Reference for Method: Li K., Hope C.M., Wang X.A., Wang J.-P. (2020) “RiboDiPA: A novel tool for differential pattern analysis in Ribo-seq data.” Nucleic Acid Research, 2020,48(21), gkaa1049, https://doi.org/10.1093
RiboDiPA main features
The RiboDiPA R package executes four major functions to perform differential pattern analysis of Ribo-seq data, with optional visualization of results. An overview of the process can be seen in Figure 1:
GTF file parsing and exon merging: For a given gene, all exons annotated in the GTF file are merged into a total transcript. This provides a global picture of changes across conditions for a gene, as the total transcript will capture changes in ribosome occupancy even when transcript isoform usage might change across conditions.
BAM file processing and P-site mapping: The Ribo-seq alignment files (.bam) are processed to calculate the P-site position for each RPF, with adaptable rules that users’ can specify to call P-sites from the data. The mapped P-site frequency at each nucleotide position along the total transcript is compiled for each gene of each sample.
Data binning: To overcome the inherent sparseness of Ribo-seq data, P-site positions are merged into bins using one of three methods: 1) an adaptive bin-width method that varies by gene, based on the Freedman-Diaconis rule 2) a fixed bin width method (as small as a single codon) that the user may specify, or 3) binning by exon, using boundaries specified in the GTF file.
Differential pattern analysis: Pattern analysis of genes is performed on binned data for a given gene, comparing bin by bin across conditions to identify regions with statistically significant differences. The results of this testing are output as \(p\)-values and \(q\)-values for each gene. Additionally, a supplementary statistic, the \(T\)-value, is also produced to identify genes with a larger changes across conditions among significant genes, and is calculated based on a singular value decomposition procedure. \(T\)-value is intended to account for both the magnitude and number of differential bins, thus providing a way to prioritize subsets of significant genes for further investigation.
Optionally: Visualization of Ribo-seq footprints: RiboDiPA also provides functionality for the visualization of mapped P-site frequency data for a given gene, as well as the binned data directly used for testing, with significantly different bins marked.
The RiboDiPA pipeline
The following vignette is intended to provide a walkthrough for running the RiboDiPA R package, pointing out both the main workflow and optional functionality for users. It presumes that you have successfully installed RiboDiPA package from Bioconductor.
The data provided for the purposes of the vignette are adapted from Kasari et al, and represent a high-quality dataset collected in yeast. These data compare wild type cells to cells depleted for New1, which was shown by the authors to be a regulator involved in translation termination. As is often the case for data included in vignettes, the provided files are subsets of the full data set, and are intended to illustrate the functionality of RiboDiPA. We note that a typical full-scale analysis of real data for most users will be computing intensive. The computing time depends upon the number of samples, the sequencing depth of the samples, and the complexity of the organism, in terms of number of genes and exons. For example, the total computing time of the wild type versus New1 comparison (4 samples) on a 20-core node is about 10 minutes. RiboDiPA utilizes the parallel computing functionality of R and automatically detects the number of cores available to run jobs in parallel and improve performance. While a personal computer is more than sufficient for the illustration purposes of this vignette, for optimal performance with real data, we recommend that users run RiboDiPA on a server or computing cluster.
0. Ribo-DiPA Wrapper Function
For users’ convenience, we have provided a wrapper function to permit execution of the Ribo-DiPA pipeline, which minimally requires a GTF file and BAM files, separated by experiment and replicate.
## Download sample files from GitHub
library(BiocFileCache)
file_names <- c("WT1.bam", "WT2.bam", "MUT1.bam", "MUT2.bam", "eg.gtf")
url <- "https://github.com/jipingw/RiboDiPA-data/raw/master/"
bfc <- BiocFileCache()
bam_path <- bfcrpath(bfc,paste0(url,file_names))
This will produce a list of four BAM files: WT1.bam, WT2.bam, MUT1.bam, and MUT2.bam, which represent two biological replicates each of wild type cells and New1 mutant cells, respectively. These BAM files were subset on the interval chrIV:553,166-581,762 using samtools, which is a roughly 30kb region that contains 16 genes. Alternatively, users can declare the names of their BAM files directly in a vector.
We recommend that users utilize the identical GTF file used to produce the experimental alignments. For example, a GTF file sourced from Ensembl will not work with BAM files aligned with a GTF file sourced from UCSC. The GTF file, “eg.gtf”, provided in the package is adapted from Ensembl, Saccharomyces cerevisiae release R64-1-1, and only contains features on chromosome IV. Users may also declare their GTF file directly.
## Make the class label for the experiment
classlabel <- data.frame(
condition = c("mutant", "mutant", "wildtype", "wildtype"),
comparison = c(2, 2, 1, 1)
)
rownames(classlabel) <- c("mutant1","mutant2","wildtype1","wildtype2")
The class label determines the comparison performed by RiboDiPA, and minimally requires a column named comparison
which labels the reference condition “1” and the treatment condition “2”, with the option of including conditions that should not be compared labeled with “0”. In this case “wildtype” represents the reference condition, and “mutant” represents the treatment.
## Run the RiboDiPA pipeline with default parameters
result.wrp <- RiboDiPA(bam_path[1:4], bam_path[5], classlabel, cores = 2)
The RiboDiPA()
function is a wrapper function that calls all other necessary functions in the package. The default approaches for this wrapper are to do automatic generation of P-site offsets and adaptive binning of the mapped P-sites, although all parameters available in the other functions of the package are available to be modified in the wrapper.
The argument cores
specifies the number of CPU cores to be used in the calculation. The user should replace it by the maximum number of available cores for maximum computing efficiency (or leave it unspeficied, in which case, the number of cores is set to the value of detectCores(logical = FALSE)
).
## View the table of output from RiboDiPA
head(result.wrp$gene[order(result.wrp$gene$qvalue), ])
#> tvalue pvalue qvalue
#> YDR050C 7.135543e-02 1.788608e-18 1.413000e-16
#> YDR064W 6.267031e-02 6.599787e-07 2.606916e-05
#> YDR062W 4.701957e-02 3.167373e-02 8.340748e-01
#> YDR059C 1.646677e-01 1.259123e-01 1.000000e+00
#> YDL019C 4.576056e-05 1.837478e-01 1.000000e+00
#> YDR143C 9.665726e-03 3.227685e-01 1.000000e+00
The RiboDiPA()
function outputs a list of genes with their \(T\)-value, \(p\)-value, and adjusted \(p\)-values indicated, stored in the value gene
, along with other intermediate data objects used in the calculation. In most cases, we expect that users will sort by the adjusted \(p\)-value in order to see the most significant genes genome-wide, which we show above. Genes YDR049-YDR065 are located within the interval selected for this vignette, and we can clearly see highly significant gene hits with TPI1 and RPS13 (YDR050C and YDR064W, respectively), with \(q\)-values of 1.41e-16 and 2.61e-05.
In subsequent sections we will walk through the corresponding functions in more detail.
1. P-site mapping
A P-site is the exact position on mRNA that has been translated by the ribosome, where the growing nascent chain of the polypeptide (covalently attached to tRNA) is located. In practice, RPFs that have been aligned to the genome have different lengths, therefore a procedure is required to map a given RPF to a P-site position to get a clear picture of ribosome translational activity.
The psiteMapping()
function will take the input data, and use user-specified rules to map RPFs to P-sites, or generate those rules automatically using the procedure described in Lauria et al (2018). Additionally, if there are multiple transcript isoforms in a sample that utilize the same exon in the genome, in can be difficult (or impossible) to assign a given RPF to a particular transcript in a Ribo-seq experiment. RiboDiPA circumvents this problem by combining all exons into a “total transcript” and performs testing at the gene level. Therefore, in addition to the P-site offset generation and mapping, the psiteMapping()
function also generates total transcript coordinates for each exon.
## Perform individual P-site mapping procedure
data.psite <- psiteMapping(bam_file_list = bam_path[1:4],
gtf_file = bam_path[5], psite.mapping = "auto", cores = 2)
P-site mapping outputs a list of values: coverage
, the coverage across each gene; counts
, the number of P-sites counts per gene; exons
, the total transcript coordinates and genomic coordinates for each exon in the genome; and psite.mapping
, the P-site mapping offsets. For the coverage
object, rows correspond to replicates and columns correspond to nucleotide positions with respect to the total transcript. Similarly, for the counts
object, rows represent genes and columns represent samples. Now, let us examine the offsets generated automatically by the function:
## P-site mapping offset rule generated
data.psite$psite.mapping
The read length of the RPF is listed (qwidth
), followed by the nucleotide offset from the 5’ end (psite
). For instance, reads of length 28 have an offset of 12, meaning that the P-site will be mapped 12 nucleotides from the 5’ end of the read.
As mentioned above, the optimal P-site offsets from the 5’ end of reads is calibrated using a two-step algorithm on start codons genome-wide, closely following the procedure of Lauria et al (2018). First, for a given read length, the offset is calculated by taking the distance between the first nucleotide of the start codon and the 5’ most nucleotide of the read, and then defining the offset as the 5’ position with the most reads mapped to it. This process is repeated for all read lengths and then the temporary global offset is defined to be the offset of the read length with the maximum count. Lastly, for each read length, the adjusted offset is defined to be the one corresponding to the local maximum found in the profiles of the start codons closest to the temporary global offset.
In case of insufficient reads mapped to the start codons, we recommend users to use the center
option to take the center of the read as the P-site, or to provide their own offset rules by simply using a matrix with two columns, labeled qwidth
and psite
, passed into the psite.mapping
parameter of the psiteMapping()
function. We note that specifying fixed rules for the P-site offsets might be especially useful when comparing across different experiments collected in the same organism, to insure consistency in the comparison.
## Use user specified psite mapping offset rule
offsets <- cbind(qwidth = c(28, 29, 30, 31, 32),
psite = c(18, 18, 18, 19, 19))
data.psite2 <- psiteMapping(bam_path[1:4], bam_path[5],
psite.mapping = offsets, cores = 2)
Lastly, the psiteMapping()
function uses the parallel computing package doParallel to speed up the process of mapping P-sites. To utilize this feature, specify the number of cores available for the job using the cores
parameter. If cores
is not specified, this function will automatically detect the number of cores on your computer to run jobs in parallel.
2. Data binning
Once reads have been mapped to P-sites in the various experiments, the next step is to bin mapped P-sites together to permit statistical testing. The smallest bin one could imagine is a single-codon (three nucleotides) which would provide the highest resolution of binning, but entails some practical problems. For instance, very long genes will have more codons, therefore after correction for multiple hypothesis testing, only the most pronounced perturbations would show statistical significance at large genes. Alternatively, the largest bin imaginable is to use an entire gene as one bin, although positional information across the gene will be lost. Therefore, a robust method to choose the right bin size per gene to permit discovery is needed, which is the essence of the RiboDiPA adaptive binning method.
The adaptive method uses a procedure based on the Freedman-Diaconisis rule to pick an optimal number of bins of equal width for each gene, where different genes will have different bin widths, but the positions and number of bins for a gene will be the same across replicates and conditions to permit testing.
## Merge the P-site data into bins with a fixed or an adaptive width
data.binned <- dataBinning(data = data.psite$coverage, bin.width = 0,
zero.omit = FALSE, bin.from.5UTR = TRUE, cores = 2)
The function dataBinning()
returns a list of binned P-site footprint matrices. In each matrix, rows correspond to replicates, and columns correspond to bins. If the parameter bin.width
is not specified or set to zero, this indicates that the function will run in the adaptive binning mode (as opposed to fixed-width mode, see below). In general, we recommend to use adaptive binning, due to the fact that most Ribo-seq experiments are sparse and have few numbers of reads, on a per codon basis.
If the zero.omit
argument is set to TRUE
, bins with all zeros across all replicates are removed from the differential pattern analysis. When the length of total transcript is not an integer multiple of the bin width, binning will start from the 5’ end if bin.from.5UTR
argument is TRUE
, or from the 3’ end otherwise. In general, bin width is equal for every bin across the total transcript, except for the last two bins, which are adjusted to prevent the last bin from being very small in the case where the bin width does not divide the total transcript length evenly.
## Merge the P-site data on each codon
data.codon <- dataBinning(data = data.psite$coverage, bin.width = 1,
zero.omit = FALSE, bin.from.5UTR = TRUE, cores = 2)
In cases where coverage permits, users can also specify a fixed width of bin, all the way down to 1, which represents single-codon resolution. This can be useful for examining details at regions that are very likely to have changes in translational regulation, namely near the start and stop codons. For instance, we examined 50 codons upstream and downstream of the stop and start codons respectively to identify a patterns of stacked ribosomes near the stop codon in the case of New1 deletion (see Li et al, 2020).
## Merge the P-site data on each exon and perform differential pattern analysis
result.exon <- diffPatternTestExon(psitemap = data.psite,
classlabel = classlabel, method = c('gtxr', 'qvalue'))
In cases where users would prefer to use exons as the bins for statistical testing, we have provided a function called diffPatternTestExon()
. This function rolls data binning and differential pattern testing into one function and outputs the same structure qw diffPatternTest()
function. For organisms like yeast where alternative splicing is minimal, this option may not be very useful, but for higher organisms with many exons and much more alternative splicing, it may provide useful insight.
3. Differential pattern analysis
Once appropriate bins have been generated, the RiboDiPA package performs differential pattern testing on P-site counts bin by bin for each gene. Briefly, counts are modeled by a negative binomial distribution to call bins with statistically significant differences across conditions, and then the smallest p-value for a given gene is adjusted to control for multiple hypothesis testing across all genes.
## Perform differential pattern analysis
result.pst <- diffPatternTest(data = data.binned,
classlabel = classlabel, method=c('gtxr', 'qvalue'))
The diffPatternTest()
function takes the output from data binning as input, and also requires a class label object, describing the comparison to be made. The class label object is simply a data frame with two columns, condition
and comparison
, where condition
labels the conditions tested, and comparison
labels the experimental conditions numerically, where “1” indicates the control condition, “2” indicates the treatment condition, and “0” indicates replicates that should not be compared, if present.
The output of this function is a list that contains a data frame object gene
as well as other objects that store intermediate calculations. gene
contains gene-level \(T\)-value, \(p\)-value, and \(q\)-value (if \(q\)-value is specified as the metric for multiple comparison error correction) of all genes. The bin
object contains bin-level test \(p\)-value and corresponding adjusted \(p\)-value for each bin of each gene.
\(T\)-value, bin-level \(p\)-value, and bin-level adjusted \(p\)-value and gene-level adjusted \(p\)-value and \(q\)-value (in this case measured by the qvalue) of all genes. The gene-level \(p\)-value and \(q\)-value are the main result of the testing, and therefore the main output of the package.
Additionally, the \(T\)-value is a supplementary statistic that quantifies the magnitude of difference between conditions, with larger numbers indicating a greater difference. The \(T\)-value is defined to be 1-cosine of the angle between the first right singular vectors of the footprint matrices of the two conditions under comparison. It ranges from 0-1, with larger values representing larger differences between conditions, and practically speaking, can be used to identify genes with larger magnitude of pattern difference beyond statistical significance. This might be helpful to investigators to prioritize certain genes for investigation among many that may pass the significance test for differential pattern.
Optionally, users may specify which method to use for correction of type I error for multiple hypothesis testing. The \(q\)-value method from qvalue
package is the default method of FDR control at the gene-level, and the hybrid Hochberg-Hommel method gtxr
from elitism
pacakge is the default method of multiplicity correction at bin-level. Other options defined by the package elitism
is invoked by the option to the parameter method.
4. Plotting and genome visualization
RiboDiPA implemented two plot functions for visualizing the footprint data and test results including :1) individual gene plotting in the landscape of total transcript; and 2) track plotting through genome browser using R package igvR
.
Individual gene plotting
The individual gene plotting is implemented with the package ggplot2
. Two plotting functions, plotTrack()
and plotTest()
, are provided, with the former for mapped P-site plotting and the latter for binned data that are generated from the mapped P-sites.
The plotTrack()
function visualizes reads mapped to P-site positions on a per gene basis. The input argument data
is the output object of psiteMapping()
or the psiteMapping()
output object from the wrapper RiboDiPA()
function (i.e., result.wrp$data.psite
from the example codes above). The counts of RPFs mapped to P-sites is shown on the y-axis, while the total transcript in nucleotides is shown on the x-axis.
## Plot ribosome per nucleotide tracks of specified genes.
plotTrack(data = data.psite, genes.list = c("YDR050C", "YDR064W"),
replicates = NULL, exons = FALSE)
![](data:image/png;base64,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)
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)
plotTrack()
always shows the total transcript with the 5’ end on the left and the 3’ end on the right with the corresponding genomic coordinates of the start codon and stop codon labelled. User can specify one or more genes to be plotted at a time. If the exons argument is set to TRUE
, RPFs per exon of the specified genes are also ouput.
## Plot binned ribosome tracks of siginificant genes: YDR086C and YDR210W.
## you can specify the thrshold to redefine the significant level
plotTest(result = result.pst, genes.list = NULL, threshold = 0.05)
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)
The plotTest()
plots the binned RPF footprint used in the differential pattern testing. The input argument result
for plotTest()
is the entire output object of diffPatternTest()
or diffPatternTestExon()
or RiboDiPA()
function. For replicates marked as “1” in classlabel
(see diffPatternTest()
function), the tracks are colored blue and replicates marked as “2” are colored red. Differential bins are colored black, with bin-level adjusted \(p\)-value annotated underneath the the track of the last replicate. If genes.list
is not specified, all genes with significant differential pattern will be output.
Lastly, the threshold parameter allows users to specify a threshold of signifance level for seleciton of significant genes. A threshold value of 0.05 will only plot genes with \(q\)-value less than or equal to 0.05.
Track plotting via genome browser
Three functions, bpTrack()
, binTrack()
, and exonTrack()
, are provided to support the track plotting through genome browser by utilizing igvR
. The uses can examine the ribosome footprint in the genomic landscape and the differential pattern test results. All three functions output GRanges
objects as input of igvR
for track visualization, respectively, RPF in base pair, binned RPF from diffPatternTest()
with differential pattern test results, and RPF by exons with test results.
To visualize these tracks in genome browser, users should install igvR
through Bioconductor. Some simple illustration examples are given below.
##base-bair RPF track
library(igvR)
thred <- 0.05
igv <- igvR()
setBrowserWindowTitle(igv, "ribosome footprint track example")
setGenome(igv, "saccer3")
data(data.psite)
names.rep <- c("mutant1", "mutant2", "wildtype1", "wildtype2")
tracks.bp <- bpTrack(data = data.psite, names.rep = names.rep,
genes.list = c("YDR050C", "YDR062W", "YDR064W"))
for(track.name in names.rep){
track.rep <- tracks.bp[[track.name]]
track <- GRangesQuantitativeTrack(trackName = paste(track.name, "bp"),
track.rep[,1], color = "green")
displayTrack(igv, track)
}}
## bin track and test results
data(result.pst)
data(data.psite)
tracks.bin <- binTrack(data = result.pst, exon.anno = data.psite$exons)
for(track.name in names(tracks.bin)){
track.rep <- tracks.bin[[track.name]]
resize(track.rep, width(track.rep) + 1)
track <- GRangesQuantitativeTrack(trackName = paste(track.name, "binned"),
track.rep[,1], color = "black")
displayTrack(igv, track)
}
track.rep2 <- tracks.bin[[1]]
sig.bin <- (values(track.rep2)[,5] <= thred)
log10.padj <- - log10(values(track.rep2)[,5])
mcols(track.rep2) <- data.frame(log10padj = log10.padj)
track.rep2 <- track.rep2[which(sig.bin),]
track <- GRangesQuantitativeTrack(trackName = "- log 10 of padj",
track.rep2, color = "red", trackHeight = 40)
displayTrack(igv, track)
The first input argument of bpTrack()
, data
, is the output object of psiteMapping()
or RiboDiPA()
function. If the replicate names names.rep
is not specified, column names of data$counts
will be used as track label on igvR
visualization. Also, if a list of genes for visualization is not provided, then all genes listed in data$coverage
will be plotted.
The function binTrack()
uses the output object of diffPatterbTest()
or RiboDiPA()
function for the argument data
, and the value exons
of psiteMapping()
function output for the argument exon.anno
. Besides of ribosome binned tracks, differential pattern test results is also reported in the value of binTrack()
. In Figure 2, a both base-pair RPF track and binned track are shown through igvR
. The green bars are the ribosome tracks per bp, the black bars are the binned tracks, while red bars are plotted at significant bins (i.e., adjusted bin-level p-value \(\leq 0.05\)), with \(-\log_{10}\) of adjusted p-value also plotted.
## bin track and test results
igv2 <- igvR()
setBrowserWindowTitle(igv2, "ribosome footprint per exon example")
setGenome(igv2, "saccer3")
data(result.exon)
tracks.exon <- exonTrack(data = result.exon, gene = "tY(GUA)D")
for(track.name in names(tracks.exon)){
track.rep <- tracks.exon[[track.name]]
for(tx.name in names(track.rep)){
track.tx <- tracks.exon[[track.name]][[tx.name]]
track <- GRangesQuantitativeTrack(trackName =
paste(track.name, tx.name), track.tx[,1], color = track.name)
displayTrack(igv2, track)
}
}
For higher organisms, where exons are used as the bins for statistical testing through the function diffPatternTestExon()
, exonTrack()
is the proper function to output tracks for visualization purpose. It outputs a list of lists. Each element is a list of GRanges
objects representing replicates, and each second level list element is exon-level P-site footprint counts in a transcript.
The argument data
uses the output object of diffPatternTestExon()
. The second argument gene
requires a single gene name to be plotted, since different genes may have different number of transcripts.
Conclusion
This concludes our vignette. For additional information, please consult the reference manual for each individual function, as well as the associated paper for this package for methodological details (Li et al, 2020).