DeepKin:用卷积神经网络预测低覆盖率基因组和古基因组的相关性。

IF 5.5 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Merve N Güler, Ardan Yılmaz, Büşra Katırcıoğlu, Sarp Kantar, Tara Ekin Ünver, Kıvılcım Başak Vural, N Ezgi Altınışık, Emre Akbas, Mehmet Somel
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引用次数: 0

摘要

DeepKin是一种新颖的工具,旨在使用卷积神经网络(cnn)从基因组数据中预测亲缘关系。当基因组数据有限时,传统的估计亲缘关系的方法往往会遇到困难,比如古基因组和退化的法医样本。DeepKin通过利用两个CNN模型来解决这一挑战,这两个模型仅在模拟基因组数据上进行训练,可以将亲缘关系分类到第三度,并识别父母-后代和兄弟姐妹对。我们的基准测试显示,DeepKin的性能与广泛使用的工具READv2相当,甚至更好。我们验证了使用PLINK的DeepKin。地图和。以三个考古遗址的经验古基因组为输入,证明了该方法在不同遗传背景下的鲁棒性和适应性,在10 K以上共享snp的准确率为90%。通过捕获跨基因组片段的信息,DeepKin提供了一种新的方法路径,可以在高度降解的样本设置中进行相关性估计,并应用于古代DNA,以及法医和保护遗传学。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DeepKin: Predicting Relatedness From Low-Coverage Genomes and Palaeogenomes With Convolutional Neural Networks.

DeepKin is a novel tool designed to predict relatedness from genomic data using convolutional neural networks (CNNs). Traditional methods for estimating relatedness often struggle when genomic data is limited, as with palaeogenomes and degraded forensic samples. DeepKin addresses this challenge by leveraging two CNN models, which are trained solely on simulated genomic data, to classify relatedness up to the third degree and to identify parent-offspring and sibling pairs. Our benchmarking shows DeepKin performs comparably or better than the widely used tool READv2. We validated DeepKin, which uses PLINK's .map and .ped files as input, on empirical palaeogenomes from three archaeological sites, demonstrating its robustness and adaptability across different genetic backgrounds, with accuracy > 90% above 10 K shared SNPs. By capturing information across genomic segments, DeepKin offers a new methodological path for relatedness estimation in settings with highly degraded samples, with applications in ancient DNA, as well as forensic and conservation genetics.

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来源期刊
Molecular Ecology Resources
Molecular Ecology Resources 生物-进化生物学
CiteScore
15.60
自引率
5.20%
发文量
170
审稿时长
3 months
期刊介绍: Molecular Ecology Resources promotes the creation of comprehensive resources for the scientific community, encompassing computer programs, statistical and molecular advancements, and a diverse array of molecular tools. Serving as a conduit for disseminating these resources, the journal targets a broad audience of researchers in the fields of evolution, ecology, and conservation. Articles in Molecular Ecology Resources are crafted to support investigations tackling significant questions within these disciplines. In addition to original resource articles, Molecular Ecology Resources features Reviews, Opinions, and Comments relevant to the field. The journal also periodically releases Special Issues focusing on resource development within specific areas.
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