Isoform Switch Detection Pipeline for RNASeq Data Using IsoformSwitchAnalyzer

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Snakemake Workflow for Isoform Switches

This is a snakemake pipeline to detect isoform switches from RNASeq data. The pipeline is in progress. It uses IsoformSwitchAnalyzer tool so far. The pipeline detects alternative splicing and differential

Code Snippets

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shell:
    """ 
    mkdir -p galore
    mkdir -p fastqc
    trim_galore --gzip --retain_unpaired --trim1 --fastqc --fastqc_args "--outdir fastqc" -o galore --paired {input.r1} {input.r2}
    """
SnakeMake FastQC Trim_Galore From line 30 of master/Snakefile 
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shell:
           """
           salmon quant -i {params.index} --libType {params.lib} -1 {input.r1} -2 {input.r2} -o {params.outdir} -p 3 --writeUnmappedNames --seqBias --gcBias --validateMappings
           """
SnakeMake Quant Salmon From line 48 of master/Snakefile 
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shell: 
    """ 
    mkdir -p {params[1]}2 
    mkdir -p {params[2]}2
    cp -r {params[1]}_SALMON.* {params[1]}2 
    cp -r {params[0]}_SALMON.* {params[1]}2

    cp -r {params[2]}_SALMON.* {params[2]}2
    cp -r {params[0]}_SALMON.* {params[2]}2

    Rscript {config[SRC]}/splice_all.R {params[0]} {params[1]} {params[2]} {params[3]} {params[4]} {params[5]}
    cd {params[1]}2
    Rscript {config[SRC]}/splice.R {params[0]} {params[1]} {params[3]} {params[4]} 
    cd ..
    cd {params[2]}2 
    Rscript {config[SRC]}/splice.R {params[0]} {params[2]} {params[3]} {params[5]} 
    """
SnakeMake From line 67 of master/Snakefile 
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library("IsoformSwitchAnalyzeR")

args = commandArgs(trailingOnly=TRUE)
control_condition = args[1]
treat1_condition = args[2] 
treat2_condition = args[3] 
Nc = args[4] 
Nt1 = args[5] 
Nt2 =args[6] 
workdir = getwd()
salmonQuant <- importIsoformExpression( parentDir =workdir) 
summary(salmonQuant)
myDesign <- data.frame(
   sampleID = colnames(salmonQuant$abundance)[-1],
   condition = c( rep(treat1_condition, Nt1), rep(treat2_condition, Nt2), rep(control_condition, Nc) ) 
)

myDesign 

conditions <- data.frame(condition_1 =c(control_condition, control_condition), condition_2= c(treat1_condition, treat2_condition) )
summary(conditions) 

mySwitchList <- importRdata(
    isoformCountMatrix   = salmonQuant$counts,
    isoformRepExpression = salmonQuant$abundance,
    designMatrix         = myDesign,
    isoformExonAnnoation = "gencode.v40.chr_patch_hapl_scaff.annotation.gtf",
    isoformNtFasta       = "gencode.v40.transcripts.fa",
    showProgress = TRUE,
    ignoreAfterBar = TRUE,
    comparisonsToMake=conditions 
)

head(mySwitchList$isoformFeatures,2)
write.csv(mySwitchList$isoformFeatures, "mySwitchList_isoforms.csv")

head(mySwitchList$exons,2)
write.csv(mySwitchList$exons, "mySwitchList_exons.csv")

head(mySwitchList$ntSequence,2)
write.csv(mySwitchList$ntSequence, "mySwitchList_ntSeq.csv")

mySwitchList <- subsetSwitchAnalyzeRlist(
    mySwitchList,
    subset = abs(mySwitchList$isoformFeatures$dIF) > 0.4
)

##Prefilter 

mySwitchList <- preFilter(
    mySwitchList,
    geneExpressionCutoff = 5,
    isoformExpressionCutoff = 3,
    removeSingleIsoformGenes = TRUE
)
summary(mySwitchList)
head(mySwitchList)



mySwitchList <- isoformSwitchTestDEXSeq(
    mySwitchList,
    reduceToSwitchingGenes=TRUE
)

#Summarize switching features
extractSwitchSummary(mySwitchList)



mySwitchList <- extractSequence(
    mySwitchList,
    pathToOutput = workdir,
    removeLongAAseq=TRUE, 
    alsoSplitFastaFile=TRUE,
    removeShortAAseq=TRUE, 
    writeToFile=TRUE

)

extractSwitchSummary(mySwitchList)


write.csv(mySwitchList$isoformFeatures, "part1_isoformsfeatures.csv")
write.csv(mySwitchList$exons, "part1_exons.csv")
write.csv(mySwitchList$ntSequence, "part1_ntseq.csv")


mySwitchList <- analyzeIntronRetention(
    mySwitchList,
    onlySwitchingGenes = TRUE,
    alpha = 0.05,
    dIFcutoff = 0.1,
    showProgress = TRUE,
    quiet = FALSE
)

summary(mySwitchList)


mySwitchList <- analyzeAlternativeSplicing(
   mySwitchList,
    onlySwitchingGenes=TRUE,
    alpha=0.05,
    dIFcutoff = 0.1,
    showProgress=TRUE,
    quiet=TRUE
)


mySwitchList <- analyzeCPC2( mySwitchList, 'cpc2output.txt',
    removeNoncodinORFs = TRUE,
    codingCutoff = 0.725,
    quiet=FALSE
 )


mySwitchList <- analyzeIntronRetention(
    mySwitchList,
    onlySwitchingGenes = TRUE,
    alpha = 0.05,
    dIFcutoff = 0.1,
    showProgress = TRUE,
    quiet = FALSE
)

mySwitchList <-  analyzePFAM( mySwitchList, 'pfam.txt')

mySwitchList <- analyzeSignalP( mySwitchList, 'protein_type.txt')

consequencesOfInterest <- c('intron_retention','coding_potential', 'ORF_seq_similarity') 
#consequencesOfInterest <- c('isoform_seq_similarity','isoform_length','intron_retention','coding_potential','NMD_status','domain_length','domains_identified','ORF_length','ORF_seq_similarity', 'signal_peptide_identified')
mySwitchList <- analyzeSwitchConsequences(
    mySwitchList,
    consequencesToAnalyze = consequencesOfInterest,
    dIFcutoff = 0.1, # very high cutoff for fast runtimes - you should use the default (0.1)
    showProgress=FALSE
)

pdf(file = 'ConsequenceSummary.pdf', onefile = TRUE, height=6, width = 14, pointsize =7)
consq_summary <- extractConsequenceSummary(
    mySwitchList,
    consequencesToAnalyze= consequencesOfInterest,
    plotGenes = TRUE,           # enables analysis of genes (instead of isoforms)
    asFractionTotal = FALSE,      # enables analysis of fraction of significant features
    returnResult = TRUE
)
dev.off()
write.csv(consq_summary, "consequences_summary.csv")

pdf(file = 'ConsequenceEnrichment.pdf', onefile = TRUE, height=6, width = 9)
extractConsequenceEnrichment(
    mySwitchList,
    consequencesToAnalyze = 'all',
    alpha=0.05,
    dIFcutoff = 0.1,
    countGenes = TRUE,
    analysisOppositeConsequence=TRUE,
    plot=TRUE,
    localTheme = theme_bw(base_size = 12),
    minEventsForPlotting = 5,
)
dev.off()

pdf(file = 'ConsequenceEnrichmentComparison.pdf', onefile = TRUE, height=6, width = 9)
extractConsequenceEnrichmentComparison(
    mySwitchList,
    consequencesToAnalyze = 'all',
    alpha=0.05,
    dIFcutoff = 0.1,
    countGenes = TRUE,
    analysisOppositeConsequence=TRUE,
    plot=TRUE,
    localTheme = theme_bw(base_size = 14),
    minEventsForPlotting = 10,
)
dev.off()

pdf(file = 'SplicingEnrichment.pdf', onefile = TRUE, height=6, width = 9)
extractSplicingEnrichmentComparison(
    mySwitchList,
    splicingToAnalyze = 'all',
    alpha = 0.05,
    dIFcutoff = 0.1,
    onlySigIsoforms = FALSE,
    countGenes = TRUE,
    plot = TRUE,
    localTheme = theme_bw(base_size = 14),
    minEventsForPlotting = 10,
    returnResult=FALSE
)
dev.off()


pdf(file = 'SwitchOverlap.pdf', onefile = TRUE, height=6, width = 9)
switchoverlap <- extractSwitchOverlap(
    mySwitchList,
    filterForConsequences=TRUE,
    alpha = 0.05,
    dIFcutoff = 0.1,
    scaleVennIfPossible=TRUE,
    plotIsoforms = TRUE,
    plotSwitches = TRUE,
    plotGenes = TRUE
)
head(switchoverlap)
write.csv(switchoverlap, "switchoverlap.csv")
dev.off()

pdf(file = 'splicingsummary.pdf', onefile = FALSE, height=6, width = 9)
extractSplicingSummary( mySwitchList,  asFractionTotal = FALSE,
    plotGenes=TRUE )
dev.off()

pdf(file = 'GeneEnrichment.pdf', onefile = FALSE, height=6, width = 9)
extractSplicingEnrichment(
    mySwitchList,
    minEventsForPlotting=5,
    plot = TRUE
)
dev.off()



pdf(file = 'GenomeWide.pdf', onefile = FALSE, height=6, width = 9)
myIsoforms <- extractSplicingGenomeWide(
    mySwitchList,
    featureToExtract = 'isoformUsage',
    splicingToAnalyze = 'all',
    alpha=0.05,
    dIFcutoff = 0.1,
    log2FCcutoff = 1,
    violinPlot=TRUE,
    alphas=c(0.05, 0.001),
    localTheme=theme_bw(),
    plot=TRUE,
)
dev.off()

mytoplist <- extractTopSwitches(
    mySwitchList,
    filterForConsequences = TRUE,
    n =50,
    sortByQvals = TRUE
)

head(mytoplist)
write.csv(mytoplist, "toplist_all.csv")

switchPlotTopSwitches( mySwitchList, n=20, filterForConsequences = TRUE )
R IsoformSwitchAnalyzeR From line 1 of src/splice_all.R 
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library("IsoformSwitchAnalyzeR")
packageDescription("IsoformSwitchAnalyzeR")$Version


args = commandArgs(trailingOnly=TRUE)
control_condition = args[1]
treat_condition = args[2]
Nc = args[3] 
Nt = args[4] 

workdir = getwd()
salmonQuant <- importIsoformExpression( parentDir =workdir)
summary(salmonQuant)
myDesign <- data.frame(
   sampleID = colnames(salmonQuant$abundance)[-1],
   condition =  c(rep(treat_condition, Nt), rep(control_condition,Nc) ) )

myDesign 

conditions <- data.frame(condition_1 = control_condition, condition_2=treat_condition)


mySwitchList <- importRdata(
    isoformCountMatrix   = salmonQuant$counts,
    isoformRepExpression = salmonQuant$abundance,
    designMatrix         = myDesign,
    isoformExonAnnoation = "gencode.v40.chr_patch_hapl_scaff.annotation.gtf",
    isoformNtFasta       = "gencode.v40.transcripts.fa",
    showProgress = TRUE,
    ignoreAfterBar = TRUE,
    comparisonsToMake=conditions
)

out = paste(treat_condition,"mySwitchList_isoforms.csv", sep="_")
head(mySwitchList$isoformFeatures,2)
write.csv(mySwitchList$isoformFeatures, out) 

head(mySwitchList$exons,2)
out = paste(treat_condition, "mySwitchList_exons.csv", sep ="_") 
write.csv(mySwitchList$exons, out)

head(mySwitchList$ntSequence,2)
out = paste(treat_condition, "mySwitchList_ntSeq.csv", sep = "_")
write.csv(mySwitchList$ntSequence, out)



mySwitchList <- subsetSwitchAnalyzeRlist(
    mySwitchList,
    subset = abs(mySwitchList$isoformFeatures$dIF) > 0.4
)


##Prefilter 

mySwitchList <- preFilter(
    mySwitchList,
    geneExpressionCutoff = 5,
    isoformExpressionCutoff = 3,
    removeSingleIsoformGenes = TRUE
)
summary(mySwitchList)
head(mySwitchList) 



mySwitchList <- isoformSwitchTestDEXSeq(
    mySwitchList,
    reduceToSwitchingGenes=TRUE
)

#Summarize switching features
extractSwitchSummary(mySwitchList)


mySwitchList <- extractSequence(
    mySwitchList, 
    pathToOutput = workdir,
    removeLongAAseq=TRUE,
    alsoSplitFastaFile=TRUE,
    removeShortAAseq=TRUE,
    writeToFile=TRUE 
)

extractSwitchSummary(mySwitchList)

out = paste(treat_condition, "part1_isoformsfeatures.csv", sep ="_") 
write.csv(mySwitchList$isoformFeatures, out)
out = paste(treat_condition, "part1_exons.csv", sep ="_")
write.csv(mySwitchList$exons, out)
out = paste(treat_condition, "part1_ntseq.csv", sep= "_")
write.csv(mySwitchList$ntSequence, out)


mySwitchList <- analyzeIntronRetention(
    mySwitchList,
    onlySwitchingGenes = TRUE,
    alpha = 0.05,
    dIFcutoff = 0.1,
    showProgress = TRUE,
    quiet = FALSE
)

summary(mySwitchList) 


out =paste(treat_condition, "cpc2output.txt", sep ="_")
out
mySwitchList <- analyzeCPC2( mySwitchList, out, 
    removeNoncodinORFs = TRUE,
    codingCutoff = 0.725,
    quiet=FALSE
 )



out = paste(treat_condition, 'pfam.txt', sep = "_")
mySwitchList <-  analyzePFAM( mySwitchList, out)

out = paste(treat_condition, "protein_type.txt", sep ="_") 
mySwitchList <- analyzeSignalP( mySwitchList, out)

consequencesOfInterest <- c('intron_retention','coding_potential', 'ORF_seq_similarity')
#consequencesOfInterest <- c('isoform_seq_similarity','isoform_length','intron_retention','coding_potential','NMD_status','domain_length','domains_identified','ORF_length','ORF_seq_similarity', 'signal_peptide_identified')
mySwitchList <- analyzeSwitchConsequences(
    mySwitchList,
    consequencesToAnalyze = consequencesOfInterest,
    dIFcutoff = 0.1, # very high cutoff for fast runtimes - you should use the default (0.1)
    showProgress=FALSE
)

out = paste(treat_condition, "ConsequenceSummary.pdf", sep ="_") 
pdf(file = out, onefile = TRUE, height=6, width = 14, pointsize =7)
consq_summary <- extractConsequenceSummary(
    mySwitchList,
    consequencesToAnalyze= consequencesOfInterest,
    plotGenes = TRUE,           # enables analysis of genes (instead of isoforms)
    asFractionTotal = FALSE,      # enables analysis of fraction of significant features
    returnResult = TRUE
)
dev.off()

out = paste(treat_condition, "consequence_summary.csv", sep ="_") 
write.csv(consq_summary, out)


mytoplist <- extractTopSwitches(
    mySwitchList,
    filterForConsequences = TRUE,
    n =50,
    sortByQvals = TRUE
)

head(mytoplist)
out = paste(treat_condition, "toplist.csv", sep ="_")
write.csv(mytoplist, out)

mySwitchList <- analyzeAlternativeSplicing(
   mySwitchList,
    onlySwitchingGenes=TRUE,
    alpha=0.05,
    dIFcutoff = 0.1,
    showProgress=TRUE,
    quiet=TRUE
)


summary(mySwitchList$AlternativeSplicingAnalysis)

### overview of number of intron retentions (IR)

out = paste(treat_condition, "AlternativeSplicing.csv", sep ="_") 
write.csv(mySwitchList$AlternativeSplicingAnalysis, out) 


mySwitchList <- analyzeIntronRetention(
    mySwitchList,
    onlySwitchingGenes = TRUE,
    alpha = 0.05,
    dIFcutoff = 0.1,
    showProgress = TRUE,
    quiet = FALSE
)
out = paste(treat_condition, "consequence.csv", sep ="_") 
write.csv(mySwitchList$switchConsequence, out)


out = paste(treat_condition, "splicingsummary.pdf", sep="_")
pdf(file = out, onefile = FALSE, height=6, width = 9)
extractSplicingSummary( mySwitchList,  asFractionTotal = FALSE,
    plotGenes=TRUE )
dev.off()

out = paste(treat_condition, "GeneEnrichment.pdf", sep="_") 
pdf(file = out, onefile = FALSE, height=6, width = 9)
extractSplicingEnrichment(
    mySwitchList,
    minEventsForPlotting=5,
    plot = TRUE
)
dev.off()

out = paste(treat_condition, "GenomeWide.pdf", sep ="_") 
pdf(file = out, onefile = FALSE, height=6, width = 9)
extractSplicingGenomeWide(
    mySwitchList,
    featureToExtract = 'isoformUsage',
    splicingToAnalyze = 'all',
    alpha=0.05,
    dIFcutoff = 0.1,
    log2FCcutoff = 1,
    violinPlot=TRUE,
    alphas=c(0.05, 0.001),
    localTheme=theme_bw(),
    plot=TRUE,
)
dev.off()


### Visual analysis
# Indiviudal switches
switchPlotTopSwitches( mySwitchList, n=20, filterForConsequences = TRUE )

### Summary
extractSwitchSummary(mySwitchList, filterForConsequences = TRUE)


out = paste(treat_condition, "volcanoplots.pdf", sep ="_") 
pdf(file = out, onefile = TRUE, height=6, width = 9)

ggplot(data=mySwitchList$isoformFeatures, aes(x=dIF, y=-log10(isoform_switch_q_value))) +
     geom_point(
        aes( color=abs(dIF) > 0.1 & isoform_switch_q_value < 0.05 ), # default cutoff
        size=1
    ) +
    geom_hline(yintercept = -log10(0.05), linetype='dashed') + # default cutoff
    geom_vline(xintercept = c(-0.1, 0.1), linetype='dashed') + # default cutoff
    #facet_wrap( ~ condition_2) +
    facet_grid(condition_1 ~ condition_2) + # alternative to facet_wrap if you have overlapping conditions
    scale_color_manual('Signficant\nIsoform Switch', values = c('black','red')) +
    labs(x='dIF', y='-Log10 ( Isoform Switch Q Value )') +
    theme_bw()

dev.off()

out = paste(treat_condition, "overview.pdf", sep="_") 
pdf(file = out, onefile = TRUE, height=6, width = 9)
ggplot(data=mySwitchList$isoformFeatures, aes(x=gene_log2_fold_change, y=dIF)) +
    geom_point(
        aes( color=abs(dIF) > 0.1 & isoform_switch_q_value < 0.05 ), # default cutoff
        size=1
    ) +
    #facet_wrap(~ condition_2) +
    facet_grid(condition_1 ~ condition_2) + # alternative to facet_wrap if you have overlapping conditions
    geom_hline(yintercept = 0, linetype='dashed') +
    geom_vline(xintercept = 0, linetype='dashed') +
    scale_color_manual('Signficant\nIsoform Switch', values = c('black','red')) +
    labs(x='Gene log2 fold change', y='dIF') +
    theme_bw()
dev.off()



out = paste(treat_condition, "ConsequenceEnrichment.pdf", sep ="_")
pdf(file = out, onefile = TRUE, height=6, width = 9)
extractConsequenceEnrichment(
    mySwitchList,
    consequencesToAnalyze = 'all',
    alpha=0.05,
    dIFcutoff = 0.1,
    countGenes = TRUE,
    analysisOppositeConsequence=TRUE,
    plot=TRUE,
    localTheme = theme_bw(base_size = 12),
    minEventsForPlotting = 5,
)
dev.off()
R ggplot2 IsoformSwitchAnalyzeR From line 1 of src/splice.R
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Free

Created: 1yr ago
Updated: 1yr ago
Maitainers: public
URL: https://github.com/SherineAwad/IsoformSwitches
Name: isoformswitches
Version: 1
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Copyright: Public Domain
License: None
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Metagenome-atlas is a easy-to-use metagenomic pipeline based on snakemake. It handles all steps from QC, Assembly, Binning, t...
raw sequence reads Genome assembly Annotation track checkm2 gunc prodigal snakemake-wrapper-utils MEGAHIT Atlas BBMap Biopython BioRuby Bwa-mem2 cd-hit CheckM DAS Diamond eggNOG-mapper v2 MetaBAT 2 Minimap2 MMseqs MultiQC Pandas Picard pyfastx SAMtools SemiBin Snakemake SPAdes SqueezeMeta TADpole VAMB CONCOCT ete3 gtdbtk h5py networkx numpy plotly psutil utils metagenomics
Free
175
rna-seq-star-deseq2
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RNA-seq workflow using STAR and DESeq2
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public

This workflow performs a differential gene expression analysis with STAR and Deseq2. The usage of this workflow is described ...
raw sequence reads Gene expression profiling ID list snakemake-wrapper-utils biomaRt PCAtools BiocParallel BWA Cutadapt DESeq2 GFFutils MultiQC Pandas SAMtools Snakemake STAR cli stringr tidyverse RNA-Seq
Free
41
dna-seq-gatk-variant-calling
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This Snakemake pipeline implements the GATK best-practices workflow
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public

This Snakemake pipeline implements the GATK best-practices workflow for calling small germline variants. The usage of thi...
VCF raw sequence reads Variant calling genetic variants gatk rust-bio-tools snakemake-wrapper-utils tabix BCFtools BWA FastQC MultiQC Pandas Picard SAMtools Snakemake Trimmomatic Variant Effect Predictor (VEP) common matplotlib numpy seaborn DNA
Free
57