MetDNA2 excutes knowledge-guided multi-layer metabolic network to annotate metabolites from knowns to unknowns. Generally, the KGMN supports
The KGMN accepts various data imports from common data processing tools, including XCMS, MS-DIAL, and MZmine2. It also support the connection with other metabolomics workflow, like MetFrag, MS-FINDER, MASST etc.

The completed functions are provided in the MetDNA2 webserver via a free registration. The detailed tutorial was also provided in the MetDNA2 webserver.

You can install MetDNA2 from Github.

if (!require(devtools)){
    install.packages("devtools")
}

if (!require(BiocManager)){
    install.packages("BiocManager")
}

# Required packages
required_pkgs <- c("dplyr","tidyr","readr", "stringr", "tibble", "purrr",
"ggplot2", "igraph", "pbapply", "Rdisop", "randomForest", "pryr", "BiocParallel", "magrittr", "rmarkdown", "caret")
BiocManager::install(required_pkgs)

# Install ZhuLab related packages
devtools::install_github("ZhuMetLab/SpectraTools")
devtools::install_github("ZhuMetLab/MetBioInterpretation")

# Install `MetDNA2` from GitHub
devtools::install_github("ZhuMetLab/MetDNA2")

Note: Due to the limitation of copyright, the library objects zhuMetLib, zhuMetlib_orbitrap, zhuRPlib, lib_rt, lib_ccs are removed in this package. If you want to use the R package, please use your own libray insteaded, and repackage.

Generally, MetDNA requires the import of the following files for metabolite identifications, including:

1. A MS1 peak table (.csv format, required). The first three columns must be "name" , "mz" , and "rt".
2. MS2 data files (.mgf or .msp format, required).
3. A table for sample information (.csv format, required). The first two columns must be "sample.name" and "group".
4. A RT recalibration table (.csv format, optional). If you would like to follow our published LC method and recalibrate the RT library. The gradient of LC are provided here.

The step-by-step tutorials are provided in the MetDNA2 website and the later parts.

The results should be looks like below:

• The 00_annotation_table contains annotation results:
  • The table1_identification.csv contains base peak annotated candidates.
  • The table2_peak_group.csv records annotated abiotic peaks in each peak group.
  • The table3_identification_pair.csv is same as table 1, but organized as feature-metabolite pairs.

# load package
library(MetDNA2)

# run MetDNA2
runMetDNA2(
	path_pos = "working_directory/POS",
	path_neg = "working_directory/NEG",
	metdna_version = "version2",
	polarity = "positive",
	instrument = "SciexTripleTOF",
	column = "hilic",
	ce = "30",
	method_lc = "Other",
	correct_p = FALSE,
	extension_step = "2",
	comp_group = c("W30", "W03"),
	species = "hsa",
	p_cutoff = 0.050000,
	fc_cutoff = 1.000000,
	is_rt_calibration = FALSE)

Generally, it requires 4-8 hours to complete a project, which depends on the number of features and MS/MS spectra. The raw MS data can be found the repository (NIST urine, Fruit fly).

The KGMN is a versatile tool to compatible with various data processing tools and analysis workflow in metabolomics community.

• Note: we provide two packages MetDNA2InSilicoTool and MetDNA2Vis to help user to intergrate with in-silico MS/MS tools and visualize networks, respectively.

If you have any problems or bug reports, please contact us with the following materials. We will answer your questions at 1:00 pm - 3:00 pm (Beijing time) on every Friday.

• We always welcome any discussions and bug reports about MetDNA via google group: MetDNA forum.
• For Chinese users, please join our QQ group for any discussions and bug reports: 786156544.

This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles.

Zhiwei Zhou†, Mingdu Luo†, Haosong Zhang, Yandong Yin, Yuping Cai, and Zheng-Jiang Zhu*, Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking, Nature Communications, 2022, 13: 6656 Link

This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)