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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Updated: May 26, 2026

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Challenges and future directions of AIRR-seq-based diagnostics.

Ulrik Stervbo^1,2, Paraskevas Filippidis³, Felix Breden⁴

¹Center for Translational Medicine and Immune Diagnostics Laboratory, Medical Department I, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany.

Immunoinformatics (Amsterdam, Netherlands)

|May 25, 2026

Summary

This summary is machine-generated.

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) offers promising diagnostic potential but requires standardized quality controls, privacy compliance, and compatible bioinformatics. Collaboration is key to integrating AIRR-seq into precision medicine for enhanced diagnostics.

Keywords:

AIRR-seq Clinical translation Diagnostics Interpretability Machine learning Standardization

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Last Updated: May 26, 2026

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Published on: August 4, 2016

Area of Science:

Immunology
Genomics
Bioinformatics

Background:

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a powerful tool for analyzing immune system responses.
Clinical implementation of AIRR-seq is hindered by several significant challenges.
Standardization and data interpretation remain critical barriers to widespread adoption.

Purpose of the Study:

To examine the current state of AIRR-seq diagnostics.
To identify key obstacles and opportunities for advancing AIRR-seq implementation.
To discuss the potential of AIRR-seq in precision medicine.

Main Methods:

Review of current AIRR-seq diagnostic landscape.
Analysis of challenges including quality control, data privacy (GDPR, HIPAA), and bioinformatics.
Exploration of machine learning applications for repertoire signature interpretation.

Main Results:

Standardized quality controls, robust privacy measures, and clinically compatible bioinformatics pipelines are essential.
Machine learning can aid interpretation but requires a balance between accuracy and clinical interpretability.
Future applications include early disease detection, prognosis, and treatment/vaccine response monitoring.

Conclusions:

Successful clinical integration of AIRR-seq necessitates collaboration among stakeholders (funding bodies, regulators, researchers, clinicians).
Establishing clear guidelines and expanding well-characterized patient sample repositories are crucial.
Ongoing initiatives aim to unlock AIRR-seq's potential in precision medicine and diagnostics.