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Tax4Fun2_test_code.R
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Tax4Fun2_test_code.R
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################################################################################
# 1 安装 Tax4Fun2 包，构建默认参考数据并安装所有依赖项
# 1.1 设置工作目录
setwd("D:/16sdata/Tax4Fun2_test")
getwd()
# 1.2 将Tax4Fun2安装包下载在工作目录下，安装Tax4Fun2
# https://github.com/bwemheu/Tax4Fun2/releases/download/1.1.5/Tax4Fun2_1.1.5.tar.gz
install.packages(pkgs = "Tax4Fun2_1.1.5.tar.gz", repos = NULL, source = TRUE)
library(Tax4Fun2)
# 1.3 构建默认数据库
buildReferenceData(path_to_working_directory = ".",
use_force = FALSE,
install_suggested_packages = TRUE)
# 1.4 下载测试数据
getExampleData(path_to_working_directory = ".")
# 1.5安装依赖
# 进入NCBI官方网站下载最新版本的blast，截至2021/11/4已更新到v2.12.0，
# 将二进制文件放置在由buildReferenceData() ['Tax4Fun2_ReferenceData_v2'] 生成的文件夹。
# https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ncbi-blast-2.12.0+-win64.exe
################################################################################
# 2 生成自己的参考数据集
# 2.1 提取单个基因组中的SSU序列（16S rRNA 和 18S rRNA）
extractSSU(genome_file = "OneProkaryoticGenome.fasta",
file_extension = "fasta",
path_to_reference_data = "Tax4Fun2_ReferenceData_v2")
# 2.2 为单个原核基因组分配功能
assignFunction(genome_file = "OneProkaryoticGenome.fasta",
file_extension = "fasta",
path_to_reference_data = "Tax4Fun2_ReferenceData_v2",
num_of_threads = 1, fast = TRUE)
# 2.3 生成参考数据
generateUserData(path_to_reference_data = "Tax4Fun2_ReferenceData_v2",
path_to_user_data = ".",
name_of_user_data = "User_Ref0",
SSU_file_extension = "_16SrRNA.ffn",
KEGG_file_extension = "_funPro.txt")
####################################################################
# 3 进行功能预测
# 3.1 仅使用默认参考数据进行功能预测
# 3.1.2 运行RefBlast工具参考Ref99NR进行序列同源比对
runRefBlast(path_to_otus = "KELP_otus.fasta",
path_to_reference_data = "Tax4Fun2_ReferenceData_v2",
path_to_temp_folder = "Kelp_Ref99NR",
database_mode = "Ref99NR",
use_force = T, num_threads = 6)
# 3.1.3 功能注释
makeFunctionalPrediction(path_to_otu_table = "KELP_otu_table.txt",
path_to_reference_data = "Tax4Fun2_ReferenceData_v2",
path_to_temp_folder = "Kelp_Ref99NR",
database_mode = "Ref99NR",
normalize_by_copy_number = TRUE,
min_identity_to_reference = 0.97,
normalize_pathways = FALSE)
# 3.2 使用默认数据库和用户生成的数据库（非集群）进行功能预测
# 3.2.1 生成用户数据库
generateUserData(path_to_reference_data = "Tax4Fun2_ReferenceData_v2",
path_to_user_data = "KELP_UserData",
name_of_user_data = "KELP1",
SSU_file_extension = ".ffn",
KEGG_file_extension = ".txt")
# 3.2.2 运行RefBlast工具
runRefBlast(path_to_otus = "KELP_otus.fasta",
path_to_reference_data = "Tax4Fun2_ReferenceData_v2",
path_to_temp_folder = "Kelp_Ref99NR_withUser1",
database_mode = "Ref99NR",
use_force = T, num_threads = 6, include_user_data = T,
path_to_user_data = "KELP_UserData",
name_of_user_data = "KELP1")
# 3.2.3 功能注释
makeFunctionalPrediction(path_to_otu_table = "KELP_otu_table.txt",
path_to_reference_data = "Tax4Fun2_ReferenceData_v2",
path_to_temp_folder = "Kelp_Ref99NR_withUser1",
database_mode = "Ref99NR",
normalize_by_copy_number = T,
min_identity_to_reference = 0.97,
normalize_pathways = F, include_user_data = T,
path_to_user_data = "KELP_UserData",
name_of_user_data = "KELP1")
################################################################################
# 4 计算（多）功能冗余指数（实验性）
# 计算 KEGG 函数的系统发育分布（高 FRI -> 高冗余，低 FRI -> 函数冗余较少，可能会随着社区变化而丢失）
# 4.1 运行RefBlast工具
runRefBlast(path_to_otus = "Water_otus.fna",
path_to_reference_data = "Tax4Fun2_ReferenceData_v2",
path_to_temp_folder = "Water_Ref99NR",
database_mode = "Ref99NR",
use_force = T, num_threads = 6)
# 4.2 计算FRIs
calculateFunctionalRedundancy(path_to_otu_table = "Water_otu_table.txt",
path_to_reference_data = "Tax4Fun2_ReferenceData_v2",
path_to_temp_folder = "Water_Ref99NR",
database_mode = "Ref99NR",
min_identity_to_reference = 0.97)