Search research articles

关于 JoVE

概览领导团队博客 JoVE 帮助中心

作者

出版流程编辑委员会范围与政策同行评审常见问题投稿

图书馆员

用户评价订阅访问资源图书馆顾问委员会常见问题

研究

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序

Same author

TCR-FramePose: a local-frame representation for decomposing global docking and CDR3 loop geometry in TCR-pMHC recognition.

bioRxiv : the preprint server for biology·2026

Same author

Unbiased avidity-based isolation of antigen-specific T cells.

Journal for immunotherapy of cancer·2026

Same author

Dual-degenerate TCRs target multiple KRAS hotspot and HLA-A3 family combinations.

Research square·2026

Same author

Cuproptosis-immunity crosstalk informs strategy to overcome immunotherapy resistance.

Cell·2026

Same author

Deciphering small sequence differences in T cell receptor-antigen pairing.

Nature communications·2026

Same author

Addition of Metastasis-Directed Therapy to Standard of Care for Oligometastatic Disease: Primary Aggregated Analysis of All Baskets from the Phase II Randomized EXTEND Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026

Search research articles

相关实验视频

Updated: Jul 26, 2025

Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR

Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR

Published on: December 6, 2014

一种新的统计方法用于清除T细胞受体测序数据的污染.

Ruoxing Li^1,2, Mehmet Altan³, Alexandre Reuben³

¹Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 77030, Texas, Houston, USA.

Briefings in bioinformatics

|June 20, 2023

概括

在T细胞受体测序 (TCR-seq) 数据中的污染可能会扭曲免疫过程研究. 这项研究引入了一种新的统计模型来检测和删除这些文物,确保更准确的T细胞谱系分析.

关键词:

贝叶斯模型是贝叶斯模型.污染检测检测污染的检测 TCR 测序的测序方法

更多相关视频

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Published on: January 12, 2021

ATAC-seq Assay with Low Mitochondrial DNA Contamination from Primary Human CD4+ T Lymphocytes

ATAC-seq Assay with Low Mitochondrial DNA Contamination from Primary Human CD4+ T Lymphocytes

Published on: March 22, 2019

相关实验视频

Last Updated: Jul 26, 2025

Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR

Simultaneous Quantification of T-Cell Receptor Excision Circles TRECs and K-Deleting Recombination Excision Circles KRECs by Real-time PCR

Published on: December 6, 2014

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Published on: January 12, 2021

ATAC-seq Assay with Low Mitochondrial DNA Contamination from Primary Human CD4+ T Lymphocytes

ATAC-seq Assay with Low Mitochondrial DNA Contamination from Primary Human CD4+ T Lymphocytes

Published on: March 22, 2019

科学领域:

免疫学免疫学免疫学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

T细胞受体 (TCR) 谱系对免疫反应至关重要,并使用TCR测序 (TCR-seq) 进行分析.
高通量TCR-seq实验在样本采集,准备和测序过程中容易受到污染.
现有的方法往往无法解决数据污染问题,导致免疫学见解不准确.

研究的目的:

开发一种新的统计模型,用于系统地检测和清除TCR-seq数据中的污染.
为评估污染严重程度提供工具,并实施数据纠正策略.
为了能够准确下游分析T细胞谱系数据,而无需重复实验.

主要方法:

开发一个统计模型,以识别双向和交叉队列污染源.
利用来自14个现有的TCR-seq数据集的先前信息.
实施贝叶斯模型用于统计识别受污染的样本.
用于下游分析去除受影响序列的策略.

主要成果:

拟议的模型有效地检测和量化TCR-seq数据中的污染.
可视化和统计总结有助于评估污染严重程度.
该模型在与现有方法相比,在模拟研究中表现强.
该方法成功应用于两个本地TCR-seq数据集.

结论:

这种新型的统计模型为检测和清除TCR-seq数据中的污染提供了强大的解决方案.
这种方法提高了T细胞谱系分析的可靠性.
该方法提供了一种具有成本效益的方法来挽救可能受损的数据集,避免昂贵的重试.