| scholarly article | Q13442814 |
| P356 | DOI | 10.1021/ACS.JPROTEOME.9B00313 |
| P698 | PubMed publication ID | 31589052 |
| P50 | author | Hans-Georg Rammensee | Q1577018 |
| Oliver Kohlbacher | Q29998906 | ||
| Leon Bichmann | Q90365521 | ||
| Alexander Peltzer | Q57220108 | ||
| P2093 | author name string | Christopher Mohr | |
| Leon Kuchenbecker | |||
| Stefan Stevanović | |||
| Timo Sachsenberg | |||
| Annika Nelde | |||
| Juliane S Walz | |||
| Lukas Heumos | |||
| Michael Ghosh | |||
| P2860 | cites work | Reliable prediction of T-cell epitopes using neural networks with novel sequence representations | Q27485882 |
| Probability-based protein identification by searching sequence databases using mass spectrometry data | Q27860736 | ||
| An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database | Q27861106 | ||
| OpenMS: a flexible open-source software platform for mass spectrometry data analysis | Q28278187 | ||
| An open-source computational and data resource to analyze digital maps of immunopeptidomes | Q28546655 | ||
| 2016 update of the PRIDE database and its related tools | Q28603110 | ||
| The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience | Q28654922 | ||
| MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification | Q29547200 | ||
| Andromeda: a peptide search engine integrated into the MaxQuant environment | Q29615816 | ||
| SYFPEITHI: database for MHC ligands and peptide motifs | Q29616214 | ||
| ImmunoNodes - graphical development of complex immunoinformatics workflows | Q30250980 | ||
| MHC-I Ligand Discovery Using Targeted Database Searches of Mass Spectrometry Data: Implications for T-Cell Immunotherapies. | Q31167797 | ||
| Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics | Q33308294 | ||
| Interferences and contaminants encountered in modern mass spectrometry | Q33369319 | ||
| The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding | Q33419581 | ||
| Towards a systems understanding of MHC class I and MHC class II antigen presentation. | Q34029353 | ||
| Combined Analysis of Antigen Presentation and T-cell Recognition Reveals Restricted Immune Responses in Melanoma | Q64122192 | ||
| Noncoding regions are the main source of targetable tumor-specific antigens | Q90286859 | ||
| Actively personalized vaccination trial for newly diagnosed glioblastoma | Q90648467 | ||
| Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome | Q91254308 | ||
| Quantification of epitope abundance reveals the effect of direct and cross-presentation on influenza CTL responses | Q93116192 | ||
| Comet: an open-source MS/MS sequence database search tool | Q34311405 | ||
| An automated pipeline for high-throughput label-free quantitative proteomics | Q34326747 | ||
| Complete De Novo Assembly of Monoclonal Antibody Sequences | Q34538543 | ||
| jvenn: an interactive Venn diagram viewer | Q35236034 | ||
| FRED 2: an immunoinformatics framework for Python. | Q36011145 | ||
| Immunoinformatics and epitope prediction in the age of genomic medicine | Q36304147 | ||
| Use of HLA peptidomics and whole exome sequencing to identify human immunogenic neo-antigens. | Q36905613 | ||
| MS-GF+ makes progress towards a universal database search tool for proteomics | Q37283260 | ||
| Characterization of MHC ligands for peptide based tumor vaccination | Q37622336 | ||
| Insights into MHC class I antigen processing gained from large-scale analysis of class I ligands | Q37851048 | ||
| Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity. | Q38612613 | ||
| A Case for a Human Immuno-Peptidome Project Consortium | Q38622057 | ||
| An immunogenic personal neoantigen vaccine for patients with melanoma | Q38692859 | ||
| Targeted Feature Detection for Data-Dependent Shotgun Proteomics | Q38695893 | ||
| Computational genomics tools for dissecting tumour-immune cell interactions | Q38885361 | ||
| Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-allelic Cells Enables More Accurate Epitope Prediction | Q38947679 | ||
| How to talk about protein-level false discovery rates in shotgun proteomics | Q39509752 | ||
| Global proteogenomic analysis of human MHC class I-associated peptides derived from non-canonical reading frames | Q41906355 | ||
| A deeper look into Comet--implementation and features | Q41973072 | ||
| The SysteMHC Atlas project | Q41990341 | ||
| Semi-supervised learning for peptide identification from shotgun proteomics datasets. | Q45965072 | ||
| Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. | Q46111623 | ||
| Antigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens. | Q46398502 | ||
| Informatics for cancer immunotherapy | Q47290509 | ||
| NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data | Q47696633 | ||
| qPortal: A platform for data-driven biomedical research | Q47722568 | ||
| In Immunopeptidomics We Need a Sniper Instead of a Shotgun | Q48150567 | ||
| Nextflow enables reproducible computational workflows | Q48290835 | ||
| Personalized peptide vaccine-induced immune response associated with long-term survival of a metastatic cholangiocarcinoma patient | Q49178445 | ||
| High-throughput and Sensitive Immunopeptidomics Platform Reveals Profound Interferonγ-Mediated Remodeling of the Human Leukocyte Antigen (HLA) Ligandome. | Q49836890 | ||
| A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. | Q50046798 | ||
| Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. | Q50906889 | ||
| T-Cell Immunopeptidomes Reveal Cell Subtype Surface Markers Derived From Intracellular Proteins. | Q52680130 | ||
| Python for Scientists and Engineers | Q56532722 | ||
| Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry | Q57178228 | ||
| A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands | Q57472593 | ||
| Global Identification of Post-Translationally Spliced Peptides with Neo-Fusion | Q57804186 | ||
| MHCflurry: Open-Source Class I MHC Binding Affinity Prediction | Q60111456 | ||
| TAPBPR mediates peptide dissociation from MHC class I using a leucine lever | Q60931379 | ||
| MS-Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments | Q62749149 | ||
| P433 | issue | 11 | |
| P304 | page(s) | 3876-3884 | |
| P577 | publication date | 2019-10-22 | |
| P1433 | published in | Journal of Proteome Research | Q3186939 |
| P1476 | title | MHCquant: Automated and Reproducible Data Analysis for Immunopeptidomics | |
| P478 | volume | 18 |
| Q91787445 | Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis |
| Q99631836 | DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics |
| Q92641664 | Guidance Document: Validation of a High-Performance Liquid Chromatography-Tandem Mass Spectrometry Immunopeptidomics Assay for the Identification of HLA Class I Ligands Suitable for Pharmaceutical Therapies |
| Q111150052 | Immunopeptidomics toolkit library (IPTK): a python-based modular toolbox for analyzing immunopeptidomics data |
| Q89932276 | MAPDP: A Cloud-Based Computational Platform for Immunopeptidomics Analyses |
| Q89944200 | Mass Spectrometry-Based Identification of MHC-Associated Peptides |
Search more.