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快速增长的基因组序列数据的系统发育稳健扩展。

Robust expansion of phylogeny for fast-growing genome sequence data.

机构信息

State Key Laboratory of Emerging Infectious Diseases, School of Public Health, The University of Hong Kong, Hong Kong SAR, P. R. China.

Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong SAR, P. R. China.

出版信息

PLoS Comput Biol. 2024 Feb 8;20(2):e1011871. doi: 10.1371/journal.pcbi.1011871. eCollection 2024 Feb.

Abstract

Massive sequencing of SARS-CoV-2 genomes has urged novel methods that employ existing phylogenies to add new samples efficiently instead of de novo inference. 'TIPars' was developed for such challenge integrating parsimony analysis with pre-computed ancestral sequences. It took about 21 seconds to insert 100 SARS-CoV-2 genomes into a 100k-taxa reference tree using 1.4 gigabytes. Benchmarking on four datasets, TIPars achieved the highest accuracy for phylogenies of moderately similar sequences. For highly similar and divergent scenarios, fully parsimony-based and likelihood-based phylogenetic placement methods performed the best respectively while TIPars was the second best. TIPars accomplished efficient and accurate expansion of phylogenies of both similar and divergent sequences, which would have broad biological applications beyond SARS-CoV-2. TIPars is accessible from https://tipars.hku.hk/ and source codes are available at https://github.com/id-bioinfo/TIPars.

摘要

大规模的 SARS-CoV-2 基因组测序促使人们开发新的方法,利用现有的系统发育学数据来高效地添加新样本,而不是从头推断。‘TIPars’就是为应对这一挑战而开发的,它将简约分析与预先计算的祖先序列相结合。使用 1.4GB 内存,TIPars 将 100 个 SARS-CoV-2 基因组插入到一个包含 10 万个分类单元的参考树中,耗时约 21 秒。在四个数据集上的基准测试中,TIPars 在处理中度相似序列的系统发育时具有最高的准确性。对于高度相似和分化的情况,完全基于简约法和基于似然法的系统发育定位方法分别表现最好,而 TIPars 则排名第二。TIPars 能够高效准确地扩展相似和分化序列的系统发育,这将在 SARS-CoV-2 之外具有广泛的生物学应用。TIPars 可在 https://tipars.hku.hk/ 上访问,其源代码可在 https://github.com/id-bioinfo/TIPars 上获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4dd/10898724/46901704bd74/pcbi.1011871.g001.jpg

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