home.social

#immunopeptidomics — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #immunopeptidomics, aggregated by home.social.

  1. Pipeline release! nf-core/mhcquant v3.2.0 - 3.2.0 - Solitude!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  2. Pipeline release! nf-core/mhcquant v3.2.0 - 3.2.0 - Solitude!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  3. Pipeline release! nf-core/mhcquant v3.2.0 - 3.2.0 - Solitude!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  4. Pipeline release! nf-core/mhcquant v3.2.0 - 3.2.0 - Solitude!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  5. Pipeline release! nf-core/mhcquant v3.2.0 - 3.2.0 - Solitude!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  6. Pipeline release! nf-core/mhcquant v3.1.0 - 3.1.0 - BlüBa!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  7. Pipeline release! nf-core/mhcquant v3.1.0 - 3.1.0 - BlüBa!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  8. Pipeline release! nf-core/mhcquant v3.1.0 - 3.1.0 - BlüBa!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  9. Pipeline release! nf-core/mhcquant v3.1.0 - 3.1.0 - BlüBa!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  10. Pipeline release! nf-core/mhcquant v3.1.0 - 3.1.0 - BlüBa!
    Identify and quantify MHC eluted peptides from mass spectrometry raw data
    Please see the changelog: github.com/nf-core/mhcquant/re

    #dda #immunopeptidomics #massspectrometry #mhc #openms #peptides #nfcore #openscience #nextflow #bioinformatics

  11. From these MS/MS spectrum and PSM characteristics, can you guess whether it's #immunopeptidomics or #BottomUpProteomics data?

    Join us in the #BioinformaticsHub at #HUPO2023 today to discuss #MachineLearning applications for immunopeptidomics data analysis.

    Slides: doi.org/10.5281/zenodo.8353779
    Notebook to generate the figures: gist.github.com/bittremieux/af

  12. From these MS/MS spectrum and PSM characteristics, can you guess whether it's #immunopeptidomics or #BottomUpProteomics data?

    Join us in the #BioinformaticsHub at #HUPO2023 today to discuss #MachineLearning applications for immunopeptidomics data analysis.

    Slides: doi.org/10.5281/zenodo.8353779
    Notebook to generate the figures: gist.github.com/bittremieux/af

  13. From these MS/MS spectrum and PSM characteristics, can you guess whether it's #immunopeptidomics or #BottomUpProteomics data?

    Join us in the #BioinformaticsHub at #HUPO2023 today to discuss #MachineLearning applications for immunopeptidomics data analysis.

    Slides: doi.org/10.5281/zenodo.8353779
    Notebook to generate the figures: gist.github.com/bittremieux/af

  14. From these MS/MS spectrum and PSM characteristics, can you guess whether it's #immunopeptidomics or #BottomUpProteomics data?

    Join us in the #BioinformaticsHub at #HUPO2023 today to discuss #MachineLearning applications for immunopeptidomics data analysis.

    Slides: doi.org/10.5281/zenodo.8353779
    Notebook to generate the figures: gist.github.com/bittremieux/af

  15. From these MS/MS spectrum and PSM characteristics, can you guess whether it's #immunopeptidomics or #BottomUpProteomics data?

    Join us in the #BioinformaticsHub at #HUPO2023 today to discuss #MachineLearning applications for immunopeptidomics data analysis.

    Slides: doi.org/10.5281/zenodo.8353779
    Notebook to generate the figures: gist.github.com/bittremieux/af

  16. Immunopeptidomics data analysis is very challenging:
    - A massive search space has to be considered (all subsequences of the proteins under consideration).
    - Immunopeptides have different characteristics than standard tryptic peptides (non-tryptic peptide termini, predominantly singly charged).

    This leads to low spectrum annotation rates in #immunopeptidomics. 😥

  17. Immunopeptidomics data analysis is very challenging:
    - A massive search space has to be considered (all subsequences of the proteins under consideration).
    - Immunopeptides have different characteristics than standard tryptic peptides (non-tryptic peptide termini, predominantly singly charged).

    This leads to low spectrum annotation rates in #immunopeptidomics. 😥

  18. Immunopeptidomics data analysis is very challenging:
    - A massive search space has to be considered (all subsequences of the proteins under consideration).
    - Immunopeptides have different characteristics than standard tryptic peptides (non-tryptic peptide termini, predominantly singly charged).

    This leads to low spectrum annotation rates in #immunopeptidomics. 😥

  19. Immunopeptidomics data analysis is very challenging:
    - A massive search space has to be considered (all subsequences of the proteins under consideration).
    - Immunopeptides have different characteristics than standard tryptic peptides (non-tryptic peptide termini, predominantly singly charged).

    This leads to low spectrum annotation rates in #immunopeptidomics. 😥

  20. Immunopeptidomics data analysis is very challenging:
    - A massive search space has to be considered (all subsequences of the proteins under consideration).
    - Immunopeptides have different characteristics than standard tryptic peptides (non-tryptic peptide termini, predominantly singly charged).

    This leads to low spectrum annotation rates in #immunopeptidomics. 😥

  21. Our work on fragment ion intensity prediction for the analysis of timsTOF #immunopeptidomics data is now online on #bioRxiv: biorxiv.org/content/10.1101/20

    Check out this great effort by Charlotte Adams, who basically learned #DeepLearning/#MachineLearning from scratch for this project.

    Thread. 👇

  22. Our work on fragment ion intensity prediction for the analysis of timsTOF #immunopeptidomics data is now online on #bioRxiv: biorxiv.org/content/10.1101/20

    Check out this great effort by Charlotte Adams, who basically learned #DeepLearning/#MachineLearning from scratch for this project.

    Thread. 👇

  23. Our work on fragment ion intensity prediction for the analysis of timsTOF #immunopeptidomics data is now online on #bioRxiv: biorxiv.org/content/10.1101/20

    Check out this great effort by Charlotte Adams, who basically learned #DeepLearning/#MachineLearning from scratch for this project.

    Thread. 👇

  24. Our work on fragment ion intensity prediction for the analysis of timsTOF #immunopeptidomics data is now online on #bioRxiv: biorxiv.org/content/10.1101/20

    Check out this great effort by Charlotte Adams, who basically learned #DeepLearning/#MachineLearning from scratch for this project.

    Thread. 👇

  25. Our work on fragment ion intensity prediction for the analysis of timsTOF #immunopeptidomics data is now online on #bioRxiv: biorxiv.org/content/10.1101/20

    Check out this great effort by Charlotte Adams, who basically learned #DeepLearning/#MachineLearning from scratch for this project.

    Thread. 👇

  26. The slides from my presentation at the #BSPREUPA2023 conference on timsTOF fragment ion intensity prediction for #immunopeptidomics are available here: doi.org/10.5281/zenodo.8163554

    Fantastic work by Charlotte Adams. Stay tuned for the full preprint very soon.

  27. The slides from my presentation at the #BSPREUPA2023 conference on timsTOF fragment ion intensity prediction for #immunopeptidomics are available here: doi.org/10.5281/zenodo.8163554

    Fantastic work by Charlotte Adams. Stay tuned for the full preprint very soon.

  28. The slides from my presentation at the #BSPREUPA2023 conference on timsTOF fragment ion intensity prediction for #immunopeptidomics are available here: doi.org/10.5281/zenodo.8163554

    Fantastic work by Charlotte Adams. Stay tuned for the full preprint very soon.

  29. The slides from my presentation at the #BSPREUPA2023 conference on timsTOF fragment ion intensity prediction for #immunopeptidomics are available here: doi.org/10.5281/zenodo.8163554

    Fantastic work by Charlotte Adams. Stay tuned for the full preprint very soon.

  30. The slides from my presentation at the #BSPREUPA2023 conference on timsTOF fragment ion intensity prediction for #immunopeptidomics are available here: doi.org/10.5281/zenodo.8163554

    Fantastic work by Charlotte Adams. Stay tuned for the full preprint very soon.