AbstractMetagenomic sequencing has provided great advantages in the characterisation of microbiomes, but currently available analysis tools lack the ability to combine subspecies-level taxonomic resolution and accurate abundance estimation with functional profiling of assembled genomes. To define the microbiome and its associations with human health, improved tools are needed to enable comprehensive understanding of the microbial composition and elucidation of the phylogenetic and functional relationships between the microbes.

摘要宏基因组测序在微生物组的表征方面提供了巨大的优势，但目前可用的分析工具缺乏将亚种级分类学分辨率和准确的丰度估计与组装基因组的功能分析相结合的能力。为了定义微生物组及其与人类健康的关系，需要改进的工具来全面了解微生物组成并阐明微生物之间的系统发育和功能关系。

Here, we present MAGinator, a freely available tool, tailored for profiling of shotgun metagenomics datasets. MAGinator provides de novo identification of subspecies-level microbes and accurate abundance estimates of metagenome-assembled genomes (MAGs). MAGinator utilises the information from both gene- and contig-based methods yielding insight into both taxonomic profiles and the origin of genes and genetic content, used for inference of functional content of each sample by host organism.

在这里，我们介绍MAGinator，一种免费提供的工具，专门用于分析鸟枪宏基因组学数据集。MAGinator提供了亚种级微生物的从头鉴定和宏基因组组装基因组（MAG）的准确丰度估计。MAGinator利用来自基于基因和重叠群的方法的信息，深入了解分类学概况以及基因和遗传内容的起源，用于推断宿主生物每个样品的功能含量。

Additionally, MAGinator facilitates the reconstruction of phylogenetic relationships between the MAGs, providing a framework to identify clade-level differences..

此外，MAGinator有助于重建MAG之间的系统发育关系，为识别进化枝水平差异提供了一个框架。。

IntroductionDNA sequencing has revolutionised our ability to gain insight into microbial compositions without relying on the ability to cultivate organisms. To explore these compositions, various methods have been developed that either rely on databases of marker genes of known organisms or attempt to reconstruct the chromosomes directly from the short reads by first assembling them into longer contigs and then binning these based on co-occurrences or DNA composition.

引言DNA测序彻底改变了我们在不依赖培养生物的能力的情况下深入了解微生物组成的能力。为了探索这些组成，已经开发了各种方法，这些方法要么依赖于已知生物的标记基因数据库，要么试图通过首先将它们组装成更长的重叠群，然后根据共现或DNA组成将它们分类，直接从短读段重建染色体。

Mapping reads against marker gene databases with tools such as MetaPhlAn1, MetaPhyler2 and mOTUs3 is a fast and effective way of recovering the microbial composition both because the library depth required can be quite shallow and because the computational requirements are smaller. However, such methodologies have limitations originating from the reliance on predefined databases, limited ability to estimate abundances at higher taxonomic resolution4,5, and the lack of information on the functional repertoire of the identified taxa.

使用MetaPhlAn1，MetaPhyler2和mOTUs3等工具对标记基因数据库进行映射读取是一种快速有效的恢复微生物组成的方法，因为所需的文库深度可能很浅，并且计算要求较小。然而，这种方法的局限性源于对预定义数据库的依赖，在更高的分类学分辨率下估计丰度的能力有限4,5，以及缺乏关于已鉴定分类群功能库的信息。

Conversely, de novo binning strategies require high sequencing depth but can recover high-quality metagenome-assembled genomes (MAGs) from which the functional gene content can be directly linked to a specific organism. Ideally, this can recover genomes at the subspecies level that can be used in downstream analysis to generate more specific hypotheses about associations with outcomes.

相反，从头分箱策略需要高测序深度，但可以恢复高质量的宏基因组组装基因组（MAG），从中功能基因含量可以直接与特定生物体相关联。理想情况下，这可以在亚种水平上恢复基因组，可用于下游分析，以产生有关与结果关联的更具体假设。

One example of this is to be able to identify organisms, which have the capacity of degrading Human Milk Oligosaccharides (HMOs), which are an important energy source for breastfed infants. Especially Bifidobacteria have this functionality, where certain strains or subspecies have specific preferences for certain HMO types6,7,8,9.

其中一个例子是能够识别具有降解母乳低聚糖（HMO）能力的生物体，HMO是母乳喂养婴儿的重要能量来源。特别是双歧杆菌具有这种功能，其中某些菌株或亚种对某些HMO类型具有特定的偏好6,7,8,9。

Previously, it has been established that the presence of Bifidobact.

以前，已经确定双歧杆菌的存在。

Input

输入

The input to the MAGinator workflow comprises a set of samples with (1) shotgun metagenomic sequenced reads, (2) their sample-wise assembled contigs, and (3) sample-wise MAGs (groups of contigs from the same genome), clustered across samples, as defined by a metagenomic binning tool (see below).Reads should be provided in a comma-separated file giving the location of the fastq files and formatted as: SampleName,PathToForwardReads,PathToReverseReads.

。读取应以逗号分隔的文件提供，给出fastq文件的位置，并格式化为：SampleName、PathToForwardReads、PathToReverseReads。

The contigs should be nucleotide sequences in FASTA format. The MAGs should be given as a tab-separated file including the MAG identifier and contig identifier. The sample-wise MAGs should be grouped into MAG clusters representing a taxonomic entity found across the samples, which will usually be species but can also be at the subspecies level, depending on the characteristics of the input data.

重叠群应该是FASTA格式的核苷酸序列。MAG应作为制表符分隔的文件提供，包括MAG标识符和重叠群标识符。样本MAG应分组为MAG簇，代表样本中发现的分类实体，通常是物种，但也可以是亚种水平，具体取决于输入数据的特征。

MAGinator is flexible regarding which tool is being used for creating the MAGs, however we recommend using VAMB18. If other binners are used, MAG clustering across samples would have to be implemented before running VAMB. As MAGinator relies on the input MAGs a larger sample size is recommended. The specific number of samples relies both on the sequencing depth and the diversity of the community being analysed.

MAGinator在创建MAGs时使用哪种工具是灵活的，但是我们建议使用VAMB18。如果使用其他binner，则必须在运行VAMB之前实现跨样本的MAG聚类。由于MAGinator依赖于输入MAG，因此建议使用较大的样本量。样本的具体数量取决于测序深度和所分析社区的多样性。

We advise the user to look at the number of MAG clusters created and assess them according to the environment being analysed.DependenciesThe dependencies to run MAGinator are mamba21 and Snakemake22—all other dependencies are installed automatically by Snakemake through MAGinator. Additionally, MAGinator needs the GTDB-tk database downloaded for taxonomic annotation of MAGs and as a reference for the phylogenetic SNV-level analysis of the signature genes.Output generatedMAGinator generates multiple outputs and intermediate fil.

我们建议用户查看创建的MAG集群的数量，并根据所分析的环境对其进行评估。依赖项运行MAGinator的依赖项是mamba21和Snakemake22所有其他依赖项都是由Snakemake通过MAGinator自动安装的。此外，MAGinator需要下载GTDB tk数据库以进行MAG的分类学注释，并作为特征基因系统发育SNV水平分析的参考。Output generatedMAGinator生成多个输出和中间文件。

COPSAC dataset - data characteristics and preparation

COPSAC数据集-数据特征和准备

The COPSAC2010 cohort consists of 700 unselected children recruited during pregnancy week 24 and followed closely throughout childhood with extensive sample collection, exposure assessments and longitudinal clinical phenotyping38,39,40. From the cohort, we used 662 deeply sequenced metagenomics samples taken at 1 year of age.

COPSAC2010队列由700名未经选择的儿童组成，这些儿童在怀孕第24周招募，并在整个儿童时期进行了广泛的样本收集，暴露评估和纵向临床表型38,39,40。从该队列中，我们使用了1岁时采集的662个深度测序的宏基因组学样本。

The details of the study and sequencing protocol have previously been published40. The samples consist of 150-bp paired-end reads per with mean ± SD: 48 ± 15.5 million reads.The data was analysed using the same approach as for the strain-madness data set, with the exception of filtering away reads shorter than 50 bp in the preprocessing step.

研究和测序方案的详细信息先前已发布40。样本由每个150 bp的配对末端读数组成，平均SD:4850万个读数。使用与应变疯狂数据集相同的方法分析数据，除了在预处理步骤中过滤掉短于50 bp的读数。

This workflow yielded 880 MAG clusters for the samples.MAGinator was run using the reads, contigs and MAGs from VAMB as input. Thus creating a set of signature genes for each MAG cluster which has been found de novo for this particular dataset.CHILD dataset - data characteristics and preparationThe Canadian Healthy Infant Longitudinal Development (CHILD) study comprises a large longitudinal birth cohort with stool collection in infancy for microbiome analysis41.

该工作流程为样本产生了880个MAG集群。。因此，为每个MAG簇创建了一组签名基因，这些基因是针对该特定数据集从头发现的。儿童数据集-数据特征和准备加拿大健康婴儿纵向发育（CHILD）研究包括一个大型纵向出生队列，在婴儿期收集粪便用于微生物组分析41。

Stool samples used in this analysis were sequenced to an average depth of 4.85 million reads (SD: 1.79 million), and samples which included >1 million reads after preprocessing were kept for the current analysis7.We analysed a subset of the CHILD cohort, consisting of 2846 metagenomic sequenced faecal samples from infants.

本分析中使用的粪便样本的平均深度为485万读数（标准差：179万），预处理后包含>100万读数的样本保留用于当前分析7。我们分析了儿童队列的一个子集，由2846个宏基因组测序的婴儿粪便样本组成。

To overcome the shallow sequencing, the signature genes of the COPSAC2010 cohort were used to profile the samples instead of running MAGinator. To ensure that the process of the read mappings was identical to COPSAC, the read mapping was carried out using the full gene catalogue. Next, the read counts for the signature genes were ext.

为了克服浅测序，使用COPSAC2010队列的特征基因来分析样品，而不是运行MAGinator。为了确保读取映射的过程与COPSAC相同，使用完整的基因目录进行读取映射。接下来，签名基因的读取计数是ext。

Data availability

数据可用性

All relevant data supporting the key findings of this study are available within the article and its Supplementary Information files. Supplementary dataset 1 contain the Supplementary Figs. and tables. The CAMI II strain-madness benchmarking dataset is available at https://frl.publisso.de/data/frl:6425521/strain/short_read/.

本文及其补充信息文件中提供了支持本研究主要发现的所有相关数据。补充数据集1包含补充图和表。CAMI II菌株疯狂基准数据集可在https://frl.publisso.de/data/frl:6425521/strain/short_read/.

The gold standard and benchmark profiles are found at https://github.com/CAMI-challenge/second_challenge_evaluation/tree/master/profiling. The dataset from Franzosa et al. used for benchmarking is available as supplementary from their paper and the raw data is available at ENA accession SAMN08049618.

金标准和基准概况见https://github.com/CAMI-challenge/second_challenge_evaluation/tree/master/profiling.Franzosa等人用于基准测试的数据集可作为其论文的补充，原始数据可在ENA登录号SAMN08049618上获得。

The raw COPSAC fastq files are available at NCBI under BioProject PRJNA715601. The honey-bee data is publicly available and found in the sequence read archive (SRA) with the accession SRP150166. The Tara Oceans data set is publicly available and found at ENA with Study accession PRJEB1787. The CHILD shotgun metagenomics sequencing data is available at NCBI BioProject PRJNA838575 . Source data are provided in this paper.

。蜜蜂数据可公开获得，并可在序列读取档案（SRA）中找到，登录号为SRP150166。塔拉海洋数据集可在ENA公开获得，研究登录号为PRJEB1787。儿童霰弹枪宏基因组测序数据可在NCBI生物项目PRJNA838575获得。本文提供了源数据。

Availability and implementation: MAGinator is available as a Python module at https://github.com/Russel88/MAGinator..

可用性和实现：MAGinator在https://github.com/Russel88/MAGinator..

Code availability

代码可用性

MAGinator is available at GitHub (https://github.com/Russel88/MAGinator)48.

MAGinator可在GitHub获得(https://github.com/Russel88/MAGinator)48页。

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Csardi，G。和Nepusz，T。用于复杂网络研究的igraph软件包。InterJournal，复杂系统1695,1-9（2006）。Zachariasen T＆Russel J.MAGinator能够准确分析具有菌株水平系统发育的从头MAG。https://github.com/Russel88/MAGinator，https://doi.org/10.5281/zenodo.11485929（2024年）。下载参考文献致谢我们对COPSAC队列研究的儿童和家庭的所有支持和承诺表示最深切的感谢。

We acknowledge and appreciate the unique efforts of the COPSAC research team. All funding received by COPSAC is listed on www.copsac.com. The Lundbeck Foundation (Grant no R16-A1694); The Ministry of Health (Grant no 903516); Danish Council for Strategic Research (Grant no 0603-00280B) and The Capital Region Research Foundation have provided core support to the COPSAC research centre.

我们承认并赞赏COPSAC研究团队的独特努力。COPSAC收到的所有资金都列在www.COPSAC.com上。伦贝克基金会（批准号R16-A1694）；卫生部（批准号903516）；丹麦战略研究委员会（批准号0603-00280B）和首都地区研究基金会为COPSAC研究中心提供了核心支持。

JS has received funding from the Danish Council for Independent Research (Grant no. 8045-00081B). We thank the CHILD Cohort Study (CHILD) participant families for their dedication and commitment to advancing health research. CHILD was initially funded by CIHR and AllerGen NCE, and the metagenomic data reported here was generated with support from Genome Canada and Genome BC (274CHI).Author informationAuthors and AffiliationsDepartment of Health and Technology, Section of Bioinformatics, Technical University of Denmark, Lyngby, DenmarkTrine Zachariasen, Gisle A.

JS已获得丹麦独立研究委员会的资助（批准号8045-00081B）。我们感谢儿童队列研究（CHILD）参与者家庭对推进健康研究的奉献和承诺。CHILD最初由CIHR和过敏原NCE资助，这里报道的宏基因组数据是在加拿大基因组和BC基因组（274CHI）的支持下产生的。。

Vestergaard, Ole Lund & Asker BrejnrodDepartment of Biology, Section of Microbiology, University of Copenhagen, Copenhagen, DenmarkJakob Russel, Søren J. Sørensen & Jakob StokholmDepartment of Pediatrics, BC Children’s Hospital, University of British Columbia, 950 West 28th Avenue, Vancouver, BC, CanadaCharisse Petersen & Stuart E.

维斯特加德（Vestergaard）、奥勒·隆德（Ole Lund）和阿斯克·布雷诺德（Asker Brejnrod）哥本哈根大学微生物学系，丹麦·雅各布·拉塞尔（DenmarkJakob Russel）、Søren J.Sørensen&Jakob Stokholm不列颠哥伦比亚大学儿童医院儿科，不列颠哥伦比亚省温哥华西28大道950号，CanadaCharisse Petersen&Stuart E。

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PubMed Google ScholarContributionsThe figures and tables were created by T.Z, P.A.L, A.B and J.T. T.Z, A.B, J.R, J.T and S.S draughted the manuscript. The MAGinator software was developed and set up by T.Z and J.R. T.Z, J.R, C.P, G.V, S.S, P.A.L, M.P, S.T, S.J.S, O.L, J.S, A.B and J.T provided intellectual input and aided in the theoretical aspects of shaping this study.

PubMed谷歌学术贡献这些数字和表格是由T.Z，P.A.L，A.B和J.T.创建的。T.Z，A.B，J.R，J.T和S.S起草了手稿。MAGinator软件由T.Z和J.R.T.Z、J.R、C.P、G.V、S.S、P.A.L、M.P、S.T、S.J.S、O.L、J.S、A.B和J.T开发和设置，提供了智力投入，并有助于形成这项研究的理论方面。

The corresponding author had full access to the data and held the final responsibility for deciding to submit the manuscript for publication. T.Z, J.R, C.P, G.V, S.S, P.A.L, M.P, S.T, S.J.S, O.L, J.S, A.B and J.T guarantee that the accuracy and integrity of any part of the work have been appropriately investigated and resolved and all have approved the final version of the manuscript.

通讯作者可以完全访问数据，并对决定提交稿件发表负有最终责任。T、 Z，J.R，C.P，G.V，S.S，P.A.L，M.P，S.T，S.J.S，O.L，J.S，A.B和J.T保证工作任何部分的准确性和完整性都得到了适当的调查和解决，并且都已经批准了稿件的最终版本。

None of the authors received any honorarium, grant, or other forms of payment for creating this manuscript.Corresponding authorCorrespondence to.

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Competing interests

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Nature Communications thanks Stephen Nayfach and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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Reprints and permissionsAbout this articleCite this articleZachariasen, T., Russel, J., Petersen, C. et al. MAGinator enables accurate profiling of de novo MAGs with strain-level phylogenies.

转载和许可本文引用本文Zachariasen，T.，Russel，J.，Petersen，C。等人。MAGinator能够准确分析具有菌株水平系统发育的从头MAG。

Nat Commun 15, 5734 (2024). https://doi.org/10.1038/s41467-024-49958-8Download citationReceived: 18 September 2023Accepted: 21 June 2024Published: 09 July 2024DOI: https://doi.org/10.1038/s41467-024-49958-8Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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