microRNAs靶基因数据库哪家强

生信技能树

发布于 2020-04-21 06:47:13

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microRNAs早就不再是科研热点，但毕竟还是遗留下来了不少数据，而且好歹是TCGA计划的多组学中的一环。在自己的研究增加miRNA的角度也是极好的，通常大家有4个需求：

想知道自己感兴趣的一个或者多个miRNA有哪些靶基因
想知道自己感兴趣的一个或者多个基因由哪些miRNA调控
想知道自己感兴趣的一个或者多个miRNA跟哪些疾病或者药物相关
想知道自己感兴趣的一个或者多个miRNA是否调控自己感兴趣的一个或者多个基因

如果你也有上述需求，那么一个R包推荐给你，发表在Nucleic Acids Res. 2014 Sep的The multiMiR R package and database: integration of microRNA–target interactions along with their disease and drug associations

关于R包的下载安装，我就不多说了：

options(BioC_mirror="https://mirrors.tuna.tsinghua.edu.cn/bioconductor/")
options("repos" = c(CRAN="http://mirrors.cloud.tencent.com/CRAN/"))
options("repos" = c(CRAN="https://mirrors.aliyun.com/CRAN/"))
options(download.file.method = 'libcurl')
options(url.method='libcurl')
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install("multiMiR",ask = F,update = F)

安装并且加载multiMiR后，可以看到multiMiR的更新历史：

> library(multiMiR)
> db.ver = multimir_dbInfoVersions()
> db.ver[,1:3]
  VERSION    UPDATED                      RDA
1   2.3.0 2020-04-15 multimir_cutoffs_2.3.rda
2   2.2.0 2017-08-08 multimir_cutoffs_2.2.rda
3   2.1.0 2016-12-22 multimir_cutoffs_2.1.rda
4   2.0.0 2015-05-01     multimir_cutoffs.rda

这也就是我为什么推荐它的原因，首先当然是因为基于R，无需理会讨厌的网页工具，其次，它最近一次更新是2020-04-15 ，疫情如此严重，还坚持更新，值得鼓励！

当然，需要R编程基础从看得懂这个包的用法，有一个学习班推荐给大家：

生信爆款入门-全球听（买一得五）（第4期），你的生物信息学入门课
数据挖掘第2期（两天变三周，实力加量），医学生/临床医师首选技能提高课

miRWalk是12个网页工具的集合

如果你确实不喜欢R语言，也不想学，当然也可以使用网页工具哈：

一篇2018年6月的文章利用该miRWalk工具，选择被7个工具预测到的MiRNA–mRNA相互作用关系作为最后的结果。文献标题是：FABP4 as a key determinant of metastatic potential of ovarian cancer，网页工具描述如下：

miRWalk2.0 not only documents miRNA binding sites within the complete sequence of a gene, but also combines this information with a comparison of binding sites resulting from 12 existing miRNA-target prediction programs (DIANA-microTv4.0, DIANA-microT-CDS, miRanda-rel2010, mirBridge, miRDB4.0, miRmap, miRNAMap, doRiNA i.e.,PicTar2, PITA, RNA22v2, RNAhybrid2.1 andTargetscan6.2) to build novel comparative platforms of binding sites for the promoter (4 prediction datasets), cds (5 prediction datasets), 5’- (5 prediction datasets) and 3’-UTR (13 prediction datasets) regions. It also documents experimentally verified miRNA-target interaction information collected via an automated text-mining search and data from existing resources (miRTarBase, PhenomiR,miR2Disease and HMDD) offer such information.

其实还有 miRSystem 整合了其他的预测软件: DIANA, miRanda, miRBridge, PicTar, PITA, rna22和TargetScan，包含TarBase和miRecords的验证数据。

当然了，各取所需，完成科研目标为主！

但是，我们要推荐的multiMiR，有14个数据库源哦。

multiMiR的数据库源头

来自于：http://multimir.org/，数据库的详细网址如下：

                                                                                source_url
1           http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=microT_CDS/index
2                                  http://www.mirz.unibas.ch/miRNAtargetPredictionBulk.php
3                http://www.ebi.ac.uk/enright-srv/microcosm/cgi-bin/targets/v5/download.pl
4                                                               http://www.mir2disease.org
5                                         http://www.microrna.org/microrna/getDownloads.do
6                                                                         http://mirdb.org
7                                                http://mirecords.biolead.org/download.php
8                                       http://mirtarbase.mbc.nctu.edu.tw/php/download.php
9                                       http://www.pharmaco-mir.org/home/download_VERSE_db
10                                             http://mips.helmholtz-muenchen.de/phenomir/
11                                                             http://dorina.mdc-berlin.de
12                                  http://genie.weizmann.ac.il/pubs/mir07/mir07_data.html
13 http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=tarbasev8%2Findex
14               http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_61

收录了常见模式生物，人，小鼠，大鼠的miRNA数据

> db.count
       map_name human_count mouse_count rat_count total_count
1  diana_microt     7664602     3747171         0    11411773
2         elmmo     3959112     1449133    547191     5955436
3     microcosm      762987      534735    353378     1651100
4   mir2disease        2875           0         0        2875
5       miranda     5429955     2379881    247368     8057204
6         mirdb     1990425     1091263    199250     3280938
7     mirecords        2425         449       171        3045
8    mirtarbase      544588       50673       652      595913
9  pharmaco_mir         308           5         0         313
10     phenomir       15138         491         0       15629
11       pictar      404066      302236         0      706302
12         pita     7710936     5163153         0    12874089
13      tarbase      433048      209831      1307      644186
14   targetscan    13906497    10442093         0    24348590

从miRNA到mRNA

查询自己感兴趣的一个miRNA有哪些靶基因

注意，这个时候的miRNA的ID是有规则的哦，miRNA成熟体简写成miR，再根据其物种名称，及被发现的先后顺序加上阿拉伯数字，如hsa-miR-122；高度同源的miRNA在数字后机上英文小写字母(a,b,c,…)，如hsa-miR-34a，hsa-miR-34b，hsa-miR-34c等；通常一个miRNA前体长度大约为70~80nt，很可能两个臂分别产生miRNA，则继续在名称之后加上-5p/-3p等，如hsa-miR-122-5p。

所以下面代码里面的例子miRNA的ID是 hsa-miR-18a-3p你应该是明白了的！

# The default is to search validated interactions in human
example1 <- get_multimir(mirna = 'hsa-miR-18a-3p', summary = TRUE)
names(example1)
# Check which types of associations were returned
table(example1@data$type)
# Detailed information of the validated miRNA-target interaction
head(example1@data)
dim(example1@data)
# Which interactions are supported by Luciferase assay?
example1@data[grep("Luciferase", example1@data[, "experiment"]), ]
example1@summary[example1@summary[,"target_symbol"] == "KRAS",]

既然可以查询一个miRNA，当然是可以批量查询多个，示例代码如下，top_miRNAs是差异分析后挑选的miRNA的ID组成的向量：

multimir_results <- get_multimir(org     = 'mmu',
                                 mirna   = top_miRNAs,
                                 table   = 'validated',
                                 summary = TRUE)

从mRNA到miRNA

查询自己感兴趣的一个或者多个基因由哪些miRNA调控，代码分别如下：

example3 <- get_multimir(org     = "mmu",
                         target  = "Gnb1",
                         table   = "predicted",
                         summary = TRUE,
                         predicted.cutoff      = 35,
                         predicted.cutoff.type = "p",
                         predicted.site        = "all")
names(example3)
table(example3@data$type)
head(example3@data)
head(example3@summary)


apply(example3@summary[, 6:13], 2, function(x) sum(x > 0))


example4 <- get_multimir(org     = 'hsa',
                         target  = c('AKT2', 'CERS6', 'S1PR3', 'SULF2'),
                         table   = 'predicted',
                         summary = TRUE,
                         predicted.cutoff.type = 'n',
                         predicted.cutoff      = 500000)

example4.counts <- addmargins(table(example4@summary[, 2:3]))
example4.counts <- example4.counts[-nrow(example4.counts), ]
example4.counts <- example4.counts[order(example4.counts[, 5], decreasing = TRUE), ]
head(example4.counts)

因为查询的数据集，虽然记录了miRNA和mRNA的关系，但有很多筛选阈值可以选择，就需要熟练掌握数据库源头。

从miRNA到疾病或者药物

主要是数据库记录：

example2 <- get_multimir(disease.drug = 'cisplatin', table = 'disease.drug')
names(example2)
nrow(example2@data)
table(example2@data$type)
head(example2@data)

miRNA集合是否调控mRNA集合

load(url("http://multimir.org/bladder.rda"))

## ----Example5_part2, eval=TRUE, echo=TRUE---------------------------------------------------------
# search all tables & top 10% predictions
example5 <- get_multimir(org     = "hsa",
                         mirna   = DE.miRNA.up,
                         target  = DE.entrez.dn,
                         table   = "all",
                         summary = TRUE,
                         predicted.cutoff.type = "p",
                         predicted.cutoff      = 10,
                         use.tibble = TRUE)

table(example5@data$type)
result <- select(example5, keytype = "type", keys = "validated", columns = columns(example5))
unique_pairs <- 
  result[!duplicated(result[, c("mature_mirna_id", "target_entrez")]), ]

result

## ----Example5_part4, eval=TRUE, echo=TRUE---------------------------------------------------------
mykeytype <- "disease_drug"

mykeys <- keys(example5, keytype = mykeytype)
mykeys <- mykeys[grep("bladder", mykeys, ignore.case = TRUE)]

result <- select(example5, keytype = "disease_drug", keys = mykeys,
                 columns = columns(example5))
result

## ----Example5_part4_fortext, echo=FALSE, include=FALSE, eval=TRUE---------------------------------
unique_pairs <- 
  result[!duplicated(apply(result[, c("mature_mirna_id", "disease_drug")], 2,
                           tolower)), ]

一个示例

下面是使用edgeR包，对普通的转录组counts表达矩阵（miRNA）做差异分析，并且拿到感兴趣的miRNA基因集：

library(edgeR)
library(multiMiR)

# Load data
counts_file  <- system.file("extdata", "counts_table.Rds", package = "multiMiR")
strains_file <- system.file("extdata", "strains_factor.Rds", package = "multiMiR")
counts_table   <- readRDS(counts_file)
strains_factor <- readRDS(strains_file)
table(strains_factor)

# Standard edgeR differential expression analysis
design <- model.matrix(~ strains_factor)

# Using trended dispersions
dge <- DGEList(counts = counts_table)
dge <- calcNormFactors(dge)
dge$samples$strains <- strains_factor
dge <- estimateGLMCommonDisp(dge, design)
dge <- estimateGLMTrendedDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)

# Fit GLM model for strain effect
fit <- glmFit(dge, design)
lrt <- glmLRT(fit)

# Table of unadjusted p-values (PValue) and FDR values
p_val_DE_edgeR <- topTags(lrt, adjust.method = 'BH', n = Inf)

# Getting top differentially expressed miRNA's
top_miRNAs <- rownames(p_val_DE_edgeR$table)[1:10]

有了感兴趣的miRNA基因集，就可以查询它们的靶基因

library(multiMiR)
# Plug miRNA's into multiMiR and getting validated targets
multimir_results <- get_multimir(org     = 'mmu',
                                 mirna   = top_miRNAs,
                                 table   = 'validated',
                                 summary = TRUE)
head(multimir_results@data)
table(multimir_results@data$mature_mirna_id)
dim(multimir_results@data)