circRNAprofiler is an R-based framework that only requires an R installation and offers 15 modules for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis. The pipeline used by circRNAprofiler is highly automated and customizable. Furthermore, circRNAprofiler includes additional functions for data visualization which facilitate the interpretation of the results.
This vignettes provides a guide of how to use the R package circRNAProfiler.
As practical example the RNA-sequencing data from human left ventricle tissues previously analyzed by our group for the presence of circRNAs (Khan et al. 2016), was here re-analyzed. Multiple detection tools (CircMarker(cm), MapSplice2 (ms) and NCLscan (ns)) were used this time for the detection of circRNAs and an additional sample for each condition was included reaching a total of 9 samples: 3 control hearts, 3 hearts of patients with dilated cardiomyopathies (DCM) and 3 hearts with hypertrophic cardiomyopathies (HCM). After the detections we ran through Modules 1-3 to generate the object backSplicedJunctions and mergedBSJunctions that will be used in this vignettes.
Raw RNA sequencing data are available at NCBI BioProject accession number PRJNA533243.
You can install circRNAprofiler using:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("circRNAprofiler")
To download the development version on github use:
BiocManager::install("Aufiero/circRNAprofiler")
library(circRNAprofiler)
# Packages needed for the vignettes
library(ggpubr)
library(ggplot2)
library(VennDiagram)
library(gridExtra)
An important step of the analysis workflow is to initialize the project folder, which can be done with the helper function initCircRNAprofiler() available in the package. The project folder should be structured as in the example Figure 2 if seven detection tools are used to detect circRNAs in 6 samples.
Helper function
Project folder contains project files and circRNA prediction results. In detail, project files include the genome-annotation file (.gtf), the file containing information about the experimental design (experiment.txt) and optional files (motifs.txt, miRs.txt, transcripts.txt, traits.txt) which contain user specifications that are used to customize the analysis to execute. In order to initialize the project folder the helper function initCircRNAprofiler() can be used to streamline the process. To initialize the project folder run the following command specifying the tools used to predict circRNAs. The following options are allowed: “mapsplice”, “nclscan”, “knife”, “circexplorer2”, “uroborus”, “circmarker”, and “other” (see below when to use the option “other”). The function will automatically create the project folder with the corresponding subfolders named as the circRNA detection tools used, and the 5 *.txt files templates with the corresponding headers. These files should be filled with the appropriate information (see files description below).
If only MapSplice is used for the circRNA detection run the following command:
initCircRNAprofiler(projectFolderName = "circRNAprofiler", detectionTools =
"mapsplice")
If circRNA detection is performed by using multiple detection tools then run the command with the name of the detection tools used, e.g.:
initCircRNAprofiler(
projectFolderName = "circRNAprofiler",
detectionTools = c("mapsplice", "nclscan", "circmarker")
)
Next, set project folder circRNAprofiler as your working directory.
Project files description
The file experiment.txt contains the experiment design information. It must have at least 3 columns with headers:
label (1st column): unique names of the samples (short but informative).
fileName (2nd column): name of the input files - e.g. circRNAs_X.txt, where x can be can be 001, 002 etc.
condition (3rd column): biological conditions - e.g. A or B; healthy or diseased if you have only 2 conditions.
The functions for the filtering and the differential expression analysis depend on the information reported in this file. The differential expression analysis is performed by comparing the condition positioned forward against the conditions positioned backward in the alphabet (column condition of experiment.txt), so that, circRNAs with positive log2FC are up-regulated in condition B compared to condition A (and vice versa for circRNA with negative log2FC).
label | fileName | condition |
---|---|---|
C1 | circRNAs_006.txt | A |
C2 | circRNAs_009.txt | A |
C3 | circRNAs_007.txt | A |
D1 | circRNAs_005.txt | B |
D2 | circRNAs_002.txt | B |
D3 | circRNAs_001.txt | B |
H1 | circRNAs_003.txt | C |
H2 | circRNAs_004.txt | C |
H3 | circRNAs_008.txt | C |
The file *.gtf contains the genome annotation. circRNAprofiler works well and was tested with ensamble gencode, UCSC or NCBI based-genome annotations. It is suggested to use the same annotation file used during the read mapping procedure.
The file motifs.txt (optional) contains motifs/regular expressions specified by the user. It must have 3 columns with header:
id (1st column): name of the motif. - e.g. RBM20 or motif1.
motif (2nd column): motif/pattern to search.
length (3rd column): length of the motif.
If this file is absent or empty only the motifs of RNA Binding Proteins in the ATtRACT database (Giudice et al. 2016) are considered in the motifs analysis.
id | motif | length |
---|---|---|
RBM20 | [ACU]UUCU[ACU] | 6 |
The file traits.txt (optional) contains diseases/traits specified by the user. It must have one column with header id. Type data(“gwasTraits”) to have an image (dated on the 31st October 2018) of the traits reported in the GWAS catalog (MacArthur et al. 2017). The GWAS catalog is a curated collection of all published genome-wide association studies and contains~ 90000 unique SNP-trait associations. If the file traits.txt is absent or empty, all SNPs associated with all diseases/traits in the GWAS catalog are considered in the SNPs analysis.
id |
---|
AR-C124910XX levels in individuals with acute coronary syndromes treated with ticagrelor |
Anthracycline-induced cardiotoxicity in childhood cancer |
Atrial Septal Defect |
Atrial fibrillation |
Atrial fibrillation (SNPxSNP interaction) |
Atrial fibrillation (interaction) |
The file miRs.txt (optional) contains the microRNA ids from miRBase (Griffiths-Jones et al. 2006) specified by the user. It must have one column with header id. The first row must contain the miR name starting with the “>”, e.g >hsa-miR-1-3p. The sequences of the miRs will be automatically retrieved from the mirBase latest release or from the given mature.fa file, that should be present in the project folder. If this file is absent or empty, all miRs of the specified species are considered in the miRNA analysis.
id |
---|
>hsa-miR-1-3p |
>hsa-miR-126-3p |
>hsa-miR-145-5p |
>hsa-miR-133a-3p |
>hsa-miR-451a |
>hsa-miR-23b-3p |
The file transcripts.txt (optional) contains the transcript ids of the circRNA host genes. It must have one column with header id. If this file is empty the longest transcript of the circRNA host genes whose exon coordinates overlap with that of the detected back-spliced junctions are considered in the annotation analysis.
id |
---|
ENST00000514743.1 |
circRNA prediction results
The circRNAs_X.txt contains the detected circRNAs. Once the project folder has been initialized the circRNAs_X.txt file/s must go in the corresponding subfolders. There must be one .txt file per sample named circRNAs_X.txt, where X can be 001, 002 etc. If there are 6 samples, 6 .txt files named circRNAs_001.txt, circRNAs_002.txt, circRNAs_003.txt, circRNAs_004.txt, circRNAs_005.txt, circRNAs_006.txt must be present in each subfolder named as the name of the tool that has been used for the circRNA detection. If the detection tool used is mapsplice, nclscan, knife, circexplorer2, uroborus or circmarker, the only thing to do after the detection is to rename the files to circRNAs_X.txt and put them in the corresponding subfolder. A specific import function will be called internally to adapt and format the content as reported below (Figure 3).
If the tool is not mapsplice, nclscan, knife, circexplorer2, uroborus or circmarker, first check that the tool used is an annotation-based circRNA detection tool, then rename the files to circRNAs_X.txt and put them in the subfolder “other”. In this last case, you must ensure that each circRNAs_X.txt file must have at least the following 6 columns with the header (Figure 3):
gene: represents the gene from which the circRNA arises.
strand: is the strand where the gene is transcribed.
chrom: represents the chromosome from which the circRNA is derived.
startUpBSE: is the 5’ coordinate of the upstream back-spliced exon in the transcript. This corresponds with the back-spliced junction / acceptor site.
endDownBSE: is the 3’ coordinate of the downstream back-spliced exon in the transcript. This corresponds with the back-spliced junction / donor site.
coverage: corresponds to the number of reads mapping to the back-spliced junction in the sample.
NOTE: If more columns are present they will be discared.
The coordinates for startUpBSE and endDownBSE are relative to the reference strand, i.e. if strand is positive startUpBSE < endDownBSE, if strand is negative startUpBSE > endDownBSE.
The circRNAprofiler package can be extended in the future with further import functions specifically designed to import the output files of the different circRNA detection tools. At the moment only import functions for circRNA detection from annotation-based circRNA detection tools (e.g. MapSplice2, NCLscan, CircMarker, CircExplorer2, KNIFE, UROBORUS) are supported.
chrom | gene | strand | startUpBSE | endDownBSE | coverage |
---|---|---|---|---|---|
chr15 | ABHD2 | + | 89656956 | 89659752 | 35 |
chr19 | AGTPBP1 | - | 88248289 | 88190230 | 40 |
The function checkProjectFolder() helps to verify that the project folder is set up correctly.
check <- checkProjectFolder()
check
If the project folder is set up correctly, check should be equal to 0.
The function formatGTF() formats the given annotation file from ensemble gencode, UCSC or NCBI. The gtf object is then called in other functions.
# For example purpose load a short version of the already formatted annotation
# file gencode.V19.annotation.gtf (downloaded from https://www.gencodegenes.org/)
data("gtf")
head(gtf)
## chrom start end width strand type gene_name transcript_id
## 1 chr1 237205505 237205869 365 + exon RYR2 ENST00000366574.2
## 2 chr1 237433797 237433916 120 + exon RYR2 ENST00000366574.2
## 3 chr1 237494178 237494282 105 + exon RYR2 ENST00000366574.2
## 4 chr1 237519265 237519285 21 + exon RYR2 ENST00000366574.2
## 5 chr1 237527658 237527672 15 + exon RYR2 ENST00000366574.2
## 6 chr1 237532834 237532908 75 + exon RYR2 ENST00000366574.2
## exon_number
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5
## 6 6
# Alternatively put the gtf file in the project folder, then run:
# gtf <- formatGTF(pathToGTF = "gencode.V19.annotation.gtf")
The function getBackSplicedJunctions() reads the circRNAs_X.txt, adapts the content and generates a unique data frame with the circRNAs detected by each detection tool and the occurrences found in each sample.
# Load the object containing the detected circRNAs
data("backSplicedJunctions")
head(backSplicedJunctions)
## id gene strand chrom startUpBSE
## 1 ABHD2:+:chr15:89656956:89659752 ABHD2 + chr15 89656956
## 2 ACVR2A:+:chr2:148653870:148657467 ACVR2A + chr2 148653870
## 3 AFF1:+:chr4:87967318:87968746 AFF1 + chr4 87967318
## 4 AGTPBP1:-:chr9:88248289:88190230 AGTPBP1 - chr9 88248289
## 5 AGTPBP1:-:chr9:88248289:88200378 AGTPBP1 - chr9 88248289
## 6 AGTPBP1:-:chr9:88248289:88211277 AGTPBP1 - chr9 88248289
## endDownBSE tool C1 C2 C3 D1 D2 D3 H1 H2 H3
## 1 89659752 ms 35 11 38 48 38 12 34 44 41
## 2 148657467 ms 56 11 76 56 121 10 37 85 112
## 3 87968746 ms 47 5 34 48 47 5 54 10 30
## 4 88190230 ms 40 23 63 56 65 18 95 67 53
## 5 88200378 ms 13 3 19 26 43 7 20 32 23
## 6 88211277 ms 5 11 24 27 38 5 54 28 41
# Alternatively run:
# backSplicedJunctions <- getBackSplicedJunctions(gtf)
Plot the number of circRNAs identified by each detection tool.
# Plot
p <- ggplot(backSplicedJunctions, aes(x = tool)) +
geom_bar() +
labs(title = "", x = "Detection tool", y = "No. of circRNAs") +
theme_classic()
# Run getDetectionTools() to get the code corresponding to the circRNA
# detection tools.
dt <- getDetectionTools() %>%
dplyr::filter( name %in% c("mapsplice","nclscan", "circmarker"))%>%
gridExtra::tableGrob(rows=NULL)
# Merge plots
gridExtra::grid.arrange(p, dt, nrow=1)
The function mergeBSJunctions() is called to shrink the data frame by grouping back-spliced junction coordinates commonly identified by multiple detection tools. For the grouped back-spliced junction coordinates, the counts of the tool which detected the highest total mean across all analyzed samples will be taken. All the tools that detected the back-spliced junction are then listed in the column “tool” of the final table. Run getDetectionTools() to get the code correspoding to each circRNA detection tool.
NOTE: In this module circRNAs that derived from the antisense strand of the reported gene are identified. In detail, in our pipeline a circRNA is defined antisense if the strand from which the circRNA arises (i.e. reported in the prediction results) is different from the strand where the gene is transcribed (i.e. reported in the genome annotation file). This might be explained by technical artifacts or by the presence of a gene transcribed from the opposite strand that is not annotated. Due to the ambiguous nature of these predictions the antisense circRNAs (if any) are removed from the dataset and if specified by the user they can be exported in a file (i.e. antisenseCircRNAs.txt) for user consultation. Modules in circRNAprofiler are specific for the analysis of circRNAs that derive from the sense strand of the corresponding gene.
# Load object containing the merged back-spliced junctions
data("mergedBSJunctions")
head(mergedBSJunctions)
## id gene strand chrom
## 1 SYCP2:-:chr20:58497514:58497445 SYCP2 - chr20
## 2 AL132709.8:+:chr14:101432017:101432103 AL132709.8 + chr14
## 3 SLC8A1:-:chr2:40657441:40655613 SLC8A1 - chr2
## 4 SLC8A1:-:chr2:40657444:40655613 SLC8A1 - chr2
## 5 LGMN:-:chr14:93207524:93200027 LGMN - chr14
## 6 EXOC6B:-:chr2:72960247:72945232 EXOC6B - chr2
## startUpBSE endDownBSE tool C1 C2 C3 D1 D2 D3 H1 H2
## 1 58497514 58497445 cm 4858 577 4256 6561 6347 253 11050 8792
## 2 101432017 101432103 cm 4310 502 3824 5870 5609 203 10094 7953
## 3 40657441 40655613 cm,ms 2687 539 2718 2862 5623 428 2527 4446
## 4 40657444 40655613 cm,ms,ns 2064 389 2357 2263 4677 324 2196 3894
## 5 93207524 93200027 cm 1367 194 1114 1652 2164 195 1428 1551
## 6 72960247 72945232 cm,ms,ns 641 89 764 741 1186 277 1974 1154
## H3
## 1 5804
## 2 5256
## 3 6113
## 4 5291
## 5 1579
## 6 1649
# Alternatively run:
# mergedBSJunctions <-
# mergeBSJunctions(backSplicedJunctions, gtf)
Plot commonly identified circRNAs.
# Plot
p <- ggplot(mergedBSJunctions, aes(x = tool)) +
geom_bar() +
labs(title = "", x = "Detection tool", y = "No. of circRNAs") +
theme_classic()
gridExtra::grid.arrange(p, dt, nrow=1)
The use of multiple detection tools leads to the identification of a higher number of circRNAs. To rule out false positive candidates a filtering step can be applied to the detected circRNAs. The function filterCirc() filters circRNAs on different criteria: condition and read counts. The user can decide the filtering criteria. In the example below by setting allSamples = FALSE the filter is applied to the samples of each condition separately meaning that a circRNA is kept if at least 5 read counts are present in all samples of one of the conditions (A or B or C). If allSamples = TRUE, the filter is applied to all samples. We suggest to set allSamples = FALSE, since the presence of a disease/treatment can decrease the expression of subset of circRNAs thus by applying the filtering to all samples(allSamples = TRUE) those circRNAs are discarded.
filteredCirc <-
filterCirc(mergedBSJunctions, allSamples = FALSE, min = 5)
Plot circRNAs after the filtering step.
# Plot
p <- ggplot(filteredCirc, aes(x = tool)) +
geom_bar() +
labs(title = "", x = "Detection tool", y = "No. of circRNAs") +
theme_classic()
gridExtra::grid.arrange(p, dt, nrow=1)
Alternatively:
# Plot using Venn diagram
cm <- filteredCirc[base::grep("cm", filteredCirc$tool), ]
ms <- filteredCirc[base::grep("ms", filteredCirc$tool), ]
ns <- filteredCirc[base::grep("ns", filteredCirc$tool), ]
p <- VennDiagram::draw.triple.venn(
area1 = length(cm$id),
area2 = length(ms$id),
area3 = length(ns$id),
n12 = length(intersect(cm$id, ms$id)),
n23 = length(intersect(ms$id, ns$id)),
n13 = length(intersect(cm$id, ns$id)),
n123 = length(Reduce(
intersect, list(cm$id, ms$id, ns$id)
)),
category = c("cm", "ms", "ns"),
lty = "blank",
fill = c("skyblue", "pink1", "mediumorchid")
)
The helper functions getDeseqRes() identifies differentially expressed circRNAs. The latter uses the R Bioconductor packages DESeq2 which implement a beta-binomial model to model changes in circRNA expression. The differential expression analysis is performed by comparing the condition positioned forward against the condition positioned backward in the alphabet (values in the column condition in experiment.txt). E.g. if there are 2 conditions A and B then a negative log2FC means that in the conditions B there is a downregulation of the corresponding circRNA. If a positive log2FC is found means that there is an upregulation in the condition B of that circRNA.
# Compare condition B Vs A
deseqResBvsA <-
getDeseqRes(
filteredCirc,
condition = "A-B",
fitType = "local",
pAdjustMethod = "BH"
)
head(deseqResBvsA)
## id gene log2FC pvalue
## 1 SYCP2:-:chr20:58497514:58497445 SYCP2 -0.21872628 0.6882730
## 2 AL132709.8:+:chr14:101432017:101432103 AL132709.8 -0.23032811 0.6743554
## 3 SLC8A1:-:chr2:40657441:40655613 SLC8A1 -0.06724610 0.8141522
## 4 SLC8A1:-:chr2:40657444:40655613 SLC8A1 -0.04007647 0.8877923
## 5 LGMN:-:chr14:93207524:93200027 LGMN 0.04748208 0.8536426
## 6 EXOC6B:-:chr2:72960247:72945232 EXOC6B 0.51441043 0.1338385
## padj C1 C2 C3 D1 D2 D3
## 1 0.9984061 3359.6544 1895.7608 2591.0545 3555.2959 2357.5131 824.6553
## 2 0.9984061 2980.6732 1649.3448 2328.0528 3180.8546 2083.3923 661.6800
## 3 0.9984061 1858.2526 1770.9100 1654.7195 1550.8698 2088.5924 1395.0691
## 4 0.9984061 1427.4036 1278.0779 1434.9426 1226.2818 1737.2127 1056.0803
## 5 0.9984061 945.3782 637.3962 678.2037 895.1911 803.7905 635.6039
## 6 0.9984061 443.2973 292.4137 465.1235 401.5355 440.5247 902.8835
# Compare condition C Vs A
deseqResCvsA <-
getDeseqRes(
filteredCirc,
condition = "A-C",
fitType = "local",
pAdjustMethod = "BH"
)
head(deseqResCvsA)
## id gene log2FC
## 1 SYCP2:-:chr20:58497514:58497445 SYCP2 0.66419598
## 2 AL132709.8:+:chr14:101432017:101432103 AL132709.8 0.69952766
## 3 SLC8A1:-:chr2:40657441:40655613 SLC8A1 0.16723172
## 4 SLC8A1:-:chr2:40657444:40655613 SLC8A1 0.31640317
## 5 LGMN:-:chr14:93207524:93200027 LGMN -0.08792522
## 6 EXOC6B:-:chr2:72960247:72945232 EXOC6B 0.90322591
## pvalue padj C1 C2 C3 H1 H2
## 1 0.136048385 0.52337347 4634.2050 2498.0706 3487.7840 7990.915 5750.6484
## 2 0.110824859 0.46971699 4111.4499 2173.3647 3133.7608 7299.574 5201.8775
## 3 0.556461506 0.88083258 2563.2172 2333.5529 2227.3959 1827.425 2908.0281
## 4 0.251497581 0.66669722 1968.9171 1684.1412 1931.5571 1588.059 2546.9774
## 5 0.700433109 0.93530126 1304.0260 839.9059 912.9209 1032.672 1014.4740
## 6 0.002513725 0.03898947 611.4709 385.3176 626.0966 1427.517 754.8053
## H3
## 1 3099.3849
## 2 2806.7483
## 3 3264.3935
## 4 2825.4386
## 5 843.1993
## 6 880.5799
Use volcanoPlot() function
# We set the xlim and ylim to the same values for both plots to make them
# comparable. Before setting the axis limits, you should visualize the
# plots with the default values to be able to define the correct limits
p1 <-
volcanoPlot(
deseqResBvsA,
log2FC = 1,
padj = 0.05,
title = "DCMs Vs. Con",
setxLim = TRUE,
xlim = c(-8 , 7.5),
setyLim = FALSE,
ylim = c(0 , 4),
gene = FALSE
)
p2 <-
volcanoPlot(
deseqResCvsA,
log2FC = 1,
padj = 0.05,
title = "HCMs Vs. Con",
setxLim = TRUE,
xlim = c(-8 , 7.5),
setyLim = TRUE,
ylim = c(0 , 4),
gene = FALSE
)
ggarrange(p1,
p2,
ncol = 1,
nrow = 2)
Alternatively, the helper functions edgerRes() can also be used to identifies differentially expressed circRNAs. The latter uses the R Bioconductor packages EdgeR which implement a beta-binomial model to model changes in circRNA expression. The differential expression analysis is perfomed by comparing the condition positioned forward against the condition positioned backward in the alphabet (values in the column condition of experiment.txt). E.g. if there are 2 conditions A and B then a negative log2FC means that in the conditions B there is a downregulation of the corresponding circRNA. If a positive log2FC is found means that there is an upregulation in the condition B of that circRNA.
# Compare condition B Vs A
edgerResBvsA <-
getEdgerRes(
filteredCirc,
condition = "A-B",
normMethod = "TMM",
pAdjustMethod = "BH"
)
head(edgerResBvsA)
# Compare condition C Vs A
edgerResCvsA <-
getEdgerRes(
filteredCirc,
condition = "A-C",
normMethod = "TMM",
pAdjustMethod = "BH"
)
head(edgerResCvsA)
The function liftBSJCoords() maps back-spliced junction coordinates between different species and genome assemblies by using the liftOver utility from UCSC. Type data(ahChainFiles) to see all possibile options for annotationHubID E.g. if “AH14155” is specified, the hg19ToMm9.over.chain.gz will be used to convert the hg19 (Human GRCh37) coordinates to mm9 (Mouse GRCm37).
NOTE: Only back-spliced junction coordinates where the mapping was successful are reported. Back-spliced junction coordinates that could not be mapped might be not conserved between the analyzed species.
liftedBSJcoords <- liftBSJcoords(filteredCirc, map = "hg19ToMm9",
annotationHubID = "AH14155")
The function annotateBSJs() annotates circRNAs internal structure and the flanking introns. The genomic features are exracted from the user provided gene annotation. We first define the circRNA parental transcript as a linear transcript whose exon coordinates overlap with that of the detected back-spliced junctions and then the features are extracted from the selected transcript. Since the coordinates of the detected back-spliced junctions might not exactly correspond to annotated exonic coordinates, a gap of 10 nucleotides is allowed. As default, in situations where genes have multiple transcripts whose exons align to the back-spliced junction coordinates, the transcript that will produce the longest sequence (exon only) will be selected. Alternatively, the transcript to be used can be specified in transcripts.txt. The output data frame will have the following columns:
id: unique identifier.
gene: represents the gene from which the circRNA arises.
allTranscripts: are all transcripts of a circRNA’s host gene which exon coordinates overlap with the detected back-spliced junction coordinates.
transcript: as default, this is the transcript producing the longest sequence and whose exon coordinates overlap with the detected back-spliced junction coordinates. The transcript reported in this column is used in the downstream analysis.
totExons: total number of exons in the selected transcript (reported in the column transcript)
strand: is the strand from which the gene is transcribed.
chrom: is the chromosome from which the circRNA is derived.
startUpIntron: is the 5’ coordinate of the intron immediately upstream the acceptor site in the selected transcript.
endUpIntron: is the 3’ coordinate of the intron immediately upstream the acceptor site in the selected transcript.
startUpBSE: is the 5’ coordinate of upstream back-spliced exon in the selected transcript. This corresponds with the back-spliced junction / acceptor site.
endUpBSE: is the 3’ coordinate of the upstream back-spliced exon in the selected transcript.
startDownBSE: is the 5’ coordinate of downstream back-spliced exon in the selected transcript.
endDownBSE: is the 3’ coordinate of downstream back-spliced exon in the selected transcript. This corresponds with the back-spliced junction / donor site.
startDownIntron: is the 5’ coordinate of the intron immediately downstream the donor site in the selected transcript.
endDownIntron: is the 3’ coordinate of the intron immediately downstream the donor site in the selected transcript.
exNumUpBSE: is the position of the upstream back-spliced exon in the selected transcript (e.g. if it is the 1st, the 2nd etc).
<