Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

RNA Splicing

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...

Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Representation learning for multi-modal spatially resolved transcriptomics data.

Bioinformatics (Oxford, England)·2026

Same author

Estimation of Physiological Metrics from Resting ECGs Using Deep Learning in the UK Biobank, Including submaximal exercise derived V̇O <sub>2</sub> max, Body Fat Percentage, and Grip Strength.

medRxiv : the preprint server for health sciences·2026

Same author

Rawsamble: overlapping raw nanopore signals using a hash-based seeding mechanism.

Bioinformatics (Oxford, England)·2026

Same author

CAMP: a modular metagenomics analysis system for integrated multistep data exploration.

NAR genomics and bioinformatics·2026

Same author

RMS: a ML-based system for ICU respiratory monitoring and resource planning.

NPJ digital medicine·2025

Same author

Efficient and accurate search in petabase-scale sequence repositories.

Nature·2025

Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026

Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026

Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026

Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 15, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

ImmunoPepper: extracting personalized peptides from complex splicing graphs.

Laurie Prélot^1,2,3, Jiayu Chen¹, Matthias Hüser^1,3

¹Department of Computer Science, ETH Zürich, Zürich 8092, Switzerland.

Bioinformatics (Oxford, England)

|October 9, 2025

Summary

This summary is machine-generated.

ImmunoPepper software identifies cancer-specific peptides from RNA sequencing data for personalized therapy. It analyzes splicing variants and validates potential neoantigens, aiding in cancer immunotherapy development.

More Related Videos

mRNA Interactome Capture from Plant Protoplasts

mRNA Interactome Capture from Plant Protoplasts

Published on: July 28, 2017

Utilization of Grafix for the Detection of Transient Interactors of Saccharomyces cerevisiae Spliceosome Subcomplexes

Utilization of Grafix for the Detection of Transient Interactors of Saccharomyces cerevisiae Spliceosome Subcomplexes

Published on: November 9, 2020

Related Experiment Videos

Last Updated: Jan 15, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

mRNA Interactome Capture from Plant Protoplasts

mRNA Interactome Capture from Plant Protoplasts

Published on: July 28, 2017

Utilization of Grafix for the Detection of Transient Interactors of Saccharomyces cerevisiae Spliceosome Subcomplexes

Utilization of Grafix for the Detection of Transient Interactors of Saccharomyces cerevisiae Spliceosome Subcomplexes

Published on: November 9, 2020

Area of Science:

Computational Biology
Immunology
Genomics

Background:

RNA sequencing reveals transcript isoforms in health and disease.
Cancer's transcript variability can yield tumor-associated peptides for immune recognition.
Identifying these peptides is crucial for personalized cancer therapy.

Purpose of the Study:

To introduce ImmunoPepper, an open-source software tool for extracting biologically plausible peptides from RNA sequencing data.
To enable personalization of peptide sets with germline and somatic variations, accounting for novel splice variants.
To provide filtering options including normal tissue background subtraction, MHC-binding affinity prediction, and Mass Spec-based validation.

Main Methods:

ImmunoPepper processes RNA-seq datasets to construct splicing graphs.
It incorporates germline and somatic variations for personalized peptide identification.
Filtering includes normal tissue subtraction, MHC-binding prediction, and Mass Spec validation.

Main Results:

Analysis of ovarian and breast cancer samples yielded hundreds of cancer-specific predicted MHC-I binding 9-mers per sample.
Mass Spec validation confirmed a subset of these peptides, with an average validation rate of 4.5-5.3%.
An average of 20-25 validated MHC-I binding 9-mers were identified per sample.

Conclusions:

ImmunoPepper effectively generates splicing-derived neoepitopes.
Joint data structures are recommended for processing cancer and normal cohorts.
Reproducibility across generation pipelines is essential for candidate identification.