A Walk-through of RiboR
Michael Geng, Hakan Ozadam, Can Cenik
Introduction
Ribosome profiling is a sequencing based method to study protein synthesis transcriptome-wide. Actively translating mRNAs are engaged with ribosomes and protein synthesis rates can be approximated by the number of ribosomes that are translating a given mRNA. Ribosome profiling employs an RNase digestion step to recover fragments of RNA protected by ribosomes which are called Ribosome Protected Footprints (RPFs).
Ribosome profiling data analyses involve several quantifications for each transcript. Specifically, the lengths of the RPFs provide valuable biological information (see, for example, Lareau et al. and Wu et al.). To facilitate ribosome profiling data analyses as a function of RPF length in a highly efficient manner, we implemented a new data format, called ribo. Files in ribo format are called .ribo files. The associated paper for the .ribo file and its larger ecosystem can be found here.
RiboR package is an R interface for .ribo files. The package offers a suite of reading functions for .ribo files, and provides plotting functions that are most often employed in ribosome profiling analyses. Using RiboR, one can import .ribo files into the R environment, read ribosome profiling data into data frames and generate essential plots in a few lines of R code.
This document is structured into several sections. First, we give an overview of the Ribo File format and define transcript regions. Second, we provide instructions and requirements for the installation of RiboR. Third, we describe how to import a .ribo file to R environment and demonstrate essential ribosome profiling data analyses in three sections:
In the following two sections, we describe some advanced features including renaming transcripts using aliases, accessing .ribo file attributes, and getting region boundaries that define transcript regions.
In the last section, we explain the three optional types of data, which may exist in a .ribo file.
- Metadata: A .ribo file may contain metadata for each experiment and for the .ribo file itself.
- Coverage: A .ribo file can keep the nucleotide level transcriptome coverage.
- RNA-Seq: A ribosome profiling experiment can be paired with an RNA-Seq experiment to study ribosome occupancy together with transcript abundance.
In the final section, we outline a simple analysis to study translational efficiency.
.ribo File Format
.ribo files are built on an HDF5 architecture and has a predefined internal structure (Figure 1). For a more detailed explanation of the ribo format, we refer to readthedocs page of ribo.
Figure 1: .ribo File Internal Structure
While many features are required in .ribo files, quantification from paired RNA-Seq data and nucleotide-level coverage are optional.
Transcript Regions
The main protein coding region of a transcript is called the coding sequence (CDS). Its boundaries are called transcription start / stop sites. The region consisting of the nucleotides, between the 5’ end of the transcript and the start site, not translated to protein, is called 5’ untranslated region (5’UTR). Similarly, the region having the nucleotides between the stop site and the 3’ end of the transcript, is called 3’ untranslated region (3’UTR). To avoid strings and variable names starting with a number and containing an apostrophe character, we use the names UTR5 and UTR3 instead of 5’UTR and 3’UTR respectively.
Installation
Availability
The source code of RiboR package is available in a public Github repository.
Bioconductor
RiboR is a Bioconductor package. The following R code installs RiboR via Bioconductor.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ribor")Installation From Source Code
RiboR can also be installed directly using the source code. For this, the following are required.
R: RiboR requires version 3.6 or later. Previous R versions are known to cause installation and run time problems.
Devtools: Installing the package requires
devtools, a CRAN package that provides a wide suite of development tools in the R environment.
To install the package, run the following lines of code.
install.packages("devtools")
library("devtools")
install_github("ribosomeprofiling/ribor")For Linux distributions, there are some dependencies, required by devtools. Installation of these dependencies is necessary to install devtools. For Ubuntu-based distributions, the following command installs these dependencies.
sudo apt-get install libxml2-dev libcurl4-openssl-dev libssl-dev -yGetting Started
We start with loading the RiboR package.
library(ribor)Generating a ribo object
To interact with a .ribo file, it is necessary to create a ribo object that provides a direct handle to the file and displays its various attributes. As an example, we processed publicly available ribosome profiling data (GEO accession number GSE65778) from HEK293 cells using RiboFlow pipeline to generate the .ribo file in this document. More precisely, we picked two ribosome profiling experiments coming from untreated HEK293 cells (accession numbers: GSM1606107 and GSM1606108) and two RNA-Seq experiments coming from the same line of untreated cells (accession numbers: GSM1606099 and GSM1606100). Each ribo experiment can be paired with a single RNA-Seq experiment. So we arbitrarily paired the ribosome profiling experiment GSM1606107 with RNA-Seq experiment GSM1606099 and GSM1606108 with GSM1606100, when making the example .ribo file in this document.
#file path to the example ribo file
file.path <- system.file("extdata", "HEK293_ingolia.ribo", package = "ribor")
#generates the 'ribo' class object
original.ribo <- Ribo(file.path, rename = rename_default )Do not modify the values of the returned ribo object. Certain values in the ribo object’s list are used by later functions, so manually changing these values can alter the correctness of later function calls.
Once the ribo object is created, we can inquire about the contents of the .ribo file.
original.ribo
#> General File Information:
#> info
#> format version 1
#> reference appris_human_24_01_2019
#> min read length 28
#> max read length 32
#> left span 35
#> right span 15
#> transcript count 19822
#> has.metadata TRUE
#> metagene radius 35
#> has.alias TRUE
#>
#> Dataset Information:
#> experiment total.reads coverage rna.seq metadata
#> GSM1606107 164536 TRUE TRUE TRUE
#> GSM1606108 215678 TRUE TRUE TRUEThe above output provides information about the individual experiments that are contained in the given ribo object. In addition, this output displays some of the parameters, that were used in generating the .ribo file, such as left span, right span and metagene radius.
For a detailed explanation of the contents of this output, we refer to the online documentation of the ribo format.
In what follows, we demonstrate a typical exploration of ribosome profiling data. We start with the length distribution of RPFs.
Length Distribution
Several experimental decisions including the choice of RNase can have a significant impact on the RPF length distribution. In addition, this information is generally informative about the quality of the ribosome profiling data.
We use the function plot_length_distribution to generate the distribution of the reads mapping to a specific region. This function has also a boolean argument called fraction. When fraction is FALSE, the y-axis displays the total number of reads mapping to the specified region. When fraction is TRUE, the y-axis displays the quotient of the same number as above divided by the total number of reads reported in the experiment.
The following code plots the coding region mapping RPF length distribution in the range of 28 to 32 nucleotides.
plot_length_distribution(x = original.ribo,
region = "CDS",
range.lower = 28,
range.upper = 32,
fraction = TRUE)
We can also plot the absolute number of reads instead of the fraction of total reads by changing the argument fraction = FALSE.
plot_length_distribution(x = original.ribo,
region = "CDS",
range.lower = 28,
range.upper = 32,
fraction = FALSE)
We can extract the numerical values used to produce the above plot using the function get_length_distribution as follows. The parameters of this function will be explained in more detail later in this document.
rc <- get_length_distribution(ribo.object = original.ribo,
region = "CDS",
range.lower = 28,
range.upper = 32)
rc
#> DataFrame with 10 rows and 3 columns
#> experiment length count
#> <Rle> <Rle> <integer>
#> 1 GSM1606107 28 24669
#> 2 GSM1606107 29 28585
#> 3 GSM1606107 30 31893
#> 4 GSM1606107 31 33347
#> 5 GSM1606107 32 28225
#> 6 GSM1606108 28 26487
#> 7 GSM1606108 29 39570
#> 8 GSM1606108 30 48489
#> 9 GSM1606108 31 45940
#> 10 GSM1606108 32 31087It is also possible to generate the above plot from the DataFrame returned by get_region_counts. This can be useful if the user wants to further process the data prior to plotting.
# further DataFrame manipulation and filtering can ensue here
# before calling the plot function
plot_length_distribution(rc, fraction = TRUE)Metagene Analysis
A common quality control step in ribosome profiling analyses is the inspection of the pileup of sequencing reads with respect to the start and stop site of annotated coding regions. Given that ribosomes are predominantly translating annotated coding regions, these plots are informative about the enrichment at the boundaries of coding regions and also provide information regarding the periodicity of aligned sequencing reads. This type of plot is called a metagene plot as the reads are aggregated around translation start and stop sites across all transcripts.
The parameter “metagene radius” is the number of nucleotides surrounding the start/stop site and hence defines the region of analysis. For each position, read counts are aggregated across transcripts. This cumulative read coverage (y-axis) is plotted as a function of the position relative to the start/stop site (x-axis).
We can identify the metagene radius of .ribo file using the get_info function.
get_info(original.ribo)$attributes$metagene_radius
#> [1] 35We can plot the ribosome occupancy around the start or stop sites using plot_metagene. The following code produces the metagene plot at the start site for the experiment GSM1606107. The values on the y-axis are the raw read counts.
plot_metagene(original.ribo,
site = "start",
experiment = c("GSM1606107"),
range.lower = 28,
range.upper = 32)
If we don’t provide a specific value for the the experiments parameter, all available experiments will be plotted. To better compare these experiments, we can normalize the coverage by setting normalize = TRUE. In the following example, in addition to these parameters, we set site = "stop" to see the stop site coverage.
plot_metagene(original.ribo,
site = "stop",
normalize = TRUE,
title = "Stop Site Coverage",
range.lower = 28,
range.upper = 32)
There are two functions that allow the user to obtain metagene coverage data.
get_metagene- This function returns a DataFrame of metagene coverage.
get_tidy_metagene- This function returns a tidy DataFrame of metagene coverage. It is a wrapper of the
get_metagenefunction, and in order to keep manageable DataFrame dimensions, it will always sum across the transcripts.
- This function returns a tidy DataFrame of metagene coverage. It is a wrapper of the
When using length = TRUE and transcript = TRUE, the user will obtain the most concise data corresponding to the sum of reads surrounding the start / stop site across all read lengths and transcripts. As a result, one row of values will be reported for each experiment.
#get the start site across read lengths 28 to 32
meta.start <- get_metagene(ribo.object = original.ribo,
site = "start",
range.lower = 28,
range.upper = 32,
length = TRUE,
transcript = TRUE)
#only print first ten columns
print(meta.start[ , 1:10])
#> DataFrame with 2 rows and 10 columns
#> experiment -35 -34 -33 -32 -31 -30
#> <Rle> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1 GSM1606107 70 65 67 75 69 59
#> 2 GSM1606108 102 97 87 82 84 71
#> -29 -28 -27
#> <numeric> <numeric> <numeric>
#> 1 62 54 58
#> 2 84 85 74To obtain the tidy version of the above DataFrame,
tidy.meta.start <- get_tidy_metagene(ribo.object = original.ribo,
site = "start",
range.lower = 28,
range.upper = 32,
length = TRUE)
head(tidy.meta.start, 2)
#> DataFrame with 2 rows and 3 columns
#> experiment position count
#> <Rle> <Rle> <numeric>
#> 1 GSM1606107 -35 70
#> 2 GSM1606108 -35 102To maintain the counts at each individual read length summed across transcripts, use length = FALSE and transcript = TRUE. If the metagene data of a single read length, say 30, is needed, set range.lower and range.upper to 30. By default, data retrieval functions return data from all available experiments. To obtain data from a particular subset of experiments, provide a list of experiment names in the experiments parameter.
meta.start <- get_metagene(ribo.object = original.ribo,
site = "start",
range.lower = 30,
range.upper = 30,
experiment = c("GSM1606108"),
length = FALSE,
transcript = TRUE)
print(meta.start[, 1:10])
#> DataFrame with 1 row and 10 columns
#> experiment length -35 -34 -33 -32 -31 -30
#> <Rle> <Rle> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1 GSM1606108 30 35 24 26 26 24 23
#> -29 -28
#> <numeric> <numeric>
#> 1 15 14If we wish to preserve the read counts for individual transcript sum across a range of read lengths, we set transcript = FALSE and length = TRUE. While it is possible to set transcript = FALSE and length = FALSE, run times might be slower for this option, and running the function with these options requires a substantial amount of memory.
meta.start <- get_metagene(ribo.object = original.ribo,
site = "start",
range.lower = 28,
range.upper = 32,
length = TRUE,
transcript = FALSE,
alias = TRUE)
print(meta.start[1:2, 1:10])
#> DataFrame with 2 rows and 10 columns
#> experiment transcript -35 -34 -33 -32 -31
#> <Rle> <factor> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1 GSM1606107 OR4F5-201 0 0 0 0 0
#> 2 GSM1606107 OR4F29-201 0 0 0 0 0
#> -30 -29 -28
#> <numeric> <numeric> <numeric>
#> 1 0 0 0
#> 2 0 0 0As we discussed above for read length analysis, we also provide the option to generate metagene plots using the DataFrames returned by get_metagene.
plot_metagene(tidy.meta.start, normalize = TRUE)
A Note on Aggregating the Output
In the above function calls, we used two parameters length and transcript, which are used, in general, by reading and plotting functions in RiboR. These parameters determine how data is aggregated.
More precisely:
- length:
- If
TRUE, the counts will be summed up across the given read length interval. Otherwise, if length isFALSE, the counts at each individual read length will be included separately in the output.
- If
- transcript:
- If
TRUE, the counts will be summed up across all the transcripts. Otherwise, if transcript isFALSE, the counts at each individual transcript will be reported separately.
- If
Region Counts
Another important aspect of ribosome profiling data is the number of reads mapping to the different regions of the transcripts, namely, 5’UTR, CDS and 3’UTR. A large number of reads mapping to UTR5 or UTR3 regions might indicate a poor quality ribosome profiling data since ribosomes occupy CDS. Furthermore, the distribution of reads across these regions can be associated with the RNase choice in the experiment. For example in Miettinen and Bjorklund, it was shown that ribosome profiling experiments are dependent on digestion conditions.
Before going into the R functions, we briefly explain how region counts are computed, introduce our naming convention and define the regions used in ribo format.
For each read mapped to the transcriptome, we take the first nucleotide on the 5’ end of the read and determine the corresponding region. After doing this for all reads, the accumulated values give us the region counts.
As mentioned earlier, a messenger RNA transcript is partitioned into three regions: 5’UTR, CDS and 3’UTR. For technical reasons, we rename 5’UTR as UTR5 and 3’UTR as UTR3.
It is well-known that ribosomes pause, or move slower, around start and stop sites. As a result, we observe peaks around start and stop sites in metagene plots. This behavior of ribosome makes it harder to perform certain analyses such as coverage, translation efficiency, periodicity and uniformity analysis with accuracy. To tackle this problem, we introduce two additional regions called UTR5 junction and UTR3 junction, and modify the definition of the regions UTR5, CDS and UTR3 as shown in Figure 2. This way, we keep regions around start and stop sites separate when doing such analyses.
Figure 2: Extended Region Definitions
More precisely, first, we fix two integers: left span (l) and right span (r) and define the junction regions as follows.
UTR5 junction: This region consists of l nucleotides to the left of the start site , and r nucleotides to the right of the start site.
UTR3 junction: This region consists of l nucleotides to the left of the stop site , and r nucleotides to the right of the stop site.
Using these junction regions, we re-define the conventional regions as follows.
UTR5: This region is the set of nucleotides between the 5’ end of the transcript and the UTR5 junction.
CDS: This region is the set of nucleotides between the UTR5 junction and UTR3 junction.
UTR3: This region is the set of nucleotides between the UTR3 junction and the 3’ end of the transcript.
Similar to the get_metagene function, the get_region_counts function has the transcript and length parameters. As previously mentioned, transcript specifies whether or not to sum across the transcripts, and length specifies whether or not to sum across the read lengths.
The following code will plot the number of sequencing reads whose 5’ ends map to the UTR5, CDS, and UTR3 as a stacked bar plot. To facilitate comparison between experiments, the percentage of the regions counts are plotted and the percentage of reads mapping to CDS are printed on the plot.
plot_region_counts(x = original.ribo,
range.lower = 28,
range.upper = 32)
To get the region counts summed across both lengths and transcripts, set length = TRUE and transcript = TRUE.
rc <- get_region_counts(original.ribo,
range.lower = 28,
range.upper = 32,
length = TRUE,
transcript = TRUE,
region = c("CDS"))
rc
#> DataFrame with 2 rows and 3 columns
#> experiment region count
#> <Rle> <Rle> <numeric>
#> 1 GSM1606107 CDS 146719
#> 2 GSM1606108 CDS 191573When presented with the option of preserving the region counts at each individual read length, it may be preferable to present the transcript names as their shortened aliases.
To get the data only summed across the read lengths, set length = TRUE and transcript = FALSE. Note that the alias = TRUE in this case, and instead of original.ribo, we are using alias.ribo.
rc <- get_region_counts(original.ribo,
range.lower = 28,
range.upper = 32,
length = TRUE,
transcript = FALSE,
alias = TRUE,
region = c("CDS"))
rc
#> DataFrame with 39644 rows and 4 columns
#> experiment transcript region count
#> <Rle> <factor> <Rle> <integer>
#> 1 GSM1606107 OR4F5-201 CDS 0
#> 2 GSM1606107 OR4F29-201 CDS 0
#> 3 GSM1606107 OR4F16-201 CDS 0
#> 4 GSM1606107 SAMD11-202 CDS 8
#> 5 GSM1606107 NOC2L-201 CDS 88
#> ... ... ... ... ...
#> 39640 GSM1606108 MT-CO2-201 CDS 28
#> 39641 GSM1606108 MT-ATP8-201 CDS 4
#> 39642 GSM1606108 MT-ATP6-201 CDS 35
#> 39643 GSM1606108 MT-ND4L-201 CDS 2
#> 39644 GSM1606108 MT-ND5-201 CDS 30To get the data in its full form, preserving the information each individual read length and transcript, set length = FALSE and transcript = FALSE.
rc <- get_region_counts(original.ribo,
range.lower = 28,
range.upper = 32,
length = FALSE,
transcript = FALSE,
alias = TRUE,
region = c("CDS"))
rc
#> DataFrame with 198220 rows and 5 columns
#> experiment transcript length region count
#> <Rle> <factor> <Rle> <Rle> <integer>
#> 1 GSM1606107 OR4F5-201 28 CDS 0
#> 2 GSM1606107 OR4F29-201 28 CDS 0
#> 3 GSM1606107 OR4F16-201 28 CDS 0
#> 4 GSM1606107 SAMD11-202 28 CDS 0
#> 5 GSM1606107 NOC2L-201 28 CDS 22
#> ... ... ... ... ... ...
#> 198216 GSM1606108 MT-CO2-201 32 CDS 13
#> 198217 GSM1606108 MT-ATP8-201 32 CDS 2
#> 198218 GSM1606108 MT-ATP6-201 32 CDS 10
#> 198219 GSM1606108 MT-ND4L-201 32 CDS 1
#> 198220 GSM1606108 MT-ND5-201 32 CDS 8By default, the DataFrame output is in a tidy form. If non-tidy data set is desired, then set tidy = FALSE.
rc <- get_region_counts(original.ribo,
range.lower = 28,
range.upper = 32,
tidy = FALSE)
rc
#> DataFrame with 2 rows and 6 columns
#> experiment UTR5 UTR5J CDS UTR3J UTR3
#> <Rle> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1 GSM1606107 7487 6244 146719 3280 806
#> 2 GSM1606108 10413 8732 191573 4052 908Also, by default, the counts are not normalized, but there is an option to normalize the region counts. Set normalize = TRUE to instead obtain the normalized counts per million reads.
rc <- get_region_counts(original.ribo,
range.lower = 28,
range.upper = 32,
normalize = TRUE,
region = c("CDS"))
rc
#> DataFrame with 2 rows and 3 columns
#> experiment region count
#> <Rle> <Rle> <numeric>
#> 1 GSM1606107 CDS 891713.667525648
#> 2 GSM1606108 CDS 888236.166878402Similar to the case of metagene and length distribution, the bar plot above can be generated directly from a DataFrame.
rc.info <- get_region_counts(ribo.object = original.ribo,
region = c("UTR5", "CDS", "UTR3"),
range.lower = 28,
range.upper = 32)
# further data frame manipulation and filtering can ensue here
# before calling the plot function
plot_region_counts(rc.info)
A Note on the Memory Footprint
In the above function calls, the compact parameter is set to TRUE by default, and this parameter is available for all of the offered reading functions. When set to TRUE, it applies Rle (run-length encoding) and factor to several of the columns (notably the columns with repeated values such as experiment, length, and region), and the purpose of this is to reduce the memory footprint of the return values and better accomodate any range of devices using the package.
# default return value with compact = TRUE
compact_rc <- get_region_counts(ribo.object = original.ribo,
range.lower = 28,
range.upper = 32,
length = TRUE,
transcript = FALSE,
compact = TRUE,
alias = TRUE)
head(compact_rc)
#> DataFrame with 6 rows and 4 columns
#> experiment transcript region count
#> <Rle> <factor> <Rle> <numeric>
#> 1 GSM1606107 OR4F5-201 UTR5 0
#> 2 GSM1606107 OR4F29-201 UTR5 0
#> 3 GSM1606107 OR4F16-201 UTR5 0
#> 4 GSM1606107 SAMD11-202 UTR5 0
#> 5 GSM1606107 NOC2L-201 UTR5 1
#> 6 GSM1606107 KLHL17-201 UTR5 0
class(compact_rc)
#> [1] "DataFrame"
#> attr(,"package")
#> [1] "S4Vectors"
object.size(compact_rc)
#> 3808904 bytes
# return value with compact = FALSE
# Note that in this example, it takes up twice as much memory
noncompact_rc <- get_region_counts(ribo.object = original.ribo,
range.lower = 28,
range.upper = 32,
length = TRUE,
transcript = FALSE,
compact = FALSE,
alias = TRUE)
head(noncompact_rc)
#> experiment transcript region count
#> 1 GSM1606107 OR4F5-201 UTR5 0
#> 2 GSM1606107 OR4F29-201 UTR5 0
#> 3 GSM1606107 OR4F16-201 UTR5 0
#> 4 GSM1606107 SAMD11-202 UTR5 0
#> 5 GSM1606107 NOC2L-201 UTR5 1
#> 6 GSM1606107 KLHL17-201 UTR5 0
class(noncompact_rc)
#> [1] "data.frame"
object.size(noncompact_rc)
#> 7608984 bytesIf compact is FALSE, the called function will return a data.frame instead of a DataFrame, and it will strip the Rle and factor that is applied to several of columns, resulting in a notably larger memory footprint. However, this makes it easier to manipulate and filter the data frame for any downstream analyses, and the data.frame is more readily compatible with other popular packages (i.e. dplyr, tidyr, and ggplot2).
Advanced Features
Alias
In the beginning, when we created the ribo object, we used an optional parameter rename = rename_default.
original.ribo <- Ribo(file.path, rename = rename_default )
original.ribo
#> General File Information:
#> info
#> format version 1
#> reference appris_human_24_01_2019
#> min read length 28
#> max read length 32
#> left span 35
#> right span 15
#> transcript count 19822
#> has.metadata TRUE
#> metagene radius 35
#> has.alias TRUE
#>
#> Dataset Information:
#> experiment total.reads coverage rna.seq metadata
#> GSM1606107 164536 TRUE TRUE TRUE
#> GSM1606108 215678 TRUE TRUE TRUENote that has.alias attribute is TRUE in the above output. When the ribo object is created with the rename parameter, providing alias allows us to rename transcripts, by explicitly setting alias = TRUE.
meta.start <- get_metagene(ribo.object = original.ribo,
site = "start",
range.lower = 28,
range.upper = 32,
length = FALSE,
transcript = FALSE,
alias = TRUE)
meta.start[1:2 , c(1,2,3,38,39,40)]
#> DataFrame with 2 rows and 6 columns
#> experiment transcript length -1 0 1
#> <Rle> <factor> <Rle> <integer> <integer> <integer>
#> 1 GSM1606107 OR4F5-201 28 0 0 0
#> 2 GSM1606107 OR4F29-201 28 0 0 0The transcriptome reference, that we used to generate this .ribo file, is from GENCODE. In particular, transcript names in this reference are long for the sake of completeness. For example, they include Ensemble transcript and gene ids in addition to standard gene symbols (HGNC).
head(get_reference_names(original.ribo), 2)
#> [1] "ENST00000335137.4|ENSG00000186092.6|OTTHUMG00000001094.4|-|OR4F5-201|OR4F5|1054|protein_coding|"
#> [2] "ENST00000426406.3|ENSG00000284733.1|OTTHUMG00000002860.3|OTTHUMT00000007999.3|OR4F29-201|OR4F29|995|protein_coding|"A user might want to work with simplified ids for convenience. To provide this functionality, an optional parameter called rename can be provided when initializing a ribo object as shown above.
rename parameter can be used in two major ways. The first option is providing a renaming function. This is often a short piece of code that parses the original name and returns a shorter gene, or transcript, name. The RiboR package provides a default renaming function, named rename_default for the appris human transcriptome. However, an alternative custom renaming function can also be supplied for example if using a different transcriptome annotation.
The default renaming function extracts the fifth entry in the transcript name, separated by “|”.
rename_default <- function(names){
return(unlist(strsplit(names, split = "[|]"))[5])
}
rename_default("ENST00000335137.4|ENSG00000186092.6|OTTHUMG00000001094.4|-|OR4F5-201|OR4F5|1054|protein_coding|")
#> [1] "OR4F5-201"Using the above function, the following code will generate a ribo object and assign an alias to all of the transcripts in a .ribo file.
alias.ribo <- Ribo(file.path, rename = rename_default)
alias.ribo
#> General File Information:
#> info
#> format version 1
#> reference appris_human_24_01_2019
#> min read length 28
#> max read length 32
#> left span 35
#> right span 15
#> transcript count 19822
#> has.metadata TRUE
#> metagene radius 35
#> has.alias TRUE
#>
#> Dataset Information:
#> experiment total.reads coverage rna.seq metadata
#> GSM1606107 164536 TRUE TRUE TRUE
#> GSM1606108 215678 TRUE TRUE TRUENotice that when rename is not provided, the alias attribute will be false.
noalias.ribo <- Ribo(file.path)
noalias.ribo
#> General File Information:
#> info
#> format version 1
#> reference appris_human_24_01_2019
#> min read length 28
#> max read length 32
#> left span 35
#> right span 15
#> transcript count 19822
#> has.metadata TRUE
#> metagene radius 35
#> has.alias FALSE
#>
#> Dataset Information:
#> experiment total.reads coverage rna.seq metadata
#> GSM1606107 164536 TRUE TRUE TRUE
#> GSM1606108 215678 TRUE TRUE TRUEWe also provide a second option of supplying a string vector containing the alias names directly. The string vector should correspond to the order of reference names in the output of get_reference_names. A subsequent call to set_aliases can be used to add aliases to an already existing ribo object. Similar to the above use of the rename parameter, set_aliases can be invoked with an appropriate renaming function.
#create a ribo file
alias.ribo <- Ribo(file.path)
#generate the vector of aliases
aliases <- rename_transcripts(ribo = alias.ribo, rename = rename_default)
#add aliases
alias.ribo <- set_aliases(alias.ribo, aliases)
head(aliases, 3)
#> [1] "OR4F5-201" "OR4F29-201" "OR4F16-201"
alias.ribo
#> General File Information:
#> info
#> format version 1
#> reference appris_human_24_01_2019
#> min read length 28
#> max read length 32
#> left span 35
#> right span 15
#> transcript count 19822
#> has.metadata TRUE
#> metagene radius 35
#> has.alias TRUE
#>
#> Dataset Information:
#> experiment total.reads coverage rna.seq metadata
#> GSM1606107 164536 TRUE TRUE TRUE
#> GSM1606108 215678 TRUE TRUE TRUEFile Attributes
If we want a returned list of previously printed attributes, then the function get_info will return all of the attributes found in the root of the .ribo file as well as information on each of the experiments. For a detailed explanation, see the readthedocs page of RiboPy.
Note that this simply returns a named list of many of the ribo object contents. The returned list organizes the information into three separate values, has.metadata, attributes, and experiment.info. This is a more efficient alternative to simply referencing the values in the ribo object since the method reads directly from the .ribo file handle and not the downstream declared ribo object.
#retrieves the experiments
original.info <- get_info(ribo.object = original.ribo)
original.info
#> $has.metadata
#> [1] TRUE
#>
#> $attributes
#> $attributes$format_version
#> [1] "1.0"
#>
#> $attributes$left_span
#> [1] 35
#>
#> $attributes$length_max
#> [1] 32
#>
#> $attributes$length_min
#> [1] 28
#>
#> $attributes$metagene_radius
#> [1] 35
#>
#> $attributes$reference
#> [1] "appris_human_24_01_2019"
#>
#> $attributes$ribogadgets_version
#> [1] "0.0.1"
#>
#> $attributes$right_span
#> [1] 15
#>
#>
#> $experiment.info
#> experiment total.reads coverage rna.seq metadata
#> 1 GSM1606107 164536 TRUE TRUE TRUE
#> 2 GSM1606108 215678 TRUE TRUE TRUERegion Boundaries
A .ribo file contains the region boundary information. This information can be useful to compare CDS lengths of different transcripts or perform region specific analysis using coverage data.
Some transcripts might have very short regions, compared to left or right span, in their original annotation. In this case, when attempting to set the junction boundaries, the UTR5 and/or UTR3 junctions may take the entirety of any combination of the transcript’s original regions (5’ UTR, CDS, and 3’UTR). Then, UTR5 or UTR3 junctions will take over their nucleotides. Thus, in our modified region definitions, the coordinates of these regions are marked as NA and their lengths will be 0.
Region coordinates can be obtained using get_region_coordinates.
region_coord <- get_region_coordinates(ribo.object = alias.ribo , alias = TRUE)
head(region_coord, 2)
#> transcript UTR5_start UTR5_stop UTR5J_start UTR5J_stop CDS_start CDS_stop
#> 1 OR4F5-201 1 1 2 51 52 916
#> 2 OR4F29-201 NA NA 1 34 35 920
#> UTR3J_start UTR3J_stop UTR3_start UTR3_stop
#> 1 917 966 967 1054
#> 2 921 970 971 995Length of regions can be useful to normalize data, say by CDS length, or to compare different transcripts. The function get_region_lengths gives region lengths for each transcript.
region_lengths <- get_region_lengths(ribo.object = alias.ribo, alias = TRUE)
head(region_lengths, 2)
#> transcript UTR5 UTR5J CDS UTR3J UTR3
#> 1 OR4F5-201 1 50 865 50 88
#> 2 OR4F29-201 0 34 886 50 25Functionality for Multiple Ribo Files
The functions within RiboR take in a single Ribo object. However, the following helper function, read_multiple_files, returns an aggregated data frame, given a named list of Ribo objects and any RiboR reader function of interest (get_metagene, get_region_counts, get_length_distribution, get_coverage, or get_rnaseq) with its associated parameters.
# helper function
read_multiple_files <- function(ribo_list, getter_function, ...) {
df_list <- lapply(ribo_list, getter_function, ...)
df_list <- mapply(cbind, df_list, "study" = names(df_list), SIMPLIFY=FALSE)
df <- dplyr::bind_rows(df_list)
return (df)
}Note that the compact parameter should be set to FALSE and that the parameters given will be consistently used across all of the Ribo objects in the given list. This means that lists of Ribo objects containing Ribo files with different properties (e.g. Transcript Aliases, RNA-Seq, Coverage) may need to be filtered for consistency before use.
# create a named Ribo object list of length >= 1
ribo_file_list <- list(original.ribo)
names(ribo_file_list) <- c("GSE65778")
# try out a function
read_multiple_files(ribo_list = ribo_file_list, getter_function = get_region_counts, compact = FALSE, tidy = FALSE)
#> experiment UTR5 UTR5J CDS UTR3J UTR3 study
#> 1 GSM1606107 7487 6244 146719 3280 806 GSE65778
#> 2 GSM1606108 10413 8732 191573 4052 908 GSE65778As seen in the above output, the names of the Ribo object list allow for a new study column in the resulting data frame to allow for easier filtering and processing in downstream analyses.
Optional Data
Length distribution, metagene coverage and region counts are essential to ribosome profiling data analysis and these data exist in every .ribo file. However, for certain types of analysis, additional data might be required.
For example, periodicity and uniformity analyses require the knowledge of number of reads at each nucleotide position, aka coverage data. Another analysis, called translational efficiency, can be done when transcript abundance information is present. An example of this is seen below in the section, Sample Analysis. For these types of analyses, .ribo files offer two types of optional data: coverage data and RNA-Seq data.
It might be helpful to have data explaining how ribosome profiling data is collected, prepared and processed. For this, .ribo files has an additional field, called metadata, to store such data for each experiment and for the .ribo file itself.
Optional data don’t necessarily exist in every .ribo file. Their existence can be checked as follows.
original.ribo
#> General File Information:
#> info
#> format version 1
#> reference appris_human_24_01_2019
#> min read length 28
#> max read length 32
#> left span 35
#> right span 15
#> transcript count 19822
#> has.metadata TRUE
#> metagene radius 35
#> has.alias TRUE
#>
#> Dataset Information:
#> experiment total.reads coverage rna.seq metadata
#> GSM1606107 164536 TRUE TRUE TRUE
#> GSM1606108 215678 TRUE TRUE TRUEIn the above output, we see that both of the experiments have all optional data as the values in the columns ‘coverage’, ‘rna.seq’ and ‘metadata’ are TRUE. Also the .ribo file has metadata as the ‘has.metadata’ attribute is TRUE.
Metadata
A .ribo file can contain metadata for each individual experiment as well as the ribo file itself. If we want to see the metadata of a given experiment, then we can use the get_metadata function and specify the experiment of interest.
To view the metadata of the .ribo file, we use the get_metadata function.
get_metadata(ribo.object = original.ribo,
print = TRUE)
#The output is omitted due to its lengthMetadata can be saved in a variable for later use by setting print = FALSE.
info <- capture.output(get_metadata(ribo.object = original.ribo,
print = FALSE))
info
#The output is omitted due to its lengthTo find the experiments that have metadata and subsequently retrieve it from one of the experiments, consider the following.
#obtain a list of experiments with metadata
experiment.info <- get_info(ribo.object = original.ribo)[['experiment.info']]
has.metadata <- experiment.info[experiment.info$metadata == TRUE, "experiment"]
has.metadata
#> [1] "GSM1606107" "GSM1606108"
#store the name of the first experiment with metadata and gets its metadata
experiment <- has.metadata[1]
get_metadata(ribo.object = original.ribo,
name = experiment,
print = TRUE)
#> 3padapter: CTGTAGGCACCATCAAT
#> GEO: GSM1606107
#> Lab: Ingolia
#> Notes: One nuc. clipped from fivep
#> Notes_2: Randomly picked 0.3 M reads from the original data.
#> SRA: SRR1795425,SRR1795426
#> cell-line: HEK 293
#> link: https://elifesciences.org/articles/05033Coverage
For all quantifications, we first map the sequencing reads to the transcriptome and use the 5’ most nucleotide of each mapped read. Coverage data is the total number of reads whose 5’ends map to each nucleotide position in the transcriptome.
Within a .ribo file, the coverage data, if exists, is typically the largest data set in terms of storage, and it accounts for a substantial portion of a .ribo file’s size, when present. The get_coverage function returns the coverage information for one specific transcript at a time.
Since coverage data is an optional field of .ribo files, it is helpful to keep track of the experiment names with coverage data. Once the list is obtained, the experiments of interest can easily be chosen and extracted.
#get a list of experiments that have coverage data
experiment.info <- get_info(ribo.object = original.ribo)[['experiment.info']]
has.coverage <- experiment.info[experiment.info$coverage == TRUE, "experiment"]
has.coverage
#> [1] "GSM1606107" "GSM1606108"Because get_coverage takes the transcript name, for annotations with long transcriptome names, it is recommended to use aliases and generate a ribo object with a valid rename parameter.
The get_coverage function retrieves the coverage across any subset of the experiments. If length parameter is TRUE, then the coverage counts are summed up across the read lengths.
cov <- get_coverage(ribo.object = original.ribo,
name = "MYC-206",
range.lower = 28,
range.upper = 32,
length = TRUE,
alias = TRUE,
tidy = TRUE,
experiment = has.coverage)
cov
#> DataFrame with 2730 rows and 3 columns
#> experiment position count
#> <Rle> <Rle> <integer>
#> 1 GSM1606107 1 0
#> 2 GSM1606108 1 0
#> 3 GSM1606107 2 0
#> 4 GSM1606108 2 0
#> 5 GSM1606107 3 0
#> ... ... ... ...
#> 2726 GSM1606108 1363 0
#> 2727 GSM1606107 1364 0
#> 2728 GSM1606108 1364 0
#> 2729 GSM1606107 1365 0
#> 2730 GSM1606108 1365 0To preserve the information at ribosome foot print length, set length = FALSE.
#only using one experiment for this
exp.names <- has.coverage[1]
#length is FALSE, get coverage information
#at each read length
cov <- get_coverage(ribo.object = original.ribo,
name = "MYC-206",
range.lower = 28,
range.upper = 32,
length = FALSE,
alias = TRUE,
tidy = TRUE,
experiment = exp.names)
cov
#> DataFrame with 6825 rows and 4 columns
#> experiment length position count
#> <Rle> <Rle> <Rle> <integer>
#> 1 GSM1606107 28 1 0
#> 2 GSM1606107 29 1 0
#> 3 GSM1606107 30 1 0
#> 4 GSM1606107 31 1 0
#> 5 GSM1606107 32 1 0
#> ... ... ... ... ...
#> 6821 GSM1606107 28 1365 0
#> 6822 GSM1606107 29 1365 0
#> 6823 GSM1606107 30 1365 0
#> 6824 GSM1606107 31 1365 0
#> 6825 GSM1606107 32 1365 0RNA-Seq
Most ribosome profiling experiments generate matched RNA-Seq data to enable analyses of translation efficiency. We provide the ability to store RNA-Seq quantification in .ribo files to facilitate these analyses. We store RNA-seq quantifications in a manner that parallel the region counts for the ribosome profiling experiment. Specifically, the RNA-Seq data sets contain information on the relative abundance of each transcript at each of the following transcript regions.
- 5’ Untranslated Region (UTR5)
- 5’ Untranslated Region Junction (UTR5J)
- Coding Sequence (CDS)
- 3’ Untranslated Region Junction (UTR3J)
- 3’ Untranslated Region (UTR3)
To get started, it might be helpful to store a character vector of the experiment names with RNA-Seq data.
#get a list of experiments that have RNA-Seq data
experiment.info <- get_info(ribo.object = original.ribo)[['experiment.info']]
has.rnaseq <- experiment.info[experiment.info$rna.seq == TRUE, "experiment"]
has.rnaseq
#> [1] "GSM1606107" "GSM1606108"To get all of the RNA-Seq data in the sample .ribo file in a non-tidy format, set tidy = FALSE.
#get a list of experiments that have RNA-Seq data
rnaseq <- get_rnaseq(ribo.object = original.ribo,
tidy = FALSE,
alias = TRUE,
experiment = has.rnaseq)
#print out the first 2 rows of the DataFrame
head(rnaseq, 2)
#> DataFrame with 2 rows and 7 columns
#> experiment transcript UTR5 UTR5J CDS UTR3J UTR3
#> <Rle> <factor> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1 GSM1606107 OR4F5-201 0 0 0 0 0
#> 2 GSM1606107 OR4F29-201 0 0 0 0 0To get the the RNA-Seq data of the experiment at the UTR5J, CDS, and UTR3J regions only, in a tidy format, set regions = c("UTR5J", "CDS", "UTR3J") and tidy = TRUE.
experiment <- has.rnaseq[1]
rnaseq <- get_rnaseq(ribo.object = original.ribo,
tidy = TRUE,
alias = TRUE,
region = c("UTR5J", "CDS", "UTR3J"),
experiment = experiment)
head(rnaseq, 2)
#> DataFrame with 2 rows and 4 columns
#> experiment transcript region count
#> <Rle> <factor> <Rle> <numeric>
#> 1 GSM1606107 OR4F5-201 UTR5J 0
#> 2 GSM1606107 OR4F29-201 UTR5J 0Sample Analysis
Here, we outline a simple analysis highlighting some capabilities of our package.
A common use case for the RNA-Seq data (example seen in the RNA-Seqsection in Optional Data) is to measure translational efficiency. We first filter out poorly expressed transcripts, and visualize the relationship between the RNA-Seq and ribosome profiling region counts.
# obtain the rnaseq and ribosomal occupancy of the CDS region
rnaseq_CDS <- get_rnaseq(ribo.object = original.ribo,
tidy = TRUE,
alias = TRUE,
region = "CDS",
compact = FALSE,
experiment = "GSM1606108")
rc_CDS <- get_region_counts(ribo.object = original.ribo,
tidy = TRUE,
alias = TRUE,
transcript = FALSE,
region = "CDS",
compact = FALSE,
experiment = "GSM1606108")
# filter out the lowly expressed transcripts
plot(density(log2(rnaseq_CDS$count)), xlab = "Log 2 of RNA-Seq Count", main = "RNA-Seq Density")
From the above plot, we decided to use a read count threshold of 2 for both the RNA-Seq and ribosome profiling region counts. We then plot the relationship between ribosome profiling and RNA-Seq counts.
# filter out transcripts with count value less than 2 for either ribosome profiling region counts or rnaseq
filtered_transcripts <- which(rnaseq_CDS$count > 2 & rc_CDS$count > 2)
rnaseq_CDS <- rnaseq_CDS[filtered_transcripts, ]
rc_CDS <- rc_CDS[filtered_transcripts, ]
plot(log2(rnaseq_CDS$count), log2(rc_CDS$count), xlab = "RNA-Seq", ylab = "Ribosome Profiling", main = "log2 CDS Read Counts", pch = 19, cex = 0.5)
Next, we calculate the ratio of ribosome profiling to RNA-Seq counts. This ratio has been used as a rough proxy for translational efficiency in the literature. We then visually inspect the distribution of this ratio.
# get the translational efficiency of CDS
te_CDS <- rc_CDS$count/rnaseq_CDS$count
plot(density(log2(te_CDS)), xlab = "Log 2 of Translation Efficiency", main = "")
Using the above density distribution, we next select genes with a log2 ratio of greater than -2. From this threshold value, we identify 229 transcripts that exhibit relatively high translational efficiency.
# filter for translational efficiency of the observed elbow at -2 (on the log2 scale, untransformed value of .25)
high_te <- rc_CDS$transcript[which(te_CDS > .25)]
# found 229 transcripts, first 20 shown here
head(high_te, n = 20)
#> [1] "CPTP-201" "FBXO6-201" "ACTL8-201" "SH3BGRL3-201" "TRNP1-201"
#> [6] "IFI6-203" "HEYL-201" "COL9A2-202" "HYI-201" "CYP4X1-201"
#> [11] "FOXD2-201" "CYR61-201" "S1PR1-201" "CSF1-201" "TENT5C-201"
#> [16] "MLLT11-201" "S100A11-201" "SYT11-201" "HSPA6-201" "PLEKHA6-201"