基于高密度单核苷酸多态性的共祖远亲缘关系预测算法准确性研究

目的 研究构建基于共祖（identity-by-descent，IBD）片段算法预测远亲缘关系分析流程并评估预测准确性。方法 采用高密度单核苷酸多态性（single nucleotide polymorphism，SNP）芯片对253份家系样本进行检测，研究基于IBD片段算法的分析流程进行两两个体间亲缘关系预测，评估预测准确性。随机减少SNP位点，评估位点数对算法预测准确性的影响。结果 IBD片段算法预测1~7级亲缘关系平均置信区间准确率为94.72%，预测可信度为99.77%，6级及以上亲缘关系预测时出现假阴性。随着SNP数量减少，预测准确性会出现一定程度的下降。结论 IBD片段算法可用于7级以内亲缘关系的预测，该算法在群体遗传学、法医遗传学等领域有重要应用价值。     

单细胞RNA测序数据分析方法研究进展

单细胞RNA测序(Single cell RNA sequencing,scRNA-Seq)已经广泛应用于细胞分化、肿瘤微环境及多种疾病病因学研究。目前,由于scRNA-Seq具有低捕获率、高噪声、高变异性等特点,通过优化数据分析方法提高测序结果准确性已经成为测序领域的研究热点。对近年来数据分析过程中利用的数学方法进行了总结,讨论了数据分析的优势及存在的问题,以期为新算法的开发和应用提供参考,逐步提高测序结果的可靠性。     

人工智能算法与生态环境模型耦合研究进展

人工智能算法在生态环境领域已有广泛应用,但在揭示自然科学现象规律时存在泛化能力不足、可解释性差等问题。为弥补这些不足,实现优势互补,将人工智能算法与具有物理机制的生态环境模型耦合研究已成为近些年快速发展的一种新型研究方法。本文从应用在生态环境领域的人工智能算法出发,概述了其分类和应用情况,重点梳理了人工智能算法与生态环境模型耦合研究的发展、现状及不足,提出了一个将人工智能与机理模型紧密耦合以重构机理过程的思路,分析了该网络部分参数的理论意义,提高可解释性和泛化能力的可行性,以及模拟机理过程运行的应用前景,并展望了人工智能算法与生态环境模型耦合研究的发展趋势。     

Development of a Core Set from a Large Rice Collection using a Modified Heuristic Algorithm to Retain Maximum Diversity

A new heuristic approach was undertaken for the establishment of a core set for the diversity research of rice. As a result, 107 entries were selected from the 10 368 characterized accessions. The core set derived using this new approach provided a good representation of the characterized accessions present in the entire collection. No significant differences for the mean, range, standard deviation and coefficient of variation of each trait were observed between the core and existing collections. We also compared the diversity of core sets established using this Heuristic Core Collection （HCC） approach with those of core sets established using the conventional clustering methods. This modified heuristic algorithm can also be used to select genotype data with allelic richness and reduced redundancy, and to facilitate management and use of large collections of plant genetic resources in a more efficient way.