小柯机器人

瑞士洛桑大学Christophe Dessimoz和美国贝勒医学院Fritz J. Sedlazeck共同合作，近期取得重要工作进展。他们研究提出了Read2Tree工具，可以使用Read2Tree直接从原始测序读数推断系统发育树。相关研究成果2023年4月20日在线发表于《自然—生物技术》杂志上。

据介绍，目前的系统发育树推断方法需要以巨大的计算和人工成本运行复杂的管道，并且在测序覆盖率、组装和注释质量方面有额外的限制，尤其是对于大型数据集。

为了克服这些挑战，研究人员提出了Read2Tree，它直接将原始测序读数处理成相应的基因组，并绕过系统发育推断中的传统步骤，如基因组组装、注释和全序列与全序列的比较，同时保持准确性。在包含各种数据集的基准测试中，Read2Tree比基于组装的方法快10-100倍，在大多数情况下更准确，但测序覆盖率高且参考物种非常遥远的情况除外。为了说明该工具的广泛适用性，研究人员重建了一个跨越5.9亿年进化的435个物种的酵母生命树。研究人员还将Read2Tree应用于超过10000个冠状病毒科样本，在一棵树上准确地对高度多样的动物样本和几乎相同的严重急性呼吸综合征冠状病毒2序列进行分类。

总之，Read2Tree的速度、准确性和多功能性使大规模启用比较基因组学成为可能。

附：英文原文

Title: Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree

Author: Dylus, David, Altenhoff, Adrian, Majidian, Sina, Sedlazeck, Fritz J., Dessimoz, Christophe

Issue&Volume: 2023-04-20

Abstract: Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes and bypasses traditional steps in phylogeny inference, such as genome assembly, annotation and all-versus-all sequence comparisons, while retaining accuracy. In a benchmark encompassing a broad variety of datasets, Read2Tree is 10–100 times faster than assembly-based approaches and in most cases more accurate—the exception being when sequencing coverage is high and reference species very distant. Here, to illustrate the broad applicability of the tool, we reconstruct a yeast tree of life of 435 species spanning 590 million years of evolution. We also apply Read2Tree to >10,000 Coronaviridae samples, accurately classifying highly diverse animal samples and near-identical severe acute respiratory syndrome coronavirus 2 sequences on a single tree. The speed, accuracy and versatility of Read2Tree enable comparative genomics at scale.

DOI: 10.1038/s41587-023-01753-4

Source: https://www.nature.com/articles/s41587-023-01753-4

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex