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基于精心策划的塔奇纳 (Diptera) 记录集,确定BOLD发动机的缺陷和改进建议

Frederik Stein^1,2, Oliver Gailing³

¹Julius Kühn-Institute, Federal Research Centre for Cultivated Plants, Institute for Forest Protection, Quedlinburg, Germany.

|August 29, 2025

概括

生命数据库的条形码 (BOLD) 由于不断增加的数据和算法限制,导致了物种识别问题. 需要改进数据的提交和处理,以确保可靠的DNA条形码识别.

科学领域:

分类学和系统学
生物信息学
分子生态学

背景情况:

生命的条形码数据库 (BOLD) 是DNA条形码和物种识别的重要资源.
在BOLD中增加的数据量导致了在条形码索引号 (BIN) 中保持精确的物种分配的挑战.
之前的研究强调了BOLD识别算法的潜在不一致性.

研究的目的:

评估BOLD的物种识别算法,特别是精制单一链接 (RESL) 和通过自动分区 (ASAP) 组装物种的性能.
调查数据质量的影响,包括分类错误和元数据,对BOLD标识的可靠性.
评估条形码索引号 (BIN) 在 BOLD 中物种识别中的作用,并确定导致差异的因素.

主要方法:

分析了BOLD中Tachina属记录的精选数据集,包括公布和私人数据.
将RESL算法与ASAP算法进行物种划分.
调查 BOLD v4 和 v5 ID 引擎对 BIN 和数据集成模式的依赖.

主要成果:

与RESL相比,ASAP算法表现出优异的性能,该算法严格遵循DNA条形码差距概念.
分类学错误,不一致的BIN形成和缺失的元数据被确定为不可靠的物种识别的重要贡献者.
大胆查询主要依赖于BIN,但观察到偏差,导致与分配的BIN不一致的物种匹配.
BOLD v4 ID引擎的默认模式整合了私有和公开的数据,可能会导致仅基于COI数据的公开记录.

结论:

数据提交和后提交过程中的算法限制和缺陷导致了BOLD识别的不可靠性.
需要改进数据管理和算法方法,以提高BOLD的准确性和一致性.
如果不进行必要的修改,在BIN中将继续积累相互矛盾的物种分配,从而降低BOLD对标本识别的可靠性.

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