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XinweiSong / Asgene

A manually curated arsenic functional gene tool (AsgeneDB) was developed for rapid and accurate metagenomic analysis.
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AsgeneDB: A curated orthology arsenic metabolism gene database for metagenome annotation


Description: A manually curated arsenic functional gene database (AsgeneDB) and R package (Asgene package) are developed for rapid and accurate metagenomic analysis.

Authors: Xinwei Song, Yongguan Zhu, Yongming Luo, Jianming Xu, Bin Ma*

Overview

Arsenic (As) is a kind of toxic metal-like element widely distributed in the world. To understand the microbial community of arsenic metabolism in the environment, we developed a curated arsenic functional gene database (AsgeneDB) covering five arsenic metabolic pathways (transport, respiratory, reduction, oxidative and methylation processes), 59 arsenic biotransformation functional gene families and 414773 representative sequences. Here, protein sequences for As gene families were recruited from multiple public databases such as UniProt, NCBI RefSeq, KEGG, COG, eggNOG, arCOG and KOG. AsgeneDB covers 46 phyla and 1653 genera of bacterial, archaea and fungi. It can quickly analyze the arsenic metabolism and transformation function of microbial communities by integrating multiple lineal homology databases with high specificity, comprehensiveness, representativeness and accuracy. AsgeneDB and the associated R Package will greatly promote the study of arsenic metabolism in microbial communities in various environments.

AsgeneDB Documentation

Database files

1. The database files user needs are already built into the R package (Asgene). Therefore, when using Asgene R package to analyze metagenomic data, there is no need to download AsgeneDB separately.
2. Example datasets are provided as input and output to help users better understand this package. The example datasets consist of three protein sequence files, stored as testdata1.rda, testdata2.rda, testdata3.rda.
3. In order to facilitate users to use the AsgeneDB individually for personalized analysis, AsgeneDB can be downloaded from https://data.cyverse.org/dav-anon/iplant/home/xinwei/AsgeneDB/AsgeneDB.zip.
AsgeneDB.zip details: includes 4 files

  1. AsgeneDB.fa: Fasta format representative sequences obtained by clustering curated sequences at 100% sequence identity. This file can be used for “BLAST” searching arsenic genes in shotgun metagenomes.

  2. asgene.map: A mapping file that maps sequence IDs to gene names, only sequences belonging to arsenic gene families are included. This file is used to generate arsenic gene profiles from BLAST-like results against the database.

  3. id_gene_tax_pathway_total.csv: Species table of sequences in AsgeneDB.

    Columns included:

    1. Gene name (colnames:gene)
    2. Corresponding arsenic metabolic pathway (colnames:pathway)
    3. Taxid (colnames:taxid)
    4. Protein ID (colnames:protein_id)
    5. Kindom classification of species (colnames:kindom)
    6. Phylum classification of species (colnames:phylum)
    7. Class classification of species (colnames:class)
    8. Order classification of species (colnames:order)
    9. Family classification of species (colnames:family)
    10. Genus classification of species (colnames:genus)
    11. Species classification of species (colnames:species)

  4. length.txt: The file contains the length of amino acid sequences in AsgeneDB for standardizing arsenic gene abundance statistics.

Dependent Tools

  1. R Studio
  1. database searching tools:

Asgene Package

Installation

You can install the development version of Asgene from GitHub with:

install.packages("devtools")
devtools::install_github("XinweiSong/Asgene")

Usage

Description:
we provide Asgene Package for metagenomic alignment (nucleic acid or protein sequence), subsequent gene family abundance statistics and sample abundance standardization. The database files user needs are built into the Asgene. Therefore, users only need to choose a database search tool according to their needs (e.g., USEARCH, BLAST and DIAMOND) and input three parameters (e.g., working path, search parameters of tool and filetype) to automatically analyze statistics and output statistical results. Users can select gene abundance statistics (Option: abundance) to normalize read counts per kilobase per million reads (RPKM) to eliminate differences in sequencing depth and reference sequence length between samples. In addition, if the user selects functional species statistics (Option: taxonomy), the driveing species of each arsenic metabolism gene at different classification levels in the sample can be generated automatically.

Example

This is a basic example which shows you how to use the package:

library(Asgene)
#Arsenic metabolism gene abundance analysis
Asgene(analysis = "abundance", workdir = "./", method = "diamond", toolpath = "./", search_parameters = "-e 1e-4 -p 28 --query-cover 80 --id 50",seqtype = "nucl", filetype = "fasta", PE = TRUE , output = "./")
#Arsenic metabolism taxonomy analysis
Asgene(analysis = "taxonomy", workdir = "
./", method = "diamond", toolpath = "./", search_parameters = "-e 1e-4 -p 28 --query-cover 80 --id 50",seqtype = "nucl", filetype = "fasta",PE = TRUE, output = "./")
#Example datasets using
Asgene(analysis = "abundance", workdir = "./", method = "diamond", toolpath = "./", search_parameters = "-e 1e-4 -p 28 --query-cover 80 --id 50",seqtype = "prot", output = "./", test.data = TRUE)
Asgene(analysis = "taxonomy", workdir = "./", method = "diamond", toolpath = "./", search_parameters = "-e 1e-4 -p 28 --query-cover 80 --id 50",seqtype = "prot", output = "./", test.data = TRUE)

Output

Output 1
Output of As metabolic gene abundance analysis

Output of As metabolic gene abundance analysis

Output 2
Output of As metabolic taxonomy analysis

Output of As metabolic taxonomy analysis

NOTE: