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![Image of logo]( # About APAlyzer [![](]( [![](]( [![](]( [![](]( [![](]( APAlyzer is a toolkit for bioinformatic analysis of alternative polyadenylation (APA) events using RNA sequencing data. Our main approach is comparison of sequencing reads in regions demarcated by high quality polyadenylation sites (PASs) annotated in the PolyA_DB database ( The current version (v3.0) uses RNA-seq data to examine APA events in 3’ untranslated regions (3’UTRs) and in introns. The coding regions are used for gene expression calculation. ## Authors * **Ruijia Wang** * **Bin Tian** ## License This project is licensed under the LGPL-3 License. # Program installation APAlyzer should be installed using BiocManager: ```{r eval=FALSE} if (!"BiocManager" %in% rownames(installed.packages())) install.packages("BiocManager") BiocManager::install("APAlyzer") ``` Alternatively, it can also be installed as follows: ```{r eval=FALSE} R CMD INSTALL APAlyzer.tar.gz ``` In additions, user can also install development version of APAlyzer directly from GitHub: ```{r eval=FALSE} BiocManager::install('RJWANGbioinfo/APAlyzer') ``` After installation, APAlyzer can be used by: ```{r eval=FALSE} library(APAlyzer) ``` # Sample data and PAS references ## RNA-seq BAM files The package reads BAM file(s) to obtain read coverage information in different genomic regions. The following example shows that we first specify paths to example BAM files in the `r BiocStyle::Biocpkg("TBX20BamSubset")` [@TBX20BamSubset] data package. In this example, BAM files correspond to mouse RNA-seq data, (mapped to mm9). ```{r eval=TRUE} suppressMessages(library("TBX20BamSubset")) suppressMessages(library("Rsamtools")) flsall = getBamFileList() flsall ``` ## Genomic reference PAS references in the genome (both 3’UTRs and introns) are required by our package. We have pre-built a reference file for the mouse genome (mm9), which can be loaded from `extdata`: ```{r eval=TRUE} library(repmis) URL="" file="mm9_REF.RData" source_data(paste0(URL,file,"?raw=True")) ``` This `extdata` covers 3’UTR APA regions (refUTRraw), IPA regions (dfIPA), and 3’-most exon regions (dfLE). The `refUTRraw` is a data frame containing 6 columns for genomic information of 3’UTR PASs: ```{r eval=TRUE} head(refUTRraw,2) ``` `dfIPA` is a data frame containing 8 columns for Intronic PASs; ‘upstreamSS’ means the closest 5’ or 3’ splice site to IPA, ‘downstreamSS’ means closest 3’ splice site: ```{r eval=TRUE} head(dfIPA,2) ``` `dfLE` is a data frame containing 5 colmuns for 3’ least exon; ‘LEstart’ means the start genomic position of last 3’ exon. ```{r eval=TRUE} head(dfLE,2) ``` In additions to mouse mm9, our package has also a pre-build version for mouse mm10, human hg38 and human hg19 genome: ```{r eval=TRUE} URL="" file="hg19_REF.RData" source_data(paste0(URL,file,"?raw=True")) ``` More pre-build refercence can be found at the reference and testing data repo: ## Building 3'UTR and intronic PAS reference region at once To quantify the relative expression of PAS, we will need to build the reference regions for them, although this can be build separately in previous version. We also provide a new fouction `REF4PAS` starting from APAlyzer 1.2.1 to build these regions at once: ```{r eval=TRUE} refUTRraw=refUTRraw[which(refUTRraw$Chrom=='chr19'),] dfIPAraw=dfIPA[which(dfIPA$Chrom=='chr19'),] dfLEraw=dfLE[which(dfLE$Chrom=='chr19'),] PASREF=REF4PAS(refUTRraw,dfIPAraw,dfLEraw) UTRdbraw=PASREF$UTRdbraw dfIPA=PASREF$dfIPA dfLE=PASREF$dfLE ``` In this case, `UTRdbraw` is the reference region used for 3'UTR APA analysis, while `dfIPA` and `dfLE` are needed in the intronic APA analysis. ## Building 3'UTR PAS and IPA reference using GTF files Although we highly suggest user use references regions genrated from PolyA_DB. Start from APAlyzer 1.2.1, we also provide a new fouction that can help users to build their reference directly from gene annotation GTF files, we hope this can help the species which are not covered by the PolyA_DB yet: ```{r eval=FALSE} ## build Reference ranges for 3'UTR PASs in mouse download.file(url='', destfile='Mus_musculus.GRCm38.99.gtf.gz') GTFfile="Mus_musculus.GRCm38.99.gtf.gz" PASREFraw=PAS2GEF(GTFfile) refUTRraw=PASREFraw$refUTRraw dfIPAraw=PASREFraw$dfIPA dfLEraw=PASREFraw$dfLE PASREF=REF4PAS(refUTRraw,dfIPAraw,dfLEraw) ``` Start from APAlyzer 2.0, two annotation methods can be set through `AnnoMethod=` to annotate the PAS, 'legacy' or 'V2' (default), 'legacy' is the old method used within previous versions, while 'V2' is the updated and improved version. # Analysis of APA in 3’UTRs ## Building aUTR and cUTR references To calculate 3’UTR APA relative expression (RE), we first need to define the refence regions of aUTR and cUTR using `refUTRraw`. Since the sample data only contains mapping information on chr19, we can zoom into reference regions on chr19 only: ```{r eval=FALSE} refUTRraw=refUTRraw[which(refUTRraw$Chrom=='chr19'),] UTRdbraw=REF3UTR(refUTRraw) ``` The `REF3UTR` function returns a genomic range containing aUTR(pPAS to dPAS) and cUTR(cdsend to pPAS) regions for each gene: ```{r} head(UTRdbraw,2) ``` ## Calculation of relative expression Once cUTR and aUTR regions are defined, the RE of 3’UTR APA of each gene can be calculated by `PASEXP_3UTR`: ```{r eval=TRUE} DFUTRraw=PASEXP_3UTR(UTRdbraw, flsall, Strandtype="forward") ``` The `PASEXP_3UTR` 3UTR requires two inputs: 1) aUTR and cUTR reference regions, and 2) path of BAM file(s). In additions to input, one can also define the strand of the sequencing using `Strandtype`. The detailed usage can also be obtained by the command `?PASEXP_3UTR`.The output data frame covers reads count (in aUTR or cUTR), RPKM (in aUTR or cUTR) and RE (log2(aUTR/cUTR)) for each gene: ```{r eval=TRUE} head(DFUTRraw,2) ``` # Analysis of APA in introns ## Building intronic polyA references Analysis of IPA requires two genomic regions: IPA regions and 3’-most exons. As mentioned above, these regions in mouse and human genomes have been pre-built in the package: ```{r eval=TRUE} #mouse(mm9): URL="" file="mm9_REF.RData" source_data(paste0(URL,file,"?raw=True")) ``` ```{r eval=TRUE} #human(hg19): URL="" file="hg19_REF.RData" source_data(paste0(URL,file,"?raw=True")) ``` ## Calculation of relative expression Similar to 3’UTR APA, RE of IPAs can be calculated using PASEXP_IPA: `PASEXP_IPA`: ```{r eval=FALSE} dfIPA=dfIPA[which(dfIPA$Chrom=='chr19'),] dfLE=dfLE[which(dfLE$Chrom=='chr19'),] IPA_OUTraw=PASEXP_IPA(dfIPA, dfLE, flsall, Strandtype="forward", nts=1) ``` Note that, as a specific feature for IPA, one can set more threads using ‘nts=’(the parameter passed to Rsubread::featureCounts, check `?Rsubread::featureCounts` for details) to increase calculation speed. The detailed usage can be obtained by the command `?PASEXP_IPA`. The output data frame contains read count and read density IPA upstream (a), IPA downstream (b) and 3’-most exon region (c). The RE of IPA is calculated as log2((a - b)/c). ```{r} head(IPA_OUTraw,2) ``` # Significance analysis of APA events Once the read coverage information is obtained for each sample, one can compare APA regulation difference between two different groups. In this analysis, there are two types of experimental design: 1) without replicates; 2) with replicates. A sample table will be generated according to the design: ```{r eval=TRUE} # Build the sample table with replicates sampleTable1 = data.frame(samplename = c(names(flsall)), condition = c(rep("NT",3),rep("KD",3))) sampleTable1 ``` ```{r eval=TRUE} # Build the sample table without replicates sampleTable2 = data.frame(samplename = c("SRR316184","SRR316187"), condition = c("NT","KD")) sampleTable2 ``` ## Significantly regulated APA in 3’UTRs To analyze 3’UTR APA between samples (KD and NT groups in the example) without replicates, `sampleTable2` is used. The function used here is called `APAdiff` (detailed information can be obtained by the command `?APAdiff`). It will fist to go through the sample table to determine whether it is a replicate design or non-replicate design. Then the APA compassion will be performed. ```{r eval=TRUE} # Analysis 3'UTR APA between KD and NT group using non-repilicate design test_3UTRsing=APAdiff(sampleTable2,DFUTRraw, conKET='NT', trtKEY='KD', PAS='3UTR', CUTreads=0) ``` The `APAdiff` function requires two inputs: 1) A sample table defining groups/conditions of the samples, and 2) read coverage information of aUTRs and cUTRs, which can be obtained by `PASEXP_3UTR` from the previous step. The group name, i.e., treatment or control, can be defined b `trtKEY=` and `conKET=`; the PAS type analyzed should be defined by `PAS=`; and the read cutoff used for aUTR and cUTR is defined by `CUTreads=` with the default value being 0. In the non-replicate design, the APA pattern will be compared between two samples and output will be shown in a data frame: ```{r eval=TRUE} head(test_3UTRsing,2) table(test_3UTRsing$APAreg) ``` The output contains 4 columns: ‘gene symbol’ describes gene information; ‘RED’ is relative expression difference between two groups; ‘pvalue’ is statistical significance based on the Fisher’s exact test; ‘p_adj’ is FDR adjusted pvalue and ‘APAreg’ is 3’UTR APA regulation pattern in the gene. We define 3 types in ‘APAreg’, ‘UP’ means aUTR abundance in the treatment group (‘KD’ in this case) is at least 5% higher than that in control (‘NT’ in this case), and ‘pvalue’<0.05; ‘DN’ means aUTR abundance is 5% lower in treatment than that in control and p-value<0.05; ‘NC’ are the remaining genes. With respect to 3’UTR size changes, ‘UP’ means 3’UTR lengthening, and ‘DN’ 3’UTR shortening. For the replicate design, we use t-test for significance analysis. However, other tools based on negative binomial data distribution, such as `r BiocStyle::Biocpkg("DEXSeq")` [@DEXSEQ] might also be used. ```{r eval=TRUE} # Analysis 3'UTR APA between KD and NT group using multi-repilicate design test_3UTRmuti=APAdiff(sampleTable1, DFUTRraw, conKET='NT', trtKEY='KD', PAS='3UTR', CUTreads=0, p_adjust_methods="fdr", MultiTest='unpaired t-test') head(test_3UTRmuti,2) table(test_3UTRmuti$APAreg) ``` In the replicate design, additional parameter `MultiTest` can be used to set the statistical method. In the current version, there are 3 methods can be set: "unpaired t-test" (default), "paired t-test", "ANOVA". After the running of `APAdiff`, a few columns will be generated: ‘RED’ is difference of averaged relative expression between two groups; ‘pvalue’ is the p-value calculated through the method defined by `MultiTest`. 'APAreg' is the predicted APA regulation direction; ‘UP’ is defined as ‘RED’ >0 and ‘pvalue’ <0.05; while ‘DN’ is the opposite; and ‘NC’ is the remaining genes. ## Significantly regulated APA in introns IPA comparison is similar to 3’UTR APA using `APAdiff`, except that it (1) uses IPA expression as input, and (2) ‘PAS=’ needs to be defined as ‘IPA’, and (3) the analysis is performed on each IPA. Note that, the direction of IPA regulation is similar to that of 3’UTR APA. This means ‘UP’ is defined as up-regulation of IPA (RED > 0); ‘DN’ is the opposite; and ‘NC’ is the remaining genes. Analysis of IPA between KD and NT groups without replicates is shown here: ```{r eval=TRUE} test_IPAsing=APAdiff(sampleTable2, IPA_OUTraw, conKET='NT', trtKEY='KD', PAS='IPA', CUTreads=0) head(test_IPAsing,2) ``` Analysis of IPA between KD and NT groups using replicate data is shown here: ```{r eval=TRUE} test_IPAmuti=APAdiff(sampleTable1, IPA_OUTraw, conKET='NT', trtKEY='KD', PAS='IPA', CUTreads=0) head(test_IPAmuti,2) ``` ## Significantly regulated APA in multi-condition design Starting from APAlyzer 1.9.2, `APAdiff` can also be used on multiple conditions design (with replicates in each condition). To trigger such analysis, simply define your sample table with multiple conditions/groups, e.g.: ```{r eval=TRUE} # Build the sample table with replicates sampleTable3 = data.frame(samplename = c(names(flsall)), condition = c(rep("NT",2),rep("KD1",2),rep("KD2",2))) sampleTable3 ``` then call the 3'UTR APA analysis through: ```{r eval=TRUE} test_3UTRmuti=APAdiff(sampleTable3, DFUTRraw, PAS='3UTR', CUTreads=0, p_adjust_methods="fdr") head(test_3UTRmuti,2) ``` Or call the differential IPA analysis through: ```{r eval=TRUE} test_IPAmuti=APAdiff(sampleTable3, IPA_OUTraw, PAS='IPA', CUTreads=0, p_adjust_methods="fdr") head(test_IPAmuti,2) ``` In the multi-condition design, the output contains 2 result columns: ‘pvalue’ is statistical significance based on the one-way ANOVA across all conditions; ‘p_adj’ is FDR adjusted pvalues. # Visualization of analysis results Start from APAlyzer 1.2.1, we provides two new fouction called `APAVolcano` and `APABox` for users to plot their RED results using volcano plot and box plot. In the volcano plot, users can also label the top genes using `top=` or a set of specific gene using `markergenes=`, for example: ```{r eval=FALSE} APAVolcano(test_3UTRsing, PAS='3UTR', Pcol = "pvalue", top=5, main='3UTR APA') ``` In the box plot, RED is ploted on 'UP', 'DN', and 'NC' genes: ```{r eval=FALSE} APABox(test_3UTRsing, xlab = "APAreg", ylab = "RED", plot_title = NULL) ``` In addtion to volcano and box plots, APA comparison result can be also plotted using either boxplots or violin plots or CDF curves. For the previous 3’UTR APA and IPA comparison outputs, one needs to first build the plotting data frame: ```{r eval=TRUE} test_3UTRmuti$APA="3'UTR" test_IPAmuti$APA="IPA" dfplot=rbind(test_3UTRmuti[,c('RED','APA')],test_IPAmuti[,c('RED','APA')]) ``` To make violin plots and CDF curves using `r BiocStyle::Biocpkg("ggplot2")`: ```{r eval=TRUE} library(ggplot2) ``` ```{r fig1, fig.height = 4, fig.width = 4, fig.align = "center"} ###violin ggplot(dfplot, aes(x = APA, y = RED)) + geom_violin(trim = FALSE) + geom_boxplot(width = 0.2)+ theme_bw() + geom_hline(yintercept=0, linetype="dashed", color = "red") ``` ```{r fig2, fig.height = 4, fig.width = 5, fig.align = "center"} ###CDF ggplot(dfplot, aes( x = RED, color = APA)) + stat_ecdf(geom = "step") + ylab("cumulative fraction")+ geom_vline(xintercept=0, linetype="dashed", color = "gray")+ theme_bw() + geom_hline(yintercept=0.5, linetype="dashed", color = "gray") ``` # Gene expression analysis using coding regions APA is frequently involved in gene expression regulation. To compare gene expression vs. APA in different samples, our package provides a simple function to assess the expression changes using RNA-seq reads mapped to coding sequences. ## Building coding region references ```{r eval=TRUE} suppressMessages(library("GenomicFeatures")) suppressMessages(library("")) extpath = system.file("extdata", "mm9.chr19.refGene.R.DB", package="APAlyzer") txdb=loadDb(extpath, packageName='GenomicFeatures') IDDB = CDSdbraw=REFCDS(txdb,IDDB) ``` ## Calculation of expression ```{r eval=TRUE} DFGENEraw=GENEXP_CDS(CDSdbraw, flsall, Strandtype="forward") ``` # Extract 3'most alignment from the pair-end bam file Start from APAlyzer 1.5.5, we provides a new fouction called `ThreeMostPairBam` for users to extract three prime most alignment from the paired-end bam file and save it into a new bam file. For example: ```{r eval=FALSE} ## Extract 3 prime most alignment of a paired-end ## bam file and saved into a new bam file Bamfile='/path/to/inputdir/input.bam' Outdir='/path/to/outdir/' StrandType="forward-reverse" ## "forward-reverse", or "reverse-forward" or "NONE" ThreeMostPairBam (BamfilePath=Bamfile, OutDirPath=Outdir, StrandType='forward-reverse') ``` The output bamfile (A bam file named as *.3most.bam located in `Outdir`) can be used as input file in `PASEXP_3UTR`. To use this bam file for `PASEXP_IPA`, the user need to set the `SeqType` to "ThreeMostPairEnd", for example: ```{r eval=FALSE} ThreemostBamfile="test.3most.bam" IPA_OUTraw=PASEXP_IPA(dfIPA, dfLE, ThreemostBamfile, Strandtype="forward", SeqType="ThreeMostPairEnd") ``` # Complete Analysis Example: APA analysis in mouse testis versus heart ## About this dataset To provide a complete tutorial of APA analysis using our package, we have now prepared a testing dataset through down sampling of mouse RNA-Seq data in heart (GSM900193) and testis (GSM900199): | Sample ID | SRRID | Sample Name | Down sampling reads| |-----------|:---------:|------------:|----------:| | GSM900199 | SRR453175 | Heart_Rep1 | 5 Million | | GSM900199 | SRR453174 | Heart_Rep2 | 5 Million | | GSM900199 | SRR453173 | Heart_Rep3 | 5 Million | | GSM900199 | SRR453172 | Heart_Rep4 | 5 Million | | GSM900193 | SRR453143 | Testis_rep1 | 5 Million | | GSM900193 | SRR453142 | Testis_rep2 | 5 Million | | GSM900193 | SRR453141 | Testis_rep3 | 5 Million | | GSM900193 | SRR453140 | Testis_rep4 | 5 Million | ## Download the bam files ```{r eval=FALSE} download_testbam() flsall <- dir(getwd(),".bam") flsall<-paste0(getwd(),'/',flsall) names(flsall)<-gsub('.bam','',dir(getwd(),".bam")) ``` ## Build the PAS reference regions ```{r eval=FALSE} library(repmis) URL="" file="mm9_REF.RData" source_data(paste0(URL,file,"?raw=True")) PASREF=REF4PAS(refUTRraw,dfIPA,dfLE) UTRdbraw=PASREF$UTRdbraw dfIPA=PASREF$dfIPA dfLE=PASREF$dfLE ``` ## Calculation of relative expression of 3'UTR APA and IPA ```{r eval=FALSE} UTR_APA_OUT=PASEXP_3UTR(UTRdbraw, flsall, Strandtype="invert") IPA_OUT=PASEXP_IPA(dfIPA, dfLE, flsall, Strandtype="invert", nts=1) ``` ## Significantly regulated APA in 3’UTRs ```{r eval=FALSE} ############# 3utr APA ################# sampleTable = data.frame(samplename = c('Heart_rep1', 'Heart_rep2', 'Heart_rep3', 'Heart_rep4', 'Testis_rep1', 'Testis_rep2', 'Testis_rep3', 'Testis_rep4'), condition = c(rep("Heart",4), rep("Testis",4))) test_3UTRAPA=APAdiff(sampleTable,UTR_APA_OUT, conKET='Heart', trtKEY='Testis', PAS='3UTR', CUTreads=5) ``` ```{r eval=TRUE} table(test_3UTRAPA$APAreg) APAVolcano(test_3UTRAPA, PAS='3UTR', Pcol = "pvalue", plot_title='3UTR APA') APABox(test_3UTRAPA, xlab = "APAreg", ylab = "RED", plot_title = NULL) ``` ## Significantly regulated APA in Intron ```{r eval=FALSE} ############# IPA ################# test_IPA=APAdiff(sampleTable, IPA_OUT, conKET='Heart', trtKEY='Testis', PAS='IPA', CUTreads=5) ``` ```{r eval=TRUE} table(test_IPA$APAreg) APAVolcano(test_IPA, PAS='IPA', Pcol = "pvalue", plot_title='IPA') APABox(test_IPA, xlab = "APAreg", ylab = "RED", plot_title = NULL) ``` # FAQs **How to generate a BAM file list for analysis?** A BAM file list containing both BAM file names and paths of the files. Let’s say all the BAM files are stored in bamdir, then BAM file lists can be obtained through: ```{r eval=FALSE} flsall = dir(bamdir,".bam") flsall=paste0(bamdir,flsall) names(flsall)=dir(bamdir,".bam") ``` **Why am I getting error messages when I try to get txdb using makeTxDbFromUCSC?** You can try either upgrade your Bioconductor, or load the genome annotation using GTF, or load the prebuild genome annotation using ‘.R.DB’ file, e.g., mm9.refGene.R.DB.