ArticleNucleic acids research2017
GibbsCluster: unsupervised clustering and alignment of peptide sequences.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 1 of them a synthesis that pooled it.
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Who cites it
135 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Immunopeptidomics reveals conserved and abundant Francisella tularensis proteins as immunogenic CD4 T cell antigens in mice.PLoS pathogens · 2026Article
- A targetable dependency on nonsense-mediated decay for cellular homeostasis and immune control in small cell lung cancer.Molecular cancer · 2026Article
- Skewed MHC Class I Peptide Presentation in Psoriatic Arthritis.International journal of molecular sciences · 2026Article
- Harnessing Tumor-Specific Transcript Diversity Uncovers a Shared Neoantigen Reservoir for Pancreatic Ductal Adenocarcinoma.MedComm · 2026Article
- PepGen: conditional generation of peptides for MHC binding.Bioinformatics (Oxford, England) · 2026Article
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- Discovery and structural characterization of newcastle disease virus mimetic peptides identified through phage display.Virus research · 2026Article
- Immunopeptidome profiling in pulmonary fibrosis provides a platform for identifying therapeutic targets.Nature immunology · 2026Article
- A high-speed attention network for MHC-bound peptide identification and 3D modeling.Cell reports methods · 2026Article
- Benchmarking Software for DDA-PASEF Immunopeptidomics.Molecular & cellular proteomics : MCP · 2026Article
- Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A.Briefings in bioinformatics · 2026Article
- Recurrent Immunogenic Neoantigens and Their Cognate T-cell Receptors in Treatment-Resistant Metastatic Prostate Cancer.Cancer discovery · 2026Article
- MHC1-TIP enables single-tube multimodal immunopeptidome profiling and uncovers intratumoral heterogeneity in antigen presentation.Communications biology · 2026Article
- BeitAI-pHLA: Multiallele Peptide-HLA Class I Binding Prediction Using Protein Language Model and Multi-Instance Learning.Computational and structural biotechnology journal · 2026Article
75 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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Abstract
Receptor interactions with short linear peptide fragments (ligands) are at the base of many biological signaling processes. Conserved and information-rich amino acid patterns, commonly called sequence motifs, shape and regulate these interactions. Because of the properties of a receptor-ligand system or of the assay used to interrogate it, experimental data often contain multiple sequence motifs. GibbsCluster is a powerful tool for unsupervised motif discovery because it can simultaneously cluster and align peptide data. The GibbsCluster 2.0 presented here is an improved version incorporating insertion and deletions accounting for variations in motif length in the peptide input. In basic terms, the program takes as input a set of peptide sequences and clusters them into meaningful groups. It returns the optimal number of clusters it identified, together with the sequence alignment and sequence motif characterizing each cluster. Several parameters are available to customize cluster analysis, including adjustable penalties for small clusters and overlapping groups and a trash cluster to remove outliers. As an example application, we used the server to deconvolute multiple specificities in large-scale peptidome data generated by mass spectrometry. The server is available at http://www.cbs.dtu.dk/services/GibbsCluster-2.0.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.