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).