Immunopeptidomics for Dummies: Detailed Experimental Protocols and Rapid, User-Friendly Visualization of MHC I and II Ligand Datasets with MhcVizPipe
暂无分享,去创建一个
Qing Ma | Chen Li | Mathieu Courcelles | Pierre Thibault | Kevin A. Kovalchik | Pouya Faridi | Anthony Purcell | Kevin A. Kovalchik | Laura Wessling | Frederic Saab | Jérôme Despault | Peter Kubiniok | David Hamelin | Marco Tognetti | Lukas Reiter | Roland Bruderer | Joël Lanoix | Éric Bonneil | Etienne Caron | Isabelle Sirois | David J. Hamelin | L. Reiter | E. Caron | P. Thibault | A. Purcell | P. Faridi | Peter Kubiniok | Qing Ma | I. Sirois | M. Tognetti | É. Bonneil | J. Lanoix | Chen Li | Jérôme Despault | Laura Wessling | Frederic Saab | R. Bruderer | M. Courcelles
[1] Tanveer S. Batth,et al. Protein Aggregation Capture on Microparticles Enables Multipurpose Proteomics Sample Preparation* , 2019, Molecular & Cellular Proteomics.
[2] T. Veres,et al. Polymer-based microfluidic chip for rapid and efficient immunomagnetic capture and release of Listeria monocytogenes. , 2015, Lab on a chip.
[3] X. D. Hoa,et al. Development of a multiplexed microfluidic proteomic reactor and its application for studying protein-protein interactions. , 2011, Analytical chemistry.
[4] Dario Neri,et al. High‐sensitivity HLA class I peptidome analysis enables a precise definition of peptide motifs and the identification of peptides from cell lines and patients’ sera , 2016, Proteomics.
[5] Mark Cobbold,et al. Complementary IMAC enrichment methods for HLA-associated phosphopeptide identification by mass spectrometry , 2015, Nature Protocols.
[6] Robert P. Luoma,et al. Digital microfluidic magnetic separation for particle-based immunoassays. , 2012, Analytical chemistry.
[7] Morten Nielsen,et al. IEDB-AR: immune epitope database—analysis resource in 2019 , 2019, Nucleic Acids Res..
[8] Kevin A. Kovalchik,et al. The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases* , 2019, Molecular & Cellular Proteomics.
[9] Sri H. Ramarathinam,et al. Response to Comment on “A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands” , 2019, Science Immunology.
[10] H. Röst. Deep learning adds an extra dimension to peptide fragmentation , 2019, Nature Methods.
[11] A. Goldberg,et al. Degradation of cell proteins and the generation of MHC class I-presented peptides. , 1999, Annual review of immunology.
[12] Sébastien Lemieux,et al. MAPDP: a cloud-based computational platform for immunopeptidomics analyses. , 2020, Journal of proteome research.
[13] C. Perreault,et al. Exploiting non-canonical translation to identify new targets for T cell-based cancer immunotherapy , 2018, Cellular and Molecular Life Sciences.
[14] Ruedi Aebersold,et al. A Case for a Human Immuno‐Peptidome Project Consortium , 2017, Immunity.
[15] Jürgen Cox,et al. High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis , 2019, Nature Methods.
[16] Ngoc Hieu Tran,et al. Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry , 2018, Nature Methods.
[17] Mathias Wilhelm,et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning , 2019, Nature Methods.
[18] Morten Nielsen,et al. GibbsCluster: unsupervised clustering and alignment of peptide sequences , 2017, Nucleic Acids Res..
[19] M. Nielsen,et al. The Length Distribution of Class I–Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele–Specific Binding Preference , 2016, The Journal of Immunology.
[20] W. van Eden,et al. An Unexpected Major Role for Proteasome-Catalyzed Peptide Splicing in Generation of T Cell Epitopes: Is There Relevance for Vaccine Development? , 2017, Front. Immunol..
[21] S. Stevanović,et al. Purification and Identification of Naturally Presented MHC Class I and II Ligands. , 2019, Methods in molecular biology.
[22] M. Nielsen,et al. NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data , 2017, The Journal of Immunology.
[23] Alex Rubinsteyn,et al. MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing. , 2020, Cell systems.
[24] Morten Nielsen,et al. Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data. , 2020, Journal of proteome research.
[25] Teodor Veres,et al. Active pneumatic control of centrifugal microfluidic flows for lab-on-a-chip applications. , 2015, Lab on a chip.
[26] Sri H. Ramarathinam,et al. Spliced peptides and cytokine driven changes in the immunopeptidome of melanoma , 2019, bioRxiv.
[27] Juliane Liepe,et al. Post-translational peptide splicing and T cell response , 2017 .
[28] R Higuchi,et al. High-resolution, high-throughput HLA genotyping by next-generation sequencing. , 2009, Tissue antigens.
[29] Morten Nielsen,et al. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data , 2020, Nucleic Acids Res..
[30] H. Rammensee,et al. The HLA Ligand Atlas. A resource of natural HLA ligands presented on benign tissues , 2019, bioRxiv.
[31] Etienne Caron,et al. Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry* , 2015, Molecular & Cellular Proteomics.
[32] Hanspeter Pfister,et al. UpSet: Visualization of Intersecting Sets , 2014, IEEE Transactions on Visualization and Computer Graphics.
[33] Sri H. Ramarathinam,et al. A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands , 2018, Science Immunology.
[34] M. Mishto. What We See, What We Do Not See, and What We Do Not Want to See in HLA Class I Immunopeptidomes , 2020, Proteomics.
[35] Roman A. Zubarev,et al. The SysteMHC Atlas project , 2017, Nucleic Acids Res..
[36] Ruedi Aebersold,et al. The SysteMHC Atlas: a Computational Pipeline, a Website, and a Data Repository for Immunopeptidomic Analyses. , 2020, Methods in molecular biology.
[37] Morten Nielsen,et al. Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data , 2019, Immunogenetics.
[38] Martin Eisenacher,et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data , 2018, Nucleic Acids Res..
[39] Juan Antonio Vizcaíno,et al. Minimal Information About an Immuno‐Peptidomics Experiment (MIAIPE) , 2018, Proteomics.
[40] A. Tholey,et al. Miniaturized sample preparation on a digital microfluidics device for sensitive bottom-up microproteomics of mammalian cells using magnetic beads and mass spectrometry-compatible surfactants. , 2019, Lab on a chip.
[41] Ilan Beer,et al. Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome* , 2018, Molecular & Cellular Proteomics.
[42] D. Harlan,et al. Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion , 2016, Science.
[43] G. Learn,et al. Contribution of proteasome-catalyzed peptide cis-splicing to viral targeting by CD8+ T cells in HIV-1 infection , 2019, Proceedings of the National Academy of Sciences.
[44] Sri H. Ramarathinam,et al. Mass spectrometry–based identification of MHC-bound peptides for immunopeptidomics , 2019, Nature Protocols.
[45] Alessandro Sette,et al. An open-source computational and data resource to analyze digital maps of immunopeptidomes , 2015, eLife.
[46] Xiaohui Liu,et al. In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics , 2020, Nature Communications.
[47] M. Bassani-Sternberg,et al. High-Throughput, Fast, and Sensitive Immunopeptidomics Sample Processing for Mass Spectrometry. , 2019, Methods in molecular biology.
[48] P. Kloetzel,et al. Multi-level Strategy for Identifying Proteasome-Catalyzed Spliced Epitopes Targeted by CD8+ T Cells during Bacterial Infection. , 2017, Cell reports.
[49] S. Lemieux,et al. Global proteogenomic analysis of human MHC class I-associated peptides derived from non-canonical reading frames , 2016, Nature Communications.
[50] Alex Rubinsteyn,et al. MHCflurry: Open-Source Class I MHC Binding Affinity Prediction. , 2018, Cell systems.
[51] G. Coukos,et al. Mass spectrometry–driven exploration reveals nuances of neoepitope-driven tumor rejection , 2019, JCI insight.
[52] Michal Bassani-Sternberg,et al. Mass Spectrometry Based Immunopeptidomics for the Discovery of Cancer Neoantigens. , 2018, Methods in molecular biology.
[53] George Coukos,et al. Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes , 2019, Nature Biotechnology.
[54] Timo Sachsenberg,et al. MHCquant: Automated and reproducible data analysis for immunopeptidomics. , 2019, Journal of proteome research.
[55] Patrick G. A. Pedrioli,et al. A tissue-based draft map of the murine MHC class I immunopeptidome , 2018, Scientific Data.
[56] S. Lemieux,et al. MHC class I-associated peptides derive from selective regions of the human genome. , 2016, The Journal of clinical investigation.
[57] M. D. Chamberlain,et al. Digital Microfluidics for Immunoprecipitation. , 2016, Analytical chemistry.
[58] Mathieu Courcelles,et al. Comparison of the MHC I Immunopeptidome Repertoire of B‐Cell Lymphoblasts Using Two Isolation Methods , 2018, Proteomics.
[59] P. Kloetzel,et al. A large fraction of HLA class I ligands are proteasome-generated spliced peptides , 2016, Science.
[60] S. Stevanović,et al. Biochemical large-scale identification of MHC class I ligands. , 2013, Methods in molecular biology.
[61] H. Ovaa,et al. Why do proteases mess up with antigen presentation by re-shuffling antigen sequences? , 2018, Current opinion in immunology.
[62] P. Gendron,et al. Noncoding regions are the main source of targetable tumor-specific antigens , 2018, Science Translational Medicine.