Data for raxtax: A k-mer-based non-Bayesian Taxonomic Classifier

This repository contains the input databases and summarized results for evaluating our preprint: raxtax: A k-mer-based non-Bayesian Taxonomic Classifier (BioRxiv) Abstract Motivation: Taxonomic classification in biodiversity studies is the process of assigning the anonymous sequences ofa marker gene (barcode) to a specific lineage using a reference database that contains named sequences in a knowntaxonomy. This classification is important for assessing the complexity of biological systems. Taxonomic classificationfaces two inherent challenges: first, accuracy is critical as errors can propagate to downstream analysis results; andsecond, the classification time requirements can limit study size and study design, in particular when consideringthe constantly growing reference databases. To address these two challenges, we introduce raxtax, an efficient, noveltaxonomic classification tool that uses common k-mers between all pairs of query and reference sequences. We alsointroduce two novel uncertainty scores which take into account the fundamental biases of reference databases.Results: We validate raxtax on three widely used empirical reference databases and show that it is 2.7-100 times fasterthan competing state-of-the-art tools on the largest database while being equally accurate. In particular, raxtax exhibitsincreasing speedups with growing query and reference sequence numbers compared to existing tools (for 100,000 and1,000,000 query and reference sequences overall, it is 1.3 and 2.9 times faster, respectively), and therefore alleviates thetaxonomic classification scalability challenge.Availability and Implementation: raxtax is available at https://github.com/noahares/raxtax under a CC-NC-BY-SA license. The scripts and summary metrics used in our analyses are available at https://github.com/noahares/raxtax_paper_scripts. Original Data Sources UNITE: https://doi.plutof.ut.ee/doi/10.15156/BIO/2959332 Greengenes: http://ftp.microbio.me/greengenes_release/gg_13_5/ BOLD: https://boldsystems.org/ (exact database version no longer available)本仓库包含用于评估我们预印本文章的输入数据库与汇总结果： raxtax：一款基于k聚体（k-mer）的非贝叶斯分类器（BioRxiv） 摘要 研究背景：生物多样性研究中的分类学分类，是指借助包含已知命名序列的参考数据库，将标记基因（条形码）的未知序列指派至特定演化支的过程。该分类步骤对于解析生物系统的复杂性至关重要。分类学分类面临两项固有挑战：其一，分类精度至关重要，因为错误会传导至下游分析结果；其二，分类耗时会限制研究规模与研究设计，在参考数据库持续增长的背景下这一问题尤为突出。为解决这两项挑战，本文提出raxtax——一款高效的新型分类学分类工具，其利用查询序列与参考序列所有配对间的共有k聚体。此外，本文还提出两种新颖的不确定性评分方法，可兼顾参考数据库的固有偏倚。 研究结果：我们在三款广泛使用的实证参考数据库上验证了raxtax，结果显示，在最大规模的数据库上，其运行速度比当前主流顶尖同类工具快2.7至100倍，且分类精度保持一致。具体而言，相较于现有工具，随着查询与参考序列数量的增加，raxtax的加速比不断提升：当总查询与参考序列数分别为10万和100万时，其运行速度分别提升1.3倍和2.9倍，因此有效缓解了分类学分类的可扩展性难题。 可用性与实现：raxtax已以CC-NC-BY-SA许可协议发布于https://github.com/noahares/raxtax。本分析所用的脚本与汇总指标可从https://github.com/noahares/raxtax_paper_scripts获取。 原始数据来源 UNITE：https://doi.plutof.ut.ee/doi/10.15156/BIO/2959332 Greengenes：http://ftp.microbio.me/greengenes_release/gg_13_5/ BOLD：https://boldsystems.org/（具体数据库版本现已无法获取）