小柯机器人

德国慕尼黑工业大学Julien Gagneur小组的研究建立了人体组织异常剪接的预测方法。该研究于2023年5月4日发表于国际学术期刊《自然-遗传学》杂志。

研究人员生成了一个异常剪接基准数据集，涵盖了基因型组织表达（GTEx）数据集中49个人体组织中超过880万个罕见变异。在20%召回率下，基于DNA碱基的模型可实现最高12%的精度。通过绘制和量化整个转录组的组织特异性剪接位点使用情况并模拟亚型竞争，研究人员在相同的召回率下将精度提高了三倍。将临床可及组织的RNA测序数据整合到该模型-AbSplice可使精度达到60%。这些结果在两个独立的队列中重现，有助于非编码功能丧失变异的鉴别以及遗传诊断的设计和分析。

研究人员表示，异常剪接是遗传性疾病的主要原因之一，但仅能在临床可及组织的转录组中进行直接检测，如皮肤或体液。虽然基于DNA的机器学习模型可以优先考虑影响剪接的罕见变异，但其在预测组织特异性异常剪接中的效率仍未得到验证。

附：英文原文

Title: Aberrant splicing prediction across human tissues

Author: Wagner, Nils, elik, Muhammed H., Hlzlwimmer, Florian R., Mertes, Christian, Prokisch, Holger, Ypez, Vicente A., Gagneur, Julien

Issue&Volume: 2023-05-04

Abstract: Aberrant splicing is a major cause of genetic disorders but its direct detection in transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While DNA-based machine learning models can prioritize rare variants for affecting splicing, their performance in predicting tissue-specific aberrant splicing remains unassessed. Here we generated an aberrant splicing benchmark dataset, spanning over 8.8million rare variants in 49 human tissues from the Genotype-Tissue Expression (GTEx) dataset. At 20% recall, state-of-the-art DNA-based models achieve maximum 12% precision. By mapping and quantifying tissue-specific splice site usage transcriptome-wide and modeling isoform competition, we increased precision by threefold at the same recall. Integrating RNA-sequencing data of clinically accessible tissues into our model, AbSplice, brought precision to 60%. These results, replicated in two independent cohorts, substantially contribute to noncoding loss-of-function variant identification and to genetic diagnostics design and analytics.

DOI: 10.1038/s41588-023-01373-3

Source: https://www.nature.com/articles/s41588-023-01373-3

Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
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