep the number of bioinformatician accounts as low as possible due to their extended ability to modify the data,

Massists

The massist class represents MS experts who are in charge of MS data import, validation and reporting. Massists also manage user accounts and projects creation. By default massists have access to all myProMS functionalities except those normally dedicated to bioinformaticians.

Data managers and workgroups

Data managers have the same privileges as massists but restricted to projects and users of their workgroup. Workgroup usage is optional but is particularly useful for multiple MS-based research labs sharing a common MS facility. In that case, a single instance of myProMS with a workgroup attributed to each lab will insure data privacy while maintaining management centralisation by the MS facility.

Biologists

Biologists are end users of myProMS. They have access to the projects they participate to with various levels of privileges depending of their expertise and involvement in each project. Project access privileges for biologists come in multiple flavors:

Project involvement-based privileges:

Guest: A guest user can only access the project data but cannot modify them.

User: Can access and modify project data.

Administrator: Same as a user. In addition, a project Administrator can grant other users access to project.

Expertise-based privileges:

Biologists (users and administrators) can be granted partial access to MS data validation if their knowledge of the procedures involved is judged sufficient:

Power (User/Administrator): Can enter “Validation” mode to validate protein identification data only.

Super (User/Administrator): Can enter “Validation” mode to validate both protein and peptide identification data.

Account management¶

From myProMS main window, follow the Users link (this link is named Account for biologists).

Once in the User section, users can either edit their account and change their password. Massists and managers can view and modify other users accounts and create new ones.

Note

A user cannot create an account class higher than its own (eg. a manager cannot create a massist account).

A bioinformatician can be grant extended privileges to a massist or manager to allow this user to perform specified annotation management tasks. In addition, if Mascot is used with group and user ids enabled, this information can be specified for each massist and manager to insure equivalent data access restriction when importing Mascot data directly from myProMS.

Annotation data management¶

Sequence databanks¶

The sequence databanks used by the search engines must be referenced in myProMS so that protein annotations (identifier, description, species and sequence) sometimes not present in search result files (eg. Mascot) can be retrieved from the corresponding fasta file during analysis import.

A referenced sequence databank is also associated with a specific parse rule that allows myProMS to properly match and extract the annotation from the fasta file.

Databank types¶

Multiple databank types are available in myProMS depending on the proteomic resource used to download the fasta file, corresponding to different fasta file entries:

UniProt:

>sp|P15311|EZRI_HUMAN Ezrin OS=Homo sapiens (Human) GN=EZR Ezrin

	UniProt - ALL	UniProt - ACC / ACC#-n	UniProt - ID
Protein identifier	sp\|P15311\|EZRI_HUMAN	P15311	EZRI_HUMAN
Description	Ezrin
Species	Homo sapiens

SWISSPROT/trEMBL #1, #2 and #3

These 3 types are equivalent of the 3 UniProt types described above except that they recognize the obsolete fasta entry format:

>sp|P15311|EZRI_HUMAN Ezrin (p81) (Cytovillin) (Villin-2) - Homo sapiens (Human)
NCBI:

>gi|125987826|sp|P15311|EZRI_HUMAN Ezrin (p81) (Cytovillin) (Villin-2) [Homo sapiens (Human)]

	NCBInr - ALL	NCBI - GI
Protein identifier	gi\|125987826\|sp\|P15311\|EZRI_HUMAN	gi\|125987826
Description	Ezrin (p81) (Cytovillin) (Villin-2)
Species	Homo sapiens

IPI:

>IPI:IPI00843975.1|SWISS-PROT:P15311| Tax_Id=9606 Gene_Symbol=EZR Ezrin`

Protein identifier	IPI00843975
Description	Tax_Id=9606 Gene_Symbol=EZR Ezrin
Species	9606

Warning

The IPI resource is no longer maintained. We do not recommend using fasta files from this resource with your MS search engines.

Undefined source: Protein (User-defined)

This type can be used as a temporary solution for any unknown or custom fasta-compatible entry: >pipe_separated_identifier_block any text

Protein identifier	Whole identifier block
Description	Everything else
Species	None recorded

Note

If you need you need to handle other entries type, please contact your local myProMS administrator or email to myproms@curie.fr.

Listing databanks¶

Only bioinformatician and massists/managers with granted appropriate privileges can access and manage the protein databanks. From myProMS main window, select Annotation data and follow the Sequence databanks link. All active databanks are listed in alphabetic order with a short summary of information as shown in the screen capture below. From this window, you can either add a new databank, edit or delete an existing one.

Adding a new databank¶

From the databank list window, click on the Add new databank button at the top or bottom of the list.

The following form will be displayed:

Fill out the form to provide information on the databank you want to add. In particular, you must select the databank type so that the server will know how to extract the protein annotation from the file. Information on the corresponding parse rule is then displayed to help insure the right databank type was selected. You must also provide a fasta file containing the protein data.

There are multiple ways to do so:

Use a databank already referenced by Mascot: myProMS allows you to directly use fasta files stored on the Mascot server to avoid data duplication. In this case, the databank will be automatically synchronized when updated by Mascot.
Use a file from a dedicated directory on server (e.g. file was previously uploaded by FTP or the directory is shared between local computer and server).
Upload a fasta file from your computer.
Download the file from the internet: You must provide an HTTP or FTP link to the file.

For the last 3 options, normal and gzip-/zip-compressed files are handled. If the databank contains both target and decoy sequences, this must be specified as well as the decoy tag used (eg. REV_).

For the first 2 options (except if a compressed file is used in the 2nd option), it is possible to test the type of annotation rules selected before actually creating the new databank: Select a databank type, the file to be used and click on the Test rules button. Annotations from up to 10 entries from the file will be extracted using the selected rules and displayed. Select another set of rules and try again if the extraction did not match your expectations.

If the databank is species-specific, it is recommended to provide the species scientific name even if already specified in the protein entry lines of the fasta file. Click on the Save button to submit the databank creation form. Once the process is completed, you will be redirected to the databank list window.

Editing a databank¶

You can edit all information concerning an existing databank except its annotation type, the sequence file used and whether it contains decoy sequences. From the databank list window, click on the Edit button on the right side of the databank row. A form similar of that used to add a databank will be displayed. Make the desired changes and click on the Save button to validate your changes. You can test your annotation rules as described above for databank addition but regardless of the databank file origine.

If your databank references a Mascot file, it is possible to check if the file has been updated on the mascot server by clicking on the Check for update button. This can take up to a few minutes for large databank files such as NCBI databanks. Checking for file update is not mandatory since it will be performed automatically once the databank is used during an Analysis import.

Deleting a databank¶

We recommend to delete any databank that will no longer be used to keep the list displayed as short as possible. Deletion of a databank has no effect on the traceability information of analyses using this databank. A databank can be deleted at any time except during import of analyses using this databank.

From the databank list window, click on the Delete button on the right side of the databank row. A prompt will asked you to confirm your decision.

Spectral (SWATH) libraries management¶

Listing spectral libraries¶

From myProMS main window, select Annotation data and follow the SWATH libraries link. All available libraries are listed in alphabetic order with few informations as shown in the screenshot below. On the left side are listed all existing libraries with the possibility to delete, export, edit or update them. On the upper part of the window, you can either add a new library, merge two existing ones, or visualize running processes. You can also search if some desired proteins are existing into one library thank to the search link and restore the previous version of an updated library.

Adding a new library¶

From the libraries list window, click on the Add new spectral library button on the top of the list to display the form below.

You need to select the following parameters in the library creation form :

Task : You can create a new library or merge new data files with an existing library (create a new library from an existing one).
Library name : Provide a name for the library.
Species : Select the species scientific name to filter the databank list.
Consensus library options : A consensus library is a spectral library in which MS2 spectrum entries with a redundant peptide sequence assignment have been collapsed into a single entry. Two options are provided for consensus library generation: a simple option that assumes that all fragment ion spectra are correctly assigned (UNSPLIT) and a more sophisticated option that additionally considers retention time when merging spectra (SPLIT).
Files : Select the DDA data files used to generate the spectral library. Data from 3 search engines can be selected : Mascot files (.dat), X! Tandem files (.xml or .tandem.pep.xml) and Sequest (.xml). For each Mascot, X! Tandem or Sequest file you need to upload the associated mzXML file (with the same name as the Mascot, X! Tandem or Sequest file). You can upload your files from your computer, you can import them from an existing project (only for the .dat files), upload an archive, or select the files in the shared directory.
Instrument : The mass spectrometer used to acquire the data.
Databank : The fasta file used by the search engines (Mascot, X! Tandem and Sequest).
Mayu options : FDR estimation with MAYU. False Discovery Rate (FDR) and number of missed cleavage can be selected.
RT file : The file containing the list of iRT retention time reference peptides.
Description : Optional description of the current library.

Once the form is filled, click on the Submit button to launch the spectral library creation process.

Danger

Update with the new version

Merging two library¶

Two libraries can be merged by clicking on the Merge two libraries button on the libraries list window. The displayed form requires the names of each of the 2 libraries, the name of the new library and an optional description. Clicking on Submit will fuse the selected libraries to create the new library.

Important

Only two libraries with the same iRT file, databank type and consensus library option (SPLIT or UNSPLIT) can be merged.

Editing a library¶

From the libraries list window, click on the Edit button on the right side of the library row. The following form will be displayed :

Note

Only the name and the description can be modified

Make the desired changes and click on the Submit button to save your changes.

Updating a library¶

It is also possible to extend a library using another databank-search data from the same organism. From the libraries list window, click on the Update button on the right of the library row. A form similar to the library creation one will be displayed.

Fill in the parameters and click on the Submit button to launch the update process.

Restoring the previous version of a library¶

An updated library can be downgraded by clicking on the Restore previous version button on the right of the library row on the libraries list interface. Every version of a library can be restored by consecutive downgrades.

Searching for proteins in a library¶

Another available option is to check whether a protein of interest is present in a library and visualize the associated peptides by clicking on the Search button of the desired library, on the libraries list window.

Several proteins can be searched at the same time by inserting the accession names, the protein id or the names of the proteins (one per line or separated by either comma or a space character) in the following form.

_images/search_protein_spectral_libraries.png

All the selected terms are searched beforehand in Uniprot, and a list of proteins is displayed. Some information such as the protein name, id, accession number, length and corresponding gene names are shown. The number of associated peptides identified is also indicated.

_images/search_results_protein_spectral_libraries.png

The peptide list and the protein’s sequence can be displayed by clicking on the number in the #Peptides column.

Some information about each peptide such as sequence, modifications, position on the protein, M/Z, charge, IRT time and specificity are shown.

Exporting a library¶

You can export a library to use it in a quantification software. From the libraries list interface, click on the Export button to display the export form.

You have to fill in the following parameters :

Export format : The library can be exported for PeakView or for OpenSWATH, or you can download the final format of the library (sptxt).
Mass range of fragment ions : Lower and upper mass limits of fragment ions. (min=350 and max=2000 by default).
Ion series and charge : The ion desired type (a, b, c, x, y, or z) and charge separated by a comma. (charge=1+ and 2+ by default).
Number of ions per peptide : Minimum and maximum number of ions per peptide. (min=3 and max=20 by default).
Files :
- Windows SWATH file : Upload the file that contain the SWATH window scheme that has been used for SWATH data acquisition.
- File with modifications delta mass : Optional file containing the modifications not specified by default.
- Labelling file : Optional file containing the amino acid isotopic labelling mass shifts. If this option is used, heavy transitions will be generated.
- Fasta file : Optional databank fasta file used to relate peptides to their proteins.
Other options : You can select another optional options such as the maximum permissible error, the time scale, the UIS order (calculated when using switching modification; if -1 is set, all transitions for each isoform will be reported; default : 2), or the list of allowed fragment mass modifications.
Protein list : You can select a file containing a protein list to export just these proteins from the library.

Then you can click on the Submit button to launch the export process. Once the process is complete, you can download the final file with a download link that will appear.

Deleting a spectral library¶

A library can be deleted from the list window (by clicking on the Delete button of the corresponding library) only if this library was not used to create another library (merge option, in that case, a prompt will inform you).

GO files management¶

GO analyses require two types of GO files: an ontology file and an annotation file. These files are not project-specific and are thus managed globally in myProMS. From myProMS main window, select Annotation data and follow the GO annotations link to display the list of GO files recorded.

Note

Only bioinformaticians and authorized massists/managers can manage GO files

Ontology files¶

Ontology files contain the GO terms identifiers, description and relationships between. To add a new ontology file, click on Add new Gene Ontology file:

The displayed form requires the following information:

Name: A relevant name for the ontology. This name will be displayed in all GO analysis starting forms in ontology selecting section.
File: The file containing the ontology must be in OBO format (not XML nor database dump). Daily updated ontology files can be fetched from GO website. The file can be uploaded directly from user computer or directly retrieved from remote FTP by writing its full URL (e.g. ftp://ftp.geneontology.org/pub/go/ontology/obo_format_1_2/gene_ontology.1_2.obo).
Scope: Specify if the ontology file contains the full gene ontology or a slim version.

Note

A slim version gives a broad overview of the ontology content without the detail of the specific fine grained terms. If a slim file is used, make sure to select the slim option.

To be able to use a slim ontology for GO analyses, at least one full ontology file must have been also recorded to allows myProMS to reconstructs missing associations between proteins and the GO terms recorded in the slim file.

Warning

Running a slim GO analysis without a corresponding full ontology will cause an error

Saved ontologies can be edited. If the file was retrieved by FTP and a most recent version available on the distant server, it can be downloaded again directly by clicking on Update file.

Annotation files¶

Annotation files contains mapping of protein identifiers to GO terms. They are species-specific and must be in Gene Association File (GAF) format. A large number of updated annotation files for many species can be fetched from the Uniprot-GOA database. To add a new annotation, click on Add new annotation file:

The displayed form requires the following information:

Name: A relevant name for the annotation, that will be displayed on each GO analysis starting form in annotation selection section.
Description: An optional description for the annotation.
Species: Select the targeted species from the list of available ones (See Species below for more information).
File: file can be uploaded from your computer or retrieved remotely from a FTP server (e.g. ftp://ftp.ebi.ac.uk/pub/databases/GO/goa/HUMAN/gene_association.goa_human.gz for the human annotation file).
Identifier used: Select the protein identifier that must be used in myProMS to match the annotation’s one (eg: select Uniprot ID or Uniprot AC for Uniprot-GOA files). If “Default” is selected, the default protein identifier displayed in myProMS will be used.

Warning

Identifier must be set carefully to insure proper GO annotation mapping

Species¶

myProMS automatically records the species associated with any protein validated. Because different strains or variants of the same species are also recorded, it is necessary to manually link these entries to the same reference species. Furthermore, reference species must be recorded for Gene Ontology analyses. A species management section is provided so that bioinformaticians and authorized massists/managers can manually record or correct species information. By default, a list of 5 model organisms species data is provided with myProMS as reference.

Listing species¶

From myProMS main window, select Annotation data and follow the Species link to access the species management interface.

As shown is the above screen capture, a subset of species can be listed either by scientific or common name by selecting the appropriate initial letter in one of the 2 alphabets displayed.

Adding or editing a species¶

A species can be added or edited by clicking on “Add species“ or “Edit” buttons respectively. The following form is then displayed:

The common name, scientific name and taxonID fields are mandatory. A link to the NCBI Taxonomy resource is provided to help you find this information if not known. You can either set this species as reference by checking the Is reference or link it to a reference one In addition an optional field allows you to link any species with a reference one by selecting a target species in the drop-down menu.

Deleting a species¶

A species can be deleted from the list interface (by clicking on the Delete button of the corresponding species) only if this species is no longer associated with validated protein, not set as reference species nor used in a GO analysis.

Sequence modifications¶

myProMS automatically records the post-translational modifications (PTMs) found in imported analyses on protein sequences. Once an analysis has been imported, PTMs found on this analysis are added to the list and you can edit the properties of those PTMs. myProMS keeps track of every imported modifications and displays by ascending name as defined on UNIMOD website.

In this list, all PTMs are depicted by five informations ; names (PSI-MS and interim name), description, specificity and status (red or green). The specificity describes on which residue the PTM tends to occur. It could be on a specific residue including or not a context (like “Any N-term”, “Protein N-term”,etc.).

The status displayed on the upper left corner by a circle tells if a PTM is valid or not. To be valid, a PTM should be characterized by a monoisotopic and an average mass like the ones defined on UNIMOD website. If a PTM is not valid ( ptm_not_valid ), it means that myProMS could not retrieved this PTM through the UNIMOD current list of PTMs. The origin of that issue comes from one reason : you entered a “home-named” modification that is not referenced in UNIMOD.

Two solutions exist to solve this issue :

If this modification was already imported on another referenced name, you should merge this “home-named” modification to this one by editing the non-valid PTM. In the future, myProMS will automatically applies to this “home-named” modification the properties of the referenced one.
If this modification was not imported through another name, you should edit the PTM and provide mass and specificity.

Make sure that all PTMs retrieved are valid in order to avoid the other features available in myProMS to give wrong output (like fragmentation table of peptides for example).

Editing or merging PTMs¶

A PTM can be edited by clicking on Edit button.

In this mode, you can update the description or the delta-mass of this PTM. A link to UNIMOD is provided by giving the Unimod Accession number. Specificity can be updated given your expertise on the PTM and reviews articles you may have read.

The option Merge with gives the opportunity to merge two PTMs into one single entry. This could be useful if you wish to give an alternative name to a modification. Select the modification you want to merge with the current PTM and click on Save. This action will add the name of the current modification to the list of alternative names of the one selected.

For PTMs that you want to make appear in your projects and give special attention to, you need to enter a code (usually, a single letter) and a color. Those PTMs will become relevant and will be selectable in every project you manage.

Here is a list of relevant PTMs and their associated code-color designation:

Note

For more information on that topic, please, see Project Creation.

Server administration¶

Projects settings¶

All MS search results and data subsequently generated are organized in projects. A project regroups sets of data that belong to the same user or group of users and generated in the context of a defined scientific project. End-user (biologists and managers outside their workgroup) accessibility to the data is defined at the project level.

Selection¶

From myProMS main window, follow the Projects link to display the Project selection interface.

For biologists, a straightforward list of the projects they have access to will be displayed. For other classes of users, projects can be organized based on the following topics: Workgroups (default), Project owners, Active projects and Archived projects. Corresponding projects are listed by ascending name. Their description, owner and/or workgroup together with the access credentials of current user. If On-going analyses is selected, the list generated is composed of Analyses still undergoing validation. Their name, description, data file name, creation date and corresponding project are displayed. This list can be sorted by Import date, Name, MS type, Validation status and Data file name.

Alternatively, a Search can be performed using various criterias:

Projects are then listed together with the items that were matched during the search. Once listed, click on the Open button to go to the selected project.

Creation¶

Only bioinformaticians, massists and data managers can create projects. From the Project selection interface click on the Create a new Project button. The following form is then displayed:

Name: Provide a mandatory name for the project.
Description: an optional description for the project.
Protein visibility: Specify the project-wide protein visibility rule to be used. See Match groups and protein visibility below for detailed information on this concept.
Identifier conversion: myProMS tries to map protein identifiers to their synonyms in multiple biological resources. If a conversion is selected, the default identifiers used for protein sequence identification during the MS search process can be replaced by synonyms more meaningful to end users. Unmapped identifiers will not be changed.
Relevant PTMs: Post-translational modifications relevant to project can be selected here. The list of selectable modifications can be modified in Sequence Modification section. Information regarding relevant modifications will then be available when performing multiple tasks such as listing, comparing, quantifying and displaying proteins or modification sites.
Project owner: Specify the owner of the project here. This information can be used to sort projects in the projects selection window.
Workgroup: Specify which workgroup this project should belong to if any.
Comments: an optional comments for the project.

Click on the Save button to create project.

Note

Projects can be edited at any time to modify any of these settings.

Accessibility¶

Bioinformaticians and massists have full access to all projects recorded in myProMS. Data managers have full access to all projects within their workgroup. Biologists and managers outside their workgroup must be explicitly granted access to projects when needed. The project access management interface is accessible from the project’s home page by clicking on the Project Accessibility button in the option frame.

The interface summarizes the list of users able to access the project together with their credentials. New users can be added one at a time. Once added to the access list, users are guests by default. Select the credentials you wish to provided each user with. The access rights available are listed below the user access form. See also User classes and access privileges for more information.

Click on the Save button to validate any changes.

Organization¶

Data in a project are hierarchically organized as shown in the figure below:

Experiments¶

An Experiment item represents an actual biological experiment for which MS data will be collected. To create a new experiment, select the project element in the top left navigation frame and click on Add Experiment(s) in the option frame.

Provide a name and optional description and/or comments. Multiple experiments can be created at once if the field Multiple entries labels is filled in. Labels defined in this field will be sequentially appended to the name each experiment created. Labels can be defined individually using a comma-separated string (eg. “A,D,G”) or a range string using a ‘-‘ (eg. “1-5”);

Samples¶

A Sample item is a “loose” entity that can represent a single or multiple mixed (e.g. for labelled quantification) biological samples. It can be viewed as a sub-experiment or Analysis-containing item. It is up the user to define its function depending on the experimental context of the analyses it contains. To create a new sample, select its parent experiment in the navigation frame and click on Add Sample(s) in the option frame.

Provide a name and optional description and/or comments. Multiple samples can be created as described for experiments (see Experiments).

Analyses¶

An Analysis corresponds to a dataset imported from a single search engine result file: mostly the MS/MS spectra (except for PMF runs), the peptide/protein identifications and associated quantifications when present in the file. Analysis data must be imported, validated and reported before end users can access them and further process their results. These procedures are described in the chapter Analysis data import and validation below.

Life span¶

On-going: Once created, a project is set as active and on-going. This means that it can be populated with new items and data. On-going projects are flagged with a yellow icon in the project selection window.
Ended: If the project is judged completed, it can be edited and ended by clicking on the End button at the bottom of the edition form. Ending a project will automatically end all partially-validated analyses without new reporting (see Validations and Reporting sections in the Analysis management chapter below for more information). Once ended, a project is still active and accessible but can no longer be edited or populated. Ended projects are flagged with a green icon in the project selection window.
Archived: As time passes, some project might no longer be accessed by any users. These projects can be archived to save space on the server. All data files stored outside the database will be compressed. Archived projects are flagged with a red icon and are no longer accessible for data exploration. They can however be listed in the Project selection window by selecting “List of: Archived projects”.
Restoration: Archived and Ended projects can be fully restored to any activity state if necessary by clicking on the appropriate button in the project home page.

Analysis import¶

The collection of spectra/peptides/proteins(/quantification) data contained in a search result file are imported into myProMS as an Analysis.

Note

Only bioinformaticians, massists and managers can import Analyses.

Select the Experiment, Sample or 2D Gel into which the Analyses must be imported and click Process Analyses in the option frame. From the selection menu displayed, select Analysis Management to display the list of available options.

Supported search engines¶

myProMS allows to import from various search engines :

Mascot (DAT file or MSF file from Proteome Discoverer Software by Thermo Scientific).
Paragon (XML generated from ProteinPilotTM Software by AB SCIEX, group2xml.exe).
Sequest (MSF file from Proteome Discoverer Software by Thermo Scientific).
Phenyx (XML file generated through Phenyx platform by GeneBio). DEPRECATED!
Andromeda/MaxQuant (mqpar.xml and 3-4 txt files are required).
X!Tandem (XML file from X! Tandem pipeline (PAPPSO) or from Trans-Proteomic Pipeline (TPP )).
PeakView (Exported Excel XLXS file for SWATH data).
OpenSwath (TSV file from OpenSWATH workflow).
Spectronaut (TSV file generated from SpectraunotTM by Biognosys).

Attention

If you perform Mascot searches with Proteome Discoverer (PD) Software, make sure you dit not use Protein and Peptide filters. In the Workflow Editor, click on Mascot node and then, set the four filters Peptide Cutt Off Score, Peptide Without Protein Cut, Protein Relevance Threshold and Protein Relevance Factor to 0. If these filters are not turned-off, myProMS import options such as predefined False Discovery Rate (FDR) will not be accurate.

Collecting search files¶

Multiple analyses can be imported at once as long as the corresponding searches were performed with the same search engine and protein databank(s).

To select data files in Mascot, Proteome Discoverer and X!Tandem:

Click on Proceed next to the Import multiple analyses process as shown below. The following form will be displayed to select the source of the search files to be imported.

Multiple import sources are available:

A user directory on server: Following upload, files are stored in a user-dedicated directory on server. These files will stay available to the user until he decides to delete them; either just after import or later. In the later case the user can still access this directory for file management purpose by clicking on the Clean My Directory button.
Any directory on server: This option is available to bioinformaticians only. The user can provide any path on the server where myProMS should look for search results files.
Mascot server: If a Mascot server is declared in myProMS configuration file, it can be accessed, searched for specific search results and files directly uploaded into myProMS user directory.

User can search results files by date or job number range or keywords in the files search title. The list matching files is then displayed and grouped by day of creation. Specific information on a file (name, availability, search title and user ID) can be displayed by clicking on the file name. If access to Mascot is restricted (user accounts set up), the Mascot userID must be defined in myProMS as well (see Account management).

In this case, only Mascot files accessible to the user will be displayed.

Upload Zip archive: If a large number of files must be imported, they can be uploaded at once as the zip archive. The archive will be unzipped on the server.
Upload multiple files: Alternatively, up to 10 files can be uploaded as separate files.

Once your files have been selected, click on Proceed to initiate file retrieval from the selected source. This procedure may take a few minutes depending of the number and size of the files. Once the transfer is complete, a file import interface will be displayed.

Important

Most browsers do not support upload of files with (total) size > 2 Gb. If files larger than 2 Gb must be uploaded, we recommend to use Google Chrome. This limitation does not apply when retrieving files directly from a Mascot server.

Import parameters (Mascot, Proteome Discoverer and X!Tandem)¶

Files retrieved are listed in alphabetical order together with pertinent information about the search performed: the MS file, search type (MS2, MS1 or mix of both), databank(s) and taxonomy and search title used.

Note

The same Proteome Discoverer msf file can contain multiple searches results (e.g. a search performed with Mascot and another with Sequest). In this case, separate entries will be listed for each search performed together with more details on the parameters used. Each search result can be imported separately and distinct Analysis items will be created.

Proceed as follow to continue data import:

Select the files to be imported from the list by checking the boxes on the left-hand side of the files name.
Provide a name for the Analysis to be created (Analysis name column). This name can be typed by the user or set to match either the name of the search file or that of the original MS file used for the search.
If the parent item was an Experiment (or a 2D Gel), each new Analysis must be associated with a specific Sample (or Spot). Pre-existing Samples (or Spots) can be selected from a dropdown menu or the can be created on the fly (Samples only): To create a new Sample, select “New” from the dropdown menu of Parents column. A popup window will ask you to provide a name for the new Sample.
Select the databanks to be used to extract protein annotations (Databank(s) field). If multiple databanks were used during the search (possible with Mascot for instance), the corresponding number should also be selected here. All search files to be imported in the same batch should have been performed using the same or equivalent (set of) databank(s).
Define a filtering rule for the data to be imported (Threshold score):
- If a decoy search was performed, data can be filtered to based on a user-defined False Discovery Rate (FDR) on peptide identification (default is 1%). A threshold score for peptide identification will be determined so that the data imported will (tentatively) match the defined FDR value as illustrated in the figure below. Threshold score calculation can either uses the qvality algorithm [1], the Mayu algorithm or the DT count algorithm. In DT count mode, decoy (D) and target (T) peptides are simultaneously counted in descending score order until the proportion of the 2 populations matches the selected FDR value.
- If no decoy search was performed or the FDR value was set equal or less than 0, the filtering will be performed according to a minimum (threshold) score for peptide identification. A default (search engine-specific) threshold score will be applied unless a different one is provided by the user.
Select the maximum number of interpretations allowed of the same fragmentation spectrum (Max. rank). The default is 1, but up to 10 can be chosen.

Note

When performing FDR filtering, it is recommended to set this value to 1 since the FDR calculation is based on 1 interpretation per spectrum.
Provide optional description and comments.
Decide whether the files should be deleted after import or not (Delete imported files afterwards). Unless selected for deletion, file will remain on the server for new import until the user decides to delete them.
Click on Proceed to initiate the data import into myProMS database.

Importing MaxQuant data¶

3 to 5 files are required to import a MaxQuant search/quantification into myProMS:

mqpar.xml (usually located in the root directory of the MaxQuant search),
evidence.txt (file from the Combined/txt directory),
Peptides.txt (idem),
proteinGroups.txt (idem. Optional, only to import protein quantification data),
msms.txt (idem. Optional, only to display peptide fragmentation spectra).

Files 2 to 5 must be compressed in a common archive before import. Select an Experiment in which to import the data. From the Process Analyses window, select either the Import multiple Analyses or Import quantification processes and click on Proceed next to Import MaxQuant quantification to display the form below.

<Image>

Provide the files mentioned above and the protein sequence databank(s) used for the search. If contaminants were searched, provide a matching contaminant databank. Finally, select the rule you wish to use for protein aggregation into match groups: myProMS or MaxQuant. Specifiy also if you wish to import protein quantification data (the proteinGroups.txt file must be provided in the archive in that case). Submit the form to start data import. Data import will take a few minutes. Samples, Analyses and an experimental Design, a peptide quantification and 1 to multiple protein quantifications will be added to the selected Experiment according to the information extracted from the files uploaded. Peptides and proteins will be automatically validated since they were used in protein quantification.

Import DIA data¶

User can import DIA quantification data from three different software : PeakView, OpenSWATH and Spectronaut. Into myProMS select an Experiment and from the Process Analyses window, select the Analysis quantification process and click on Proceed next to Import PeakView/OpenSWATH/Spectronaut data to display the associated form.

From PeakView¶

Two files are required : the Excel worksheet file generated by PeakView and the spectral library. Into PeakView, once the experimental SWATH data analysis is over, you can export the result into an Excel file by clicking on the Quantitation tab on the toolbar and selecting SWATH Processing/Export/All. The PeakView search parameters can be filled in the following form to be saved in myProMS database to ensure traceability. Then submit the form to launch data import.

_images/analysis_import_DIA_PeakView.png

From OpenSWATH¶

The file from OpenSWATH is required, this is a TSV file generated by the last step of the workflow (TRIC). The library that will be used to analyse the experimental data is also needed as the associated export parameter file. Then, the user must provide the TRIC method used and the number version of OpenSWATH.

_images/analysis_import_DIA_OpenSwath.png

Running OpenSWATH quantification¶

The OpenSWATH workflow can be launched directly from myProMS. The process will analyse the experimental files and import the result in a same step. Some parameters are required as the library name, the mzXML results files, the iRT file (in TraML format) and the DIA windows file. The user can import his own library converted for OpenSWATH, and choose to merge this result with other existing analysis.

_images/analysis_import_DIA_OpenSwath_Quanti.png

Danger

A MODIFIER AVEC LE NOUVEAU WORKFLOW

From Spectronaut¶

The import Spectronaut data form is similar to OpenSwath’s form.

_images/analysis_import_DIA_Spectronaut.png

Once the process is over, samples and analyses will be added to the selected Experiment. Peptides and proteins will be automatically validated. Transition quantification data will be imported but not the peptide or the protein quantification results as a dedicated pipeline is available in myProMS to perform this task (see the Protein quantification chapter below for more information).

Analysis summary¶

Files will be imported sequentially and the Analyses (and Samples if any) newly generated will appear in the top left navigation window. The Analysis summary is shown below:

Diverse information is available to the user such as the MS type, search engine, databank(s) (selected in myProMS), protein identifier type, taxonomy, labeling method if any, threshold score value and strategy (FDR-based or user-defined), validation status,… More search parameters and score distribution for FDR computation can be displayed on demand.

[1]	Qvality algorithm : Käll et al. Bioinformatics 2008, 25

Analysis validation¶

Search result data associated with an Analysis must be validated before being accessible by end-user for further interpretation. Data processing of Analysis data is a multi-step process.

Analyses icons are color-coded based on their data processing status to help users to easily determine the validation level of each of them:

: Protein annotation import not yet completed.
: Data not yet validated.
: Data partially validated.
: Data validated and reported.

Note

Protein annotation import from the databank file(s) is not part of the validation process. It is triggered as a background task immediately after search result data import. This process can take several minutes depending on the number of proteins identified. However, validated data cannot be reported (see the Reporting section below for more details) before protein annotation data import has been completed.

Automated peptide/protein validation¶

FDR (False discovery rate) - based validation¶

FDR-based validation is initiated at the data import step to filter out data below the corresponding threshold score (see Analysis import).

Qualitative validation¶

Comparative validation¶

Validation templates¶

Manual peptide/protein validation¶

Peptides selection/exclusion¶

Protein exclusion and filtering¶

Lower-scoring peptides activation¶

Clear peptide/protein selections¶

Sequence modification validation¶

Phosphorylation sites validation with PhosphoRS¶

PhosphoRS [1] is an algorithm used to determine phosphorylation positions on a peptide, based on its sequence and MS2 fragmentation spectrum. This tool is included into myProMS and can be used to correct imported phosphorylation data. From “Process analyses” menu, select Peptide/Protein Selection process type, and click on Start PhosphoRS analysis.

The following form is then displayed:

PhosphoRS parameters can be set in PhosphoRS Analysis Rules section: - : - Activation type: select the fragmentation mechanism of the analysis instrument. PhosphoRS is optimized for each activation type. - Mass Deviation: mass error tolerance when PhosphoRS matches experimental spectra with theoretical spectra.

Manual validation of modifications¶

Validation traceability¶

Reporting¶

[1]	PhosphoRS: T. Taus et al., J. Proteome Res., 2011

Biological Samples¶

Properties¶

Treatments¶

Recording a biological sample¶

Linking biological samples to MS Analyses¶

Validated proteins¶

Match groups and protein visibility¶

Match groups are a key feature in myProMS. Understanding and setting properly the rules that control match group organization and protein visibility is essential for accurate data analysis and interpretation.

Because the shotgun mass spectrometry technique (used to generate all Analysis data) allows to identify peptides and not protein, multiple proteins can be matched with the same (set of) peptides. This creates an inherent ambiguity on the identity of the proteins contained in the extract analysed. myProMS deals with this problem by organizing the proteins identified in match groups representing groups of proteins sharing the same (sub-)set of peptides.

To avoid protein inflation, by default only 1 (top) protein per match group is made visible and all other will be hidden (not considered as identified in the sample studied). The top protein is also used as alias for the match group. Only visible proteins are considered as present in the sample analyzed. Hidden proteins will not be listed (unless the user specifies otherwise) and not used in all subsequent data processing such as protein quantification or Gene Ontology analyses.

Top protein selection rules¶

For each match group, myProMS attempts to set the protein most likely identified by the corresponding set of peptides as top protein using the following rules sequentially until only 1 protein remains:

The protein is matched by all peptides in the set.

*The protein is the most often found as top proteins in previously validated analyses.

The best scoring protein among those meeting the previous criteria.

The protein with best sequence coverage among those meeting the previous criteria.

The best annotated protein among those meeting the previous criteria. Annotation quality is estimated as follows:

SwissProt identifier.

trEMBL identifier.

None of the following keywords found in the protein description.

The hypothetical keyword is found in the protein description.

The unknown or unnamed keywords are found in the protein description.

The protein description is missing.

the shortest protein among those meeting the previous criteria.

the protein with identifier first in alphabetic order.

* myProMS will preferentially select a protein that has been identified often in previous samples.

Project-wide protein visibility rules¶

By default, only the top protein of each match group is visible. However, the user can use 1 of 3 predefined project-wide protein visibility rules to alter this default behavior. Edit the corresponding project. The following section is part of the edition form:

These rules are self-explanatory and ordered by decreasing stringency. Selecting rule #2 or #3 will alter myProMS default behavior and can potentially lead to multiple visible proteins per match group. Click on Save to validate your changes. Visibility of all identified proteins will re-evaluated based on the rule selected except if involved in quantifications or GO analyses. Keep also in mind that protein lists and saved comparisons (see Saving a comparison) can be modified by the resulting changes in protein visibility.

Checking for conflicting match groups¶

It is possible to check for inconsistency in match groups across multiple analyses; meaning to detect proteins with inconsistent visibility (visible vs hidden) across multiple analyses. From the Summary view of any project item containing at least 2 validated analyses, click on the Scan for conflicts button right of the number of visible/total proteins validated. A list of such proteins (if any) will be displayed with the number of analyses where each protein is found visible or hidden as shown in figure below.

Click on the [+] icon to display the list of the analyses involved. From this list, you can either:

Edit a specific match group by selecting an analysis (radio list) and clicking on the Edit match group button at the top of the table (see Manual edition).
Display detailed information on a protein in the context of the analysis of your choice by directly clicking on that analysis’ name.

Displaying match group composition¶

List Proteins at Analysis-level (see Project-based protein lists) and check Show Match groups at the top of the list. Click on the [+] icon next to the alias protein identifier to display the content of the match group. No [+] icon indicates that the protein is alone in its group. As shown in the screen capture below, visible proteins are listed in bold while hidden ones appear in light font.

Manual edition¶

Aside from project-wide protein visibility rules, any match group can be manually edited to modify the top protein selection as well as the visibility of any protein in the group. Modification is allowed only if corresponding analysis is not associated with protein quantifications nor GO analyses. Click on the Edit Match Group button at the bottom of the match group list to enter editing mode.

In part 1) of the form, the top (alias) protein can be changed and any other protein can be made visible meaning that you believe they are indeed present in the biological sample analysed.

In part 2) of the form, you can propagate the changes made in part 1) upward in the project tree. You can chose to propagate independently the alias, visible and hidden protein selection.

Finally, part 3) lets you decide whether your changes can contradict or not the current project-wide protein visibility rule. If this option is unchecked, any changes made that do not agree with the project-wide rule will be ignored. Click on Proceed to validate your changes.

Peptide distribution in match group¶

It is possible to visualize graphically the set of peptides of the match group and where they match each protein sequence. To do so, click on the “Peptide Distribution” button at the bottom of the match group list.

See Peptide Distribution in Single protein view for more information on how to interpret the displayed data.

Identifier mapping¶

Single protein view¶

Protein lists¶

Project-based protein lists¶

User-defined protein lists¶

Export protein lists¶

Search for proteins¶

Compare project proteins¶

Full protein-level comparison¶

Pairwise protein-level comparison¶

Pairwise peptide-level comparison¶

Saving a comparison¶

Peptide Quantification¶

Peptide quantification is a necessary step for peptide-based protein quantification; whether the quantification is based on MS-spectra (SILAC, TnPQ, XIC-based label-free quantification,…) or on MS/MS fragments (iTRAQ, TMT, SWATH…).

Data import from search results file¶

Some search result files already contain peptide quantification data. It is always the case for MS/MS fragment-based quantification such as iTRAQ for which the peptide ion intensities are part of the MS/MS spectrum data.

Some search results files (Proteome Discoverer MSF or MaxQuant) may also contain peptide XIC data if a quantification was performed after the search process. When peptide quantification data are contained in the imported search results file(s) myProMS will automatically import these data either during search data import or at the Validated data Report step if data validation must be performed.

Only quantification data related to validated peptide will be kept (see the Virtual peptides section below for important additional information).

Data extraction from LC/MS file with MassChroQ¶

XIC-based peptide quantification can be performed within myProMS whether or not peptide quantification data were already available in the search results file. myProMS uses the tool MassChroQ [1] to perform this task. However, the corresponding LC/MS file(s) must be provided in mzXML format.

Managing mzXML files¶

To manage the list of mzXML files available within a given project, select any Experiment or Sample from the project navigation window and click on the Process Analyses button in the option frame. From the selection menu displayed, select Analysis Quantification to display the list of available options. Click on Proceed next to the Manage mzXML files process as shown below.

The following form will be displayed to allow you to either import a new mzXML file or delete already imported ones.

Note

We perform LC/MS files (RAW & WIFF formats) conversion to mzXML with ProteoWizard tool using default settings. Other format conversion tools were not tested. We also recommend not to change the files name (except for the mzXML extension) to ease Analysis/mzXLM file matching in the quantification launch step.

Running XIC extraction¶

Go to the Analysis Quantification options (as shown above) and click on Proceed next to the XIC extraction with MassChroQ process to display the form shown below.

In the first part of the form, multiple parameters can be set for the extraction:

Name of the quantification : All extraction data collected will be regrouped in a single quantification carrying this name.
Extraction type : Profile or centroid
Isotope labeling : If isotope labeling was performed on your sample, it is possible to use XIC extraction to retrieve it. To do so, you need to choose SILAC. Up to 3 different channels can be retrieved at a time (e.g. heavy, light and medium) that have to be named. For each channel, one or more quantification label can be added given the experimental design. Each quantification label is linked to a post-translational modification that explains it. Specify the modification target on which it occurs (side chain, n-ter or c-ter). If side chain is chosen, don’t forget to give the residue where the post-translational modification occurs.

Here is provided an example of the filled form in a SILAC experiment where lysine and arginine residues were designed as heavy isotope. 13C6-15N4 was renamed to Arg10 for clarity.

Important

It is really important at this stage to define a light channel if a biological experiment/condition/analysis match the light version. Otherwise, the light version of the peptide will not be retrieved in the end.

Note

Your modification is not selectable in the modification target option ? Check the status of the modification (see Sequence modification section below). Maybe the modification you are using is not tagged as label. If not, change this and save it by editing the modification.
Alignment settings: Multiple LC/MS runs can be quantified at once. MassChroQ can align all runs to match features across different runs. User must provide an alignment algorithm (OBI-Warp or ms2), a reference run by selecting the corresponding analysis and an m/z window (for OBI-Warp algorithm).
Peptide selection: Whether to extract or not all charge states of a given peptide.
Type of XIC extraction to be performed: basePeak area (most intense peak in the range of masses) or TIC area (summed intensity across the range of masses).
More settings are also available by clicking on the corresponding button.

Finally, click on Launch Quantification to start the extraction. A pop-up window will appear to allow you to monitor the quantification progress. XIC extraction is a long process that can last up to an hour or more depending on the number of Analyses to be aligned, the complexity of the LC/MS run and the computer power available. You can continue using myProMS in the mean time and even launch other quantifications. All on-going quantification jobs are displayed in the Monitor Quantifications window (see figure below).

Note

As new jobs are launched or old ones completed, they will appear or disappear from the list. Additionally, if an error occurs during quantification, a message will appear for the corresponding job. The user will be able to display the content of the error message and delete the failed job and all associated temporary data.

If this window is closed inadvertently (closing it has no effects on the on-going jobs) or did not appear (pop-up windows for myProMS URL must be enabled in your browser), it can be displayed again by clicking on the Monitor on-going quantifications button in the Analysis Quantification options (Process Analyses > Analysis Quantification).

In the second part of the form, you must select the analyses corresponding to the extraction and associate its mzXML file to each of them. If the mzXML file name matches the MS data file recorded for the Analysis, myProMS will do the job for you. (Check the MassChroQ manual for help on setting these parameters properly).

Note

Only runs with reproducible retention-time values (e.g biological or technical replicates) should be selected for alignment. Runs potential very different set of feature (e.g.sample fractions separated on a gel) should be extracted separately.

Virtual/Ghost peptides and proteins¶

During the quantification process, intensities of parent ions can be calculated even though the corresponding peptide did not end up in the list of peptides validated. For instance, in the case of a SILAC-labeled analysis, the label-free form of a peptide can be validated but not its labeled counterpart; either because the later falls under the threshold score used or was not identified at all. However, these data are valuable for the quantification since both peptide forms are required for ratio calculation.

Note

myProMS solves this issue by adding these missing peptides to the list of validated peptides but with a special status: virtual peptides (also called ghost peptide).

This strategy of peptide addition also applies to alternative charge states of a given peptides (e.g. if the 2+ charge state of a peptide is validated, all other quantified charge states will be added as virtual peptides). Virtual peptides remain hidden unless their presence is required for proper data interpretation.

Important

When applied to a label-free quantification where 2 or more analyses are aligned, a peptide validated in the reference analysis but missing (or not validated) in an aligned one can be reextracted as a virtual peptide. If this peptide does not belong to any validated proteins of the aligned analysis, the protein(s) matching this peptide in the reference will be added to the analysis aligned as ghost protein(s). Ghost proteins appear in italics in most protein lists.

Displaying peptide quantification data¶

Once the peptide quantification data are available (after a Report for Search file extraction or an XIC extraction within myProMS), they can be displayed for individual analysis by selecting the corresponding analysis in the Project navigation frame and clicking on the Internal Quantifications button in the option frame.

From new window displayed in the result frame, select the name of the quantification (in the Peptide quantification section). A window similar to the one below will be displayed showing a summary of the quantification parameters used (if any: no parameters are displayed in case of a direct extraction from a search results file) and a list of proteins with identified peptides and corresponding XICs or fragments area for DIA extraction. In case of labeled quantification, peptide sets (label isoforms) are grouped into a single peptide row. The peptide set sequence, variable modification, position, charge, score(s) and XIC(s) are displayed.

Legend: Case of a direct extraction of SILAC-labeled peptide XIC from a search result file

Virtual peptides can be easily identified as they do not have score.

Legend: Case of a MassChroQ extraction with alignment of multiple Analyses

[1]	MassChroQ : Valot B et al, Proteomics, 2011

Protein Quantification¶

Absolute abundance quantification¶

emPAI (label-free)¶

The Exponentially Modified Protein Abundance Index (emPAI) is a spectral-count method that estimates the relative quantitation of proteins in a complex mixture[1] based on protein coverage by peptide matches. myProMS uses the built-in implementation of the Mascot server 2.3 software which is a slightly modified version of the original emPAI value (for more details, have a look to mascot help ). As this value is retrieved from Mascot web-server, this label-free method can only be applied to Analyses generated from Mascot DAT files directly imported from a connected Mascot server.

SIn (label-free)¶

The Spectral Index Normalized (SIn) is a normalized label-free quantitative method which combines three abundance features : peptide and spectral count with fragment-ion (MS/MS) intensity[2] . This label-free method is currently available only for Analyses generated from Mascot DAT files. Support for other search results formats is plan in future versions of myProMS.

MaxQuant: Intensity, LFQ, iBAQ¶

Proteomic Ruler¶

The Proteomic Ruler yields absolute quantification values such as protein copy number per cell, concentration or mass of protein per cell but it can also give abundance values or ranks of abundance in the sample. The idea is that the quantity of a specific protein compared to all the proteins in the sample is proportional to the protein intensity, measured by MS, compared to the total MS signal.

Thus, the computation of quantification values is based on previous measures of intensities, typically Intensity or LFQ values as output by MaxQuant (see MaxQuant: Intensity, LFQ, iBAQ above). For this reason, the Proteomic Ruler is available only from a MaxQuant design, with quantifications already computed.

Proteomic Ruler Type : You can choose between three different methods of Proteomic Ruler : the Total Protein Approach, the Histone Proteomic Ruler or the Custom Proteins Ruler.

Total Protein Approach : This is based on the work of Wiśniewski et al., 2012 [3]. The copy numbers per cell are determined by comparison of the individual intensities with the sum of all the MS intensities, then dividing this ratio by the molecular weight and multiplying by the Avogadro constant and by the total protein content (mass) of a single cell. Therefore you must specify this total protein content per cell as input. The formula is then :

\[{Protein\ copy\ number\ per\ cell} = {Total\ amount\ of\ proteins\ per\ cell}\ \cdot\ \frac{N_A}{M}\ \cdot\ \frac{Protein\ MS\ intensity}{Total\ MS\ intensity}\]

Histone Proteomic Ruler : Wiśniewski and colleagues published this as an extension of their Total Protein Approach in 2014 [4]. Instead of relying on a user-given total amount of proteins per cell (most probably calculated from the total amount of proteins in the sample and the number of cells in the sample), the Histone Proteomic Ruler is based on the Histone MS signal and on the ploidy of the studied cells.

In their paper, Wiśniewski et al. show that the total protein mass per cell can be estimated from the cellular DNA mass and the ratio of the Histone MS signal to the Total MS signal, following the formula :

\[{Cellular\ protein\ mass} = {Cellular\ DNA\ mass}\ \cdot\ \frac{Total\ MS\ signal}{Histone\ MS\ signal}\]

For a specific cell type, we can obtain the amount of DNA per cell from the genome size and the ploidy of the cell type. For example, for a diploid human cell, considering a genome size of \(3.2\cdot10^9 bp\) and an average base pair weight of \(615.8771 Da\), the expected amount of DNA is approximatively \(6.5 pg/cell\).

Once the total protein mass per cell has been computed, the estimation of absolute copy numbers per cell is then obtained in the same way as for the Total Protein Approach (see above).

Warning

Because this method relies heavily on the histone content of the sample(s), Wiśniewski et al. state in their paper that for accurate results, “the only prerequisite is a eukaryotic, whole-cell proteome dataset where the chromatin fraction is not over- or underrepresented as a result of sample handling” and that “a reasonable depth of proteomic analysis is needed to ensure a robust contribution of the histone MS signal” (depth of around 12 000 peptides).
Custom Proteins Ruler : This method follows the same rationale as the two above, namely that the proportion of a given protein in the sample compared to the total protein content can be estimated from the proportion of its MS signal compared to the total MS signal (intensity). The only difference in this case is that instead of using the total protein content per cell or the cellular DNA mass/histone MS signal, the scaling is done with proteins of known quantity (hereafter referred to as custom proteins). You can use a spike-in or any other proteins of your sample as custom proteins, as long as you can provide the total quantity per cell of these proteins in each of your samples.

For example, let’s say you want to quantify two samples, 1 and 2, and your custom proteins are protein A, B and C. In each sample you know that you have :
- \(100ng\) of protein A,
- \(70ng\) of protein B,
- \(30ng\) of protein C,
so a total of \(200ng\) of custom proteins in each sample. However, you also have :
- 20 000 cells in sample 1,
- 25 000 cells in sample 2
You will have to provide the software with your custom proteins identifiers (either a list that you have already saved or a text file with one identifier per line) and with the values :
- \(\frac{200\cdot10^{-9}}{20000} = 10pg\) for sample 1,
- \(\frac{200\cdot10^{-9}}{25000} = 8pg\) for sample 2

Normalization mode: In the default mode, each sample is scaled individually according to the method you chose. If you quantify more than one sample at a time, you may want to normalize them in another way. Your different options are :

All samples separately : The default mode. A normalization factor is computed for each sample and applied to it individually.
Same normalization for all : A normalization factor is computed for each sample, but only the average of all these normalization factors is applied to scale the intensities of every sample.
Same normalization within groups : In an experiment, some samples are often related to some others, like biological replicates for instance. In that case, you may want to group related samples together and scale them according to the group they belong to. A normalization factor is thus computed for every sample and the average normalization factor of each group is retained to scale the data of samples from this group.

This option requires that you later select the groups for each sample you want to quantify (see figure …)

Average all samples : This is a very particular case where the data (intensities) from all your samples is averaged (actually, the software takes the median intensities) for each protein before any other computation, which creates an virtual “average sample”. The normalization is then done on this average sample. This mode yields a unique quantification value per protein, corresponding to the average sample. It is meaningful only in some particular cases, for example if you quantify only some biological or technical replicates together and want to have a global view on them.

MaxQuant metric : This is the metric computed by MaxQuant on your samples and that you want to use as the basis for this quantification. You can choose between Intensity and LFQ metrics. The samples for which the chosen metric is available are displayed after selection of the metric.

Total cellular protein concentration : To compute concentration values for each protein, the software calculates the volume of the sample. To do so it needs the protein concentration of your sample in g/L.

Protein selection : Use this feature if you want to exclude one of your lists of proteins from the quantification before launching the computation, or, on the contrary, if you want to restrict the considered proteins to a specific subset. For example, you can exclude contaminants. You need to previously create your own list of proteins (see User-defined protein lists) to use this feature.

Warning

You should not exclude proteins that are not contaminants because the Proteomic Ruler relies on the total MS signal to quantify the proteins. If you exclude viable proteins that were in your samples, your results will not be accurate and some features such as Mass or Molecular abundance will simply become meaningless. Do not exclude proteins only because you don’t need to quantify them.

Organism name : Provide the name of the organism from which your proteins are from (used mostly with the Histone Proteomic Ruler method).

Desired output : Select at least one type of quantification value that you are interested in. You can select multiple outputs by maintaining the mouse button clicked during the selection or by clicking on multiple features while holding the Ctrl key on your keayboard. The quantification types available are Copy number per cell, Concentration, Mass per cell, Mass Abundance, Molecular Abundance, Copy number rank and Relative copy number rank.

Note

The Copy number per cell is the basis to compute all the other quantification values, so it will be computed anyway. We suggest that you select it even if that is not the main feature you are interested in.

Displaying single abundance quantification data¶

Relative abundance quantification¶

Single-Analysis quantification (labeled)¶

If a labeled Analysis has to be quantified, labeling parameters and all peptide XIC data should be readily available in the corresponding search results file. Therefore, a straightforward protein quantification can be performed as follow: Go to the Analysis Quantification options (Process Analyses > Analysis Quantification) and click on Proceed next to the (SILAC/iTRAQ)-based quantification process to display the quantification form shown below.

Name : A name for the quantification.
Labeled states : Select the different conditions to be compared. Available labeled states are identified based on labeling design extracted from the search result file. Each condition defined will be used as a reference for the following one(s). 1 state is usually associated with 1 condition. However, if more than 2 states are identified (e.g. iTRAQ 4/8-plex) an additional option will be displayed for grouping different states as replicates of the same condition. In addition, if more than 2 conditions are defined, all corresponding ratios will be calculated except reverse ratios (cond B/cond A but not cond A/cond B).
Note

It is possible to quantify multiple Analyses at once. Make sure they share identical labeling design. If not, they should be quantified separately.

Multiple filter can be applied on Peptide selection:
Specificity : Whether to restrict quantification to proteotypic peptides or not.

Missed cleav. : Include or not miss-cleaved peptides.

PTMs : Peptides with sequence modification can be allowed, not allowed or extend exclusion to corresponding non-modified peptide.

Charges : Include all charge states of a peptide set or restrict to set that gives the best signal (set containing peptide with highest XIC value).

Sources : If the search results files is a merge of multiple LC/MS runs (e.g. Proteome Discoverer), use peptide sets from all runs or use only the one with best signal.
Quantification settings : Additional options are available to control experimental bias, outliers detection and differential analysis.
- Bias correction : Select whether to correct or not for signal bias between label states and which method to apply: If Scale normalization is selected, the assumption is made that the total XIC signal between all states should be equal. Alternatively if Reference protein(s) is selected, a pre-recorded List of proteins must be provided. When using this option, it is assumed that a subset of proteins (e.g. House keeping proteins) is unchanged amongst all states and therefore only the sums of the XICs matching these proteins are set equal. In both cases, a state-specific correction factor is computed and applied to each individual peptide XIC.
- Avoid infinite ratios : Infinite ratios (log values) can occur when XIC values are missing in 1 of the 2 conditions being compared. When a mixture of normal and infinite peptide ratios exists for the same protein, myProMS must either use the most abundant type of ratios to quantify the protein (e.g. set protein ratio to +/-infinite (log values) if more than 50% of matching peptides have infinite log ratios) or only use the “normal” ratios even if they are less frequent that the infinite ones (to avoid infinite ratios whenever possible). This later option is automatically selected if more than 2 conditions are compared to prevent excessive data exclusion.
- More advanced settings can be used for outlier detection, comparison hypothesis test (Two-sided/Lesser/Greater), FDR control, …

Finally, select the analysi(e)s to be quantified. If multiple peptide quantification datasets are available for an Analysis, one must be selected. Click on the Launch Quantification button. Multiple quantifications will be queued and processed as up to 3 parallel jobs. As described above for Peptide Quantification, a popup window will appear with the list of all jobs launched with their progress status.

Design-based quantifications¶

The use of a design for a quantification is highly recommended, even if it requires only single labeled analysis. It is mandatory to create a design for a quantification that requires more than 1 analysis. Designs are automatically generated when importing protein quantification data from MaxQuant analyses.

Conditions

Observations

Displaying relative abundance quantification data¶

Label-free quantifications¶

Label-free quantifications are methods that allow to determine the relative amount of proteins in two or more biological samples without any use of stable isotope or chemical tag. It is based on precursor signal intensity or the number of spectra made for each peptide of a protein. Here is a brief description of several methods available in myProMS that you can use from top panel button Process Analyses and then, Analysis Quantification.

TnPQ¶

Silva et al. showed in their work[5] on a Q-ToF type instrument that it is possible to quantify unknown protein samples with a known unified signal response factor in absolute manner . Then, the Top 3 Protein Quantification[6] extended this method to ion trap instruments. The method premises that for each protein identified by a set of peptides, the average of the three most efficiently ionized and therefore highest MS signals directly correlated with the input amount of the corresponding protein. In myProMS, we extended this definition to “all available peptides” for a given protein and called it TnPQ.

Steps involved in TnPQ computation:

Retrieval of all available XICs (area) of each peptide of the protein for all conditions

Removal of incomplete peptide information i.e. peptide with no XIC information in at least one of the replicates of a condition will be removed

Warning

when creating a quantification, avoid to add too many conditions because you will lose a lot of peptide information given the fact that all conditions must provide a XIC for a peptide to be considered more further

If a bias correction setting was selected (scale or reference protein normalization), a normalization step is introduced by computing bias estimates on unique peptides [7] . All XIC are divided by those bias factors.

Note

If None was chosen, nothing is done to the data

Removal of extreme XIC values (outliers) based on the coefficient of variation (standard deviation divided by the mean) of all identified peptides along the replicates in the conditions.

Compute for each protein the geometrical mean of peptide XICs

Quality control of the data (normality test on the data and variance sameness)

Compute the ratio between paired conditions and make a test to assess equality of mean depending on the design made before

For 2 conditions : use Student t-test comparison (or Welch t-test if variance are not the same)

For more than 2 conditions : use Tuckey HSD (honestly significant difference) test

If chosen, adjust p-values to control FDR level

Comparing multiple protein quantifications¶

Exporting multiple quantifications¶

For the explanation of all features see section “Exploratory analyses” except for the following item:
- Gene Name : transform all protein name in gene name (<which?geneID/unigene>).
- Keep most changing proteins between:
  Sample: For a given isoform (or protein), a standard deviation is calculated for all selected sample and only the isoform (or protein) with the best value is kept. The standard deviation is a measure that is used to quantify the amount of variation or dispersion of a set of data values.
  
  Group: For a given isoform (or protein), an Anova is applied, a p-value is calculated between group. Analysis of variance (ANOVA) is a collection of statistical models and their associated procedures (such as “variation” among and between groups) used to analyze the differences among group.
After clicking on export dataset button, a compressed directory is downloaded which contains following file :
- R_parameter.txt: all the parameters used by the statistical analysis.
- parameter.txt: all the parameters used for data filtering.
- matrix_pvalue_processed.txt:
- matrix_pep_processed.txt: peptide number
- matrix_log2ratio_processed.txt:
- annotation_processed.txt:
- sd.txt: standard deviation value (between sample)

[1]	emPAI : Hishima et al, Mol Cell Proteomics, 2005

[2]	SIn : Griffin NM et al, Nat Biotechnol., 2009

[3]	Total Protein Approach : Wiśniewski et al, Mol Syst Biol., 2012

[4]	Histone Proteomic Ruler : Wiśniewski et al, Mol Cell Proteomics, 2014

[5]	TnPQ : Silva et al, Mol Cell Proteomics, 2006

[6]	T3PQ : Grossmann et al, J Proteomics, 2010

[7]	TnPQ bias correction (scale normalization part) : Yang et al. 2002

PTMs quantification¶

Set PTMs relevance to project¶

Display modification sites distribution¶

Compare PTMs between projects¶

Quantify modification sites¶

Exploratory analysis¶

In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Two types of exploratory analyses are available in myProMS : Principal Component Analysis (PCA) and Clustering. They can be launch on protein quantification or peptide count/abundance datasets.

Launching exploratory analyses on protein quantification¶

First select the Experiment containing the dataset to analyze. Then click on the Start Exploratory Analyses button. The following form will be displayed:

_images/exploratory_analysis_protein_settings.png

Name: Provide a name for the PCA and/or the clustering analysis. The analysis is saved and can be retrieved by this name in the Exploratory analyses tree displayed in the sub-navigation frame.
Focus: 3 types of data can be analyze, Proteins, Proteins modifications (phosphorylation , acetylation….) and Normalized proteins modifications.
Features: run exploratory analysis on Protein quantifications or Peptide count.
Quantification type: Select one the protein quantification methods available as listed below:

Quantification type for protein quantification
emPAI	emPAI
	emPAI (Mol %)
	emPAI (Mr %)
MaxQuant Intensities	Intensities
	iBAQ
	LFQ
	MS/MS count
Normalized spectral index	SIN
Protein ratio	Protein ratio

Data transform: by default data are not logged, user can transform data in log2 or log10. This is critical, in particular for protein ratio in order to make ratio data symmetrical and centered on 1 (0 in log mode)
Data filtering: - Abs. fold change in at least n quantifications (only for protein ratio): Only proteins with an absolute fold change (before log) greater than or equal to the value provided found in at least n quantifications will be kept . - Infinite ratios (only for protein ratio): choose how to deal with infinite ratios. - p-value in at least n quantifications (only for protein ratio): Only proteins with p-value lesser than or equal to the value provided found in n quantifications will be kept. - Peptides: Select the counting method: All, Distinct, Razor, … (the exact list of available options depends of the quantification method selected) and the minimum value allowed in a quantification to consider the protein to be present or absent. - Missing values: choose how to deal with missing values. Different options are available: None allowed, Allow x%, Allow all. Value is per individual proteins across dataset. Proteins with more missing values than allowed will be excluded. - Exclude ambiguous site (only for modifications proteins): exclude or not sites with ambiguous (uncertain) modification position (ambiguous site contains “~” eg, xxx-254~262:1/5). - Aggregate (by best modifications sites, only for modifications proteins): keep only 1 modification site per protein (gene). The site with the highest variance across the dataset will be kept as a proxy for the whole protein (gene).
Protein filtering: - Custom list: by default all proteins that passed above filters are used for the analysis. It is also possible to focus only or exclude a set of proteins based on the custom list selected. - Keep n most changing proteins: Only the n proteins with highest variance across the dataset will be kept.
Clustering parameters: choose the method and the metric parameters (default method is “Ward” and metric is “pearson”)
Data selection: choose the quantifications to analyze. - Auto-extend selection: after selecting a quantification, all the following ones are automatically selected (or unselected). - Copy selection: copy the dataset used in a previously performed exploratory analysis.

Launching exploratory analyses on peptides count¶

_images/exploratory_analysis_peptides_settings.png

Name: Provide a name for the PCA and/or the clustering analysis. The analysis is saved and can be retrieved by this name in the Exploratory analyses tree displayed in the sub-navigation frame.
Features: run exploratory analysis on protein quantifications or peptide count
Quantification type: different types of quantification are available.
Peptide ID: <to be completed>
Peptide Count: <to be completed>
Spectral Count: <to be completed>
Peptide XIC: <to be completed>
Grouping method: 4 methods are available (except for peptide ID).
Min. <to be completed>
Max. <to be completed>
Mean <to be completed>
Sum <to be completed>
Missing values: choose how to deal with missing values. Different options are available: “None allowed”, “Allow x%”, “Allow all”.
Clustering parameters: choose the method and the metric parameter (by default, method is “Ward” and metric is “pearson”).

Principal Component Analysis (PCA)¶

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

The number of distinct principal components is equal to the smaller of the number of original variables or the number of observations minus one. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components.

The resulting vectors are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables.

Summary / edit / delete¶

_images/exploratory_analysis_summary.png

If a PCA analysis is selected, a summary of the information available for that analysis is displayed in the mainframe.

PCA name
Description
Focus
Features
Data transform
Data Filtering
Status: There are 3 possible status: Ongoing, Finished or Error
Quantifications used: list of quantifications used for the analysis (or name of the list)
List of graphical highlight used (see below).

The PCA name and the description can be modify by clicking the Edit button. The analysis can be deleted by clicking the Delete button.

Displaying a PCA¶

_images/exploratory_analysis_pca_options.png

PCA can be viewed with 4 differents options:

Quantifications view.
Quantifications scaled view.
Protein (or isoforms for modification sites) view.
Protein scaled (or isoforms scaled if modifications sites).

The PCA dimensions can be changed, a 2.1D view is available by selecting a third dimension. In this case, the points’ size becomes proportional to the values of the selected dimension.

Danger

Add the new PCA 3D display

_images/exploratory_analysis_pca_dimension.png

For each dimension the list of proteins (or isoforms) can be displayed.

_images/exploratory_analysis_pca_isoform.png

Danger

2D-Clustering¶

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).

Summary / edit / delete¶

The summary part of clustering is exactly the same than the PCA summary plus the information of clustering parameters.

Displaying clustering¶

_images/exploratory_analysis_clustering.png

Danger

Functional analysis¶

Gene Ontology¶

Different types of analyses using Gene Ontology (GO) can be performed on validated protein lists. The GO project provides a controlled vocabulary of terms for describing gene products such as proteins. For more details, see the GO website. A GO analysis can regroup proteins into standardized categories of terms belonging to 3 domains: Biological Process, Cellular Component and Molecular Function.

In myProMS, all GO analyses need 2 types of GO files that are managed from GO files management section (See corresponding chapter below for more information) :

Ontology file: the file that contains all term descriptions and their relationships between each other
Annotation file: the file that maps each protein identifier to the most specific terms that characterize the protein.

GO summary¶

The GO summary tool can be used to simply regroup proteins sharing common GO terms. This tool can be run from the option frame on any project item, by clicking on the Gene Ontology summary button. The following form is then displayed:

Ontology	The file containing terms that will be used to regroup proteins
Annotation	The file containing protein annotations to GO terms
Domain	Select one of the 3 GO domains the analysis will be focused on
Depth	Only terms at the specified depth in the GO graph structure will be used. Depth is calculated by counting the distance between a term and the root term of the corresponding ontology domain. If a high depth is selected, a very large number of terms will be displayed and the results may be difficult to read
Minimal protein per term (Optional)	If a selected term contains less proteins that this specified value, this term will be ignored and the matching proteins will be added to the “Other” category

Click on Display to launch the process. After a short calculation time, results are displayed as shown in the example below:

An interactive bar plot shows each term frequency. Click on a bar to display the proteins mapped to the corresponding term. Each protein group can also be viewed by browsing the list of terms displayed below the plot.

GO enrichment analysis¶

Enrichment analysis is performed to determine which GO terms are significantly enriched in a tested set of proteins when compared with a given background set (eg. the whole proteome of the species studied). All terms will be tested regardless of their depth. In myProMS, GO enrichment analysis is calculated with the GO::TermFinder package developed for perl[1]. Briefly, a p-value using a hypergeometric distribution is computed to determine whether any GO terms annotate a specified list of proteins at a frequency greater than that would be expected by chance. Multiple hypothesis correction is available with FDR computing. This tool is accessible by clicking on an experiment and selecting the Start GO Analysis button in the option frame. The following form in then displayed:

Name	Provide a name for the enrichment analysis. The analysis is saved and can be retrieved by this name in the GO analyses tree displayed in the sub-navigation frame
Description (Optional)	Description of the current analysis
Ontology file	The file containing term relationships
Domain(s)	Select one or more domains to test
Advanced parameters (Optional)	Estimated number of proteins in organism	If the background population consists of the whole proteome (more exactly the whole protein set contained in the annotation file), this value can be set to calculate properly the enrichment ratio of GO terms in the tested protein set(s), supposing that the annotation file is incomplete. This option artificially adds unannotated proteins to the background
	Background population	Select the population to which the tested protein set will be compared. A previously built custom list can be selected, or a local file can be used instead. This file must contains all protein identifiers that compose the background (1 identifier per row). These identifiers must match the ones contained in the annotation file. If selected background is set to “Unspecified”, the whole protein set contained in the annotation file will be used as background. In this case, be sure that the annotation file contains only proteins from the current species. This can be considered as a whole proteome background if the annotation has a very good coverage of current species proteome. The background population selection strongly affects the significance of terms and must be chosen carefully and coherently with your biological question
	Statistical settings	These settings can be set to control the significance cut-off of GO terms. False Discovery Rate (FDR) or p-value criteria can be selected.
	Show non-significant terms in graph	If this option is disabled, non-significant terms will be represented with small dots in graphical view. This can increase significantly the visibility of the graph if the dataset contains a large number of significant terms
	Include only proteins with at least n peptide(s)	Proteins which contains less peptides than the value specified will be excluded from the tested set
Select a protein set	Select the protein set to be tested. It can be selected from any project item or custom list

Once all parameters have been set, click on Start Analysis. The computation may last several minutes depending on the sizes of the protein sets being compared. The results are directly displayed after the process but can also be accessed later on by selecting the analysis name in the GO analyses tree displayed in the sub-navigation frame.

For each domain, results can be displayed in 3 different views accessible at the top of the page:

Cloud view: Highly significant terms (low p-value) are represented with a large font, and less significant terms with a small font. The proteins mapped to a term can be listed by clicking and each term.
Table view: More details can be viewed in table format which contains the p-value and enrichment ratio of each term.
Graph view: Displays a graph of the significant terms as nodes with their relationships as edges. Each node colour is based on the corresponding term’s p-value significance. Proteins that are mapped to a term can be viewed by clicking on the corresponding node.

Quantitative gene enrichment analysis¶

When a quantification is available, a quantitative gene enrichment analysis can be performed as it was originally done for SILAC experiments[2]. The quantified proteome is divided into five bins corresponding to log2 ratios or bin proportion. Enrichment of GO terms in each bin is then calculated compared to a provided background and a cluster analysis allow to visualize a heatmap of enriched GO-terms in all bins. Here is how you should proceed to do it. This option is accessible by clicking on an experiment and selecting the Start Q. GO Analysis button in the option frame. After loading a protein set of an Analysis or a Design related quantification, you need to select the parameters in the following form:

Name	Provide a name for the enrichment analysis. The analysis is saved and can be retrieved by this name in the GO analyses tree displayed in the sub-navigation frame
Description (Optional)	Description of the current analysis
Ratio	Choose the ratio considered for the enrichment in the quantitation (like heavy/medium or heavy/light for SILAC experiments).
Peptides (Optional)	Make a threshold upon the number of peptides used to compute the ratio
Protein-ratio p-value threshold	Make a selection on the associated p-value of the ratio
Ontology file	The file containing term relationships
Annotation	The file containing protein annotations to GO terms
Domain	Select one domain to test
Advanced parameters (Optional)	Background population	Select the population to which the tested protein set will be compared. See GO enrichment analysis section for custom list recommendations
	Enrichment test statistical settings	These settings can be set to control the significance cut-off of GO terms. False Discovery Rate (FDR) or p-value criteria can be selected

When the enrichment is done, you can get information of the GO-Analysis by clicking on the sub-navigation frame the item generated and Summary.

Click on the Heatmap button to see the output you can get:

Each row represent a GO-Term and each cell is the -log10 of the p-value of the enrichment test for the GO-Term in the specific bin (put to 1 and then log-transformed to 0 if that ontology is not enriched/significant in the bin).

Each line is z-scored. Then, these z-scores are clustered by one-way hierarchical clustering using the function hclust in R (the distance function used is euclidean and the agglomeration method used is average).

Note

The heatmap is interactive and can be exported as a jpeg image. Clicking on a cell updates the frame and provides the list of proteins containing the annotated GO-Term in the bin.

[1]	GO::TermFinder package : Boyle et al., Bioinformatics, 2004

[2]	Quantitative GO SILAC : Pan C et al, MCP, 2009

Pathway enrichment¶

Pathway is the term from molecular biology which depicts an artificial simplified model of a process within a cell or tissue. In bioinformatics research, pathway analysis is used to identify related proteins within a pathway. This is helpful when analyzing any omics dataset with a large number of proteins. By examining the changes in proteins in a pathway, its biological causes can be explored.

Typical pathway model starts with extracellular signaling molecule that activates a specific protein. Thus triggers a chain of protein-protein or protein-small molecule interactions. Pathway analysis helps to understand or interpret omics data from the point of view of canonical prior knowledge structured in the form of pathways diagrams. It allows finding distinct cell processes (cellular processes), diseases or signalling pathway that are statistically associated with selection of differentially expressed proteins between two samples. To do so, myProms uses Reactome’s web-service.

Launch pathway analysis¶

Name	Provide a name for the Pathway analysis. The analysis is saved and can be retrieved by this name in the Functional analyses tree displayed in the sub-navigation frame
Description (Optional)	Free text
Advanced parameters (Optional)	Set a p-value threshold
Select proteins	Select proteins to use in the analysis, from custom list or project item

Summary / edit / delete¶

If a pathway analysis is selected, a summary of the information available for that analysis is displayed in the mainframe.

Displaying pathway analysis¶

3 different views are available : Cloud, graphical and table view :

Cloud view :

_images/pathway_analysis_results_cloud.png

The displaying pathway is ordered by p-value. The color corresponds to the enrichment factor value.

Users have access to the unannotated proteins, which does not match with the reactome database.

_images/pathway_analysis_results_unannotated.png

_images/pathway_analysis_results_unannotated_details.png

Graphical view :

_images/pathway_analysis_results_graph.png

_images/pathway_analysis_results_details.png

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A COMPLETER

Table view :

_images/pathway_analysis_results_table.png

By default, pathways are ordered by p-value. They can be sorted by enrichment factor or FDR by clicking the corresponding name. A pathway can be display in Reactome by clicking on its name.

Export¶

An excel file can be downloaded which correspond to the table view information.

Motif enrichment analysis¶

Motifs are recurring short sequence elements. Their over-representation usually implies some functional significance. myProMS uses rmotifx R package for the enrichment analysis and ggseqlogo R package for drawing the motif.

Launch analysis¶

Name: provide a name for the PCA and/or the clustering analysis. The analysis is saved and can be retrieved by this name in the Exploratory analyses tree displayed in the sub-navigation frame.
Modification: choose the type of modifications (phosphorylation, acetylation, methylation….).
Foreground selection: select a quantification or a previously saved list.
Foreground filtering:
- Ratio: all proteins with a ratio corresponding to the chosen value are kept.
- Infinite ratio: exclude or not.
p-value: all proteins with a p-value smaller than or equal to the chosen value are kept.
Central residue: following the type of modifications, the drop down menu is automatically generated. For instance, phosphorylation modification generate 3 type of residue (S, T, Y).
Width: the width is the number of total characters is the motif. It should be an odd number between 3 and 35. However, choosing a motif width that is too narrow can result in the exclusion of motifs with critical longer-range dependencies and choosing a motif width that is too wide (without adjusting the significance threshold accordingly) can yield spurious motif results.
Min. occurrence: the occurrence threshold refers to the minimum number of times you wish each of your extracted motifs to occur in the data set. An occurrence threshold of 20 usually is appropriate, although this parameter may be adjusted to yield more specific or less specific motifs. This parameter can be used to tune the specificity of motifs since motifs with greater specificity (i.e., more “fixed” positions) are expected to occur less often than those motifs with lower specificity. Users that wish to extract a maximal number of motifs should set this parameter to a low value (for example, “5”) and rely solely on the significance parameter (see step 8) to extract motifs. On occasion it may be useful to set this parameter as a fractional percent of the total number of modification sites in order to compare motifs with similar specificities across data sets that vary in size (e.g., to compare motifs from data sets of 300 and 3000 sites, one may opt to set the occurrences parameter to 5 and 50, respectively).
Significance: The significance refers to the P-value threshold for the binomial probability. This is used for the selection of significant residue/position pairs in the motif. t is critical to note that this value does not take into account a correction for multiple hypotheses (such as the Bonferroni correction). On any given motif-x search step there are (number of possible characters at each position) * (number of non fixed positions) hypotheses being tested. For example, in an S-centered analysis of width 15, there would be (20) * (14) = 280 hypotheses tested. To ensure an alpha-value of at least 0.05 by the Bonferroni method, one would need to divide the desired alpha-value by the total number of hypotheses tested (i.e., 0.05/280 = 0.00018).We suggest a threshold of 0.000001 to maintain a low false positive rate in standard protein motif analyses.
Background selection: The background simply refers to the item from which the data set was taken. This is important for accurate statistical analysis. You may choose the selected quantification or generate a random background sequence based on the probability of each amino acid within the proteome.

Summary/editing/deleting¶

If a motif enrichment analysis is selected, a summary of the information available for that analysis is displayed in the mainframe.

The motif name and the description can be modify by clicking the edit button. The analysis can be deleted by clicking the delete button.

Important

If the motif analysis is involved in a heatmap, you have to delete the heatmap before deleting the motif analysis.

Displaying motif enrichment¶

Motif: the logo picture made by ggseqlogo with the over-represented motif. Motif positions are labeled below the x-axis and residues are colored according to their chemical and physical properties.
Fold change: is an indicator of the enrichment level of the extracted motifs. Specifically, it is calculated as (foreground matches/foreground size)/(background matches/background size).
Score: The “motif score” is calculated by taking the sum of the negative log probabilities used to fix each position of the motif. As such, higher motif scores typically correspond to motifs that are more statistically significant as well as more specific (i.e., greater number of fixed positions).
Fg. matches / Bg. matches: indicate the number of peptides containing a given motif in those respective data sets following the removal of all peptides containing previously extracted motifs. Because of this iterative “set reduction” strategy, the “foreground matches” and “background matches” statistics may be less than or equal to the total number of instances of a given motif in the whole data set.
Fg. size / Bg. size: indicate the total number of peptides contained in these data sets. The size of these data sets decreases as motifs are extracted (i.e., down a column) due to the fact that peptides are removed from both the foreground and background data sets following motif extraction. The total number of foreground peptides not falling into any extracted motif class can therefore be calculated as the difference between the “foreground size” and the “foreground matches” of the final motif class (e.g., 163 – 32 = 131 unclassified peptides in Figure 5). The “fold increase” statistic is an indicator of the enrichment level of the extracted.

Clicking on either the links in the motif logo, brings users to the peptide data used to extract the given motifs. As such, the number of peptides found for each motif in this section corresponds exactly to the number of “foreground matches” for the motif in appropriate column of the results table.

Heatmap motif analysis¶

Launch HM motif analysis

_images/motif_analysis_heatmap_settings.png

Select the previously saved motif analysis in the left panel, click on the Add button and the selected analysis are moved in the right panel to be cluster.

Summary / Edit / Delete

_images/motif_analysis_heatmap_summary.png

Display Heatmap motif analysis

_images/motif_analysis_heatmap_results.png

Indices and tables¶

Search Page

Installation¶

Git Repository¶

Docker deployment¶

Login¶

Server Architecture¶

Users management¶

User classes and access privileges¶

Account management¶

Annotation data management¶

Sequence databanks¶

Databank types¶

Listing databanks¶

Adding a new databank¶

Editing a databank¶

Deleting a databank¶

Spectral (SWATH) libraries management¶

Listing spectral libraries¶

Adding a new library¶

Merging two library¶

Editing a library¶

Updating a library¶

Restoring the previous version of a library¶

Searching for proteins in a library¶

Exporting a library¶

Deleting a spectral library¶

GO files management¶

Ontology files¶

Annotation files¶

Species¶

Listing species¶

Adding or editing a species¶

Deleting a species¶

Sequence modifications¶

Editing or merging PTMs¶

Server administration¶

Projects settings¶

Selection¶

Creation¶

Accessibility¶

Organization¶

Experiments¶

Samples¶

Analyses¶

Navigation¶

Life span¶

Analysis import¶

Supported search engines¶

Collecting search files¶

Import parameters (Mascot, Proteome Discoverer and X!Tandem)¶

Importing MaxQuant data¶

Import DIA data¶

From PeakView¶

From OpenSWATH¶

Running OpenSWATH quantification¶

From Spectronaut¶

Analysis summary¶

Analysis validation¶

Automated peptide/protein validation¶

FDR (False discovery rate) - based validation¶

Qualitative validation¶

Comparative validation¶

Validation templates¶

Manual peptide/protein validation¶

Peptides selection/exclusion¶

Protein exclusion and filtering¶

Lower-scoring peptides activation¶

Clear peptide/protein selections¶

Sequence modification validation¶

Phosphorylation sites validation with PhosphoRS¶

Manual validation of modifications¶

Validation traceability¶

Reporting¶

Biological Samples¶

Properties¶

Treatments¶

Recording a biological sample¶

Linking biological samples to MS Analyses¶

Validated proteins¶

Match groups and protein visibility¶

Top protein selection rules¶