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MHCMotifDecon - 1.3

Motif deconvolution of Multi-allele immunopetidomics data

MHCMotifDecon-1.3 is a supervised method for motif deconvolution of MHC peptidome data. The method uses MHC binding predictions from NetMHCpan-4.2 (for MHC class I) and NetMHCIIpan-4.3 (for MHC class II) to deconvolute and assign likely MHC restriction elements to MHC peptidome data.

In the deconvolution, MS co-immunoprecipitated contaminants are identified and placed in a trash bin.

WHAT's NEW? In MHCMotifDecon-1.3, the annotation of peptides to individual MHC molecules in a given cell line is now based on the prediction scores obtained by predicting with all networks in the NetMHCpan/NetMHCIIpan ensemble for each allele individually. This is different from previous versions, in which the MHC annotation was based on a majority vote between the networks in the ensemble. This means that the final prediction score from MHCMotifDecon-1.3 for each peptide-MHC pair is equivalent to the score that NetMHCpan/NetMHCIIpan outputs.

SUBMISSION

Hover the mouse cursor over the symbol for a short description of the options

INPUT TYPE:

Paste an input into the field below:

... or upload a file in format "Ligand [Cell_line_ID]" directly from your local disk:

... or load some sample data (currently only available for class II):

If input data is for multiple cell lines:

paste an input into the field below:


... or load a file with the allele information for each cell line in the format [Cell_line_ID HLA1,HLA2,...] directly from your local disk:


else if input data is for a single cell line:

select species/loci:



Select Allele(s)


... or type a list of molecules names (i.e. DRB1_0101) separated by commas without spaces


For a list of available molecule names click here

ADDITIONAL CONFIGURATION:

Define allowed peptide length to include (default 12-21) 

Include peptide count histogram

Include length histogram

Include consistency matrix plot

Threshold to discard as trash: % Rank 

Minimum quantity of sequences allowed for logo plotting 

Output file with prediction score per MHC in each cell line 



Restrictions:

Confidentiality:
The sequences are kept confidential and will be deleted after processing.

CITATIONS

For publication of results, please cite:

  • Accurate MHC Motif Deconvolution of immunopeptidomics data reveals high relevant contribution of DRB3, 4 and 5 to the total DR Immunopeptidome.
    Saghar Kaabinejadian, Carolina Barra, Bruno Alvarez, Hooman Yari, William Hildebrand, Morten Nielsen
    Frontiers in Immunology 26 January 2022. Sec. Antigen Presenting Cell Biology, DOI: 10.3389/fimmu.2022.835454

DATA RESOURCES

INSTRUCTIONS

INPUT DATA

In this section, the user must define the input for the prediction server following these steps:

1) Specify the desired type of input data (Class II or Class I) using the drop down menu.

2) Provide the input data by means of pasting the data into the blank field, uploading it using the "Choose File" button or by loading sample data using the "Load Data" button. The input must be in the format "Ligand [Cell_line_ID]", where ligand is an MS identified peptide, and Cell_line_ID the ID for the corresponding cell line. This ID is optional if analysing single MS data sets. All the input peptide sequences must be in one-letter amino acid code. The alphabet is as follows (case sensitive):

A C D E F G H I K L M N P Q R S T V W Y and X (unknown)

Any other symbol will be converted to X before processing.

MHC SELECTION

Here, the user must define which MHC(s) molecule(s) the input MS data is going to be deconvoluted against:

1)If the input data is for a single cell line, the alleles can be selected from a Dropdown list. Here, first, select the "species/loci.

2)After selecting the "species/loci, the user will be able to select a single or multiple MHC molecules from the updated "Select Allele(s)" list. On the other hand, the user may opt to directly type the MHC names in the provided blank field (separated by commas and without blank spaces); if this is the case, there will be no need to select an MHC supertype familiy from the drop-down menu. Click here for a list of MHC molecule names (use the names in the first column). Please note that a maximum of 20 MHC types is allowed per submission.

3)If the input data is covering MS data from multiple cell lines, the user must paste oin, upload or load sample data describing the MHC molecules expressed in each cell line. The input must be in the format "Cell_line_ID HLA1,HLA2,HLA3", where each Cell_line_ID must match the cell-line ID given in the MS peptide input data. Please note that steps 1-2) and 3) are mutually exclusive, and are only labeled this way for explanation purposes.

ADDITIONAL CONFIGURATION

In this section, the user may define additional parameters to further customize the run:

1) Specify the allowed peptide lengths to include (default 12-21 for class II, and 8-14 for class I). All peptides with length outside the specified range are excluded from the analysis.

2)-4) Include peptide count histograms, Include length histogram, and Include consistency matrix plot. These boxes can be selected to include the different additional plots. If "Include consistency matrix plot" is selected an option to select "Threshold for correlation matrix plotting" appears. The threshold specifies how many examples of a given allele must be present in the MS data to include the correlation plot analysis.

5) Specify a %Rank threshold to filter out Trash.

6) Specify a minimum number of peptide annotations needed to generate a given logo.

7) Output a file for each cell line in the input data, containing the predictions across alleles. For a cell line with the alleles DRB1_0401 and DRB1_0701, the format would be
peptide cell_line DRB1_0401_core DRB1_0401_rank DRB1_0401_rawscore DRB1_0701_core DRB1_0701_rank DRB1_0701_rawscore best_mhc best_core best_rank best_rawscore

SUBMISSION

After the user has finished the "INPUT DATA", "MHC SELECTION" and "ADDITIONAL CONFIGURATION" steps, the submission can now be done. To do so, the user can click on "Submit" to submit the job to the processing server, or click on "Clear fields" to clear the page and start over.

The status of your job (either 'queued' or 'running') will be displayed and constantly updated until it terminates and the server output appears in the browser window.

At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; when it terminates you will be notified by e-mail with a URL to your results. They will be stored on the server for 24 hours.

OUTPUT FORMAT

Below is the output from running the MHC class II sample data with default options, and turning on the option 'Output file with prediction score per MHC in each cell line', using MHCMotifDecon-1.3. Note, that the sample data set is very small, and the generated MHC motif and length distributation plots hence are rather noisy. 


###################################################
# Running MHC_Motif_Decon 1.3 ...
#
# Call from /var/www/services/services/MHCMotifDecon-1.3/tmp/
#
# Input file: /var/www/webface/tmp/server/mhcmotifdecon-1.3/688B71370024E34FED6BA180/file.0
# Number of sequences: 100
# Label-MHC file: /var/www/webface/tmp/server/mhcmotifdecon-1.3/688B71370024E34FED6BA180/file.1
# MHC class: II
# Length range: 12-21
# RANK threshold: 20
# Minimum quantity of sequences for logo plotting: 10
# File with per-mhc scores per peptide will be generated.
# MHC counts plots will be included.
# MHC length histograms will be included.
# Run ID: run_18162
# Dirty mode enabled.
#
# Creating output folder /var/www/services/services/MHCMotifDecon-1.3/tmp//run_18162...
# DONE.
#
# Deconvoluting Peptide-MHCs...
# 100/100
# Deconvolution DONE.
#
# Reading alleles for Abelin...
# Reading alleles for Heyder...
# Reading alleles for Khoda...
#
# WARNING! Low quantity of sequences (1) found for DRB1_0301 in Abelin. This allele will not be plotted.
# WARNING! Low quantity of sequences (6) found for DRB1_1101 in Abelin. This allele will not be plotted.
# WARNING! Low quantity of sequences (6) found for Trash in Abelin. This allele will not be plotted.
#
# Found 14 sequences for DRB1_0401 in Heyder
# WARNING! Low quantity of sequences (1) found for DRB1_0701 in Heyder. This allele will not be plotted.
# WARNING! Low quantity of sequences (3) found for Trash in Heyder. This allele will not be plotted.
#
# Found 32 sequences for DRB1_0402 in Khoda
# Found 25 sequences for DRB1_0701 in Khoda
# Found 12 sequences for Trash in Khoda
#
# 1/4 Making logo for DRB1_0401 in Heyder...
# 2/4 Making logo for DRB1_0402 in Khoda...
# 3/4 Making logo for DRB1_0701 in Khoda...
# 4/4 Making logo for Trash in Khoda...
#
# Generating peptide count histograms... DONE.
#
# Generating peptide length histograms...#
# SAVING LOGOS PLOT...
# DPI: 200.0
# PDF: Abelin_peptide_length_histogram.pdf
# PNG: Abelin_peptide_length_histogram.png
#
# SAVING LOGOS PLOT...
# DPI: 200.0
# PDF: Heyder_peptide_length_histogram.pdf
# PNG: Heyder_peptide_length_histogram.png
#
# SAVING LOGOS PLOT...
# DPI: 200.0
# PDF: Khoda_peptide_length_histogram.pdf
# PNG: Khoda_peptide_length_histogram.png
 DONE.
#
# Plotting /var/www/services/services/MHCMotifDecon-1.3/tmp/run_18162/data/Heyder_count_histogram.png
# Plotting /var/www/services/services/MHCMotifDecon-1.3/tmp/run_18162/data/Heyder_peptide_length_histogram.png
# Plotting /var/www/services/services/MHCMotifDecon-1.3/tmp/run_18162/logos/Heyder@DRB1_0401.logo-001.png
# Plotting /var/www/services/services/MHCMotifDecon-1.3/tmp/run_18162/data/Khoda_count_histogram.png
# Plotting /var/www/services/services/MHCMotifDecon-1.3/tmp/run_18162/data/Khoda_peptide_length_histogram.png
# Plotting /var/www/services/services/MHCMotifDecon-1.3/tmp/run_18162/logos/Khoda@DRB1_0402.logo-001.png
# Plotting /var/www/services/services/MHCMotifDecon-1.3/tmp/run_18162/logos/Khoda@DRB1_0701.logo-001.png
# Plotting /var/www/services/services/MHCMotifDecon-1.3/tmp/run_18162/logos/Khoda@Trash.logo-001.png
#
# SAVING LOGOS PLOT...
# DPI: 200.0
# PDF: /var/www/services/services/MHCMotifDecon-1.3/tmp//run_18162/logos.pdf
# PNG: /var/www/services/services/MHCMotifDecon-1.3/tmp//run_18162/logos.png
#
# MHC_Motif_Decon has finished.
#
# Results are stored in /var/www/services/services/MHCMotifDecon-1.3/tmp//run_18162
###################################################

logos/
logos/Heyder_count_histogram.png
logos/Khoda_count_histogram.png
logos/Khoda@DRB1_0701.logo.eps
logos/Heyder@DRB1_0401.logo.txt
logos/Heyder@DRB1_0401.logo.eps
logos/Khoda@Trash.logo_freq.mat
logos/Abelin_count_histogram.png
logos/Khoda@DRB1_0402.logo.eps
logos/Khoda@DRB1_0701.logo.txt
logos/Khoda@Trash.logo.txt
logos/Khoda@DRB1_0701.logo_freq.mat
logos/Khoda@Trash.logo.eps
logos/Khoda@DRB1_0402.logo_freq.mat
logos/Abelin_peptide_length_histogram.png
logos/Khoda@DRB1_0402.logo.txt
logos/Khoda@Trash.logo-001.png
logos/Khoda_peptide_length_histogram.png
logos/Heyder_peptide_length_histogram.png
logos/Khoda@DRB1_0402.logo-001.png
logos/Heyder@DRB1_0401.logo-001.png
logos/Heyder@DRB1_0401.logo_freq.mat
logos/Khoda@DRB1_0701.logo-001.png

Motif Deconvolution plot:

Link to LOGO image logos.png logos.pdf

Link to prediction file Output_file.xls Link to file with per-MHC prediction scores per_mhc_scores.txt

Link to tar.gz file with logo/image files data.tar.gz 




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ARTICLE ABSTRACTS








MAIN REFERENCE







Accurate MHC Motif Deconvolution of immunopeptidomics data reveals high relevant contribution of DRB3, 4 and 5 to the total DR Immunopeptidome



Saghar Kaabinejadian, Carolina Barra, Bruno Alvarez, Hooman Yari, William Hildebrand,  Morten Nielsen





Mass spectrometry (MS) based immunopeptidomics is used in several biomedical applications including neo-epitope discovery in oncology, next-generation vaccine development and protein-drug immunogenicity assessment. Immunopeptidome data are highly complex given the expression of multiple HLA alleles on the cell membrane and presence of co-immunoprecipitated contaminants. The absence of tools that  deal with these challenges effectively and guide the analysis and interpretation of this complex type of data is currently a major bottleneck for the large-scale application of this technique. To resolve this,  we here present the MHCMotifDecon that benefits from state-of-the-art HLA class-I and class-II predictions to accurately deconvolute immunopeptidome datasets and assign individual ligands to the most likely HLA molecule, allowing to identify and characterize HLA binding motifs while discarding co-purified contaminants. We have benchmarked the tool against other state-of-the-art methods and illustrated its application on experimental datasets for HLA-DR demonstrating a previously underappreciated role for HLA-DRB3/4/5 molecules in defining HLA class II immune repertoires. With its ease of use, MHCMotifDecon can efficiently guide interpretation of immunopeptidome datasets, serving the discovery of novel T cell targets. MHCMotifDecon is available at https://services.healthtech.dtu.dk/service.php?MHCMotifDecon-1.0.



Frontiers in Immunology 26 January 2022.
Sec. Antigen Presenting Cell Biology, DOI: 10.3389/fimmu.2022.835454

Full text





EARLIER REFERENCES













  


Software Downloads


    

  • Version 1.3a
    • 


  • Version 1.2c
    • 


  • Version 1.2b
    • 


  • Version 1.2a
    • 


  • Version 1.0a
    • 











GETTING HELP




If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0) and the options you have selected. If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).

If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.

Correspondence: 
Technical Support: