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Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....

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Updated: Sep 10, 2025

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Personalized Peptide Arrays for Detection of HLA Alloantibodies in Organ Transplantation

Published on: September 6, 2017

consHLA: a next generation sequencing consensus-based HLA typing workflow.

Rachel Bowen-James^1,2,3, Weilin Wu¹, Marie Wong-Erasmus^1,4

¹Children's Cancer Institute, Lowy Cancer Research Centre, UNSW, Kensington, NSW, Australia.

BMC Bioinformatics

|August 20, 2025

Summary

This summary is machine-generated.

We developed consHLA, a tool combining germline and tumor sequencing data for high-resolution Human Leukocyte Antigen (HLA) typing. This approach improves accuracy and identifies tumor-specific HLA changes, aiding cancer immunotherapy and transplant matching.

Keywords:

Bioinformatics Computational Biology Consensus algorithm Human leukocyte antigen Major histocompatibility complex Tissue typing Transcriptome sequencing Whole genome sequencing

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Last Updated: Sep 10, 2025

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Area of Science:

Genomics
Immunogenetics
Bioinformatics

Background:

Human Leukocyte Antigens (HLA) are critical for immune responses and transplantation.
Current HLA typing methods offer limited resolution, impacting clinical applications.
Next-generation sequencing (NGS) data presents an opportunity for higher-resolution HLA typing.

Purpose of the Study:

To develop a consensus HLA typing approach using combined germline and tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq) data.
To improve the accuracy and confidence of HLA typing in cancer patients.
To identify clinically relevant HLA type changes occurring in tumors.

Main Methods:

Developed consHLA, an automated workflow integrating germline WGS, tumor WGS, and tumor RNA-seq data.
Utilized the HLA-HD package for Class I and II HLA gene analysis at three-field resolution.
Validated the workflow using data from 86 high-risk pediatric cancer patients.

Main Results:

Achieved 97.9% concordance with gold standard HLA typing results.
Observed 90.5% allele consistency across the three NGS data types.
Identified typing inconsistencies in 29 brain tumor cases, with 32% having clinically relevant explanations.

Conclusions:

The consHLA workflow provides high-resolution, consensus HLA typing from combined sequencing data.
It offers improved accuracy over current gold standards and identifies tumor-specific HLA alterations.
The automated workflow generates clinically relevant, user-friendly reports.