用深度学习方法估计部分克隆群体中生殖模式的比例。

IF 1 Q3 AGRICULTURE, MULTIDISCIPLINARY
T A Nikolaeva, A A Poroshina, D Yu Sherbakov
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引用次数: 0

摘要

生物实体(包括种群、物种和群落)之间的遗传多样性是了解其结构和功能的基本信息来源。然而,许多生态和进化问题产生于有限和复杂的数据集,使传统的分析方法复杂化。在这种背景下,我们的研究应用了基于深度学习的方法来解决进化生物学中的一个关键问题:有性繁殖和无性繁殖之间的平衡。有性生殖通常会破坏选择所支持的有利基因组合,而无性生殖无需雄性就能更快地增殖,有效地保持有益的基因型。本研究的重点是探索单一物种内有性繁殖和无性繁殖的共存模式。我们开发了一个卷积神经网络模型,专门用于分析在变化的环境中表现出混合生殖策略的种群动态。这里开发的模型允许人们估计来自有性生殖的种群成员与由孤雌生殖的雌性产生的无性生殖生物的比例。该模型假设在具有双重繁殖策略和稳定的种群规模的种群中,生殖比率随时间保持不变。该方法适用于微卫星重复序列等中性多等位基因标记性状。结果表明,该模型估计生殖模式比例的精度高达0.99,有效地处理了小样本量带来的复杂性。当训练数据集的维度与实际数据一致时,模型收敛到最小误差的速度要快得多,这凸显了数据集设计对预测性能的重要性。这项工作有助于理解进化生物学中的生殖策略动力学,展示了深度学习在增强遗传数据分析方面的潜力。我们的发现为未来在波动的生态环境中研究遗传多样性和生殖模式的细微差别铺平了道路,强调了先进的计算方法在进化研究中的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning approach to the estimation of the ratio of reproductive modes in a partially clonal population.

Genetic diversity among biological entities, including populations, species, and communities, serves as a fundamental source of information for understanding their structure and functioning. However, many ecological and evolutionary problems arise from limited and complex datasets, complicating traditional analytical approaches. In this context, our study applies a deep learning-based approach to address a crucial question in evolutionary biology: the balance between sexual and asexual reproduction. Sexual reproduction often disrupts advantageous gene combinations favored by selection, whereas asexual reproduction allows faster proliferation without the need for males, effectively maintaining beneficial genotypes. This research focuses on exploring the coexistence patterns of sexual and asexual reproduction within a single species. We developed a convolutional neural network model specifically designed to analyze the dynamics of populations exhibiting mixed reproductive strategies within changing environments. The model developed here allows one to estimate the ratio of population members who originate from sexual reproduction to the clonal organisms produced by parthenogenetic females. This model assumes the reproductive ratio remains constant over time in populations with dual reproductive strategies and stable population sizes. The approach proposed is suitable for neutral multiallelic marker traits such as microsatellite repeats. Our results demonstrate that the model estimates the ratio of reproductive modes with an accuracy as high as 0.99, effectively handling the complexities posed by small sample sizes. When the training dataset's dimensionality aligns with the actual data, the model converges to the minimum error much faster, highlighting the significance of dataset design in predictive performance. This work contributes to the understanding of reproductive strategy dynamics in evolutionary biology, showcasing the potential of deep learning to enhance genetic data analysis. Our findings pave the way for future research examining the nuances of genetic diversity and reproductive modes in fluctuating ecological contexts, emphasizing the importance of advanced computational methods in evolutionary studies.

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来源期刊
Vavilovskii Zhurnal Genetiki i Selektsii
Vavilovskii Zhurnal Genetiki i Selektsii AGRICULTURE, MULTIDISCIPLINARY-
CiteScore
1.90
自引率
0.00%
发文量
119
审稿时长
8 weeks
期刊介绍: The "Vavilov Journal of genetics and breeding" publishes original research and review articles in all key areas of modern plant, animal and human genetics, genomics, bioinformatics and biotechnology. One of the main objectives of the journal is integration of theoretical and applied research in the field of genetics. Special attention is paid to the most topical areas in modern genetics dealing with global concerns such as food security and human health.
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