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Updated: Aug 14, 2025

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

Haplotype-aware pantranscriptome analyses using spliced pangenome graphs.

Jonas A Sibbesen¹, Jordan M Eizenga¹, Adam M Novak¹

¹UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA.

|January 16, 2023

Summary

This summary is machine-generated.

Pangenomics is now applied to transcriptomics with the pantranscriptome, improving RNA sequencing analysis. This new approach enhances accuracy and enables haplotype-specific expression quantification without prior sample characterization.

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Area of Science:

Bioinformatics
Computational Biology
Genomics and Transcriptomics

Background:

Pangenomics utilizes population-level genome references (sequence graphs) to overcome limitations of single reference genomes.
Traditional reference-based methods struggle with population-level genomic and transcriptomic analyses.
Reference bias in genome analysis can hinder accurate interpretation of sequencing data.

Purpose of the Study:

To extend pangenomic approaches to transcriptomics by introducing the pantranscriptome.
To develop a toolchain for constructing and analyzing pantranscriptome graphs.
To enable accurate haplotype-aware transcript expression quantification from RNA sequencing data.

Main Methods:

Development of additions to the VG toolkit and a new tool, RPVG.
Construction of spliced pangenome graphs representing population transcriptomes.
Mapping of RNA sequencing data to pantranscriptome graphs.
Haplotype-aware expression quantification using the pantranscriptome reference.

Main Results:

The developed toolchain improves the accuracy of RNA sequencing mapping compared to state-of-the-art methods.
The workflow enables efficient quantification of haplotype-specific transcript expression.
Accurate analysis is achieved without the need for prior haplotype characterization of the sample.

Conclusions:

Pantranscriptome analysis represents a significant advancement in transcriptomics.
The developed tools offer a more accurate and comprehensive approach to RNA sequencing data analysis.
This method facilitates population-level transcriptomic studies and haplotype-specific expression analysis.