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MTSplice 预测遗传变异对组织特异性剪接的影响。

MTSplice predicts effects of genetic variants on tissue-specific splicing.

机构信息

Department of Informatics, Technical University of Munich, Boltzmannstraße, Garching, 85748, Germany.

Department of Computer Science, Stanford University, Stanford, CA, USA.

出版信息

Genome Biol. 2021 Mar 31;22(1):94. doi: 10.1186/s13059-021-02273-7.

Abstract

We develop the free and open-source model Multi-tissue Splicing (MTSplice) to predict the effects of genetic variants on splicing of cassette exons in 56 human tissues. MTSplice combines MMSplice, which models constitutive regulatory sequences, with a new neural network that models tissue-specific regulatory sequences. MTSplice outperforms MMSplice on predicting tissue-specific variations associated with genetic variants in most tissues of the GTEx dataset, with largest improvements on brain tissues. Furthermore, MTSplice predicts that autism-associated de novo mutations are enriched for variants affecting splicing specifically in the brain. We foresee that MTSplice will aid interpreting variants associated with tissue-specific disorders.

摘要

我们开发了免费开源的多组织剪接模型(Multi-tissue Splicing,MTSplice),用于预测遗传变异对 56 个人体组织中剪接盒外显子的影响。MTSplice 将 MMSplice(用于建模组成性调控序列)与新的神经网络(用于建模组织特异性调控序列)相结合。在预测 GTEx 数据集大多数组织中与遗传变异相关的组织特异性变化方面,MTSplice 优于 MMSplice,在脑组织方面的改进最大。此外,MTSplice 预测自闭症相关的新生突变富集了影响大脑特异性剪接的变异。我们预计 MTSplice 将有助于解释与组织特异性疾病相关的变异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2909/8011109/1bdc4daa4bbd/13059_2021_2273_Fig1_HTML.jpg

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