转录本的可变剪切工具-IsoformSwitchAnalyzeR

可变剪切是指mRNA前体可以有多种方式将exon连接在一起的过程。 由于可变剪切使一个基因产生多个转录本，不同转录本可能翻译成不同蛋白。在传统的转录组未考虑可变剪接的存在，这意味着RNA-seq数据的全部信息通常利用不充分。

IsoformSwitchAnalyzeR它能够根据RNA序列衍生的新型和/或带注释的全长同工型的定量数据对同工型转换进行统计鉴定。IsoformSwitchAnalyzeR有助于集成（预测的）注释的许多来源，例如开放阅读框（ORF / CDS），蛋白结构域（通过Pfam），信号肽（通过SignalP），无固定三维结构的蛋白（IDR，通过NetSurfP-2或IUPred2A），编码潜力（通过CPAT或CPC2）以及转录调控机制（NMD）的敏感性等。

总之，IsoformSwitchAnalyzeR能够更细致的分析RNA-seq数据，重点是同工型转换（具有预期的结果）及其相关的可变剪接，从而扩大了RNA-seq数据的可用性。

1）安装

if (!requireNamespace("BiocManager", quietly = TRUE)){
    install.packages("BiocManager")
    BiocManager::install("IsoformSwitchAnalyzeR")
}

2）Workflow

3）一个简单的示例

library(IsoformSwitchAnalyzeR)

# 导入salmon数据
salmonQuant <- importIsoformExpression(
    parentDir = system.file("extdata/",package="IsoformSwitchAnalyzeR")
)

# 试验设计
myDesign <- data.frame(
    sampleID = colnames(salmonQuant$abundance)[-1],
    condition = gsub('_.*', '', colnames(salmonQuant$abundance)[-1])
)

# 构建 switchAnalyzeRlist 对象
aSwitchList <- importRdata(
    isoformCountMatrix   = salmonQuant$counts,
    isoformRepExpression = salmonQuant$abundance,
    designMatrix         = myDesign,
    isoformExonAnnoation = system.file("extdata/example.gtf.gz"             , package="IsoformSwitchAnalyzeR"),
    # 转录本的 fasta 序列文件
    isoformNtFasta       = system.file("extdata/example_isoform_nt.fasta.gz", package="IsoformSwitchAnalyzeR"),
    fixStringTieAnnotationProblem = TRUE,
    showProgress = FALSE
)

# 过滤
aSwitchList <- preFilter(aSwitchList)

# 分析差异表达的isoform
aSwitchListAnalyzed <- isoformSwitchTestDEXSeq(
    switchAnalyzeRlist = aSwitchList,
    reduceToSwitchingGenes=TRUE
)

# OFR分析
exampleSwitchListAnalyzed <- analyzeORF(
    aSwitchListAnalyzed,
    orfMethod = "longest",
    showProgress=FALSE
)

exampleSwitchListAnalyzed <- extractSequence(
    exampleSwitchListAnalyzed, 
    pathToOutput = '<insert_path>',
    writeToFile=FALSE
)

# 还有很多分析，如：
# analyzeCPAT() # OR
# analyzeCPC2()
# analyzePFAM()
# analyzeSignalP()
# analyzeIUPred2A() # OR
# analyzeNetSurfP2()

### Add CPC2 analysis
exampleSwitchListAnalyzed <- analyzeCPC2(
    switchAnalyzeRlist   = exampleSwitchListAnalyzed,
    pathToCPC2resultFile = system.file("extdata/cpc2_result.txt", package = "IsoformSwitchAnalyzeR"),
    removeNoncodinORFs   = TRUE   # because ORF was predicted de novo
)
#> Added coding potential to 162 (100%) transcripts

### Add PFAM analysis
exampleSwitchListAnalyzed <- analyzePFAM(
    switchAnalyzeRlist   = exampleSwitchListAnalyzed,
    pathToPFAMresultFile = system.file("extdata/pfam_results.txt", package = "IsoformSwitchAnalyzeR"),
    showProgress=FALSE
)
#> Converting AA coordinats to transcript and genomic coordinats...
#> Added domain information to 127 (78.4%) transcripts

### Add SignalP analysis
exampleSwitchListAnalyzed <- analyzeSignalP(
    switchAnalyzeRlist       = exampleSwitchListAnalyzed,
    pathToSignalPresultFile  = system.file("extdata/signalP_results.txt", package = "IsoformSwitchAnalyzeR")
)
#> Added signal peptide information to 17 (10.49%) transcripts

### Add NetSurfP2 analysis
exampleSwitchListAnalyzed <- analyzeIUPred2A(
    switchAnalyzeRlist        = exampleSwitchListAnalyzed,
    pathToIUPred2AresultFile = system.file("extdata/iupred2a_result.txt.gz", package = "IsoformSwitchAnalyzeR"),
    showProgress = FALSE
)

# 进行可变剪切分析
exampleSwitchListAnalyzed <- analyzeAlternativeSplicing(
    switchAnalyzeRlist = exampleSwitchListAnalyzed,
    quiet=TRUE
)
# 提取异构体中其异构体使用量有显着变化和异构体的异构体
consequencesOfInterest <- c('intron_retention','coding_potential','NMD_status','domains_identified','ORF_seq_similarity')

exampleSwitchListAnalyzed <- analyzeSwitchConsequences(
    exampleSwitchListAnalyzed,
    consequencesToAnalyze = consequencesOfInterest, 
    dIFcutoff = 0.4, # very high cutoff for fast runtimes - you should use the default (0.1)
    showProgress=FALSE
)
# 异构体分析
exampleSwitchListAnalyzedSubset <- subsetSwitchAnalyzeRlist(
    exampleSwitchListAnalyzed, 
    exampleSwitchListAnalyzed$isoformFeatures$condition_1 == 'COAD_ctrl'
)
# 绘图
switchPlot(exampleSwitchListAnalyzedSubset, gene = 'ZAK')

也可以单独的绘制每个图：

switchPlotTranscript(exampleSwitchListAnalyzedSubset, gene = 'ZAK')

switchPlotGeneExp (exampleSwitchListAnalyzedSubset, gene = 'ZAK')

switchPlotIsoExp(exampleSwitchListAnalyzedSubset, gene = 'TNFRSF1B')

switchPlotIsoUsage(exampleSwitchListAnalyzedSubset, gene = 'ZAK')

IsoformSwitchAnalyzeR该包含了诸多的分析内容，是转录本分析的一大神器，推荐阅读官方文档进行全面了解和学习。

4）IsoformSwitchAnalyzeR中用到的一些方法及其参考文献/资料

• Import of data from Salmon/Kallisto/RSEM/StringTie (importRdata() function): Please cite reference 10.
• Import of data from Salmon via Tximeta (importSalmonData() function): Please cite reference 10 and 17.
• Inter-library normalization of abundance values: Please cite reference 10 and 11.
• Isoform switch test implemented utilizing DEXSeq via IsoformSwitchAnalyzeR (Default) : Please cite reference 1, 12 and 13.
• Isoform switch test implemented in the DRIMSeq package: Please cite reference 1 and 3.
• Prediction of open reading frames (ORF) analysis: Please cite reference 1 and 4.
• Prediction of pre-mature termination codons (PTC) and thereby NMD-sensitivity: Please cite reference 1, 4, 5 and 6.
• CPAT: Please cite reference 7.
• CPC2: Please cite reference 14.
• Pfam: Please cite reference 8.
• SignalP: Please cite reference 9.
• NetSurf2-P: Please cite reference 15.
• IUPred2A: Please cite reference 16.
• Prediction of consequences: Please cite reference 1.
• Visualizations (plots) implemented in the IsoformSwitchAnalyzeR package: Please cite reference 1.
• Alternative splicing analysis: Please cite both reference 1 and 4.
• Genome-wide enrichment analysis: Please cite both reference 1 and 2.

Refrences:

1. Vitting-Seerup et al. The Landscape of Isoform Switches in Human Cancers. Cancer Res. (2017) link.
2. Vitting-Seerup et al. IsoformSwitchAnalyzeR: Analysis of changes in genome-wide patterns of alternative splicing and its functional consequences. Bioinformatics (2019) link.
3. Nowicka et al. DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics. F1000Research, 5(0), 1356. link.
4. Vitting-Seerup et al. spliceR: an R package for classification of alternative splicing and prediction of coding potential from RNA-seq data. BMC Bioinformatics 2014, 15:81. link.
5. Weischenfeldt et al. Mammalian tissues defective in nonsense-mediated mRNA decay display highly aberrant splicing patterns. Genome Biol 2012, 13:R35 link.
6. Huber et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods, 2015, 12:115-121. link.
7. Wang et al. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res. 2013, 41:e74. link.
8. Finn et al. The Pfam protein families database. Nucleic Acids Research (2012) link.
9. _Almagro et al. SignalP 5.0 improves signal peptide predictions using deep neural networks.**. Nat. Biotechnol (2019)_ link
10. Soneson et al. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research 4, 1521 (2015). link.
11. Robinson et al. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology (2010) link.
12. Ritchie et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research (2015) link.
13. Anders et al. Detecting differential usage of exons from RNA-seq data. Genome Research (2012) link.
14. Kang et al. CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res (2017) link.
15. Klausen et al. NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning. BioRxiv (2018) link
16. Meszaros et al. IUPred2A: Context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res (2018) link
17. Love et al. Tximeta: Reference sequence checksums for provenance identification in RNA-seq. PLoS Comput. Biol (2020) link

参考资料：

1.http://bioconductor.org/packages/release/bioc/vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html#importing-data-from-salmon-via-tximeta