MHCquant: A Workflow for Identification and Quantification of HLA Ligands
MHCquant is a comprehensive bioinformatics workflow designed to identify and quantify HLA ligands from mass spectrometry data. This workflow provides an automated and reproducible way to analyze large-scale datasets, enabling researchers to gain insights into the complex interactions between the immune system and disease.
Key Features:
- Identification of HLA ligands : MHCquant uses advanced algorithms to identify HLA ligands from mass spectrometry data, including both peptides and proteins.
- Quantification of HLA ligand abundance : The workflow provides accurate quantification of HLA ligand abundance, allowing researchers to understand the dynamics of antigen presentation.
- Support for multiple HLA alleles : MHCquant can handle multiple HLA alleles, making it a versatile tool for studying the genetic diversity of HLA molecules.
Benefits:
- Improved understanding of immune responses : MHCquant provides insights into the complex interactions between the immune system and disease, enabling researchers to better understand how HLA ligands shape immune responses.
- Identification of potential therapeutic targets : The workflow can be used to identify potential therapeutic targets for cancer, infectious diseases, and autoimmune disorders by analyzing the HLA ligandome.
- Enhanced reproducibility and accuracy : MHCquant provides an automated and reproducible way to analyze large-scale datasets, reducing errors and increasing confidence in results.
Tools and Resources:
- Nextflow-based workflow : The workflow is built on Nextflow, a popular bioinformatics pipeline management system that ensures reproducibility and scalability.
- Support for multiple input formats : MHCquant accepts various input formats, including mzML, mzXML, and CSV files.
- Output files in standard formats : The workflow produces output files in standard formats, such as CSV and JSON, making it easy to integrate with downstream analysis tools.
Usage:
MHCquant can be run on a variety of systems, including Linux, macOS, and Windows. The workflow is designed to be user-friendly and requires minimal bioinformatics expertise.
By using MHCquant, researchers can gain a deeper understanding of the complex interactions between the immune system and disease, ultimately driving discovery and innovation in immunology and beyond.
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Development and maintenance partially funded by de.NBI.