Introduction to GeneStructureTools
2022-11-01
Package
GeneStructureTools 1.18.0
Contents
1 Introduction
GeneStructureTools is a package for the manipulation and analysis of transcribed gene structures.
We have provided functions for importing Whippet and leafcutter alternative splicing data, and the analysis of these splicing events. Splicing events can also be defined manually if you are using a different splicing analysis tool to Whippet. For specific events - currently including exon skipping, intron retention, alternative splice site usage and alternative first/last exons - transcripts can be made in silico which use the two splicing modes - i.e. transcripts containing and transcripts skipping an exon. These transcripts do not have to be pre-annotated, and thus all potential isoforms can be compared for an event.
Current comparisons of transcripts include annotating and analysing ORF and UTR features (length, locations, difference/similarity between transcripts), and predicting nonsense mediated decay (NMD) potential.
We also have functions for re-annotation of .GTF features, such as annotating UTRs as 3’ or 5’, and assigning a broader biotype for genes and transcripts so more informative analysis can be performed between these classes.
2 Importing Differential Splicing Data
Currently, very few available tools output splicing event type information (i.e. exon skipping, intron retention) within tested genes. GeneStructureTools currently has functions for processing data from:
Data Preparation
We have pre-prepared data from mouse embryonic stem cell (ESC) development (Gloss et. al, 2017, Accession Number GSE75028), at days 0 and 5, and run whippet on each replicate using the reccomended parameters and the Gencode vM14 annotation. You can download the whippet, leafcutter, and DEXSeq files here.
You will also need to download the Gencode GTF file from here.
For the purposes of this vignette, small subsets of these data are available in the package data (inst/extdata).
Data provided is typical output for leafcutter and Whippet. For details on what each file contains, please refer to their respective manuals ( leafcutter | Whippet ).
DEXSeq data is processed as reccomended by the (DEXSeq) manual. The script used to process raw output is here
2.1 Whippet
To run a full analysis on Whippet output, you will need the raw .psi.gz (percent spliced in) and .jnc.gz (junction read counts) files for each sample. In addition, you will need to compare conditions using whippet-delta.jl and have a resulting .diff.gz file.
Read in Whippet files from downloaded data
# Load packages
library(GeneStructureTools)
library(GenomicRanges)
library(stringr)
library(BSgenome.Mmusculus.UCSC.mm10)
library(Gviz)
library(rtracklayer)
# list of files in the whippet directory
whippet_file_directory <- "~/Downloads/GeneStructureTools_VignetteFiles/"
# read in files as a whippetDataSet
wds <- readWhippetDataSet(whippet_file_directory)
# create a sample table with sample id, condition and replicate
whippet_sampleTable <- data.frame(sample=c("A01","B01","A21","B21"),
condition=c("01","01","21","21"),
replicate=(c("A","B","A","B")))
# read in gtf annotation
gtf <- rtracklayer::import("~/Downloads/gencode.vM14.annotation.gtf.gz")
exons <- gtf[gtf$type=="exon"]
transcripts <- gtf[gtf$type=="transcript"]
# add first/last annotation (speeds up later steps)
if(!("first_last" %in% colnames(mcols(exons)))){
t <- as.data.frame(table(exons$transcript_id))
exons$first_last <- NA
exons$first_last[exons$exon_number == 1] <- "first"
exons$first_last[exons$exon_number ==
t$Freq[match(exons$transcript_id, t$Var1)]] <- "last"
}
# specify the BSGenome annotation
g <- BSgenome.Mmusculus.UCSC.mm10::BSgenome.Mmusculus.UCSC.mm10Read in Whippet files from package data
# Load packages
suppressPackageStartupMessages({
library(GeneStructureTools)
library(GenomicRanges)
library(stringr)
library(BSgenome.Mmusculus.UCSC.mm10)
library(Gviz)
library(rtracklayer)
})
# list of files in the whippet directory
whippet_file_directory <- system.file("extdata","whippet/",
package = "GeneStructureTools")
# read in files as a whippetDataSet
wds <- readWhippetDataSet(whippet_file_directory)
# create a sample table with sample id, condition and replicate
whippet_sampleTable <- data.frame(sample=c("A01","B01","A21","B21"),
condition=c("01","01","21","21"),
replicate=(c("A","B","A","B")))
# read in gtf annotation
gtf <- rtracklayer::import(system.file("extdata","example_gtf.gtf",
package = "GeneStructureTools"))
exons <- gtf[gtf$type=="exon"]
transcripts <- gtf[gtf$type=="transcript"]
# add first/last annotation (speeds up later steps)
if(!("first_last" %in% colnames(mcols(exons)))){
t <- as.data.frame(table(exons$transcript_id))
exons$first_last <- NA
exons$first_last[exons$exon_number == 1] <- "first"
exons$first_last[exons$exon_number ==
t$Freq[match(exons$transcript_id, t$Var1)]] <- "last"
}
# specify the BSGenome annotation
g <- BSgenome.Mmusculus.UCSC.mm10::BSgenome.Mmusculus.UCSC.mm102.2 leafcutter
Only the file containg the leafcutter results for each intron, and the .gtf file used with leafcutter needs to be read in for results processing. The leafcutter results file is generated after running prepare_results.R on your data, then extracting out the intron data table.
First, find the location of the leafviz2table.R script:
#find location of the script
system.file("extdata","leafviz2table.R", package = "GeneStructureTools")Then run it on your leafviz output .RData file. The first argument is the leafviz output RData file, and the second is the name of the table you wish to write the intron results to.
Rscript leafviz2table.R leafviz.RData per_intron_results.tabWe have an processed example file available in extdata/leafcutter with a small sample of significant events.
# read in gtf annotation
gtf <- rtracklayer::import(system.file("extdata","example_gtf.gtf", package = "GeneStructureTools"))
exons <- gtf[gtf$type=="exon"]
# specify the BSGenome annotation
g <- BSgenome.Mmusculus.UCSC.mm10::BSgenome.Mmusculus.UCSC.mm10
# list of files in the leafcutter directory
leafcutter_files <- list.files(system.file("extdata","leafcutter/", package = "GeneStructureTools"),full.names = TRUE)
intron_results <- read.delim(leafcutter_files[grep("per_intron_results.tab", leafcutter_files)], stringsAsFactors = FALSE)3 Summarise changes in gene structures due to splicing
3.1 Whippet
# filter events for significance
wds <- filterWhippetEvents(
whippetDataSet = wds,
probability = 0.95, # min probability
psiDelta = 0.1, # min change in PSI
eventTypes = "all", # all event types
minCounts = 100, # mean of at least 100 counts in one condition
sampleTable = whippet_sampleTable)
# check for changes in gene/transcript structure
whippet_summary <- whippetTranscriptChangeSummary(wds,
exons = exons,
transcripts = transcripts,
BSgenome = g,
NMD = FALSE # ignore nonsense mediated decay
)
head(whippet_summary)## gene node coord strand type psi_a
## 1 ENSMUSG00000032883.15 3 chr1:78663141-78663280 + CE 0.35304
## 2 ENSMUSG00000024038.16 6 chr17:31527310-31528401 + CE 0.13662
## 3 ENSMUSG00000018379.17 5 chr11:88049216-88049411 + RI 0.37136
## 4 ENSMUSG00000034064.14 3 chr16:38549434-38549636 - AA 0.38094
## 5 ENSMUSG00000035478.14 2 chr10:80399122-80399217 - AD 0.45017
## 6 ENSMUSG00000026421.14 2 chr1:135729147-135729274 + AF 0.84840
## psi_b psi_delta probability complexity entropy
## 1 0.13494 0.21810 1.000 K1 0.9619
## 2 0.27537 -0.13875 0.998 K1 0.9006
## 3 0.21276 0.15861 1.000 K1 0.9590
## 4 0.23379 0.14716 0.990 K1 0.9638
## 5 0.33980 0.11036 0.953 K1 0.9937
## 6 0.99487 -0.14647 1.000 K1 0.6421
## unique_name comparison condition_1
## 1 ENSMUSG00000032883.15_chr1:78663141-78663280_CE_3 01_v_21 01
## 2 ENSMUSG00000024038.16_chr17:31527310-31528401_CE_6 01_v_21 01
## 3 ENSMUSG00000018379.17_chr11:88049216-88049411_RI_5 01_v_21 01
## 4 ENSMUSG00000034064.14_chr16:38549434-38549636_AA_3 01_v_21 01
## 5 ENSMUSG00000035478.14_chr10:80399122-80399217_AD_2 01_v_21 01
## 6 ENSMUSG00000026421.14_chr1:135729147-135729274_AF_2 01_v_21 01
## condition_2 coord_match condition_1_counts condition_2_counts node
## 1 21 1 89.5 213.5 3
## 2 21 2 706.0 456.5 6
## 3 21 3 569.5 662.0 5
## 4 21 4 103.5 109.0 3
## 5 21 5 98.5 107.5 2
## 6 21 6 51.5 196.5 2
## coord strand type psi_a psi_b psi_delta probability
## 1 chr1:78663141-78663280 + CE 0.35304 0.13494 0.21810 1.000
## 2 chr17:31527310-31528401 + CE 0.13662 0.27537 -0.13875 0.998
## 3 chr11:88049216-88049411 + RI 0.37136 0.21276 0.15861 1.000
## 4 chr16:38549434-38549636 - AA 0.38094 0.23379 0.14716 0.990
## 5 chr10:80399122-80399217 - AD 0.45017 0.33980 0.11036 0.953
## 6 chr1:135729147-135729274 + AF 0.84840 0.99487 -0.14647 1.000
## complexity entropy unique_name
## 1 K1 0.9619 ENSMUSG00000032883.15_chr1:78663141-78663280_CE_3
## 2 K1 0.9006 ENSMUSG00000024038.16_chr17:31527310-31528401_CE_6
## 3 K1 0.9590 ENSMUSG00000018379.17_chr11:88049216-88049411_RI_5
## 4 K1 0.9638 ENSMUSG00000034064.14_chr16:38549434-38549636_AA_3
## 5 K1 0.9937 ENSMUSG00000035478.14_chr10:80399122-80399217_AD_2
## 6 K1 0.6421 ENSMUSG00000026421.14_chr1:135729147-135729274_AF_2
## comparison condition_1 condition_2 coord_match condition_1_counts
## 1 01_v_21 01 21 1 89.5
## 2 01_v_21 01 21 2 706.0
## 3 01_v_21 01 21 3 569.5
## 4 01_v_21 01 21 4 103.5
## 5 01_v_21 01 21 5 98.5
## 6 01_v_21 01 21 6 51.5
## condition_2_counts id orf_length_bygroup_x
## 1 213.5 chr1:78663141-78663280 720
## 2 456.5 chr17:31527310-31528401 104
## 3 662.0 chr11:88049216-88049411 201
## 4 109.0 chr16:38549434-38549636 410
## 5 107.5 chr10:80399122-80399217 313
## 6 196.5 chr1:135729147-135729274 166
## orf_length_bygroup_y utr3_length_bygroup_x utr3_length_bygroup_y
## 1 720 1328 1328
## 2 468 74 74
## 3 253 763 411
## 4 317 1485 1488
## 5 281 185 185
## 6 166 1898 709
## utr5_length_bygroup_x utr5_length_bygroup_y filtered percent_orf_shared
## 1 460 320 FALSE 1.0000000
## 2 57 57 FALSE 0.2222222
## 3 37 37 FALSE 0.0000000
## 4 2 520 FALSE 0.7731707
## 5 192 192 FALSE 0.8977636
## 6 1135 2596 FALSE 1.0000000
## max_percent_orf_shared orf_percent_kept_x orf_percent_kept_y
## 1 1.0000000 1.0000000 1.0000000
## 2 0.2222222 1.0000000 0.2222222
## 3 0.7944664 0.0000000 0.0000000
## 4 0.7731707 0.7731707 1.0000000
## 5 0.8977636 0.8977636 1.0000000
## 6 1.0000000 1.0000000 1.0000000###leafcutter
leafcutter_summary <- leafcutterTranscriptChangeSummary(intron_results,
exons = exons,
BSgenome = g,
NMD = FALSE,
showProgressBar = FALSE)
head(leafcutter_summary[!duplicated(leafcutter_summary$cluster),])## cluster status loglr df p p.adjust genes
## 5 chr16:clu_1396 Success 20.16004 2 1.756329e-09 2.720554e-06 Eif4a2
## 1 chr10:clu_1204 Success 18.48627 2 9.365149e-09 7.253308e-06 Bclaf1
## 6 chr15:clu_1488 Success 23.03735 6 2.860878e-08 1.107875e-05 Tarbp2
## 2 chr17:clu_1281 Success 20.57316 4 2.506720e-08 1.107875e-05 Rnps1
## 8 chr5:clu_225 Success 16.41082 2 7.462287e-08 2.311817e-05 Hnrnpd
## 7 chr14:clu_1512 Success 15.00163 2 3.054042e-07 7.884518e-05 Ktn1
## FDR intron logef T01
## 5 2.720554e-06 chr16:23111896:23112351:clu_1396 0.30823371 0.2657849
## 1 7.253308e-06 chr10:20333572:20334499:clu_1204 -0.56647927 0.3521539
## 6 1.107875e-05 chr15:102518602:102519122:clu_1488 -0.47200141 0.2294254
## 2 1.107875e-05 chr17:24414734:24415041:clu_1281 -0.07302291 0.1145809
## 8 2.311817e-05 chr5:99967387:99976534:clu_225 0.55565460 0.3165242
## 7 7.884518e-05 chr14:47724970:47725929:clu_1512 -0.46578048 0.3338764
## T02 deltapsi chr start end clusterID verdict gene
## 5 0.4410376 0.1752527121 chr16 23111896 23112351 clu_1396 annotated Eif4a2
## 1 0.2269207 -0.1252331984 chr10 20333572 20334499 clu_1204 annotated Bclaf1
## 6 0.2597079 0.0302825713 chr15 102518602 102519122 clu_1488 annotated Tarbp2
## 2 0.1140728 -0.0005080613 chr17 24414734 24415041 clu_1281 annotated Rnps1
## 8 0.3905444 0.0740202209 chr5 99967387 99976534 clu_225 annotated Hnrnpd
## 7 0.1656536 -0.1682228169 chr14 47724970 47725929 clu_1512 annotated Ktn1
## ensemblID transcripts constitutive.score orf_length_bygroup_x
## 5 ENSMUSG00000022884.14 + 1 408
## 1 ENSMUSG00000037608.16 + 1 919
## 6 ENSMUSG00000023051.10 + 1 132
## 2 ENSMUSG00000034681.16 + 1 305
## 8 ENSMUSG00000000568.15 - 1 336
## 7 ENSMUSG00000021843.16 + 1 1327
## orf_length_bygroup_y utr3_length_bygroup_x utr3_length_bygroup_y
## 5 363 615 857
## 1 870 2442 2442
## 6 274 1265 334
## 2 282 710 710
## 8 361 366 366
## 7 1303 469 469
## utr5_length_bygroup_x utr5_length_bygroup_y filtered percent_orf_shared
## 5 34 34 FALSE 0.7671569
## 1 236 236 FALSE 0.9466812
## 6 224 92 FALSE 0.0000000
## 2 293 59 FALSE 0.9245902
## 8 294 294 FALSE 0.9307479
## 7 32 32 FALSE 0.9819141
## max_percent_orf_shared orf_percent_kept_x orf_percent_kept_y
## 5 0.8897059 0.7671569 0.8622590
## 1 0.9466812 0.9466812 1.0000000
## 6 0.4817518 0.0000000 0.0000000
## 2 0.9245902 0.9245902 1.0000000
## 8 0.9307479 1.0000000 0.9307479
## 7 0.9819141 0.9819141 1.00000004 Altering Gene and Transcript Structures
whippetTranscriptChangeSummary() combines several functions for analysing changes in gene structures. While this has been made to simplify analysis from whippet data, individual functions can be used on other data sources or manually annotated gene structures. It may also be helpful to run each individual step if you would like to manually investigate changes to genes.
4.1 Exon skipping
Exon skipping, or cassette exon usage, occurs when a single exon is spliced out of the mature transcript.
Removal of a skipped exon
1.a. Find skipped exon events
# filter out skipped exon events (coded as "CE")
# we will be looking at Ndufv3 (ENSMUSG00000024038.16)
wds.ce <- filterWhippetEvents(wds, psiDelta=0,probability=0,
event="CE", idList="ENSMUSG00000024038.16")
diffSplicingResults(wds.ce)## gene node coord strand type psi_a
## 2 ENSMUSG00000024038.16 6 chr17:31527310-31528401 + CE 0.13662
## psi_b psi_delta probability complexity entropy
## 2 0.27537 -0.13875 0.998 K1 0.9006
## unique_name comparison condition_1
## 2 ENSMUSG00000024038.16_chr17:31527310-31528401_CE_6 01_v_21 01
## condition_2 coord_match condition_1_counts condition_2_counts
## 2 21 2 706 456.5# psi_a = 0.137, psi_b = 0.275
# percentage of transcripts skipping exon 3 decreases from timepoint 1 to 21
# whippet outputs the skipped exon coordinates
coordinates(wds.ce)## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr17 31527310-31528401 + | chr17:31527310-31528..
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengths2. Find transcripts which overlap the skipped exon, and create normal & skipped exon isoforms
# find exons in the gtf that overlap the skipped exon event
exons.ce <- findExonContainingTranscripts(wds.ce,
exons = exons,
transcripts = transcripts)
# make skipped and included exon transcripts
# removes the skipped exon from all transcripts which contain it
skippedExonTranscripts <- skipExonInTranscript(skippedExons = exons.ce,
exons=exons,
match="skip",
whippetDataSet = wds.ce)## make Gvis models
# set up for visualisation
gtr <- GenomeAxisTrack()
# all transcripts for the gene
geneModel.all <- GeneRegionTrack(makeGeneModel(exons[exons$gene_id ==
skippedExonTranscripts$gene_id[1]]),
name="Reference Gene",
showId=TRUE,
transcriptAnnotation = "transcript")
# reference transcript
geneModelNormal <- GeneRegionTrack(makeGeneModel(
skippedExonTranscripts[skippedExonTranscripts$set=="included_exon"]),
name="Reference Isoform",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
# for the skipped exon transcript
geneModelSkipped <- GeneRegionTrack(makeGeneModel(
skippedExonTranscripts[skippedExonTranscripts$set=="skipped_exon"]),
name="Alternative Isoform",
showId=TRUE, fill="#94AFD8",
transcriptAnnotation = "transcript")
plotTracks(list(gtr,geneModel.all,geneModelNormal, geneModelSkipped),
extend.left = 1000, extend.right = 1000)
# Only the transcript isoform containing the skipped exon (exon 3)
# is used for analysis, and a 'novel' isoform is created by exon skipping4.2 Intron Retention
Intron Retention occurs when an intron is not spliced out of the mature transcript.
Addition of a retained intron
1. Create normal and retained isoform structures from whippet coordinates
# filter out retained events (coded as "RI")
# we will be looking at Srsf1 (ENSMUSG00000018379.17)
wds.ri <- filterWhippetEvents(wds, psiDelta=0,probability=0,
event="RI", idList="ENSMUSG00000018379.17")
diffSplicingResults(wds.ri)## gene node coord strand type psi_a
## 3 ENSMUSG00000018379.17 5 chr11:88049216-88049411 + RI 0.37136
## psi_b psi_delta probability complexity entropy
## 3 0.21276 0.15861 1 K1 0.959
## unique_name comparison condition_1
## 3 ENSMUSG00000018379.17_chr11:88049216-88049411_RI_5 01_v_21 01
## condition_2 coord_match condition_1_counts condition_2_counts
## 3 21 3 569.5 6622. Find transcripts which overlap the intron, and create normal & retained intron isoforms
# find exons pairs in the gtf that bound the retained intron event
exons.ri <- findIntronContainingTranscripts(wds.ri,
exons)
# make retained and non-retained transcripts
# adds the intron into all transcripts which overlap it
retainedIntronTranscripts <- addIntronInTranscript(exons.ri,
exons = exons,
whippetDataSet = wds.ri,
glueExons = TRUE)## make Gviz models
# all transcripts for the gene
geneModel.all <- GeneRegionTrack(makeGeneModel(exons[exons$gene_id == retainedIntronTranscripts$gene_id[1]]),
name="Reference Gene",
showId=TRUE,
transcriptAnnotation = "transcript")
# reference transcript
geneModelNormal <- GeneRegionTrack(makeGeneModel(
retainedIntronTranscripts[retainedIntronTranscripts$set=="spliced_intron"]),
name="Reference Isoform",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
# for the retained intron transcript
geneModelRetained <- GeneRegionTrack(makeGeneModel(
retainedIntronTranscripts[retainedIntronTranscripts$set=="retained_intron"]),
name="Alternative Isoform",
showId=TRUE, fill="#94AFD8",
transcriptAnnotation = "transcript")
# Only the transcript isoforms with exons at the boundries of the retained intron are used for analysis, and 'novel' isoforms are created by intron retention
plotTracks(list(gtr,geneModel.all,geneModelNormal, geneModelRetained),
extend.left = 1000, extend.right = 1000)
4.3 Alternative acceptor and donor splice sites
Creation of alternative donor/acceptor isoforms currently relies on junction read counts supplied by whippet.
Creation of transcripts with an alternative donor (left) or acceptor (right) splice site.
4.3.1 Aternative acceptor
1. Create normal and alternative isoform structures from whippet coordinates
# filter out alternative acceptor events (coded as "AA")
wds.aa <- filterWhippetEvents(wds, psiDelta=0,probability=0,
event="AA", idList="ENSMUSG00000034064.14")
diffSplicingResults(wds.aa)## gene node coord strand type psi_a
## 4 ENSMUSG00000034064.14 3 chr16:38549434-38549636 - AA 0.38094
## psi_b psi_delta probability complexity entropy
## 4 0.23379 0.14716 0.99 K1 0.9638
## unique_name comparison condition_1
## 4 ENSMUSG00000034064.14_chr16:38549434-38549636_AA_3 01_v_21 01
## condition_2 coord_match condition_1_counts condition_2_counts
## 4 21 4 103.5 109# AA/AD coordinates range from the normal acceptor splice site to the alternative acceptor splice site
coordinates(wds.aa)## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr16 38549434-38549636 - | chr16:38549434-38549..
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengths# find exons pairs in the gtf that bound the retained intron event
junctionPairs.aa <- findJunctionPairs(wds.aa,
type="AA")
junctionPairs.aa## GRanges object with 2 ranges and 5 metadata columns:
## seqnames ranges strand | id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr16 38549433-38550083 - | ENSMUSG00000034064.1..
## [2] chr16 38549636-38550083 - | ENSMUSG00000034064.1..
## gene whippet_id search set
## <character> <character> <character> <character>
## [1] ENSMUSG00000034064.14 chr16:38549434-38549.. right Y
## [2] ENSMUSG00000034064.14 chr16:38549434-38549.. right X
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengths# make transcripts with alternative junction usage
altTranscripts <- replaceJunction(wds.aa,
junctionPairs.aa,
exons,
type="AA")
# make transcripts using junction X
xTranscripts <- altTranscripts[altTranscripts$set=="AA_X"]
# make transcripts using junction Y
yTranscripts <- altTranscripts[altTranscripts$set=="AA_Y"]geneModel.all <- GeneRegionTrack(makeGeneModel(exons[exons$gene_id == altTranscripts$gene_id[1]]),
name="Reference Gene",
showId=TRUE,
transcriptAnnotation = "transcript")
# transcript X
geneModelX <- GeneRegionTrack(makeGeneModel(xTranscripts),
name="Isoform X",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
# transcript Y
geneModelY<- GeneRegionTrack(makeGeneModel(yTranscripts),
name="Isoform Y",
showId=TRUE, fill="#94AFD8",
transcriptAnnotation = "transcript")
plotTracks(list(gtr,geneModel.all,geneModelX, geneModelY),
extend.left = 1000, extend.right = 1000)
# Zoomed in at the alternative acceptor site
plotTracks(list(gtr,geneModel.all,geneModelX, geneModelY),
from = 38547500, to = 38551000)
4.3.2 Aternative donor
1. Create normal and alternative isoform structures from whippet coordinates
# filter out alternative acceptor events (coded as "AD")
# we will be looking at Mdbd3 (ENSMUSG00000035478.14)
wds.ad <- filterWhippetEvents(wds, psiDelta=0,probability=0,
event="AD", idList="ENSMUSG00000035478.14")
diffSplicingResults(wds.ad)## gene node coord strand type psi_a psi_b
## 5 ENSMUSG00000035478.14 2 chr10:80399122-80399217 - AD 0.45017 0.3398
## psi_delta probability complexity entropy
## 5 0.11036 0.953 K1 0.9937
## unique_name comparison condition_1
## 5 ENSMUSG00000035478.14_chr10:80399122-80399217_AD_2 01_v_21 01
## condition_2 coord_match condition_1_counts condition_2_counts
## 5 21 5 98.5 107.5# AD coordinates range from the normal donor splice site to the alternative donor splice site
coordinates(wds.ad)## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr10 80399122-80399217 - | chr10:80399122-80399..
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengths# find exons pairs in the gtf that bound the retained intron event
junctionPairs.ad <- findJunctionPairs(wds.ad, type="AD")
# make transcripts with alternative junction usage
altTranscripts <- replaceJunction(wds.ad,
junctionPairs.ad,
exons, type="AD")
# make transcripts using junction X
xTranscripts <- altTranscripts[altTranscripts$set=="AD_X"]
# make transcripts using junction Y
yTranscripts <- altTranscripts[altTranscripts$set=="AD_Y"]geneModel.all <- GeneRegionTrack(makeGeneModel(exons[exons$gene_id == altTranscripts$gene_id[1]]),
name="Reference Gene",
showId=TRUE,
transcriptAnnotation = "transcript")
# transcript X
geneModelX <- GeneRegionTrack(makeGeneModel(xTranscripts),
name="Isoform X",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
# transcript Y
geneModelY<- GeneRegionTrack(makeGeneModel(yTranscripts),
name="Isoform Y",
showId=TRUE, fill="#94AFD8",
transcriptAnnotation = "transcript")
plotTracks(list(gtr,geneModel.all,geneModelX, geneModelY),
extend.left = 1000, extend.right = 1000)
4.4 Alternative first/last exons
Creation of alternative first/last isoforms currently relies on junction read counts supplied by whippet.
Creation of transcripts with an alternative first (left) or last (right) exon.
4.4.1 Alternative first exons
1. Create normal and alternative isoform structures from whippet coordinates
# filter out alternative acceptor events (coded as "AF")
# we will be looking at Csrp1 (ENSMUSG00000026421.14)
wds.af <- filterWhippetEvents(wds, psiDelta=0,probability=0,
event="AF", idList="ENSMUSG00000026421.14")
diffSplicingResults(wds.af)## gene node coord strand type psi_a
## 6 ENSMUSG00000026421.14 2 chr1:135729147-135729274 + AF 0.8484
## psi_b psi_delta probability complexity entropy
## 6 0.99487 -0.14647 1 K1 0.6421
## unique_name comparison condition_1
## 6 ENSMUSG00000026421.14_chr1:135729147-135729274_AF_2 01_v_21 01
## condition_2 coord_match condition_1_counts condition_2_counts
## 6 21 6 51.5 196.5# whippet outputs first (or last) exon being tested only
# AF/AL coordinates range are exon coordinates for the tested first/last exon
coordinates(wds.af)## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr1 135729147-135729274 + | chr1:135729147-13572..
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengths# find junction pairs that use the same acceptor/donor as the specified first/last exon
# i.e. find the alternative first/last exon
junctionPairs.af <- findJunctionPairs(wds.af, type="AF")# make transcripts with alternative junction usage
altTranscripts <- replaceJunction(wds.af, junctionPairs.af,
exons,
type="AF")
# make transcripts using exon X
xTranscripts <- altTranscripts[altTranscripts$set=="AF_X"]
# make transcripts using exon Y
yTranscripts <- altTranscripts[altTranscripts$set=="AF_Y"]geneModel.all <- GeneRegionTrack(makeGeneModel(exons[exons$gene_id == altTranscripts$gene_id[1]]),
name="Reference Gene",
showId=TRUE,
transcriptAnnotation = "transcript")
# reference transcript
geneModelX <- GeneRegionTrack(makeGeneModel(xTranscripts),
name="Isoform X",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
# for the retained intron transcript
geneModelY<- GeneRegionTrack(makeGeneModel(yTranscripts),
name="Isoform Y",
showId=TRUE, fill="#94AFD8",
transcriptAnnotation = "transcript")
# Only the transcript isoforms with exons at the boundries of the retained intron are used for analysis, and 'novel' isoforms are created by intron retention
plotTracks(list(gtr,geneModel.all,geneModelX, geneModelY),
extend.left = 1000, extend.right = 1000)
4.4.2 Alternative last exons
1. Create normal and alternative isoform structures from whippet coordinates
# filter out alternative acceptor events (coded as "AL")
# we will be looking at Ppm1b (ENSMUSG00000061130.12)
wds.al <- filterWhippetEvents(wds, psiDelta=0,probability=0,
event="AL", idList="ENSMUSG00000061130.12")
diffSplicingResults(wds.al)## gene node coord strand type psi_a psi_b
## 7 ENSMUSG00000061130.12 8 chr17:85013566-85014776 + AL 0.6706 0.50781
## psi_delta probability complexity entropy
## 7 0.16279 0.969 K2 1.226
## unique_name comparison condition_1
## 7 ENSMUSG00000061130.12_chr17:85013566-85014776_AL_8 01_v_21 01
## condition_2 coord_match condition_1_counts condition_2_counts
## 7 21 7 155.5 187.5# whippet outputs first (or last) exon being tested only
# AF/AL coordinates range are exon coordinates for the tested first/last exon
coordinates(wds.al)## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | id
## <Rle> <IRanges> <Rle> | <character>
## [1] chr17 85013566-85014776 + | chr17:85013566-85014..
## -------
## seqinfo: 5 sequences from an unspecified genome; no seqlengths# find junction pairs that use the same acceptor/donor as the specified first/last exon
# i.e. find the alternative first/last exon
junctionPairs.al <- findJunctionPairs(wds.al, type="AL")
# make transcripts with alternative junction usage
altTranscripts <- replaceJunction(wds.al, junctionPairs.al,
exons,
type="AL")
# make transcripts using junction X
xTranscripts <- altTranscripts[altTranscripts$set=="AL_X"]
# make transcripts using junction Y
yTranscripts <- altTranscripts[altTranscripts$set=="AL_Y"]geneModel.all <- GeneRegionTrack(makeGeneModel(exons[exons$gene_id == altTranscripts$gene_id[1]]),
name="Reference Gene",
showId=TRUE,
transcriptAnnotation = "transcript")
# reference transcript
geneModelX <- GeneRegionTrack(makeGeneModel(xTranscripts),
name="Isoform X",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
# for the retained intron transcript
geneModelY<- GeneRegionTrack(makeGeneModel(yTranscripts),
name="Isoform Y",
showId=TRUE, fill="#94AFD8",
transcriptAnnotation = "transcript")
# Only the transcript isoforms with exons at the boundries of the retained intron are used for analysis, and 'novel' isoforms are created by intron retention
plotTracks(list(gtr,geneModel.all,geneModelX, geneModelY),
extend.left = 1000, extend.right = 1000)
4.5 Alternative Intron usage (leafcutter)
leafcutter uses an intron-centric view of splicing, and therefore all tested events are given as intron coordinates in clusters. Alternative isoforms are generated in sets. If possible, all downregulated introns/junctions are grouped together in a set, and all upregulated introns/junctions in another.
alternativeIntronUsage() first finds transcripts which overlap each intron set, and have perfect matches to the start and end of the intron (i.e. share splice sites). If exons are present within the range overlapping the intron set, these are replaced with exons that preserve the intron usage set.
Three intron cluster
Alternative intron leafcutter event in Eif4a2
# select a single cluster
cluster <- leafcutter_summary[leafcutter_summary$cluster=="chr16:clu_1396",]
# generate alternative isoforms
altIsoforms1396 <- alternativeIntronUsage(cluster, exons)
# downregulated isoforms
altIsoforms1396_dnreg <- altIsoforms1396[grep("dnre",
altIsoforms1396$transcript_id)]
# upregulated isoforms
altIsoforms1396_upreg <- altIsoforms1396[grep("upre",
altIsoforms1396$transcript_id)]
# visualise
gtr <- GenomeAxisTrack()
geneModel.ref <- GeneRegionTrack(makeGeneModel(
exons[exons$gene_id=="ENSMUSG00000022884.14"]),
name="Reference Gene",
showId=TRUE,
transcriptAnnotation = "transcript")
geneModel.dnreg <- GeneRegionTrack(makeGeneModel(altIsoforms1396_dnreg),
name="Downregulated isoforms",
showId=TRUE,fill="#4D7ABE",
transcriptAnnotation = "transcript")
geneModel.upreg <- GeneRegionTrack(makeGeneModel(altIsoforms1396_upreg),
name="Upregulated isoforms",fill="#94AFD8",
showId=TRUE,
transcriptAnnotation = "transcript")
plotTracks(list(geneModel.ref,geneModel.dnreg, geneModel.upreg),
extend.left = 1000, extend.right = 1000)
Five intron cluster
More sets may be used if the number of introns in each cluster is greater than three.
In this case, the downregulated introns can overlap, so are split into two sets: e+d+b, and c
Alternative intron leafcutter event in Rnps1
# select a single cluster
cluster <- leafcutter_summary[leafcutter_summary$cluster=="chr17:clu_1281",]
# generate alternative isoforms
altIsoforms1281 <- alternativeIntronUsage(cluster, exons)
# downregulated isoforms
altIsoforms1281_dnreg <- altIsoforms1281[grep("dnre",
altIsoforms1281$transcript_id)]
# upregulated isoforms
altIsoforms1281_upreg <- altIsoforms1281[grep("upre",
altIsoforms1281$transcript_id)]
# visualise
gtr <- GenomeAxisTrack()
geneModel.ref <- GeneRegionTrack(makeGeneModel(
exons[exons$gene_id=="ENSMUSG00000034681.16"]),
name="Reference Gene",
showId=TRUE,
transcriptAnnotation = "transcript")
geneModel.dnreg <- GeneRegionTrack(makeGeneModel(altIsoforms1281_dnreg),
name="Downregulated isoforms",
showId=TRUE,fill="#4D7ABE",
transcriptAnnotation = "transcript")
geneModel.upreg <- GeneRegionTrack(makeGeneModel(altIsoforms1281_upreg),
name="Upregulated isoforms",fill="#94AFD8",
showId=TRUE,
transcriptAnnotation = "transcript")
plotTracks(list(geneModel.ref,geneModel.dnreg, geneModel.upreg),
extend.left = 1000, extend.right = 1000)
5 Annotate Open Reading Frames
1. Find open reading frame features
# we will be looking at Ndufv3 (ENSMUSG00000024038.16) again
wds.ce <- filterWhippetEvents(wds, psiDelta=0,probability=0,
event="CE", idList="ENSMUSG00000024038.16")
# find exons in the gtf that overlap the skipped exon event
exons.ce <- findExonContainingTranscripts(wds.ce,
exons = exons,
transcripts = transcripts)
# make skipped and included exon transcripts
# removes the skipped exon from all transcripts which contain it
skippedExonTranscripts <- skipExonInTranscript(skippedExons = exons.ce,
exons=exons,
match="exact",
whippetDataSet=wds.ce)
# make non-skipped exon transcripts
normalTranscripts <- exons[exons$transcript_id %in%
exons.ce$transcript_id]
# get ORF details for each set of transcripts
orfs_normal <- getOrfs(normalTranscripts, BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE)
orfs_skipped <- getOrfs(skippedExonTranscripts[skippedExonTranscripts$set ==
"skipped_exon"],
BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE)
orfs_included <- getOrfs(skippedExonTranscripts[skippedExonTranscripts$set ==
"included_exon"],
BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE)
head(orfs_normal[,-8])## id gene_id frame seq_length seq_length_nt
## 1 ENSMUST00000046288.14 ENSMUSG00000024038.16 1 512 1537
## 2 ENSMUST00000046288.14 ENSMUSG00000024038.16 2 512 1537
## 3 ENSMUST00000046288.14 ENSMUSG00000024038.16 3 511 1537
## start_site stop_site orf_length start_site_nt stop_site_nt utr3_length
## 1 428 466 38 1282 1399 139
## 2 358 406 48 1073 1220 318
## 3 19 487 468 57 1464 74
## min_dist_to_junction_a exon_a_from_start min_dist_to_junction_b
## 1 78 3 140
## 2 991 2 102
## 3 143 3 75
## exon_b_from_final
## 1 0
## 2 1
## 3 0# id: transcript isoform id
# gene_id: gene id
# frame: which open reading frame (1:3)
# seq_length: sequence length (in AA)
# seq_length_nt: sequence length (in nt)
# start_site: ORF start site (in AA)
# stop_site: ORF stop site (in AA)
# orf_sequence: ORF sequence (not shown)
# orf_length: ORF length (in AA)
# start_site_nt: ORF start site (in nt) / 5'UTR length
# stop_site_nt: ORF stop site (in nt)
# utr3_length: 3'UTR length (in nt)
# min_dist_to_junction_a: distance from stop codon to upstream junction (junction A)
# exon_a_from_start: junction A exon number
# min_dist_to_junction_b: distance from stop codon to downstream junction (junction B),
# exon_b_from_final: junction B exon number (counting backwards from the final exon)We can also annotate upstream open reading frames for transcripts
# either as an indivudual data.frame with all uORFs
upstreamORFs <- getUOrfs(normalTranscripts, BSgenome = g, orfs=orfs_normal, findExonB=TRUE)
head(upstreamORFs)## id frame overlaps_main_ORF uorf_length start_site_nt
## 3 ENSMUST00000046288.14 1 downstream 468 57
## 11 ENSMUST00000046288.14 1 upstream 10 229
## 9 ENSMUST00000046288.14 1 upstream 13 382
## 4 ENSMUST00000046288.14 1 upstream 29 553
## 7 ENSMUST00000046288.14 1 upstream 18 709
## 5 ENSMUST00000046288.14 1 upstream 23 749
## stop_site_nt dist_to_start_nt min_dist_to_junction_b exon_b_from_final
## 3 1464 -182 75 0
## 11 262 1020 1060 1
## 9 424 858 898 1
## 4 643 639 679 1
## 7 766 516 556 1
## 5 821 461 501 1# id: transcript id
# frame: reading frame for ORIGINAL orf data
# overlaps_main_orf: is the entire uorf upstream of the main orf (upstream), or is there some overlap with the main orf (downsteam) - i.e. uORF stop codon is within the main ORF
# uorf_length: length of the uorf (in AA)
# start_site_nt: position (in nt) where the uorf start codon occurs within the transcript
# stop_site_nt: position (in nt) where the uorf stop codon occurs within the transcript
# dist_to_start_nt: distance (in nt) from the uorf stop codon to the main orf start codon
# min_dist_to_junction_b: distance from the uorf stop codon to the nearest downstream exon end/splice junction
# exon_b_from_final: relative exon number (from the end) of the uorf stop codon containing exon
# or as a summary by using the getOrfs() function
# with uORFS=TRUE
orfs_normal <- getOrfs(normalTranscripts, BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE,
uORFs=TRUE)
head(orfs_normal[,-8])## id gene_id frame seq_length seq_length_nt
## 1 ENSMUST00000046288.14 ENSMUSG00000024038.16 1 512 1537
## 2 ENSMUST00000046288.14 ENSMUSG00000024038.16 2 512 1537
## 3 ENSMUST00000046288.14 ENSMUSG00000024038.16 3 511 1537
## start_site stop_site orf_length start_site_nt stop_site_nt utr3_length
## 1 428 466 38 1282 1399 139
## 2 358 406 48 1073 1220 318
## 3 19 487 468 57 1464 74
## min_dist_to_junction_a exon_a_from_start min_dist_to_junction_b
## 1 78 3 140
## 2 991 2 102
## 3 143 3 75
## exon_b_from_final total_uorfs upstream_count downstream_count max_uorf
## 1 0 8 7 1 468
## 2 1 7 6 1 468
## 3 0 0 0 0 0
## uorf_maxb
## 1 1060
## 2 1060
## 3 0# this adds the following columns:
# total_uorfs: total number of uorfs found for the transcript and annotated open reading frame.
# upstream_count: number of uorfs that are located fully upstream of the main orf
# downstream_count: number of uorfs which partially overlap the main orf
# max_uorf: maximum length of an annotated uorf. If no uorfs annotated, = 0
# uorf_maxb: maximum distance from the uorf stop codon to the nearest downstream exon end/splice junction2. Compare ORFs
# compare normal and skipped isoforms
orfChange <- orfDiff(orfsX = orfs_included,
orfsY = orfs_skipped,
filterNMD = FALSE,
compareBy="gene",
geneSimilarity = TRUE,
compareUTR=TRUE,
allORFs = orfs_normal)
orfChange## id orf_length_bygroup_x orf_length_bygroup_y
## 1 chr17:31527310-31528401 468 104
## utr3_length_bygroup_x utr3_length_bygroup_y utr5_length_bygroup_x
## 1 74 74 57
## utr5_length_bygroup_y filtered percent_orf_shared max_percent_orf_shared
## 1 57 FALSE 0.2222222 0.2222222
## orf_percent_kept_x orf_percent_kept_y gene_similarity_x gene_similarity_y
## 1 0.2222222 1 1 0.2222222# id: splicing event ID
# orf_length_by_group_x: longest orf in first set of transcripts (included exon)
# orf_length_by_group_y: longest orf in second set of transcripts (skipped exon)
# utr3_length_by_group_x: 3'UTR length in first set of transcripts (included exon)
# utr3_length_by_group_y: 3'UTR length in second set of transcripts (skipped exon)
# utr5_length_by_group_x: 5'UTR length in first set of transcripts (included exon)
# utr5_length_by_group_y: 5'UTR length in second set of transcripts (skipped exon)
# filtered: filtered for NMD ?
# percent_orf_shared: percent of the ORF shared between skipped and included exon transcripts
# max_percent_orf_shared: theoretical maximum percent of the ORF that could be shared (orf_length_by_group_y / orf_length_by_group_x) or (orf_length_by_group_x / orf_length_by_group_y)
# orf_percent_kept_x: percent of the ORF in group x (included exon) contained in group y (skipped exon)
# orf_percent_kept_y: percent of the ORF in group y (skipped exon) contained in group x (included exon)
# gene_similarity_x: max percent of a normal ORF shared in the group x (included exon) transcript. If multiple ORF frames and transcripts are available, this is the maximum value from comparing the skipped isoform ORF to ALL normal isoform ORFs.
# gene_similarity_y: max percent of a normal ORF shared in the group y (skipped exon) transcript. If multiple ORF frames and transcripts are available, this is the maximum value from comparing the skipped isoform ORF to ALL normal isoform ORFs. 2.b. Compare ORFs with NMD probability
You can also use our package “notNMD” to predict nonsense-mediated decay potential in transcripts
# devtools::install_github("betsig/notNMD")
library(notNMD)
# we will be looking at Ndufv3 (ENSMUSG00000024038.16) again
wds.ce <- filterWhippetEvents(wds, psiDelta=0,probability=0,
event="CE", idList="ENSMUSG00000024038.16")
# find exons in the gtf that overlap the skipped exon event
exons.ce <- findExonContainingTranscripts(wds.ce,
exons = exons,
transcripts = transcripts)
# make skipped and included exon transcripts
# removes the skipped exon from all transcripts which contain it
skippedExonTranscripts <- skipExonInTranscript(skippedExons = exons.ce,
exons=exons,
match="exact",
whippetDataSet=wds.ce)
# make non-skipped exon transcripts
normalTranscripts <- exons[exons$transcript_id %in% exons.ce$transcript_id]
# get ORF details for each set of transcripts
# note that notNMD requires upstream orf annotations
orfs_normal <- getOrfs(normalTranscripts, BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE, uORFs=TRUE)
orfs_skipped <- getOrfs(skippedExonTranscripts[skippedExonTranscripts$set ==
"skipped_exon"],
BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE, uORFs=TRUE)
orfs_included <- getOrfs(skippedExonTranscripts[skippedExonTranscripts$set ==
"included_exon"],
BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE, uORFs=TRUE)
# calculate NMD probability
# --- note that if you have a different method for assessing NMD potential, you may substitute the values here
orfs_normal$nmd_prob <- notNMD::predictNMD(orfs_normal, "prob")
orfs_normal$nmd_class <- notNMD::predictNMD(orfs_normal)
orfs_skipped$nmd_prob <- notNMD::predictNMD(orfs_skipped, "prob")
orfs_skipped$nmd_class <- notNMD::predictNMD(orfs_skipped)
orfs_included$nmd_prob <- notNMD::predictNMD(orfs_included, "prob")
orfs_included$nmd_class <- notNMD::predictNMD(orfs_included)
orfs_normal <- orfs_normal[which(!is.na(orfs_normal$orf_length)),]
orfs_skipped <- orfs_skipped[which(!is.na(orfs_skipped$orf_length)),]
orfs_included <- orfs_included[which(!is.na(orfs_included$orf_length)),]
# compare normal and skipped isoforms
# this time setting filterNMD to TRUE, which removes NMD targeted frames/isoforms where possible
orfChange <- orfDiff(orfsX = orfs_included,
orfsY = orfs_skipped,
filterNMD = TRUE,
geneSimilarity = TRUE,
compareUTR=TRUE,
allORFs = orfs_normal)
nmdChange <- attrChangeAltSpliced(orfs_included,orfs_skipped,
attribute="nmd_prob",
compareBy="gene",
useMax=FALSE)
m <- match(orfChange$id, nmdChange$id)
orfChange <- cbind(orfChange, nmdChange[m,-1])This adds an extra two columns to the orfChange output:
nmd_prob_bygroup_x: mininmum NMD probability in first set of transcripts (normalTranscripts)
nmd_prob_bygroup_y: mininmum NMD probability in second set of transcripts (skippedExonTranscripts)
# plot ORFs on transcripts
# annotate UTR/CDS locations
geneModel.skipped <- annotateGeneModel(skippedExonTranscripts[
skippedExonTranscripts$set=="skipped_exon"], orfs_skipped)
geneModel.included <- annotateGeneModel(skippedExonTranscripts[
skippedExonTranscripts$set=="included_exon"], orfs_included)
grtr.included <- GeneRegionTrack(geneModel.included,
name="Included Isoform",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
# make tracks for non-nmd targeted CDS
grtrCDS.included <- GeneRegionTrack(
geneModel.included[geneModel.included$feature == "CDS",],
name="Included Isoform CDS",
showId=TRUE,fill="#CB3634",
transcriptAnnotation = "transcript")
grtr.skipped <- GeneRegionTrack(geneModel.skipped,
name="Skipped Isoform",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
# make tracks for non-nmd targeted CDS
grtrCDS.skipped <- GeneRegionTrack(
geneModel.skipped[geneModel.skipped$feature == "CDS",],
name="Skipped Isoform CDS",
showId=TRUE,fill="#CB3634",
transcriptAnnotation = "transcript")
plotTracks(list(gtr, grtr.included, grtr.skipped,
grtrCDS.included,grtrCDS.skipped),
extend.left = 1000, extend.right = 1000)
# Full length transcripts in blue, CDS only in redBy using GeneStructureTools and examining visually, we find that skipping of exon 3 in Ndufv3 decreases the size open reading frame (from 468 to 104AA), by removing an in frame exon - UTR lengths are unchanged and no alternative ORF sequence is generated.
# we will be looking at Srsf1 (ENSMUSG00000018379.17) again
wds.ri <- filterWhippetEvents(wds, psiDelta=0,probability=0,
event="RI", idList="ENSMUSG00000018379.17")
# find flanking exons
exons.ri <- findIntronContainingTranscripts(wds.ri,
exons)
# make retained and non-retained transcripts
# adds the intron into all transcripts which overlap it
retainedIntronTranscripts <- addIntronInTranscript(exons.ri,
exons = exons,
glueExons = TRUE,
whippetDataSet=wds.ri)
# make non-retained intron transcripts
normalTranscripts <- exons[exons$transcript_id %in%
exons.ri$transcript_id]
# get ORF details for each set of transcripts
orfs_normal <- getOrfs(normalTranscripts, BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE)
orfs_retained <- getOrfs(
retainedIntronTranscripts[retainedIntronTranscripts$set == "retained_intron"],
BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE)
orfs_spliced <- getOrfs(
retainedIntronTranscripts[retainedIntronTranscripts$set == "spliced_intron"],
BSgenome = g,
returnLongestOnly = FALSE, allFrames = TRUE)
# compare normal and retained isoforms
orfChange <- orfDiff(orfsX = orfs_spliced,
orfsY = orfs_retained,
filterNMD = FALSE,
geneSimilarity = TRUE,
compareUTR=TRUE)
orfChange## id orf_length_bygroup_x orf_length_bygroup_y
## 1 chr11:88049216-88049411 253 201
## utr3_length_bygroup_x utr3_length_bygroup_y utr5_length_bygroup_x
## 1 411 763 37
## utr5_length_bygroup_y filtered percent_orf_shared max_percent_orf_shared
## 1 37 FALSE 0 0.7944664
## orf_percent_kept_x orf_percent_kept_y
## 1 0 0# plot ORFs on transcripts
# annotate UTR/CDS locations
geneModel.retained <- annotateGeneModel(
retainedIntronTranscripts[retainedIntronTranscripts$set == "retained_intron"],
orfs_retained)
geneModel.spliced <- annotateGeneModel(
retainedIntronTranscripts[retainedIntronTranscripts$set == "spliced_intron"],
orfs_retained)
grtr.spliced <- GeneRegionTrack(geneModel.spliced,
name="Spliced Isoform",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
grtrCDS.spliced <- GeneRegionTrack(
geneModel.spliced[geneModel.spliced$feature == "CDS",],
name="Spliced Isoform CDS",
showId=TRUE,fill="#CB3634",
transcriptAnnotation = "transcript")
grtr.retained <- GeneRegionTrack(geneModel.retained,
name="Retained Isoform",
showId=TRUE, fill="#4D7ABE",
transcriptAnnotation = "transcript")
grtrCDS.retained <- GeneRegionTrack(
geneModel.retained[geneModel.retained$feature == "CDS",],
name="Retained Isoform CDS",
showId=TRUE,fill="#CB3634",
transcriptAnnotation = "transcript")
gtr <- GenomeAxisTrack()
plotTracks(list(gtr, grtr.spliced, grtrCDS.spliced,
grtr.retained, grtrCDS.retained),
extend.left = 1000, extend.right = 1000)
# Full length transcripts in blue, CDS only in redBy using GeneStructureTools and examining visually, we find that intron retention in Srsf1 decreases the size open reading frame (from 253 to 201AA), by generating a premature stop codon.
If notNMD is installed and loaded, NMD can be set to TRUE in the whippetTranscriptChangeSummary function.
summary <- whippetTranscriptChangeSummary(
whippetDataSet=wds,
exons = exons,
transcripts = transcripts,
BSgenome = g,
NMD = TRUE
)6 DEXSeq
DEXSeq tests exons (or ‘exonic parts’) for differential usage between conditions. GeneStructureTools provides a few helper functions to help further annotate where in a transcript differential exon usage occurs.
6.1 GTF reannotation
Annotation of 5’ and 3’ UTRs
Reannotates any blocks in a gtf GRanges that are annotated as a UTR and have a CDS block annotated in the same transcript.
gtf <- rtracklayer::import(system.file("extdata","example_gtf.gtf",
package = "GeneStructureTools"))
table(gtf$type)##
## gene transcript exon CDS start_codon stop_codon
## 70 453 4039 2919 225 225
## UTR
## 788gtf_UTRannotated <- UTR2UTR53(gtf)
#some transfer from exon annotation to UTR3/5 due to overlapping with a reannotated UTR
table(gtf$type, gtf_UTRannotated$type)##
## CDS UTR UTR3 UTR5 exon gene start_codon stop_codon transcript
## gene 0 0 0 0 0 70 0 0 0
## transcript 0 0 0 0 0 0 0 0 453
## exon 0 0 233 132 3674 0 0 0 0
## CDS 2919 0 0 0 0 0 0 0 0
## start_codon 0 0 0 0 0 0 225 0 0
## stop_codon 0 0 13 0 0 0 0 212 0
## UTR 0 1 457 330 0 0 0 0 0Annotation of broader transcript biotypes
Reannotates transcript biotypes into lncRNA, nmd, protein coding, pseudogene, retained intron, and short ncRNA categories.
gtf <- addBroadTypes(gtf)
table(gtf$transcript_type, gtf$transcript_type_broad)##
## lncRNA nmd protein_coding pseudogene
## antisense 25 0 0 0
## bidirectional_promoter_lncRNA 3 0 0 0
## lincRNA 7 0 0 0
## macro_lncRNA 5 0 0 0
## miRNA 0 0 0 0
## non_stop_decay 0 17 0 0
## nonsense_mediated_decay 0 1091 0 0
## polymorphic_pseudogene 0 0 0 18
## processed_pseudogene 0 0 0 2
## processed_transcript 308 0 0 0
## protein_coding 0 0 6577 0
## pseudogene 0 0 0 3
## retained_intron 0 0 0 0
## sense_intronic 3 0 0 0
## sense_overlapping 5 0 0 0
## snRNA 0 0 0 0
## snoRNA 0 0 0 0
##
## retained_intron short_ncRNA
## antisense 0 0
## bidirectional_promoter_lncRNA 0 0
## lincRNA 0 0
## macro_lncRNA 0 0
## miRNA 0 6
## non_stop_decay 0 0
## nonsense_mediated_decay 0 0
## polymorphic_pseudogene 0 0
## processed_pseudogene 0 0
## processed_transcript 0 0
## protein_coding 0 0
## pseudogene 0 0
## retained_intron 575 0
## sense_intronic 0 0
## sense_overlapping 0 0
## snRNA 0 2
## snoRNA 0 2# Ful table of all transcript types and their broader version from gencode vM14
transcript_types <- read.delim("transcript_types_broad_table.txt")
transcript_types## transcript_type transcript_type_broad freq
## 1 3prime_overlapping_ncRNA lncRNA 10
## 2 antisense lncRNA 14930
## 3 bidirectional_promoter_lncRNA lncRNA 715
## 4 lincRNA lncRNA 30775
## 5 macro_lncRNA lncRNA 9
## 6 processed_transcript lncRNA 78867
## 7 sense_intronic lncRNA 878
## 8 sense_overlapping lncRNA 215
## 9 non_stop_decay nmd 313
## 10 nonsense_mediated_decay nmd 142846
## 11 protein_coding protein_coding 1243574
## 12 polymorphic_pseudogene pseudogene 958
## 13 processed_pseudogene pseudogene 17449
## 14 pseudogene pseudogene 365
## 15 transcribed_processed_pseudogene pseudogene 513
## 16 transcribed_unitary_pseudogene pseudogene 39
## 17 transcribed_unprocessed_pseudogene pseudogene 1373
## 18 translated_processed_pseudogene pseudogene 26
## 19 unitary_pseudogene pseudogene 199
## 20 unprocessed_pseudogene pseudogene 8743
## 21 retained_intron retained_intron 101930
## 22 IG_C_gene short_ncRNA 225
## 23 IG_C_pseudogene short_ncRNA 4
## 24 IG_D_gene short_ncRNA 57
## 25 IG_D_pseudogene short_ncRNA 6
## 26 IG_J_gene short_ncRNA 42
## 27 IG_LV_gene short_ncRNA 18
## 28 IG_pseudogene short_ncRNA 5
## 29 IG_V_gene short_ncRNA 1767
## 30 IG_V_pseudogene short_ncRNA 423
## 31 miRNA short_ncRNA 4404
## 32 misc_RNA short_ncRNA 1132
## 33 Mt_rRNA short_ncRNA 4
## 34 Mt_tRNA short_ncRNA 44
## 35 ribozyme short_ncRNA 44
## 36 rRNA short_ncRNA 708
## 37 scaRNA short_ncRNA 102
## 38 scRNA short_ncRNA 2
## 39 snoRNA short_ncRNA 3016
## 40 snRNA short_ncRNA 2766
## 41 sRNA short_ncRNA 4
## 42 TEC short_ncRNA 6179
## 43 TR_C_gene short_ncRNA 98
## 44 TR_D_gene short_ncRNA 12
## 45 TR_J_gene short_ncRNA 210
## 46 TR_J_pseudogene short_ncRNA 20
## 47 TR_V_gene short_ncRNA 1206
## 48 TR_V_pseudogene short_ncRNA 816.2 DEXSeq event overlapping
DEXSeq data should be processed as per the DEXSeq manual for differential exon usage.
The script with details for how to generate the significant results table are in inst/extdata/dexseq_process.R.
You can process your own DEXSeq results from the DEXSeqResults object generated by DEXSeqResults(dxd).
# load dexseq processed data
load("dexseq_processed.Rdata")
# create results data.frame from the DEXSeqResults object
dexseq_results <- as.data.frame(dxr1)
# 3395 events significant
dexseq_results.significant <- dexseq_results[which(dexseq_results$padj < 1e-12 & abs(dexseq_results$log2fold_21_01) > 1),]
write.table(dexseq_results.significant, file="dexseq_results_significant.txt",
sep="\t", quote=FALSE)# import dexseq gtf
gtf <- rtracklayer::import(system.file("extdata","example_gtf.gtf",
package = "GeneStructureTools"))
gtf <- UTR2UTR53(gtf)
dexseq_ranges <- rtracklayer::import(system.file("extdata",
"gencode.vM14.dexseq.gtf", package = "GeneStructureTools"))
dexseq_results.significant <- read.delim(system.file("extdata",
"dexseq_results_significant.txt", package = "GeneStructureTools"))
# find the exon type of the significant events
dexseq_results.significant$overlap_types <-
findDEXexonType(rownames(dexseq_results.significant), dexseq_ranges, gtf=gtf)
overlap_types <- table(dexseq_results.significant$overlap_types)
# broader definition
dexseq_results.significant$overlap_types_broad <- summariseExonTypes(dexseq_results.significant$overlap_types)
table(dexseq_results.significant$overlap_types_broad)##
## CDS UTR3 UTR5 noncoding_exon start_codon
## 11 6 3 10 3
## stop_codon
## 77 Session Info
sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
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## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] Gviz_1.42.0 BSgenome.Mmusculus.UCSC.mm10_1.4.3
## [3] BSgenome_1.66.0 rtracklayer_1.58.0
## [5] Biostrings_2.66.0 XVector_0.38.0
## [7] stringr_1.4.1 GenomicRanges_1.50.0
## [9] GenomeInfoDb_1.34.0 IRanges_2.32.0
## [11] S4Vectors_0.36.0 BiocGenerics_0.44.0
## [13] GeneStructureTools_1.18.0 BiocStyle_2.26.0
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## [37] glue_1.6.2 GenomeInfoDbData_1.2.9
## [39] dplyr_1.0.10 rappdirs_0.3.3
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## [71] RSQLite_2.2.18 highr_0.9
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