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https://github.com/hsinyenwu/RiboPlotR
RiboPlotR package for Ribo-plot
https://github.com/hsinyenwu/RiboPlotR
Last synced: about 1 month ago
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RiboPlotR package for Ribo-plot
- Host: GitHub
- URL: https://github.com/hsinyenwu/RiboPlotR
- Owner: hsinyenwu
- Created: 2019-06-22T19:40:13.000Z (over 5 years ago)
- Default Branch: v2019a
- Last Pushed: 2023-09-09T15:03:50.000Z (over 1 year ago)
- Last Synced: 2024-08-05T15:04:02.421Z (5 months ago)
- Language: R
- Size: 42 MB
- Stars: 13
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-riboseq - Code - 021-00824-4) | R; Similar transcript plotting to RiboCrypt (w/ multiple isoforms)| (Data Resources and Visualisation Environments)
README
# RiboPlotR for visualizing the periodicity of Ribo-seq reads.
### Introduction
RiboPlotR is a R package for for visualizing RNA-seq/Ribo-seq reads in the context of a given gene, including Ribo-seq reads mapped to the annotated coding sequences (CDSs), 5' and 3' untranslated regions (UTRs), and introns, with all annotated transcript isoform models displayed in parallel in the plot. There are several advantages to the style used in RiboPlotR: (1) We can detect novel translation events in the unannotated coding regions, such as those in the UTRs and introns. (2) By including all transcript isoform models in the plot, in most cases, we can visually determine which transcript isoform(s) is/are translated. (3) By comparing sequencing data and annotated gene models in parallel, we can identify discrepancies between the Ribo-seq data and the predicted CDSs, such as frameshifts and variations in coding regions; similarly, any deviations in the mRNA profile from the annotated transcript isoforms are also easily visualized. (4) The relative Ribo-seq abundance in different transcript features, such as upstream ORFs (uORFs) and introns, can be visualized and thus used to infer potential regulatory mechanisms and generate new hypotheses. Below, we describe uses for and examples with RiboPlotR to visualize translation events in a gene with a predicted uORF and in another gene with different transcript isoforms.
RiboPlotR separately plots each transcript isoform of a given gene. Only one isoform is plotted at a time, and the default is to plot isoform 1. For each isoform, the same RNA-seq and Ribo-seq reads are used for plotting; the only difference is the expected coding region for the Ribo-seq reads, which is indicated by a black dashed line (expected translation start) and a grey dashed line (expected translation stop). Inside the expected coding region, Ribo-seq P-sites that are mapped in the expected frame, the +1 frame, and the +2 frame are presented using red, blue and green lines, respectively. Ribo-seq P-sites that are outside the expected coding region are shown in grey. Thus, for high-quality datasets, most of the P-sites will be in red if the data agree with the annotation. The x-axis below the gene models indicates the genomic coordinates, whereas the primary y-axis (left) indicates the RNA-seq count, and the secondary y-axis (right) indicates the Ribo-seq P-site count. When an isoform is translated, the majority of P-sites should cover the expected CDSs and are shown in red. If two isoforms cover a different coding region at the 3' ends, the two plots will have different color schemes at the 3' end. This design allows users to quickly see if a plotted isoform is being actively translated (see style figure and examples below).
A. Example of a commonly used single-transcript plot. RNA-seq and Ribo-seq reads are shown for one mature transcript isoform per plot. For Ribo-seq reads, either the most 5' site or the P-site position (the first nt within the peptidyl site within the ribosome) is used for plotting. The first nt of the transcript is considered frame 1. Reads that are mapped to each reading frame are shown in red, blue, and green. Thus, the Ribo-seq reads for the annotated CDS can be in one of the three colors. AUG start codons are marked as white half-lines in all three frames. The stop codons are marked as grey lines in all three frames.
B. The RiboPlotR style shows all annotated transcript isoform models in parallel with RNA-seq coverage and Ribo-seq P-site reads. Within the gene model, the grey boxes indicate 5’ UTRs, the black boxes indicate the annotated ORFs, and the white pentagonal arrows indicate 3’ UTRs. The isoform being considered is labeled in bold. In addition to the annotated ORF, one upstream ORF (yellow box in the gene models) can be shown in the same plot. For all transcript isoforms of a given gene, the same RNA-seq coverage and Ribo-seq P-sites are used for plotting. For the annotated ORF, the expected CDS range is marked between a black dashed line (translation start site) and a grey dashed line (translation stop site); for the uORF, the CDS range is marked between a green dashed line (translation start site) and an orange dashed line (translation stop site). The Ribo-seq P-sites that map to the expected frame, +1 frame, and +2 frame are marked in red, blue, and green, respectively. The Ribo-seq P-sites mapped outside of the expected CDS in either the annotated ORF or uORF are shown in grey. Thus, the majority of the Ribo-seq reads for the annotated CDS will be in red if the data agree with the annotation. Here, isoforms 1 and 2 are both expressed and translated, but isoform 3 is not expressed. Since we selected isoform 1 for plotting here, Ribo-seq P-sites that are unique to isoform 2 and not included in isoform 1 are marked in grey.
## Run RiboPlotR
### Install RiboPlotR and its required packages:
Install required packages.
```R
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("GenomicRanges")
BiocManager::install("GenomicFeatures")
BiocManager::install("GenomicAlignments")
BiocManager::install("rtracklayer")
BiocManager::install("Rsamtools")
#Install RiboPlotR
library(devtools)
install_github("hsinyenwu/RiboPlotR")
```
### The basic workflow of RiboPlotR is:
1. Run gene.structure(); load the transcriptome annotation gtf/gff3 file containing the gene, mRNA/transcript, exon and CDS ranges.
2. (Optional) To plot a uORF for a transcript, users will also load the uORF gtf/gff3 file using the uorf.structure() function.
3. Run rna_bam.ribo(); load the mapped and coordinate-sorted RNA-seq bam file and the ribo-seq P-site position file.
4. Use one of the four functions below, enter gene name and isoform number to plot the translation of the isoform.
### Files required for RiboPlotR:
RiboPlotR requires the following input files:
1. A gtf or gff3 file for transcriptome annotation, which should be recognizable with the GenomicFeatures package and the transcript name should be composed of a gene name, a period and a number (a widely used format for plant genome annotations). In other words, the current version of RiboPlotR does not work when using animal gtf/gff3.
2. A mapped and coordinate-sorted bam file(s) for RNA-seq
3. A tab-delimited file(s) for Ribo-seq P-site coordinates (see below)
4. A gtf or gff3 file for uORF coordinates is optional.
Users can read in up to two sets of bam and P-site files to compare translation under two different conditions.
### Note: the Ribo-seq P-site coordinate file should look like this:
The first to forth columns are the "total counts", "chromosome number", "P-site position" and "strand" (+ or -), respectively.
```
1 1 1000000 +
3 1 10000007 +
3 1 10000010 +
3 1 10000016 +
1 1 10000018 +
4 1 10000019 +
```
### Four styles of the plots are available:
PLOTc: plots RNA-seq and Ribo-seq in one panel (plot compact)
PLOTt: plots RNA-seq and Ribo-seq separately in two panels (plot two)
PLOTc2: plots RNA-seq and Ribo-seq in one panel for two conditions
PLOTt2: plots RNA-seq and Ribo-seq separately for two conditions
### Examples:
```R
# Load RiboPlotR and essential packages
library(RiboPlotR)
# Load example datasets
agtf <- system.file("extdata", "TAIR10.29_part.gtf", package = "RiboPlotR", mustWork = TRUE) #Annotation
ugtf <- system.file("extdata", "AT3G02468.gtf", package = "RiboPlotR", mustWork = TRUE) #uORF annotation
RRNA <- system.file("extdata", "Root_test_PE.bam", package = "RiboPlotR", mustWork = TRUE) #Root RNA-seq data
SRNA <- system.file("extdata", "Shoot_test_PE.bam", package = "RiboPlotR", mustWork = TRUE) #Shoot RNA-seq data
RRibo <- system.file("extdata", "riboRoot.bed", package = "RiboPlotR", mustWork = TRUE) #Root Ribo-seq data
SRibo <- system.file("extdata", "riboShoot.bed", package = "RiboPlotR", mustWork = TRUE) #Shoot Ribo-seq data
# Run gene.structure function to load gtf for annotated protein coding genes
gene.structure(annotation=agtf, format="gtf",dataSource="Araport",organism="Arabidopsis thaliana")
# Run uorf.structure to load uORF gtf
uorf.structure(uorf_annotation=ugtf, format="gtf",dataSource="Araport",organism="Arabidopsis thaliana")
# Run rna_bam.ribo to load root and shoot RNA-seq and Ribo-seq data sets
# Here root is the first dataset and shoot is the second dataset
rna_bam.ribo(Ribo1=RRibo,
RNAseqBam1=RRNA,
RNAlab1="RNA count",
Ribolab1="Ribo count",
S_NAME1="Root",
Ribo2=SRibo,
RNAseqBam2=SRNA,
RNAlab2="RNA count",
Ribolab2="Ribo count",
S_NAME2="Shoot",
RNAseqBamPaired="paired")
#Plot AT4G21910
PLOTc2("AT4G21910") #default using first isoform. The isoform used for plotting is marked in bold.
```
```R
PLOTc2("AT4G21910",isoform=2)
```
```R
#Plot Root data (PLOTc uses the first RNA-seq and Ribo-seq dataset by default. Here the first dataset is the Root dataset.)
PLOTc("AT3G02470",uORF = "AT3G02468",NAME=" SAMDC")
```
```R
#Plot Shoot data (Here is an example how to plot the second dataset using PLOTc)
PLOTc("AT3G02470",uORF="AT3G02468",NAME=" SAMDC",RNAbam1 = RNAseqBam2, ribo1 = Ribo2, SAMPLE1 = "Shoot")
```
```R
#Plot both dataset wiht PLOTC2
PLOTc2("AT3G02470",uORF = "AT3G02468",NAME=" SAMDC",isoform=3)
```
```R
PLOTt2("AT3G02470",uORF = "AT3G02468",NAME=" SAMDC",isoform=3)
```
```R
PLOTt("AT3G02470",uORF = "AT3G02468",NAME=" SAMDC",isoform=3)
```
### Citation:
More information:
RiboPlotR: a visualization tool for periodic Ribo-seq reads
https://link.springer.com/article/10.1186/s13007-021-00824-4
### Session Info:
```R
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils
[7] datasets methods base
other attached packages:
[1] GenomicAlignments_1.20.1 Rsamtools_2.0.0
[3] Biostrings_2.52.0 XVector_0.24.0
[5] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
[7] BiocParallel_1.18.0 matrixStats_0.54.0
[9] GenomicFeatures_1.36.3 AnnotationDbi_1.46.0
[11] Biobase_2.44.0 GenomicRanges_1.36.0
[13] GenomeInfoDb_1.20.0 IRanges_2.18.1
[15] S4Vectors_0.22.0 BiocGenerics_0.30.0
[17] RiboPlotR_0.1.0 usethis_1.5.1
[19] devtools_2.0.2
loaded via a namespace (and not attached):
[1] progress_1.2.2 remotes_2.1.0
[3] lattice_0.20-38 rtracklayer_1.44.0
[5] yaml_2.2.0 blob_1.1.1
[7] XML_3.98-1.20 rlang_0.4.0
[9] pkgbuild_1.0.3 glue_1.3.1
[11] withr_2.1.2 DBI_1.0.0
[13] bit64_0.9-7 sessioninfo_1.1.1
[15] GenomeInfoDbData_1.2.1 stringr_1.4.0
[17] zlibbioc_1.30.0 memoise_1.1.0
[19] callr_3.3.0 biomaRt_2.40.1
[21] ps_1.3.0 curl_3.3
[23] Rcpp_1.0.1 backports_1.1.4
[25] desc_1.2.0 pkgload_1.0.2
[27] fs_1.3.1 bit_1.1-14
[29] hms_0.4.2 digest_0.6.20
[31] stringi_1.4.3 processx_3.4.0
[33] grid_3.6.0 rprojroot_1.3-2
[35] cli_1.1.0 tools_3.6.0
[37] bitops_1.0-6 magrittr_1.5
[39] RCurl_1.95-4.12 RSQLite_2.1.1
[41] crayon_1.3.4 pkgconfig_2.0.2
[43] Matrix_1.2-17 prettyunits_1.0.2
[45] assertthat_0.2.1 httr_1.4.0
[47] rstudioapi_0.10 R6_2.4.0
[49] compiler_3.6.0
```