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Related Experiment Video

Updated: Aug 22, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

Analyzing Antibody Repertoire Using Next-Generation Sequencing and Machine Learning.

Shuto Hayashi¹, Shumpei Ishikawa²

¹Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Methods in Molecular Biology (Clifton, N.J.)

|November 8, 2022

Summary

This summary is machine-generated.

DeepRC, a new machine learning method, analyzes the immune repertoire using high-throughput sequencing data. This approach aids in understanding the immune system and its connection to various diseases.

Keywords:

Classification Deep learning Machine learning Next-generation sequencing Repertoire analysis

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Last Updated: Aug 22, 2025

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VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

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Published on: December 28, 2015

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

High-throughput sequencing enables comprehensive immune repertoire analysis.
Immune repertoire analysis is crucial for understanding disease mechanisms.
Existing methods for repertoire analysis have limitations.

Purpose of the Study:

To introduce DeepRC, a novel method for immune repertoire analysis.
To demonstrate the application of cutting-edge machine learning in this field.
To provide a tool for analyzing simulated and real-world immune repertoire data.

Main Methods:

DeepRC utilizes advanced machine learning techniques.
The method is validated using both simulated and real-world datasets.
Analysis focuses on high-throughput sequencing data of the immune repertoire.

Main Results:

DeepRC effectively analyzes immune repertoire data.
The method demonstrates the power of machine learning in immunological studies.
Performance is assessed on diverse datasets.

Conclusions:

DeepRC offers a powerful new approach to immune repertoire analysis.
Machine learning is a valuable tool for advancing our understanding of the immune system.
This method can contribute to disease research by elucidating immune system-disease relationships.