# ribosomeProfilingQC Guide
Author: Jianhong Ou, Mariah Hoye
Date: 2023-04-25

Jianhong Ou, Mariah Hoye

#### 25 April 2023

#### Package

ribosomeProfilingQC 1.12.0

# Contents

#Introduction

Ribosome footprinting, developed by Jonathan Weissman and Nicholas Ingolia1, measures translation by direct quantification of the coding sequence currently bound by the 80S ribosome (ribosome-protected fragments, RPFs).2 In eukaryotes, the size of RPFs is around 28-nt, where the P-site of the ribosome is typically in position 13 from the 5’ end of reads. In bacteria, Allen et. al. were able to more accurately identify the P-site from 3’ end of reads3.

Schematic representation of ribosome profiling.

There are several packages available in Bioconductor already, including, riboSeqR,4 RiboProfiling 5 and ORFik 6. These packages are powerful in analyzing the ribosome footprinting data. The ORFik package can also be used to find new transcription start sites using CageSeq data. RiboWaltz 7 is another popular package which is based on R and Bioconductor.

To help researchers quickly assess the quality of their ribosome profiling data, we have developed the ribosomeProfilingQC package. The ribosomeProfilingQC package can be used to easily make diagnostic plots to check the mapping quality and frameshifts. In addition, it can preprocess ribosome profiling data for subsequent differential analysis. We have tried to make this package as user-friendly as possible and the only input needed is a bam file of your ribosome footprinting and RNAseq data mapped to the genome.

Please note that all following analyses are based on known Open Reading Frame (ORF) annotation. The sample data provided in the package is mapped to Zebrafish UCSC danRer10 assembly; all code related to this assembly will be highlighted in light yellow background for clarity.

# 1 Quick start

Here is an example using ribosomeProfilingQC with a subset of ribo-seq data.

First install ribosomeProfilingQC and other packages required to run the examples. Please note that the example dataset used here is from zebrafish. To run analysis with dataset from a different species or different assembly, please install the corresponding Bsgenome and TxDb. For example, to analyze mouse data aligned to mm10, please install BSgenome.Mmusculus.UCSC.mm10, and TxDb.Mmusculus.UCSC.mm10.knownGene. You can also generate a TxDb object by functions `makeTxDbFromGFF` from a local gff file, or `makeTxDbFromUCSC`, `makeTxDbFromBiomart`, and `makeTxDbFromEnsembl`, from online resources in GenomicFeatures package.

    library(BiocManager)
    BiocManager::install(c("ribosomeProfilingQC",
                           "AnnotationDbi", "Rsamtools",
                           "BSgenome.Drerio.UCSC.danRer10",
                           "TxDb.Drerio.UCSC.danRer10.refGene",
                           "motifStack"))

If you have trouble in install ribosomeProfilingQC, please check your R version first. The ribosomeProfilingQC package require R >= 4.0.

    R.version

    ##                _
    ## platform       x86_64-pc-linux-gnu
    ## arch           x86_64
    ## os             linux-gnu
    ## system         x86_64, linux-gnu
    ## status         RC
    ## major          4
    ## minor          3.0
    ## year           2023
    ## month          04
    ## day            13
    ## svn rev        84269
    ## language       R
    ## version.string R version 4.3.0 RC (2023-04-13 r84269)
    ## nickname       Already Tomorrow

    ## load library
    library(ribosomeProfilingQC)
    library(AnnotationDbi)
    library(Rsamtools)

## 1.1 Load genome

In this manual, we will use the fish genome.

    library(BSgenome.Drerio.UCSC.danRer10)
    ## set genome, Drerio is a shortname for BSgenome.Drerio.UCSC.danRer10
    genome <- Drerio

If your assembly is Human hg38 please load the human library,

    library(BSgenome.Hsapiens.UCSC.hg38)
    genome <- Hsapiens

If your assembly is Mouse mm10 please load the mouse library,

    library(BSgenome.Mmusculus.UCSC.mm10)
    genome <- Mmusculus

## 1.2 Prepare annotaiton CDS

The function `prepareCDS` is used to prepare the information for downstream analysis from a `TxDb` object.

    ## which is corresponding to BSgenome.Drerio.UCSC.danRer10
    library(TxDb.Drerio.UCSC.danRer10.refGene)
    txdb <- TxDb.Drerio.UCSC.danRer10.refGene ## give it a short name
    CDS <- prepareCDS(txdb)

If your assembly is Human hg38 please try to load the library,

    library(TxDb.Hsapiens.UCSC.hg38.knownGene)
    txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene ## give it a short name
    CDS <- prepareCDS(txdb)

If your assembly is Mouse mm10 please try to load the library,

    library(TxDb.Mmusculus.UCSC.mm10.knownGene)
    txdb <- TxDb.Mmusculus.UCSC.mm10.knownGene ## give it a short name
    CDS <- prepareCDS(txdb)

You can also create a TxDb object from a gtf file by GenomicFeatures::makeTxDbFromGFF function. To get GTF file, you can download it from ensembl or get the online file info via AnnotationHub. Here we use a prepared TxDb object for testing.

    ## Create a small TxDb object which only contain chr1 information.
    library(GenomicFeatures)
    txdb <- makeTxDbFromGFF(system.file("extdata",
                                        "Danio_rerio.GRCz10.91.chr1.gtf.gz",
                                        package="ribosomeProfilingQC"),
                            organism = "Danio rerio",
                            chrominfo = seqinfo(Drerio)["chr1"],
                            taxonomyId = 7955)
    CDS <- prepareCDS(txdb)

## 1.3 Inputs

The input of ribosomeProfilingQC is bam file. To prepare bam file, different from riboSeqR package which ask reads mapped to transcriptome, ribosomeProfilingQC use the bam file mapped to whole genome. To get correctly mapped reads, first try to map adaptor trimmed sequences to genome assembly by bowtie2 with following parameters: –local –ma 5 –mp 8,4 –rdg 7,7 –rfg 7,7 –fr –nofw and then fileter the reads mapped to rRNA, tRNA, snRNA, snoRNA and misc_RNA from Ensembl and Repeatmasker annotations. After that, map the clean reads to genome assembly by tophat2 with following parameters: –library-type fr-firststrand –transcriptome-index=Transcriptome_data/genome. Because the library type of ribo-seq is usally strand-specific, please make sure to map the reads with correct library type.

    library(Rsamtools)
    ## input the bamFile from the ribosomeProfilingQC package
    bamfilename <- system.file("extdata", "RPF.WT.1.bam",
                               package="ribosomeProfilingQC")
    ## For your own data, please set bamfilename as your file path.
    ## For example, your bam file is located at C:\mydata\a.bam
    ## you want to set bamfilename = "C:\\mydata\\a.bam"
    ## or you can change your working directory by
    ## setwd("C:\\mydata")
    ## and then set bamfilename = "a.bam"
    yieldSize <- 10000000
    bamfile <- BamFile(bamfilename, yieldSize = yieldSize)

## 1.4 Estimate P site

As it shown in the above figure, P site of the ribosome is in position 13 (if using RNase I). However, in different experiments, the P site may be shifted due to various experimental conditions such as the choice of enzyme and the cell type. The `estimatePsite` function can be used to check the P site. The `estimatePsite` function will search start/stop codons that occur in the reads. The `estimatePsite` will only use 12, 13 or 14 as best P site candidates when searching from the 5’ end.

    estimatePsite(bamfile, CDS, genome)

    ## [1] 13

It has been shown that for certain enzymes, such as MNase, estimating the P site from the 3’ end works much better3. The `estimatePsite` will use 15, 16 or 17 as best P site candidates when searching from the 3’ end.

    estimatePsite(bamfile, CDS, genome, anchor = "3end")

    ## [1] -16

## 1.5 Plot start/stop windows

The `readsEndPlot` function will plot the 5’ end or 3’ end reads shifted from the start/stop position of CDS. There is no difference when assign the reading frame for most of the reads if you set best P site to 13 or 10 or 16 (from 5’ end). The `readsEndPlot` can help users to determine the real best Psite. In the example below, the start codon is enriched in position -9 from the 5’ end of reads and in position 19 from the 3’ end of reads. This means there are a lot of ribosome that are docking at the translation start position and most of the reads length are 28 nt.

Ribosome docking at TSS

    readsEndPlot(bamfile, CDS, toStartCodon=TRUE)

    readsEndPlot(bamfile, CDS, toStartCodon=TRUE, fiveEnd=FALSE)

If you see following distribution, that means lots of gene are in active expression.

Active expression

If you see a warning or error message complaining about the disagreement of chromosome sequences, please verify you are using the TxDb object with correct genome assembly. If this warning message is for the patch chromosomes you are not interested, you can ignore the warning messages.

## 1.6 Read all P site coordinates

The `getPsiteCoordinates` function is used to read all P site coordinates. Ideally, the bestpsite should be 13. To test the data quality, we set bestpsite = 13.

    pc <- getPsiteCoordinates(bamfile, bestpsite = 13)

## 1.7 Fragment size distribution

Ribosome-protected fragments should ideally be 27 to 29-nt long. To check the fragment size distribution, use the following function:

    readsLen <- summaryReadsLength(pc)

### 1.7.1 Filter the reads by fragment size

To filter reads by their length for downstream analysis, use the following script:

    ## for this QC demo, we will only use reads length of 28-29 nt.
    pc.sub <- pc[pc$qwidth %in% c(28, 29)]

## 1.8 Sense/antisense strand plot

Most of the reads should be mapped to sense strand because the ribo-seq library is strand-specific.

    strandPlot(pc.sub, CDS)

## 1.9 Genomic element distribution

For ribosome footprinting, most of the reads should map to the CDS region. The `readsDistribution` function will show the P site locations in different genomic elements: CDS, 5’UTR, 3’UTR, other type exon, intron, promoter, downstream or intergenic region. A high downstream percentage indicates that there is a high percentage of alternative polyAdenylation sites usage from annotation data. A high percentage in intronic regions indicates the possibility of intron-retaining transcripts.

    pc.sub <- readsDistribution(pc.sub, txdb, las=2)

## 1.10 Metagene analysis plot for 5’UTR/CDS/3’UTR

A metagene plot can indicate the reads distribution in 5’UTR, CDS and 3’UTR region.

    cvgs.utr5 <- coverageDepth(RPFs = bamfilename, gtf = txdb, region="utr5")
    cvgs.CDS <- coverageDepth(RPFs = bamfilename, gtf = txdb, region="cds")
    cvgs.utr3 <- coverageDepth(RPFs = bamfilename, gtf = txdb, region="utr3")
    metaPlot(cvgs.utr5, cvgs.CDS, cvgs.utr3, sample=1)

## 1.11 Reading frame

Function `assignReadingFrame` is used to set the reading frame for the P sites located within the known annotated CDS. The `plotDistance2Codon` function can be used to plot the reading frame usage in transcription initiation or termination sites. Function `plotFrameDensity` can be used to collapse all the RPFs in each frame. These plots can help you to double check if the p-site position is correct or not. If it is correct, most of the reads should be assigned to frame0.

    pc.sub <- assignReadingFrame(pc.sub, CDS)
    plotDistance2Codon(pc.sub)

    plotFrameDensity(pc.sub)

To determine how many of raw reads are mapping with P sites in frame 0.

    pc <- assignReadingFrame(pc, CDS)
    plotFrameDensity(pc)

Function `plotTranscript` can be used to view the reading frame distribution for given transcripts.

    plotTranscript(pc.sub, c("ENSDART00000161781", "ENSDART00000166968",
                             "ENSDART00000040204", "ENSDART00000124837"))

## 1.12 ORFscore vs coverageRate

ORFscore2 can be used to quantify the biased distribution of RPFs toward the first frame of a given CDS. Coverage rate for whole CDS can help researchers to check the RPFs distribution along whole CDS. Coverage is determined by measuring the proportion of in-frame CDS positions with >= 1 reads. If coverage is about 1, the whole CDS is covered by active ribosomes.

    cvg <- frameCounts(pc.sub, coverageRate=TRUE)
    ORFscore <- getORFscore(pc.sub)
    ## Following code will plot the ORFscores vs coverage.
    ## Try it by removing the '#'.
    #plot(cvg[names(ORFscore)], ORFscore,
    #     xlab="coverage ORF1", ylab="ORF score",
    #     type="p", pch=16, cex=.5, xlim=c(0, 1))

# 2 Bad case

Here, we show ribosome footprinting data that is poor quality data and should not bed used for downstream analyses.

    bamfilename <- system.file("extdata", "RPF.chr1.bad.bam",
                               package="ribosomeProfilingQC")
    yieldSize <- 10000000
    bamfile <- BamFile(bamfilename, yieldSize = yieldSize)
    pc <- getPsiteCoordinates(bamfile, bestpsite = 13)
    pc.sub <- pc[pc$qwidth %in% c(27, 28, 29)]
    ## in this example, most of the reads mapped to the antisense strand
    ## which may indicate that there are some issues in the mapping step
    strandPlot(pc.sub, CDS)

    ## in this exaple, most of the reads mapped to inter-genic regions
    ## rather than the CDS, which could indicate that ribosome protected
    ## fragments are not being properly isolated/selected
    pc.sub <- readsDistribution(pc.sub, txdb, las=2)

    ## Selection of the proper P site is also critical.
    ## If we assign the wrong P site position the frame mapping will
    ## likely be impacted.
    pc <- getPsiteCoordinates(bamfile, 12)
    pc.sub <- pc[pc$qwidth %in% c(27, 28, 29)]
    pc.sub <- assignReadingFrame(pc.sub, CDS)
    plotDistance2Codon(pc.sub)

    plotFrameDensity(pc.sub)

# 3 Prepare for downstream analysis

## 3.1 RPFs only

### 3.1.1 Count for RPFs

Downstream analysis including differential analysis, comparison with RNAseq, and so on. Function `frameCounts` will generate a count vector for each transcript or gene, which can be used for differential analysis. `countReads` can be used for count multiple files of ribo-seq.

    library(ribosomeProfilingQC)
    library(AnnotationDbi)
    path <- system.file("extdata", package="ribosomeProfilingQC")
    RPFs <- dir(path, "RPF.*?\\.[12].bam$", full.names=TRUE)
    gtf <- file.path(path, "Danio_rerio.GRCz10.91.chr1.gtf.gz")
    cnts <- countReads(RPFs, gtf=gtf, level="gene",
                       bestpsite=13, readsLen=c(28,29))
    head(cnts$RPFs)

    ##                    RPF.KD1.1.bam RPF.KD1.2.bam RPF.WT.1.bam RPF.WT.2.bam
    ## ENSDARG00000000606            23            10            0            0
    ## ENSDARG00000002285             5             5            0            1
    ## ENSDARG00000002377           112            44          124           51
    ## ENSDARG00000002634             4             3            0            0
    ## ENSDARG00000002791          3051          1313         3815         1718
    ## ENSDARG00000002840             1             1            0            0

    ## To save the cnts, please try following codes by removing '#'
    # write.csv(cbind(cnts$annotation[rownames(cnts$RPFs), ], cnts$RPFs),
    #           "counts.csv")

To get GTF file, you can download it from ensembl or get the online file info via `AnnotationHub`.

    BiocManager::install("AnnotationHub")
    library(AnnotationHub)
    ah = AnnotationHub()
    ## for human hg38
    hg38 <- query(ah, c("Ensembl", "GRCh38", "gtf"))
    hg38 <- hg38[length(hg38)]
    gtf <- mcols(hg38)$sourceurl
    ## for mouse mm10
    mm10 <- query(ah, c("Ensembl", "GRCm38", "gtf"))
    mm10 <- mm10[length(mm10)]
    gtf <- mcols(mm10)$sourceurl

### 3.1.2 Differential analysis only for RPFs

    library(edgeR)  ## install edgeR by BiocManager::install("edgeR")
    gp <- c("KD", "KD", "CTL", "CTL") ## sample groups: KD:knockdown; CTL:Control
    y <- DGEList(counts = cnts$RPFs, group = gp)
    y <- calcNormFactors(y)
    design <- model.matrix(~0+gp)
    colnames(design) <- sub("gp", "", colnames(design))
    y <- estimateDisp(y, design)
    ## To perform quasi-likelihood F-tests:
    fit <- glmQLFit(y, design)
    qlf <- glmQLFTest(fit)
    topTags(qlf, n=3) # set n=nrow(qlf) to pull all results.

    ## Coefficient:  KD
    ##                        logFC   logCPM        F        PValue           FDR
    ## ENSDARG00000100782 -9.813052 11.76492 5114.429 1.434692e-304 4.519280e-302
    ## ENSDARG00000037259 -8.925759 11.52576 4750.914 1.263738e-295 1.990388e-293
    ## ENSDARG00000096578 -9.433447 11.01108 4735.475 3.124790e-295 2.864517e-293

    ## To perform likelihood ratio tests:
    fit <- glmFit(y, design)
    lrt <- glmLRT(fit)
    topTags(lrt, n=3) # set n=nrow(lrt) to pull all results.

    ## Coefficient:  KD
    ##                        logFC    logCPM       LR PValue FDR
    ## ENSDARG00000027355 -18.49135  9.659996 3110.766      0   0
    ## ENSDARG00000090408 -12.12955 10.122565 6123.816      0   0
    ## ENSDARG00000074031 -11.33105  9.452467 4486.192      0   0

### 3.1.3 Alternative splicing, translation initiation and polyadenylation

    coverage <- coverageDepth(RPFs[grepl("KD1|WT", RPFs)],
                              gtf=txdb,
                              level="gene",
                              region="feature with extension")
    group1 <- c("RPF.KD1.1", "RPF.KD1.2")
    group2 <- c("RPF.WT.1", "RPF.WT.2")
    ## subset the data, for sample run only
    coverage <- lapply(coverage, function(.ele){##do not run this for real data
      .ele$coverage <- lapply(.ele$coverage, `[`, i=seq.int(50))
      .ele$granges <- .ele$granges[seq.int(50)]
      .ele
    })
    se <- spliceEvent(coverage, group1, group2)
    table(se$type)

    ##
    ## aSE
    ##  89

    plotSpliceEvent(se, se$feature[1], coverage, group1, group2)

## 3.2 RPFs and RNA-seq

### 3.2.1 By counts

#### 3.2.1.1 Count for RPFs and RNA-seq

The `countReads` function can be used to count multiple files of ribo-seq and RNA-seq data.

    path <- system.file("extdata", package="ribosomeProfilingQC")
    RPFs <- dir(path, "RPF.*?\\.[12].bam$", full.names=TRUE)
    RNAs <- dir(path, "mRNA.*?\\.[12].bam$", full.names=TRUE)
    gtf <- file.path(path, "Danio_rerio.GRCz10.91.chr1.gtf.gz")

    ## make sure that the order of the genes listed in the bam files for RPFs
    ## and RNAseq data is the same.
    cnts <- countReads(RPFs, RNAs, gtf, level="tx")
    ## To save the cnts, please try following codes by removing '#'
    # rn <- cnts$annotation$GeneID
    # write.csv(cbind(cnts$annotation,
    #                 cnts$RPFs[match(rn, rownames(cnts$RPFs)), ],
    #                 cnts$mRNA[match(rn, rownames(cnts$mRNA)), ]),
    #           "counts.csv")

#### 3.2.1.2 Translational Efficiency (TE)

The absolute level of ribosome occupancy is strongly correlated with RNA levels for both coding and noncoding transcripts. Translational efficiency is introduced8 to show the correlation. TE is the ratio of normalized ribosome footprint abundance to mRNA density. A common normalization method is using Fragments Per Kilobase of transcript per Million mapped reads (FPKM).

    fpkm <- getFPKM(cnts)
    TE <- translationalEfficiency(fpkm)

#### 3.2.1.3 Differential analysis for TE

We suppose that the log2 transformed translational efficiency that we calculated by the ratios of RPFs to mRNAs has a linear correlation with real translational efficiency. We then use the limma package to test the differential translational efficiency.

    library(limma)
    gp <- c("KD", "KD", "CTL", "CTL") ## sample groups: KD:knockdown; CTL:Control
    TE.log2 <- log2(TE$TE + 1)
    #plot(TE.log2[, 1], TE.log2[, 3],
    #     xlab=colnames(TE.log2)[1], ylab=colnames(TE.log2)[3],
    #     main="Translational Efficiency", pch=16, cex=.5)
    design <- model.matrix(~0+gp)
    colnames(design) <- sub("gp", "", colnames(design))
    fit <- lmFit(TE.log2, design)
    fit2 <- eBayes(fit)
    topTable(fit2, number=3) ## set number=nrow(fit2) to pull all results

    ##                          CTL       KD   AveExpr        F      P.Value
    ## ENSDART00000138070 23.095739 22.15413 22.624936 15429.50 6.238446e-06
    ## ENSDART00000152424  7.818822  1.00000  4.409411 14466.13 6.772779e-06
    ## ENSDART00000020327 13.376763 14.42394 13.900350 12401.91 8.241315e-06
    ##                       adj.P.Val
    ## ENSDART00000138070 0.0005155957
    ## ENSDART00000152424 0.0005155957
    ## ENSDART00000020327 0.0005155957

A different way is that we can use raw counts to test whether the change is coming from translation level but not transcription level.

    library(edgeR)
    gp <- c("KD", "KD", "CTL", "CTL")
    design <- model.matrix(~0+gp)
    colnames(design) <- sub("gp", "", colnames(design))
    y <- lapply(cnts[c("RPFs", "mRNA")], function(.cnt){
      .y <- DGEList(counts = .cnt,
                 samples = data.frame(gp=gp))
      .y <- calcNormFactors(.y)
      .y <- estimateDisp(.y, design)
      .y
    })

    ## To perform likelihood ratio tests:
    fit <- lapply(y, glmFit, design = design)
    lrt <- lapply(fit, glmLRT)
    topTags(lrt[["RPFs"]], n=3) # difference between CTL and KD for RPFs

    ## Coefficient:  KD
    ##                        logFC   logCPM        LR PValue FDR
    ## ENSDART00000160650 -18.74358 9.514299 11432.193      0   0
    ## ENSDART00000110824 -15.31215 8.284378  7025.735      0   0
    ## ENSDART00000075230 -14.92803 8.151413  4590.713      0   0

    topTags(lrt[["mRNA"]], n=3) # difference between CTL and KD for mRNA

    ## Coefficient:  KD
    ##                        logFC   logCPM       LR PValue FDR
    ## ENSDART00000004062 -15.65935 9.082734 19013.24      0   0
    ## ENSDART00000024743 -15.65935 8.442090  3387.86      0   0
    ## ENSDART00000054388 -15.65935 8.442090  3387.86      0   0

    results <- lapply(lrt, topTags, p.value = 0.05, n = nrow(lrt[[1]]))
    RPFs_unique <- setdiff(rownames(results[["RPFs"]]),
                           rownames(results[["mRNA"]]))
    mRNA_zero <- rownames(cnts[["mRNA"]])[rowSums(cnts[["mRNA"]]) == 0]
    RPFs_unique <- RPFs_unique[!RPFs_unique %in% mRNA_zero]
    head(results[["RPFs"]][RPFs_unique, ], n=3) ## top 3 events for translation level

    ## Coefficient:  KD
    ## [1] logFC  logCPM LR     PValue FDR
    ## <0 rows> (or 0-length row.names)

### 3.2.2 By coverage

#### 3.2.2.1 Maximum N-mer translational efficiency

If we plot the correlation mRNAs or RPFs levels to translational efficiency
calculated by all counts within a transcript, we will find that TE is not well normalized. It shows a higher value in lowly expressed transcripts and a low value in highly expressed transcripts.

    plotTE(TE, sample=2, xaxis="mRNA", log2=TRUE, pch=16, cex=.5)

    #plotTE(TE, sample=2, xaxis="RPFs", log2=TRUE, pch=16, cex=.5)

This issue can be fixed by calculating the maximum value (TE max) in the most highly ribosome-occupied 90 nt window within a feature8. Please note that the normalization method for TE max is not FPKM any more.

    cvgs <- coverageDepth(RPFs, RNAs, txdb)
    TE90 <- translationalEfficiency(cvgs, window = 90, normByLibSize=TRUE)
    plotTE(TE90, sample=2, xaxis="mRNA", log2=TRUE, pch=16, cex=.5)

    plotTE(TE90, sample=2, xaxis="RPFs", log2=TRUE, pch=16, cex=.5)

Above examples are TE90 for CDS region. Following codes show how to calculate TE90 for 3’UTR regions.

    cvgs.utr3 <- coverageDepth(RPFs, RNAs, txdb, region="utr3")
    TE90.utr3 <- translationalEfficiency(cvgs.utr3, window = 90)
    ## Following code will plot the TE90 for 3'UTR regions.
    ## Try it by removing the '#'.
    #plotTE(TE90.utr3, sample=2, xaxis="mRNA", log2=TRUE, pch=16, cex=.5)
    #plotTE(TE90.utr3, sample=2, xaxis="RPFs", log2=TRUE, pch=16, cex=.5)

#### 3.2.2.2 Ribosome Release Score (RRS)

RRS is calculated as the ratio of RPFs (normalized by RNA-seq reads) in the CDS to RPFs in the 3’UTR. Because it is hard to define the CDS region for non-coding RNAs, RRS of non-coding RNAs can not be calculated by Function `ribosomeReleaseScore`.

    RRS <- ribosomeReleaseScore(TE90, TE90.utr3, log2 = TRUE)
    ## Following code will compare RSS for 2 samples.
    ## Try it by removing the '#'.
    #plot(RRS[, 1], RRS[, 3],
    #     xlab="log2 transformed RRS of KD1",
    #     ylab="log2 transformed RRS of WT1")
    ## Following code will show RSS along TE90.
    ## Try it by removing the '#'.
    #plot(RRS[, 1], log2(TE90$TE[rownames(RRS), 1]),
    #     xlab="log2 transformed RSS of KD1",
    #     ylab="log2 transformed TE of KD1")

#### 3.2.2.3 Metagene analysis plot

Plot metagene coverage for CDS, 5’UTR and 3’UTR. You will notice that the coverage for the RPF data is much more enriched in the CDS as compared to the corresponding RNAseq data

    cvgs.utr5 <- coverageDepth(RPFs, RNAs, txdb, region="utr5")
    ## set sample to different number to plot metagene analysis
    ## for different samples
    #metaPlot(cvgs.utr5, cvgs, cvgs.utr3, sample=2, xaxis = "RPFs")
    metaPlot(cvgs.utr5, cvgs, cvgs.utr3, sample=2, xaxis = "mRNA")

# 4 Fragment Length Organization Similarity Score (FLOSS)1

FLOSS can be used to compare the distribution of reads length to a background such as a cluster of genes. The gene cluster could be extracted from gtf/gff files downloaded from ensembl.

    ## documentation: https://useast.ensembl.org/Help/Faq?id=468
    gtf <- import("Danio_rerio.GRCz10.91.gtf.gz")

The gtf files can be also download via `AnnotationHub`

    BiocManager::install("AnnotationHub")
    library(AnnotationHub)
    ah = AnnotationHub()
    ## for human hg38
    hg38 <- query(ah, c("Ensembl", "GRCh38", "gtf"))
    hg38 <- hg38[[length(hg38)]]
    ## for mouse mm10
    mm10 <- query(ah, c("Ensembl", "GRCm38", "gtf"))
    mm10 <- mm10[[length(mm10)]]
    ## because the gene ids in TxDb.Mmusculus.UCSC.mm10.knownGene and
    ## TxDb.Hsapiens.UCSC.hg38.knownGene
    ## are entriz_id, the gene_id in mm10 or hg38 need to changed to entriz_id.
    library(ChIPpeakAnno)
    library(org.Mm.eg.db)
    mm10$gene_id <- ChIPpeakAnno::xget(mm10$gene_id, org.Mm.egENSEMBL2EG)
    library(org.Hg.eg.db)
    hg38$gene_id <- ChIPpeakAnno::xget(hg38$gene_id, org.Mm.egENSEMBL2EG)

    gtf <- gtf[!is.na(gtf$gene_id)]
    gtf <- gtf[gtf$gene_id!=""]
    ## protein coding
    protein <-
      gtf$gene_id[gtf$transcript_biotype %in%
                      c("IG_C_gene", "IG_D_gene", "IG_J_gene", "IG_LV_gene",
                        "IG_M_gene", "IG_V_gene", "IG_Z_gene",
                        "nonsense_mediated_decay", "nontranslating_CDS",
                        "non_stop_decay",
                        "protein_coding", "TR_C_gene", "TR_D_gene", "TR_gene",
                        "TR_J_gene", "TR_V_gene")]
    ## mitochondrial genes
    mito <- gtf$gene_id[grepl("^mt\\-", gtf$gene_name) |
                            gtf$transcript_biotype %in% c("Mt_tRNA", "Mt_rRNA")]
    ## long noncoding
    lincRNA <-
      gtf$gene_id[gtf$transcript_biotype %in%
                      c("3prime_overlapping_ncrna", "lincRNA",
                        "ncrna_host", "non_coding")]
    ## short noncoding
    sncRNA <-
      gtf$gene_id[gtf$transcript_biotype %in%
                      c("miRNA", "miRNA_pseudogene", "misc_RNA",
                        "misc_RNA_pseudogene", "Mt_rRNA", "Mt_tRNA",
                        "Mt_tRNA_pseudogene", "ncRNA", "pre_miRNA",
                        "RNase_MRP_RNA", "RNase_P_RNA", "rRNA", "rRNA_pseudogene",
                        "scRNA_pseudogene", "snlRNA", "snoRNA",
                        "snRNA_pseudogene", "SRP_RNA", "tmRNA", "tRNA",
                        "tRNA_pseudogene", "ribozyme", "scaRNA", "sRNA")]
    ## pseudogene
    pseudogene <-
      gtf$gene_id[gtf$transcript_biotype %in%
                      c("disrupted_domain", "IG_C_pseudogene", "IG_J_pseudogene",
                        "IG_pseudogene", "IG_V_pseudogene", "processed_pseudogene",
                        "pseudogene", "transcribed_processed_pseudogene",
                        "transcribed_unprocessed_pseudogene",
                        "translated_processed_pseudogene",
                        "translated_unprocessed_pseudogene", "TR_J_pseudogene",
                        "TR_V_pseudogene", "unitary_pseudogene",
                        "unprocessed_pseudogene")]
    danrer10.annotations <- list(protein=unique(protein),
                                 mito=unique(mito),
                                 lincRNA=unique(lincRNA),
                                 sncRNA=unique(sncRNA),
                                 pseudogene=unique(pseudogene))

Here we load the pre-saved chr1 annotations for sample codes.

    danrer10.annotations <-
      readRDS(system.file("extdata",
                          "danrer10.annotations.rds",
                          package = "ribosomeProfilingQC"))

    library(ribosomeProfilingQC)
    library(GenomicFeatures)
    ## prepare CDS annotation
    txdb <- makeTxDbFromGFF(system.file("extdata",
                                        "Danio_rerio.GRCz10.91.chr1.gtf.gz",
                                        package="ribosomeProfilingQC"),
                            organism = "Danio rerio",
                            chrominfo = seqinfo(Drerio)["chr1"],
                            taxonomyId = 7955)
    CDS <- prepareCDS(txdb)

    library(Rsamtools)
    ## input the bamFile from the ribosomeProfilingQC package
    bamfilename <- system.file("extdata", "RPF.WT.1.bam",
                               package="ribosomeProfilingQC")
    ## For your own data, please set bamfilename as your file path.
    yieldSize <- 10000000
    bamfile <- BamFile(bamfilename, yieldSize = yieldSize)

    pc <- getPsiteCoordinates(bamfile, bestpsite = 13)
    readsLengths <- 20:34
    fl <- FLOSS(pc, ref = danrer10.annotations$protein,
                CDS = CDS, readLengths=readsLengths, level="gene", draw = FALSE)
    fl.max <- t(fl[c(1, which.max(fl$cooks.distance)), as.character(readsLengths)])
    matplot(fl.max, type = "l", x=readsLengths,
            xlab="Fragment Length", ylab="Fraction of Reads",
            col = c("gray", "red"), lwd = 2, lty = 1)
    legend("topright",  legend = c("ref", "selected gene"),
           col = c("gray", "red"), lwd = 2, lty = 1, cex=.5)

# References

1. Ingolia, N. T. et al. Ribosome profiling reveals pervasive translation outside of annotated protein-coding genes. Cell reports 8, 1365–1379 (2014).

2. Bazzini, A. A. et al. Identification of small orfs in vertebrates using ribosome footprinting and evolutionary conservation. The EMBO journal 33, 981–993 (2014).

3. Mohammad, F., Green, R. & Buskirk, A. R. A systematically-revised ribosome profiling method for bacteria reveals pauses at single-codon resolution. Elife 8, e42591 (2019).

4. Chung, B. Y. et al. The use of duplex-specific nuclease in ribosome profiling and a user-friendly software package for ribo-seq data analysis. Rna 21, 1731–1745 (2015).

5. Popa, A. et al. RiboProfiling: A bioconductor package for standard ribo-seq pipeline processing. F1000Research 5, (2016).

6. Tjeldnes, H. An atlas of the human uORFome and its regulation across tissues. (The University of Bergen, 2018).

7. Lauria, F. et al. RiboWaltz: Optimization of ribosome p-site positioning in ribosome profiling data. PLoS computational biology 14, e1006169 (2018).

8. Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. science 324, 218–223 (2009).