ASpli: An integrative R package for analysing alternative splicing using RNA-seq
10 November 2026
Package
ASpli 2.23.4
1 Introduction
Alternative splicing (AS) is a common mechanism of post-transcriptional gene regulation in eukaryotic organisms that expands the functional and regulatory diversity of a single gene by generating multiple mRNA isoforms that encode structurally and functionally distinct proteins.
Genome-wide analysis of AS has been a very active field of research since the early days of NGS (Next generation sequencing) technologies. Since then, evergrowing data availability and the development of increasingly sophisticated analysis methods have uncovered the complexity of the general splicing repertoire.
In this vignette we describe how to use ASpli, an integrative and user-friendly R package that facilitates the analysis of changes in both annotated and novel AS events.
Differently form other approaches, ASpli was specifically designed to integrate several independent signals in order to deal with the complexity that might arise in splicing patterns. Taking into account genome annotation information, ASpli considers bin-based signals along with junction inclusion indexes in order to assess for statistically significant changes in read coverage. In addition, annotation-independent signals are estimated based on the complete set of experimentally detected splice junctions. ASpli makes use of a generalized linear model framework (as implemented in edgeR R-package (Robinson, McCarthy, and Smyth 2010)) to assess for the statistical analysis of specific contrasts of interest. In this way, ASpli can provide a comprehensive description of genome-wide splicing alterations even for complex experimental designs.
A typical ASpli workflow involves: parsing the genome annotation into subgenic features called bins, overlapping read alignments against them, perform junction counting, fulfill inference tasks of differential bin and junction usage and, finally, report integrated splicing signals. At every step ASpli generates self-contained outcomes that, if required, can be easily exported and integrated into other processing pipelines.
2 Citation
ASpli: Integrative analysis of splicing landscapes through RNA-seq assays, Mancini E, Rabinovich A, Iserte J, Yanovsky M, Chernomoretz A, Bioinformatics, March 2021, \[[journal](https://doi.org/10.1093/bioinformatics/btab141),[preprint](https://tinyurl.com/mjvet53u)\]
3 Quick start
Here is an example included in ASpli, for a pairwise comparison between 2 conditions (Control vs Treatment, 3 replicates each) using default parameters. For the sake of computational speed we will consider a custom gtf file (included in ASpli) containing info just for 10 artificial genes. Extract features from genome, define targets data.frame with phenotype data, and mBAMs data.frame with phenotype data for merged BAMs:
library(ASpli)
library(GenomicFeatures)
# gtf preprocessing ----
gtfFileName <- aspliExampleGTF()
genomeTxDb <- txdbmaker::makeTxDbFromGFF( gtfFileName )## Warning in .makeTxDb_normarg_chrominfo(chrominfo): genome version information
## is not available for this TxDb object# feature extraction ----
features <- binGenome( genomeTxDb )
#bams and target file ----
BAMFiles <- aspliExampleBamList()
targets <- data.frame(row.names = paste0('Sample',c(1:6)),
bam = BAMFiles[1:6],
f1 = c( 'control','control','control','treatment','treatment','treatment'),
stringsAsFactors = FALSE)
mBAMs <- data.frame( bam = sub("_[012]","",targets$bam[c(1,4)]),
condition = c("control","treatment"))Read counting against annotated features:
gbcounts <- gbCounts(features=features, targets=targets,
minReadLength = 100, maxISize = 50000)
gbcountsJunction-based de-novo counting and splicing signal estimation:
asd <- jCounts(counts=gbcounts, features=features, minReadLength=100)
asdDifferential gene expression and bin usage signal estimation:
gb <- gbDUreport(gbcounts, contrast = c(-1,1))
gbDifferential junction usage analysis:
jdur <- jDUreport(asd, contrast=c(-1,1))
jdurBin and junction signal integration:
sr <- splicingReport(gb, jdur, counts=gbcounts)Summary of integration of splicing signals along genomic-regions.
is <- integrateSignals(sr,asd)Export results:
exportIntegratedSignals(is,sr=sr,
output.dir = file.path( tempdir(), "aspliExample" ),
counts=gbcounts,features=features,asd=asd,
mergedBams = mBAMs)4 Getting started
4.1 Installation
ASpli is available at the Bioconductor site and can be installed following these steps: biocLite():
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following line initializes usage of Bioc devel branch and should not be necessary after
# the next official release scheduled for October 2020
if(Sys.Date()<"2020-11-01") BiocManager::install(version='devel')
BiocManager::install("ASpli")
library(ASpli)4.2 Required input data
ASpli requires the following data in order to execute the analysis pipeline:
BAMandBAIfiles for each analyzed sample.A genome annotation file in the TxDb format, as produced by the
GenomicFeaturespackage. TheGenomicFeaturespackage provides functions to create TxDb objects based on data downloaded from UCSC Genome Bioinformatics, BioMart or gff/gtf files. See “Creating New TxDb Objects or Packages” in theGenomicFeaturesvignette for more details. In this example, a TxDb is built from a gtf file:library(GenomicFeatures) TxDb <- txdbmaker::makeTxDbFromGFF( file="genes.gtf", format="gtf")The building of a
TxDbobject might be time consuming, depending on the size of your genome. If aTxDbhas been already created, we strongly recommend saving it as asqlitefile and reloading it any time the analysis is run.saveDb(TxDB,file="gene.sqlite")Please,
make sure all files use the same coordinate system.IMPORTANT: In order to estimate uniform coverage statistics and to generate graphical reports through the
exportIntegratedSignalsfunction, replicate mergedBAMfiles for each contrasted condition are needed. These merged BAMs can easily be computed from individual replicate BAM files using samtools merge utility (you can check http://www.htslib.org/doc/samtools-merge.html). Index for these merged BAM files should also be provided. They can be easily generated with samtools index utility (https://www.htslib.org/doc/samtools-index.html).
5 ASpli overview
ASpli analyzes read coverage over bins, defined as maximal sub-genic features entirely included or entirely excluded from annotated mature tanscripts (see Figure 1). Read junctions are considered in order to provide supporting evidence of differential bin usage. Additionally, annotation-independent signals are estimated based on the complete set of experimentally detected splice junctions (see Figure 1).
We leverage on the statistical framework implemented in edgeR to asses for the statistical significance of different splicing signals in a consistent and unified way. As depicted in Figure 1.3, statistically significant evidence is collected from: bin coverage differential signals, relative changes in junction’s flanking genomic regions (junction-anchorage signal), and junction usage variations displayed inside junction clusters (junction-locale signal).
Figure 1.4 shows an example of the interactive html report generated as a final output.
6 Using ASpli
The workflow intended to be used with ASpli is divided in six main steps (Figure 2).
Extracting features from genome TxDb:
binGenome()Counting reads:
gbCounts(),jCounts()Estimating differential signals:
gbDUreport(),jDUreport()Generering reports:
splicingReport(),integrateSignals()Exporting HTML reports:
exportSplicingReport(),exportIntegrateSignals()
The main objects classes used to store data and perform operations are:
ASpliFeatures: Contains genomic coordinatesASpliCounts: Contains number of reads overlapping each genomic featureASpliAS: Contains counts of aligned junctionsASpliDU: Contains results of Differential expression and usageASpliJDU: Contains results of Differential Junctions usageASpliSplicingReport: Contains differential usage information using different evidencesASpliIntegratedSignals: Contains signals present in the region
At each step it is possible to access intermediate results. See Section sec:outputs for more details.
ASpli core Funcionalities
6.1 binGenome: Binning the genome
The binGenome method is a one-stop function to:
split genes into subgenic features called bins
extract junction, gene and bin coordinates
infer bin splicing events from annotation
binGenome’s output is an object of class ASpliFeatures. Methods featuresg, featuresb, featuresj can be used to access genes, bins and junctions coordinates as GRanges (GRanges objects defined in the GenomicRanges package.)
annFile <- aspliExampleGTF()
aTxDb <- makeTxDbFromGFF(annFile)
features <- binGenome( aTxDb )
geneCoord <- featuresg( features )
binCoord <- featuresb( features )
junctionCoord <- featuresj( features )In the case gene symbols were available, they could be appended as follow:
symbols <- data.frame( row.names = genes( aTxDb ),
symbol = paste( 'This is symbol of gene:',
genes( aTxDb ) ) )
features <- binGenome( aTxDb, geneSymbols = symbols ) 6.1.1 Bin definition
Exon and intron coordinates are extracted from the annotation, but only those from multi-exonic genes are saved for further evaluation. When more than one isoform exists, some exons and introns will overlap. In the same spirit of (Anders, Reyes, and Huber 2012), exons and introns are then subdivided into non-overlapping sub-genic features dubbed bins, defined by the boundaries of different exons across transcript variants. These so defined bins are maximal sub-genic features entirely included or entirely excluded from any mature transcript (see Fig 3).
For an hypothetical gene named GeneAAA subgenic features are labelled as follow:
GeneAAA:E001: defines first exonic bin
GeneAAA:I001: defines first intronic bin
GeneAAA:Io001: defines first
Intron originalGeneAAA:J001: defines first annotated junction of GeneAAA
chr.start.end: defines an experimental junction
Bins and junctions are consecutively named from 5’ to 3’ of reference sequence, irrespective of their gene’s strand. This implies that lower numbers are always closer to the 5’ of forward strand. Alternative splicing bins are named as exons. Exonic and intronic bins are further classified into exclusively exonic bins, exclusively intronic bins, or alternative splicing bins (AS) (See Figure 3).
Bins that overlap with the beginning or end of any transcript are labelled as external. Please note that an external bin of a transcript may overlap to a non external bin of another transcript, in these cases the bin is still labelled as external. In addition to these non overlapping bins, full introns are extracted from annotation and labelled as Io )
6.1.2 Splicing event assignment
Each AS bin is further classified considering a three-bin local splicing model. Splicing-event categories are assigned to a given bin based on the intronic/exonic character of the analyzed bin and its first neighbors (Figure 4).
For genes presenting two isoforms, this model is able to unambiguously assign a well defined splicing event category to the analyzed bin: exon skipping (ES), intron retention (IR), alternative five prime splicing site (Alt5’SS), or alternative three prime splicing site (Alt3’SS) (see first row of Figure 4).
When more than two isoforms are present, we still found it useful to use the three-bin local model to segment follow up analysis. For these cases (see rows 2-4 of Figure 4) ASpli identify splicing events that involve: intronic subgenic regions surrounded by exons in at least one isoform (bin labelled as IR*), exonic subgenic regions surrounded by two introns in at least one isoform (bin labelled as ES*), exonic regions surrounded by intronic and exonic neighbor bins (bin labelled as Alt5’SS* or Alt3’SS*).
When it is not possible to get a clear splicing-type assignation (last row of Figure 4), bins are labeled as undefined AS (UAS).
6.2 Read counting
6.2.1 Targets data.frame definition
BAM file names and experimental factors should be provided as a dataframe that has as many rows as samples. The first column should be named bam and should contain the path to a BAM file. Next columns should be used to specify the different experimental factors for each sample.
For instance, for a two-factor design (e.g. genotype and time) the targets data.frame could have been defined like this:
BAMFiles <- c("path_to_bams/CT_time1_rep1.BAM", "path_to_bams/CT_time1_rep2.BAM",
"path_to_bams/CT_time2_rep1.BAM", "path_to_bams/CT_time2_rep2.BAM",
"path_to_bams/TR_time1_rep1.BAM", "path_to_bams/TR_time1_rep2.BAM",
"path_to_bams/TR_time2_rep1.BAM", "path_to_bams/TR_time2_rep2.BAM")
(targets <- data.frame( bam = BAMFiles,
genotype = c( 'CT', 'CT', 'CT', 'CT',
'TR', 'TR', 'TR', 'TR' ),
time = c( 't1', 't1', 't2', 't2',
't1', 't1', 't2', 't2' ),
stringsAsFactors = FALSE ))The targets dataframe contains experimental factor values for each sample. ASpli generates simple code-name for each experimental condition concatenating the corresponding factor levels. We recommend to check using the getConditions() function.
getConditions( targets )Important 1 The keyword condition is internally used by ASpli to store experimental condition code-names and SHOULD NOT be used as an experimental factor name in the definition of the targets data.frame.
Important 2 Row-names can be defined for the targets data.frame to univoqually identify each sample through the analysis. However this is not mandatory. ASpli automatically use condition code-names to generate unique sample ids whenever default rownames (i.e. consecutive numbers) were detected in targets data.frame.
6.2.2 gbCounts: Summarize read overlaps against all feature levels
The method gbCounts() counts the number of reads that overlaps each defined feature (i.e. genes, bins, junctions and intron/exon flanking regions). For genes and bins, read density values are also computed as the ratio between the number of reads and the length of a given feature.
By default (PE=TRUE) library is considered as paired-end and unstranded (strandMode=1).
gbcounts <- gbCounts( features = features,
targets = targets,
minReadLength = 100, maxISize = 50000,
libType="SE",
strandMode=0)features: An object of classASpliFeatures. It is a list of GRanges at gene, bin and junction leveltargets: A dataframe containing sample, BAM and experimental factors as defined in Section Targets data.frame definition.minReadLength: Reads shorter than minReadLength will not be considered to compute E1I and IE2 read coverage (see bellow).maxISize: Maximum intron expected size. Junctions longer thanmaxISizewill be dicarded.minAnchor: Minimum percentage of minReadLength that should be aligned to an exon-intron boundary (see Additional considerations ).
The result of gbCounts() method is an object of class ASpliCounts. Count and read density dataframes can be extracted using accesors methods. An extensive summary of the information stored in these tables is included in sec:outputs.
Access data:
GeneCounts <- countsg(counts)
GeneRd <- rdsg(counts)
BinCounts <- countsb(counts)
BinRd <- rdsb(counts)
JunctionCounts <- countsj(counts)Export tables to text files:
writeCounts(counts=counts, output.dir = "example")
writeRds(counts=counts, output.dir = "example")6.2.2.1 Additional considerations
At gene-level, for a given gene, the read count number is computed as the number of reads overlapping any exon included in the corresponding annotated gene model. If a single read overlaps more than one exon, it is counted only once. Note that one read can overlap two different genes, in this case it is counted for both of them.
Every intron is considered as a potential retained intron. To analyze putative inton retention events,
ASpliconsiders the corresponding upstream and downstream exons (E1 and E2, always in the forward sense). Then, following (Braunschweig et al. 2014), new artificial ranges that overlap the two retention regions E1I (connecting exon E1 and intron I) and IE2 (connecting intron I and exon E2) are defined:E1I: \[I$_{s}$- readLength (1 - minAnchor/100), I$_{s}$+ readLength (1 - minAnchor/100)\]
IE2: \[I$_{e}$- readLength (1 - minAnchor/100), I$_{e}$+ readLength (1 - minAnchor/100)\]
where I\(_{s}\) and I\(_{e}\) are the intron start and end coordinates respectively.
minAnchoris 10% of read length by default (parameterminAnchor)Please check before start the read length of your sequenced library Only those reads whith minimum overlap minReadLength with either \(E1I\) or \(IE2\) will be considered and counted.
To access this data:
e1iCounts <- countse1i(counts) ie2Counts <- countsie2(counts)Effective length: is the sum of the length of exonic bins and alternative bins (i.e. all bins except intronic bins).
Junctions are extracted from BAM files. They are defined as those reads that aligned against disjoint region of the reference genome(N operator of CIGAR notation for aligned reads (Li et al. 2009) ), and are essential for alternative splicing event quantification and discovery. Junction alignment confidence is extremely important and it should be controlled at the alignment step.
6.2.3 jCounts: Summarize junctions inclussion indices PSI, PIR and PJU
PSI (percent of inclusion) and PIR (percent of intron retention) metrics (see bellow) are computed for each bin and experimental condition. The selection of which metric is used is based on the type of splicing event associated with each bin (see Splicing event assignment). In addition, annotation-free inclussion indices (PIR\(_J\) and PJU, see bellow) are estimated using experimentally detected junctions.
asd <- jCounts(counts = gbcounts,
features = features,
minReadLength = 100,
libType="SE",
strandMode=0)Novel arguments for this function call are:
counts: An object of class
ASpliCountsthreshold: Minimum number of reads supporting junctions (Default=5)
Results: An object of class ASpliAS.. An extensive summary of the information stored in this object is included in sec:outputs.
Accesors:
irPIR <- irPIR( asd )
altPSI <- altPSI( asd )
esPSI <- esPSI( asd )
allBins <- joint( asd )
junctionsPJU <- junctionsPIR( asd )
junctionsPIR <- junctionsPIR( asd )Export tables to text files:
writeAS(as=asd, output.dir="example")6.2.3.1 Junction supporting evidence of alternative bin usage
ASpli makes use of junction data as supporting evidence of alternative usage of bins. For a general differential splicing event affecting a given bin, it is always possible to define exclusion and inclusion junctions. The first class of junctions (noted as \(J_3\)) pass over the bin of interest, whereas the second ones (note as \(J_1\) and/or \(J_2\)) quantify and support the inclusion of start and/or end bin boundaries in the mature transcript. Figure 5 illustrates this point for the different types of splicing events that could affect a given bin. ASpli considers for this analysis junctions that are completely included within a unique gene and have more than a minimum number of reads supporting them (by default this number is five).
PSI (percent spliced in) (Schafer et al. 2015) and PIR (percent of intron retention) metrics are two well known statistics that can be used to quantify the relative weight of inclusion evidence for different kind of splicing events. For each bin, ASpli quantifies the inclusion strength in every experimental condition using the appropriate inclusion index (see Fig 5).
6.2.3.2 Annotation-free inclussion indices
ASpli relies on the direct analysis of experimentally observed splicing junctions in order to study novel (i.e. non-annotated) splicing patterns.
For every experimental junction, ASpli characterizes local splicing patterns considering two hypothetical scenarios. For one hand, assuming that every detected junction might be associated to a possible intron that could be potentially retained, a \(PIR_{junc}\) value is computed (upper panel of Figure 6).
On the other hand, every junction also defines potential 5’ and 3’ splicing sites. It can be the case that one (in an alternative 5’ or 3’ scenario), or both ends (in case of exon skipping) were shared by other junctions. In this context, it is informative to characterize the relative abundance of the analyzed junction (dubbed \(J_3\)) with respect to the locally competing ones. ASpli estimates percentage junction-usage indices, \(PJU_{J_1}\) and \(PJU_{J_2}\), in order to evaluate and quantify this quantities (see bottom panel of figure 6). In order to illustrate this point, we show in figure 7 an hypothetical splicing scenario for a given junction of interest, \(J_3\). It can be appreciated that \(PJU_{J_1}\) quantifies the participation of this junction in the context of a splicing pattern involving the two orange competing junctions, whereas \(PJU_{J_2}\) reports on the usage of \(J_3\) in connection with the green competing junction.
6.2.3.3 Additional considerations:
Only those junctions with a minimum number of counts (default=5) in all samples of at least 1 condition are used for PIR/PSI analysis.
For each bin, a PIR or a PSI metric is calculated, depending on the splicing event category assigned to that bin (see section Splicing event assignment). If no splice event was assigned ( this bin is not alternative), an exon will be considered to be involved in a putative exon skiping splicing event, and an intron will be considered to be involved in a putative intron retention splicing event.
6.3 Differential signals
ASpli leverages on the statistical framework developed by Smyth and collaborators, implemented in the edgeR R-package (Robinson, McCarthy, and Smyth 2010; McCarthy, Y., and Smyth 2012), to assess for statistically significant changes in gene-expression, bin coverage and junction splicing signals. Under this approach, count data is modelled using a negative binomial model, and an empirical Bayes procedure is considered to moderate the degree of overdispersion across units.
In order to study splicing patterns, gene expression changes should be deconvolved from overall count data. On a very general setting, what we are looking for is to test whether a given unit of a certain group of elements displays differential changes respect to the collective or average behavior. ASpli uses this general idea to assess for statistically significant changes in splicing patterns probed with different genomic features:
bin-coverage signal:
ASpliassesses for differential usage of bins comparing bin’s log-fold-changes with the overall log-fold-change of the corresponding gene.junction anchorage signal: For every experimentally detected junction,
ASplianalyzes differential intron retention changes by considering log-fold-changes of a given experimental junction relative to changes in coverage of left and right junction flanking regions.junction locale signal: In the same spirit than MAJIQ and LeafCutter,
ASplidefines junction-clusters as sets of junctions that share at least one end with another junction of the same cluster (see Figure 7). In order to characterize changes for a given junction along experimental conditions,ASpliweighs log-fold-change of the junction of interest relative to the mean log-fold-change of junctions belonging to the same cluster.
ASpli makes use of the functionality implemented in the diffSpliceDGE function of the edgeR package to perform all of this comparisons within a unified statistical framework. Given a set of elements (i.e. bins or junctions) of a certain group (i.e. genes, anchorage group or junction-cluster), a negative binomial generalized log-linear model is fit at the element level, considering an offset term that accounts for library normalization and collective changes. Differential usage is assessed testing coefficients of the GLM. At the single element-level, the relative log-fold-change is reported along with the associated p-value and FDR adjusted q-values. In addition a group-level test is considered to check for differences in the usage of any element of the group between experimental conditions (see diffSpliceDGE documentation included in edgeR package for details (Robinson, McCarthy, and Smyth 2010)).
6.3.1 gbDUreport: Bin-based coverage differential signals of AS
To run this analysis:
gb <- gbDUreport( counts,
minGenReads = 10,
minBinReads = 5,
minRds = 0.05,
contrast = NULL,
ignoreExternal = TRUE,
ignoreIo = TRUE,
ignoreI = FALSE,
filterWithContrasted = TRUE,
verbose = TRUE,
formula = NULL,
coef = NULL)Summary of arguments:
minGenReads (Default=10): Genes with at least an average of minGenReads reads for any condition are included into the differential expression test. Bins from genes with at least an average of minGenReads reads for all conditions are included into the differential bin usage test.
minBinReads (Default=5): Bins with at least an average of minGenReads reads for any condition are included into the differential bin usage test.
minRds (Default=0.05) = genes with at least an average of read density for any condition are included into the differential expression test. Bins belonging to genes with at least an average of minRds read density for all conditions are included into the differential bin usage test. Bins with at least an average of minRds read density for any condition are included into the differential bin usage test.
ignoreExternal (Default = TRUE): Ignore firts or last transcript bins
ignoreI(Default = FALSE) : Ignore intronic bins, test is performed only for exons.
ignoreIo (Default = TRUE): Ignore original introns.
contrast: Numeric vector of length equal to the number of experimental conditions (as defined by
targets). The values of this vector are the coefficients of each condition (in the order given by given bygetConditions()) to set up the contrast of interest. If contrast = NULL (and also formula=NULL ), a pairwise comparison between the second and the first conditions will be considered for calculations(i.e. contrast= c(-1,1,0,…0))filterWithContrasted: logical value. If TRUE (default) bins, genes and junctions will be filtered by read counts and read densities using data from the conditions that will be used in the sought comparison (i.e. those which coefficients in contrast argument are different from zero). If FALSE, all conditions will be considered at filtering time. It is strongly recommended do not change this value
verbose: shows details of calculations (Default=TRUE)
formula: A formula can be used to specify a specific term to be tested. If coef is specified, then that coefficient will be tested. If not, it defaults to the last term in the formula.
coef: for formula only. The coefficient to be tested. If NULL the test defaults to the last term in the formula
The result of gbDUreport() method is an object of class ASpliDU. Gene expression and bin differential usage information can be extracted using accesors methods. An extensive summary of the information stored in this object is included in sec:outputs.
Accesors:
geneX <- genesDE( gb )
binDU <- binsDU( gb )Export tab delimited tables:
writeDU(gb, output.dir = "example")6.3.1.1 Additional considerations
Bins and junctions from expressed genes are considered if they have enough supporting reads (Default=5) in at least one condition.
External bins are excluded by default in the analysis. However, an external bin for one isoform can overlap to a non external bin from other isoform that can participate in alternative splicing regulation,
ASpliallows to optionally include them.Io (original intron) bins, excluded by default.
Note that the inclusion of those bins affects the estimation of corrected p-values (fdr). The information provided by Io bins are highly correlated with their sub bins and increase largely the number of events to be analyzed. The fdr correction become more strict and there is a violation on the fdr correction assupmtion that all individual tests are independent from each other. If an Io bin shows a significant change, there is a very high chance that at least one of their sub bins also shows a significant change.
6.3.2 jDUreport: Junction-centered analysis of AS
Based on abundance information of experimentally detected junctions, jDUreport considers J\(_1\), J\(_2\) and J\(_3\) junction sets, as defined in 6, to analyze annotation-free junction-anchorage and junction-locale alternative splicing signals (see Differential signals). In addition, for every annotated bin, this functions tests junction supporting evidence of bin usage (see 5).
To run this analysis:
jdu <- jDUreport(asd,
minAvgCounts = 5,
contrast = NULL,
filterWithContrasted = TRUE,
runUniformityTest = FALSE,
mergedBAMs = NULL,
maxPValForUniformityCheck = 0.2,
strongFilter = TRUE,
maxConditionsForDispersionEstimate = 24,
formula = NULL,
coef = NULL,
maxFDRForParticipation = 0.05,
useSubset = FALSE)Summary of arguments:
asd: An object of class
ASpliASwith results of PSI and PIR using experimental junctionminAvgCounts (Default=5): Minimum average counts for filtering.
runUniformityTest (Defaults = FALSE): Run uniformity test on Intron Retention in order to detect non-uniform read coverage along the intron. This test compares the standard deviation of the inner intron region (11 bases from both ends) to the mean of both intron ends. Numbers closer to 0 mean more uniform coverage. This is an experimental feature, requires the existence of one merged BAM per experimental condition and takes some time to run.
mergedBAMs: Should be specified if runUniformityTest=TRUE. It is a two column data.frame specifying the path to replicate-merged BAMs for each tested condition. Columns should be named: bam and condition respectively. If no merged BAMs exist (for example, paired samples without replicates), use the same BAMs as targets.
maxPValForUniformityCheck: To speed up uniformity test only check junctions with pval < maxPValForUniformityCheck (Default=0.2)
strongFilter: If strongFilter is TRUE, then we discard junction clusters with at least one junction that doesn’t pass the filter.
maxConditionsForDispersionEstimate: In order to reduce resource usage, estimate dispersion for statistics tests with a reduced number of conditions.
maxFDRForParticipation: In order to calculate junctionPSI participation, only use significant junctions (ie junctions with FDR < maxFDRForParticipation). (Default=0.05)
useSubset: Experimental. It is strongly recomended to leave the default, FALSE.
Results are stored in an ASpliJDU object. Note that this analysis considers junctions that are completely included within a unique gene and have enough supporting reads (MinAvgCounts, Default = 5)
Accesors:
localej( jdu )
localec( jdu )
anchorj( jdu )
anchorc( jdu )
jir( jdu )
jes( jdu )
jalt( jdu )localc and localj together with anchorc and anchorj data.frames provide information regarding statistically significant changes observed in connection with junction usage inside locale and anchorage-junction clusters respectively. jir, jes and jalt accessors provide information regarding junction support of bin usage.
Export tab delimited table:
writeJDU(jdu,output.dir = "test")6.3.2.1 Additional considerations
As an option, jDUreport checks the non-uniformity level of the read coverage observed in intronic bins. We report the ratio between the standard deviation of the inner intronic bin region (11 bases from both ends) coverage to the mean of both bin’s external ends. Values closer to 0 mean that the coverage can be considered uniform and the event is probably an Intron Retention. In order to perform this calculation replicate merged BAMs for each condition are needed. The path to the bam files should be specify through the mergeBams argument (e.g. the mBAMs data.frame in the example of Section Quick start). The calculation takes some time to run so it defaults to FALSE.
6.4 Integrative reports
6.4.1 splicingReport: bin and junction signals integration
This function combines differential splicing information from different sources. Bin and junction usage information is integrated in three steps. First, bin and annotated junction information (jir, jes, jalt) are consolidated. Then, bins that overlap with locale-J3 junctions (junctions that cover the entire locale region) are identified and the corresponding signals are combined. Finally, the same procedure is performed for bins and anchorage-J3 junctions.
To run this analysis
sr <- splicingReport(gb,
jdur,
counts)Results are stored in an ASpliSplicingReport object.
Accesors:
binbased( sr )
localebased( sr )
anchorbased( sr )binbased(sr) data.frame combines information from bins and annotated junctions. For each case, either \(\Delta PSI\) or \(\Delta PIR\) statistics is reported based on the bin splicing type. localebased(sr) is a data.frame with information from bins and locale junctions. Statistics for each junction are located in columns prefixed with “junction” and statistics for the corresponding cluster are prefixed with the “cluster” keyword. Changes in participation coefficients are also reported. If locale clusters matched a bin, the coresponding bin statistics are also included. The data structure of anchorbased(sr) data.frame is similar to the localebased(sd) one. All data.frames also include junction count records for debugging purposes.
Export tab delimited table (see exportSplicingReport: Export splicing reports in HTML pages. for interactive HTML reports):
writeSplicingReport( sr, output.dir = "test")6.4.2 integrateSignals(): Region specific summarization of differential usage signals.
This function integrates different usage signals reported in overlaping genomic regions:
bin signal: a bin is called differentially-used by
ASpliif it displays statistically significant coverage changes (fdr \(< 0.05\), by default) and, additionally, one of the two supplementary conditions hold: either the bin fold-change level is greater than a given threshold (3 fold changes, by default) or changes in inclusion levels of bin-supporting junctions (\(\Delta PIR\) or \(\Delta PSI\) according to the bin class) surpass a predefined threshold (\(0.2\) by default).anchorage signal: statistically significant changes are found at the cluster level (cluster.fdr \(<0.05\) by default) for the considered \(\{J_1,J_2,J_3\}\) junction set (see upper panel of Fig 6) and, at the same time, \(|\Delta PIR_{J_3}|\) is larger than a given threshold (0.3 by default).
locale signal: statistically significant changes are found at the cluster level (cluster.fdr \(<0.01\) by default) for the analysed junction cluster \(\{J_1,...,J_S,...,J_n\}\) (see 7) and, at the same time, there is at least one junction \(J_S\) within the cluster presenting statistically significant changes at the single unit level (junction.fdr \(<0.05\), by default) with \(|\Delta Participation_{J_S}|\) larger than a given threshold (0.3 by default). In the case that statistically significative changes were detected at the unit-level for more than one junction of a given cluster, the one displaying the largest participation change was considered and reported as the cluster’s representative junction.
is <- integrateSignals(sr, asd,
bin.FC = 3, bin.fdr = 0.05, bin.inclussion = 0.2,
nonunif = 1, usenonunif = FALSE,
bjs.inclussion = 10, bjs.fdr = 0.01,
a.inclussion = 0.3, a.fdr = 0.01,
l.inclussion = 0.3, l.fdr = 0.01,
otherSources = NULL, overlapType = "any")Arguments:
sr: An object of class ASpliSplicingReport
asd: An object of class ASpliDU
bin.FC: fold change threshold for bin signals. By default only bin signals with bin.fc > log2(3) are retained.
bin.fdr: Maximum FDR for bin signals.
bin.inclussion: Minimum level of change for junction support inclussion signals (either \(\Delta\)PIR or \(\Delta\)PSI) associated to bins who already passed bin.FC and bin.fdr filters.
nonunif: Maximum value of non-uniforimty for intronic bins (nonunif << 1 means homogeneous coverage)
usenonunif: Whether to use non uniformity as filter.
bjs.inclussion: Minimum level of change for junction support inclussion signals (either \(\Delta\)PIR or \(\Delta\)PSI).
bjs.fdr: Maximum FDR for annotated junctions.
a.inclussion: Minimum level of inclussion change for anchorage junctions.
a.fdr: Maximum FDR for anchorage junctions.
l.inclussion: Minimum level of participation change for locale junctions..
l.fdr: Maximum FDR for locale junctions.
otherSources: If user wants to compare
ASpliresults with results from other methods, otherSources must be a GenomicRange object with all the regions found with the other methods. It will be integrated with a new column next to signals information.overlapType: Matching criterium for region overlaps. Defaults to “any” and can be any of the following: “any”, “start”, “end”, “within”, “equal”.
Results are stored in an ASpliIntegratedSignals object.
Accesors:
signals( is )
filters( is )filters(is) stores the parameter values used to filter the different usage signals to be integrated (e.g. minimum fold-changes, statistical thresholds, minimal inclussion values, etc).
signals(is) contains region-based information of splicing signals. Table 1 shows the first 5 columns of this data.frame. Columns b, bjs, ja and jl stand for bin, bin-junction-support, junction-anchorage and junction-locale signals. Non-zero entries indicate the presence of the corresponding signal for the considered genomic range. An asterisk indicates overlap but not an exact match between the genomic ranges where the examined signals were observed.
| region | b | bjs | ja | jl | …. |
|---|---|---|---|---|---|
| Chr1:45560-45645 | 1 | 1 | 0 | 0 | |
| Chr1:387673-388267 | 1 | 1 | 1 | 1 | … |
| Chr1:406793-406902 | 1 | 1 | 0 | * |
In this example a bin-signal (b) was found in region Chr1:406793-406902. A matching bin-junction supporting signal was also reported (bjs), so both discoveries were merged. Finally, a significant locale cluster overlapped the region, but genomic ranges did not match, so an * was used for that signal.
6.4.2.1 Event assignement
We adopted the following heuristics to classify the region-centered integrated splicing signals. Eventhough we found this classification scheme usefull in most situations, it is strongly advised to use it as a preliminary categorization of events. Further examination is advised in order to disentangle complex splicing patterns.
For each region with bin-coverage signal, we try to find a matching region in binbased(sr) table. If a match is found, we assign that event to the corresponding bin-event class and report the corresponding statistical information. Otherwise, the signal is marked as *.
For each region with bin-junction support (bjs) signal, we try to find a matching J3-junction in binbased(sr) table. If a match is found, junction statistics are retrieved. Otherwise, the signal is marked as * in the bjs column.
For each region with anchorage-signal, we try to find a matching J3-junction in anchorbased(sr) table. If a match is found, junction statistics are retrieved. Otherwise, we try to find a matching J3-junction of a Io bin in the binbased(sr) table. If a match is found, the event is marked as IoR. Otherwise, we try to find a bin-signal matching region. In case no match is reported at this instance the event is marked as Novel Alternative Splicing Pattern NASP. If a match is reported but the bin is marked as *, then the event is marked as Complex Splicing Pattern CSP. Finally, if no J3-junction in anchorbased(sr) table is found, the signal is marked as *.
For each region with locale signal, we look for a matching cluster in localebased(sr) table. If any, we retrieve the statistical information from that cluster. Otherwise we try to find a cluster with a matching J3 junction. If no match is found, then the locale signal columns is marked with an *. In order to categorize the event we adopte the following heuristic:
If all junctions share the same end or start coordinates, we check if all bins in between are of the same type (i.e. exons or introns). If this is the case, then this is an Alt 5’/3’ event. Otherwise, the event is marked as CSP.
If the cluster is composed of three junctions and not all junctions share the same end or start, we check it there is a cluster with a matching J3-junction. If this is the case, the event is labelled as an ES event. Otherwise, it is marked as a CSP event. If some of the junctions in the cluster were novel, then the event is classified as a novel one: NASP.
For each of the other unclassified events, we check whether they involve exonic features. If this were the case, we mark them as alternative splicing affecting a consensus exon ASCE events. On the other hand, for intronic features, if some signals were marked as * we clasify the event as CSP. Othewise, the event is marked as an IR.
6.4.3 exportSplicingReport: Export splicing reports in HTML pages.
This function makes use of the DT R-package (Xie, Cheng, and Tan 2018), a wrapper of the JavaScript library ’DataTables’ to export splicing report results into interactive HTML pages.
exportSplicingReports( sr,
output.dir="sr",
openInBrowser = FALSE,
maxBinFDR = 0.2,
maxJunctionFDR = 0.2 )Arguments:
sr: An object of class ASpliSplicingReport
output.dir: HTML reports output directory
openInBrowser: (Default = FALSE) If TRUE, results are automatically displayed just after exporting.
maxBinFDR (Default = 0.2) Export info for bins with FDR < maxBinFDR
maxJunctionFDR (Default = 0.2): Export info for junctions with FDR < maxJunctionFDR
The result is an HTML page, where you can easily browse the tables stored in an ASpliSplicingReport object.
6.4.4 exportIntegratedSignals(): Export integrated signals into HTML pages.
This function makes use of the DT R-package (Xie, Cheng, and Tan 2018), a wrapper of the JavaScript library ’DataTables’, to export region-based integrated signal results into interactive HTML pages.
exportIntegratedSignals( is, output.dir="is",
sr, counts, features, asd,
mergedBams,
jCompletelyIncluded = FALSE, zoomRegion = 1.5,
useLog = FALSE, tcex = 1, ntop = NULL, openInBrowser = F,
makeGraphs = T, bforce=FALSE
)Arguments:
is: An object of class ASpliIntegratedSignals
sr: An object of class ASpliSplicingReport
counts: An object of class ASpliCount
features: An object of class ASpliFeatures
asd: An object of class ASpliAS
output.dir: HTML reports output directory
mergedBams: Dataframe with two columns, bams and conditions. Bams are paths to merged bam files for each condition. These will be used to produce coverage plots if mageGraph=TRUE.jCompletelyIncluded: If TRUE only plot junctions completely included inside the plot region. Otherwise plot any overlapping junctions, not necessarily contained in the region of interestuseLog: Plot counts logtcex: Text sizentop: Only show n top signalsopenInBrowser: Open reports in browser when donemakeGraphs: Generate graphs in reportsbforce: Force plot generation even if plot already exists on disk
The result is an HTML page, where you can easily browse the tables stored in an ASpliIntegratedSignals object.
7 Case studies
ASpli provides a small synthetic data set to test the whole pipeline. It consists of
a GTF file for a a genome with ten genes with multiple isoforms with the corresponding annotation.
a set of twelve BAM files from an experiment with a two\(\times\)two factorial design
four merged BAM files (merged replicates, one per condition)
7.1 2x2 experimental design
The two factors in this example are called f1 and f2.f1 can have value A or B and f2 can have value C or D, defining four conditions: A.C, A.D, B.C and B.D. Table 2 summarizes the experimental design.
| sample | f1 | f2 | replicate | condition |
|---|---|---|---|---|
| 1 | A | C | 0 | A.C |
| 2 | A | C | 1 | A.C |
| 3 | A | C | 2 | A.C |
| 4 | A | D | 0 | A.D |
| 5 | A | D | 1 | A.D |
| 6 | A | D | 2 | A.D |
| 7 | B | C | 0 | B.C |
| 8 | B | C | 1 | B.C |
| 9 | B | C | 2 | B.C |
| 10 | B | D | 0 | B.D |
| 11 | B | D | 1 | B.D |
| 12 | B | D | 2 | B.D |
The firts step of the workflow is to load the small gtf, build the TxDb object and extract their features. In this case gtfFileName contains the full path to the example gtf file in your system.
#library( ASpli )
library( GenomicFeatures )
gtfFileName <- aspliExampleGTF()
genomeTxDb <- makeTxDbFromGFF( gtfFileName )
features <- binGenome( genomeTxDb )Then you should define your targets table. aspliExampleBAMList() provides the full path to the BAMs files.
BAMFiles <- aspliExampleBamList()
targets <- data.frame(
row.names = paste0('Sample',c(1:12)),
bam = BAMFiles,
f1 = c( 'A','A','A','A','A','A',
'B','B','B','B','B','B'),
f2 = c( 'C','C','C','D','D','D',
'C','C','C','D','D','D'),
stringsAsFactors = FALSE)Experimental conditions are inferred from the experimental factors in the target dataframe:
getConditions(targets)We define a dataframe with path information for merged bams:
mBAMs <- data.frame(bam = sub("_[02]","",targets$bam[c(1,4,7,10)]),
condition= c("A_C","A_D","B_C","B_D"))Next step is to overlap reads and features:
gbcounts <- gbCounts( features = features,
targets = targets,
minReadLength = 100, maxISize = 50000,
libType="SE",
strandMode=0)And also quantify junctions:
asd<- jCounts(counts = gbcounts,
features = features,
minReadLength = 100,
libType="SE",
strandMode=0,
threshold = 5,
minAnchor = 10)At this point we can start asking different questions regarding differential gene expression and splicing patterns. ASpli allows to do this by defining contrasts or by using the formula approach. This feature allows the user to choose the framework that better (i.e. more easily) serve to test the sought effect.
7.1.1 The contrast approach
Table 3 shows different contrasts that can be used to test different hypothesis:
| Test | contrast |
|---|---|
| AS between f2 C and D conditions, for f1=A samples | c(-1, 1, 0, 0) |
| AS between f2 C and D conditions, for f1=B samples | c( 0, 0,-1, 1) |
| Interaction effect (i.e. differences between the above-mentioned contrasts) | c( 1,-1,-1, 1) |
To continue our mini-tutorial we will focus on the last contrast of Table 3. We will test whether the differential patterns observed between D and C levels of f2 are different for A and B f1 levels. The interaction contrast corresponds to: \[\begin{equation} I = ( B.D - B.C ) - ( A.D - A.C ) \end{equation}\]
As Table 3 shows, taking in consideration the order given by getConditions function, the coefficients of the terms in this expression can be represented as the contrast vector \([ 1,-1,-1, 1]\).
We estimate gene differential expression and annotation-based differential splicing patterns for the contrast of interest as follow:
gb <- gbDUreport(gbcounts,contrast = c( 1, -1, -1, 1 ) )genesDE() and binsDU() accesors report the statistical significance results. We can see that in this example no evidence of a statistically significant interaction term was found at gene expression level. However, 9 out of 14 bins displayed statistically significant interaction effect (fdr < 0.01)
genesDE(gb)[1:5,]
binsDU(gb)[1:5,]The analysis of annotation-free junction-based AS events can be done with 1 Note that as we required to compute non-uniform intron coverage, we should specify merged-BAMs information:
jdur <- jDUreport(asd, contrast = c( 1, -1, -1, 1 ))Results for junction-based events matching bin ranges can be accessed through jir(jdur), jes(jdur) and jalt(jdur) accesors (see jDUreport: Junction-centered analysis of AS).
Here we show the output corresponding to results of the locale-junction cluster analysis. Information regarding locale cluster-level tests can be retrieved using:
localec(jdur)[1:5,]Rownames correspond to cluster-ids. Rows are ordered by FDR values. The participation and dParticipation values reported for each cluster correspond to the values obtained for the cluster’s junction presenting the largest and most significant participation change. Note that NA’s are reported for cluster-7 as no statistically significant changes were found for any junction of that cluster.
localej(jdur) shows locale-information for each analyzed junction, reporting results of statistical tests at junction level in the first 8 columns:
localej(jdur)[1:5,1:8]We integrate all our data:
sr <- splicingReport(gb, jdur, counts = gbcounts)
is <- integrateSignals(sr,asd)And finally we export integrate signals into an interactive HTML page
exportIntegratedSignals( is, output.dir="aspliExample",
sr, gbcounts, features, asd, mBAMs)7.1.2 The formula approach
In ASpli it is also possible to use formulas to test different hypothesis. In our example we will consider the following formula to model our data:
form <- formula(~f1+f2+f1:f2)This formula corresponds to the following model matrix:
model.matrix(form,targets)Table 4 summarizes three statistical comparisons that could be easily implemented considering the second, third and fourth coefficient of the model:
| Test | contrast | coefficient |
|---|---|---|
| B.C-A.C | c(-1, 0, 1, 0) | 2 |
| A.D-A.C | c(-1, 1, 0, 0) | 3 |
| (B.D-B.C)-(A.D-A.C) | c( 1,-1,-1, 1) | 4 |
In this way, to test for an interaction effect with the formula framework, we should run:
gb <- gbDUreport(counts, formula = form , coef = 4)
jdur <- jDUreport(asd, formula = form, coef = 4 ,
runUniformityTest = TRUE,
mergedBams = mBAMs)7.2 Paired experimental design
The formula approach becomes extremely useful, for instance, to handle paired designs. We will consider a subset of the originally included samples to simulate a paired experiment (see Table 5). In this case f1 could denote experimental units and f2 levels, treatment-control conditions.
| sample | f1 | f2 | replicate | condition |
|---|---|---|---|---|
| 1 | A | C | 0 | A.C |
| 4 | A | D | 0 | A.D |
| 7 | B | C | 0 | B.C |
| 10 | B | D | 0 | B.D |
targetPaired <- targets[c(1,4,7,10),]
gbcounts <- gbCounts( features = features,
targets = targets,
minReadLength = 100, maxISize = 50000,
libType="SE",
strandMode=0)
asd <- jCounts(counts = gbcounts,
features = features,
minReadLength = 100,
libType="SE",
strandMode=0)
form <- formula(~f1+f2)
gb <- gbDUreport(gbcounts, formula = form)
#jdur <- jDUreport(asd , formula = form)
#sr <- splicingReport(gb, jdur, counts = counts)
#is <- integrateSignals(sr,asd,bjs.fdr = 0.1, bjs.inclussion = 0.1, l.inclussion=0.001,l.fdr = 1)
# exportSplicingReports(sr,output.dir="paired")
#exportIntegratedSignals(is,output.dir="paired",sr,counts,features,asd,mBAMs,tcex=2)Note: for a large number of experimental units you might disable the graph generation option makeGraphs=FALSE in exportIntegratedSignals function call.
8 Migrating from ASpli v1
Scripts written for ASpli 1.x use functions that were removed from the
package (loadBAM, readCounts, AsDiscover, DUreport,
DUreportBinSplice, junctionDUreport, mergeBinDUAS, filterDU and
plotGenomicRegions). The following table shows which function replaces each
of them; a more detailed description is available in ?"ASpli-defunct".
| Removed function | Replacement |
|---|---|
loadBAM, readCounts |
gbCounts() |
AsDiscover |
jCounts() |
DUreport, DUreportBinSplice |
gbDUreport() (DUreport.norm() and DUreport.offset() are still available) |
junctionDUreport |
jDUreport() |
mergeBinDUAS, filterDU |
splicingReport(), integrateSignals() and filterSignals() |
plotGenomicRegions |
exportSplicingReports() and exportIntegratedSignals() |
A typical v1 script looked like this:
features <- binGenome( genomeTxDb )
bams <- loadBAM( targets )
counts <- readCounts( features, bams, targets, cores = 1,
readLength = 100, maxISize = 50000 )
du <- DUreport.norm( counts, targets, contrast = c(-1, 1) )
as <- AsDiscover( counts, targets, features, bams,
readLength = 100, threshold = 5, cores = 1 )
merged <- mergeBinDUAS( du, as, targets, contrast = c(-1, 1) )
plotGenomicRegions( merged, genomeTxDb, features, targets )and the equivalent code for the current workflow (see the Quick start) is:
features <- binGenome( genomeTxDb )
gbcounts <- gbCounts( features = features, targets = targets,
minReadLength = 100, maxISize = 50000 )
asd <- jCounts( counts = gbcounts, features = features,
minReadLength = 100 )
gb <- gbDUreport( gbcounts, contrast = c(-1, 1) )
jdur <- jDUreport( asd, contrast = c(-1, 1) )
sr <- splicingReport( gb, jdur, counts = gbcounts )
is <- integrateSignals( sr, asd )
exportIntegratedSignals( is, sr = sr, output.dir = "aspliExample",
counts = gbcounts, features = features,
asd = asd, mergedBams = mBAMs )The main differences are: BAM files are read one by one by gbCounts() (there
is no bams object anymore), readLength was renamed minReadLength,
libType and strandMode describe the library, jCounts() only needs the
output of gbCounts(), and the plots of genomic regions are produced by the
export* functions as interactive HTML reports.
9 Appendix: Ouputs Details
Here is a brief explanation of the info present in ASpli objects. Some columns are common in several tables.
ASpliCounts
Gene counts (countsg) and gene read densities (rdsg)
(see table 6 for an example)
row.names:: Gene name as reported in annotation data.
symbol:: An optional name for the gene, that must be provided at the moment of feature extraction (see section Bin definition).
locus_overlap:: overlapping loci.
gene_coordinates:: format
chromosome:start-end.start, end,length::
effective_length:: gene length using only annotated exons.
sample data:: gene read counts/densities (one column per sample).
Bin counts (countsb) and bin read densities (rdsb)
row.names:: see binGenome: Binning the genome.
feature:: E for exonic bins, I for intronic bins and Io for introns before splitting. See binGenome: Binning the genome
event:: Alternative splicing event asigned (see section Bin definition:Splicing event assignment)
sample data:: bin read counts/densities (one column per sample)
Junction counts (countsj)
row.names:: Junction’s name in format
chromosome.start.end(see binGenome: Binning the genome).junction:: If junction coincides with a junction inferred from the annotation, the name is shown as is given in section binGenome: Binning the genome, otherwise it is
noHit.gene:: Locus that contains the junction.
strand:: gene’s strand
multipleHit::
yesif junction spans multiple genes.bin_spanned:: Bin’s names spanned by the junction.
j_within_bin:: If junction falls within a single bin, the name of that bin is shown.
sample data:: Junction counts (one column per sample).
E1I (countse1i) and IE2 (countsie2)
row.names:: Junction name in format
chromosome.start.end(see binGenome: Binning the genome).junction:: If junction coincides with a junction inferred from the annotation, the name is shown as is given in section Bin definition, otherwise contains
noHit.gene:: Name of the locus that contains the junction.
strand:: Strand sense of the gene.
multipleHit::
yesif junction spans multiple genes.sample data:: Junction counts (one column per sample).
| Row names | symbol | locus_overlap | gene_coordinates | start | end | length | effective_length | Sample 1 | Sample 2 | … |
|---|---|---|---|---|---|---|---|---|---|---|
| GENE01 | GENE01 | - | chr1:1-700 | 1 | 700 | 700 | 700 | 324 | 314 | n |
| GENE02 | GENE02 | - | chr1:1001-1800 | 1001 | 1800 | 800 | 550 | 327 | 333 | n |
| GENE03 | GENE03 | - | chr1:2001-2800 | 2001 | 2800 | 800 | 650 | 342 | 321 | n |
| GENE04 | GENE04 | - | chr1:3001-3800 | 3001 | 3800 | 800 | 650 | 313 | 337 | n |
| … | … | … | … | … | … | … | … | … | … | … |
ASpliAS
irPIR
event: Type of event asigned by
ASpliwhen binning.J1: Semicolon separated list of junctions with their end matching the start of the intron.
J2: Semicolon separated list of junctions with an end matching the end of the intron.
J3: Semicolon separated list of all the junctions overlaping the intron.
*: Columns in between J1-J2 and J2-J3 represent junction counts in the samples for each bin. (junctions are the sum by condition).
PIR: for each condition, calculated as: \(PIR = (J1 + J2)/(J1 + J2 + 2*J3)\)
altPSI
event: Type of event asigned by
ASpliwhen binning.J1(J2): Semicolon separated list of junctions with an end matching the end of alt5’SS or alt3’SS.
J3: Semicolon separated list of junctions with an end matching the start of an alt5’SS or alt3’SS.
*: Columns in between J1-J2 and J2-J3 represent junction counts in the samples for each bin. (junctions are the sum by condition).
PIR: for each condition, calculated as: \[PIR = (J1 + J2)/(J1 + J2 + 2*J3)\]
PSI: for each condition, calculated as: \(PSI = (J12)/(J12 + J3)\) Where J12 is J1 if it’s an alt 5’ event or J2 if it’s an alt 3’ event
esPSI
event: Type of event asigned by
ASpliwhen binning.J1: Semicolon separated list of junctions with an end on the alternative exon.
J2: Semicolon separated list of junctions with an end on the alternative exon.
J3: Semicolon separated list of exclusion junctions of alternative exon.
*: Columns in between J1-J2 and J2-J3 represent junction counts in the samples for each bin. (junctions are the sum by condition).
PSI: for each condition, calculated as: \[PSI = (J1 + J2)/(J1 + J2 + 2*J3)\]
junctionsPIR
PIR: for each experimental junction, using e1i and ie2 counts. Exclusion junction is the junction itself. This information is crutial to discover new introns as well as retention events.
hitIntron, hitIntronEvent: If the junction matches a bin, name and type of event asigned by
ASplito this bin.Columns: in between represent J1, J2 and J3 counts in the different samples
PIR: calculated for each condition, as \(PIR = (J1 + J2)/(J1 + J2 + 2*J3)\) (junctions are the sum by condition.)
junctionsPJU
Given a junction, it is possible to analyze if it shares start, end or both with other junction. If so, it is possible due to there is alternative splicing in this region.
Rownames: J3 range.
Junction: name of the junction.
Gene: gene it belongs to.
Strand: gene’s strand.
multipleHit: if junction overlaps several genes
symbol: gene symbol
gene_coordinates: gene coordinates.
bin_spanned: semicolon separated list of bins spaned by this junction.
j_within_bin: other junctions in this region
StartHit: junctions sharing start with this junction and \(PJU_J1=J3/(J1+J3)\) for each condition.
EndHit: junctions sharing end with this junction and \(PJU_J2=J3/(J2+J3)\) for each condition.
Columns: in between are:
J3 counts in the different samples for each region.
J1 counts and \(PJU_J1=J3/(J1+J3)\) for each condition.
J2 counts and \(PJU_J2=J3/(J2+J3)\) for each condition.
StartHit is J1 range and EndHit is J2 range.
ASpliJDU
known or novel:
spanned bins:
exintron: if the junction is completely included in a bin, it would indicate this AS event is a possible exintron (Marquez et al. 2015).
localec
Information about statistically significant changes in junction’s usage within locale-juntion clusters.
size: number of junctions belonging to the cluster.
cluster.LR: cluster differential usage likelihood ratio
pvalue, FDR: cluster differential usage pvalue and corresponding FDR
range: cluster genomic coordinates
participation: junction maximal participation value inside the cluster (FDR < maxFDRForParticipation)
dParticipation: delta participation of the significant junction (FDR < maxFDRForParticipation) presenting maximal participation value inside the cluster
localej
cluster: cluster’s name to which junction belongs to
log.mean: log of mean counts accross all conditions for this junction
logFC: log fold change of junction accross conditions
pvalue and FDR: junction’s pvalue and corresponding FDR
annotated: if junction is annotated or new
participation: maximal participation value observed across contrasted condictions
dParticipation: delta participation of the maximal participation value observed across contrasted condictions
Columns in between: junction counts for all samples
anchorc
cluster.LR: likelihood ratio of cluster differential usage.
pvalue and FDR: cluster differential usage pvalue and corresponding FDR.
anchorj
log.mean: log of mean counts accross all conditions for this junction
logFC: log fold change of junction accross conditions
LR: likelihood ratio of junction differential usage.
pvalue, FDR: junction’s pvalue and corresponding FDR
J1.pvalue:: J1 junction’s pvalue
J2.pvalue:: J2 junction’s pvalue
NonUniformity: if non uniformity test was performed, numbers closer to zero mean uniformity and closer to one mean non uniformity
dPIR: junction’s delta PIR
annotated: if junction is annotated or new
J counts: junctions’ counts for all samples
jir
J3: J3 junctions
logFC: log fold change of junction accross conditions
LR: likelihood ratio of junction differential usage.
pvalue, FDR: junction’s pvalue and corresponding FDR
NonUniformity: if non uniformity test was performed, numbers closer to zero mean uniformity and closer to one mean non uniformity
dPIR: junction delta PIR
multiplicity: do multiple junctions cross the region
J counts: junctions’ counts for all samples
jes
event: Type of event asigned by
ASpliwhen binning.J3: junctions
logFC: log fold change of junction accross conditions
LR: likelihood ratio of junction differential usage.
pvalue, FDR: junction’s pvalue and corresponding FDR
dPSI: junction delta PSI
multiplicity: do multiple junctions cross the region
J counts: junctions’ counts for all samples
jalt
event: Type of event asigned by
ASpliwhen binning.J3: junctions
logFC:: log fold change of junction accross conditions
log.mean:: log of mean counts accross all conditions for this junction
logFC: log fold change of junction accross conditions
LR: likelihood ratio of junction differential usage.
pvalue, FDR: junction’s pvalue and corresponding FDR
dPSI: junction delta PSI
multiplicity:: do multiple junctions cross the region
J counts: junctions’ counts for all samples
10 References
Appendix
A Session information
sessionInfo()## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] GenomicFeatures_1.65.0 GenomicRanges_1.65.4 Seqinfo_1.3.2
## [4] ASpli_2.23.4 AnnotationDbi_1.75.2 IRanges_2.47.5
## [7] S4Vectors_0.51.10 Biobase_2.73.2 BiocGenerics_0.59.12
## [10] generics_0.1.4 edgeR_4.99.12 limma_3.99.4
## [13] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.19.0
## [3] jsonlite_2.0.0 magrittr_2.0.5
## [5] farver_2.1.2 rmarkdown_2.32
## [7] BiocIO_1.23.3 vctrs_0.7.3
## [9] memoise_2.0.1 Rsamtools_2.29.0
## [11] RCurl_1.98-1.20 base64enc_0.1-6
## [13] htmltools_0.5.9 S4Arrays_1.13.2
## [15] BiocBaseUtils_1.15.1 progress_1.2.3
## [17] curl_8.1.0 SparseArray_1.13.4
## [19] Formula_1.2-6 sass_0.4.10
## [21] bslib_0.12.0 htmlwidgets_1.6.4
## [23] plyr_1.8.9 Gviz_1.57.4
## [25] httr2_1.3.0 cachem_1.1.0
## [27] GenomicAlignments_1.49.2 igraph_2.3.4
## [29] lifecycle_1.0.5 pkgconfig_2.0.3
## [31] Matrix_1.7-6 R6_2.6.1
## [33] fastmap_1.2.0 MatrixGenerics_1.25.0
## [35] digest_0.6.39 colorspace_2.1-4
## [37] crosstalk_1.2.2 Hmisc_5.3-0
## [39] RSQLite_3.53.3 filelock_1.0.3
## [41] httr_1.4.9 abind_1.4-8
## [43] compiler_4.6.1 bit64_4.8.6
## [45] htmlTable_2.5.0 S7_0.2.2
## [47] backports_1.5.1 BiocParallel_1.47.0
## [49] DBI_1.3.0 UpSetR_1.4.1
## [51] biomaRt_2.69.6 MASS_7.3-66
## [53] DelayedArray_0.39.8 rjson_0.2.23
## [55] tools_4.6.1 foreign_0.8-92
## [57] otel_0.3.0 nnet_7.3-21
## [59] glue_1.8.1 restfulr_0.0.17
## [61] grid_4.6.1 checkmate_2.3.4
## [63] cluster_2.1.8.3 gtable_0.3.6
## [65] BSgenome_1.81.1 tidyr_1.3.2
## [67] ensembldb_2.37.3 data.table_1.18.6.1
## [69] hms_1.1.4 XVector_0.53.0
## [71] pillar_1.11.1 stringr_1.6.0
## [73] splines_4.6.1 dplyr_1.2.1
## [75] BiocFileCache_3.3.3 lattice_0.23-1
## [77] deldir_2.0-4 rtracklayer_1.73.0
## [79] bit_4.6.0 biovizBase_1.61.0
## [81] tidyselect_1.2.1 Biostrings_2.81.9
## [83] knitr_1.52 gridExtra_2.3.1
## [85] bookdown_0.48 ProtGenerics_1.45.0
## [87] SummarizedExperiment_1.43.0 xfun_0.62
## [89] statmod_1.5.2 matrixStats_1.5.0
## [91] DT_0.34.0 stringi_1.8.9
## [93] UCSC.utils_1.9.0 lazyeval_0.2.3
## [95] yaml_2.3.12 evaluate_1.0.5
## [97] codetools_0.2-20 cigarillo_1.3.1
## [99] interp_1.1-6 tibble_3.3.1
## [101] BiocManager_1.30.27 cli_3.6.6
## [103] rpart_4.1.27 pbmcapply_1.5.1
## [105] jquerylib_0.1.4 dichromat_2.0-1
## [107] Rcpp_1.1.2 GenomeInfoDb_1.49.2
## [109] dbplyr_2.6.0 png_0.1-9
## [111] XML_3.99-0.25 ggplot2_4.0.3
## [113] blob_1.3.0 prettyunits_1.2.0
## [115] jpeg_0.1-11 latticeExtra_0.6-31
## [117] AnnotationFilter_1.37.0 bitops_1.1-0
## [119] txdbmaker_1.9.1 VariantAnnotation_1.59.4
## [121] scales_1.4.0 purrr_1.2.2
## [123] crayon_1.5.3 rlang_1.3.0
## [125] KEGGREST_1.53.6