1、背景知识#
- The usage of Alternative Transcription Start sites (aTSS可选择转录起始位点), Alternative Splicing (AS可选择剪切位点) and alternative Transcription Termination Sites (aTTS可选择终止位点) are collectively collectively results in the production of different isoforms.
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Alternative isoforms are widely used as recently demonstrated by The ENCODE Consortium, which found that on average, 6.3 different transcripts are generated per gene; a number which may vary considerably per gene.
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目前基因转录本水平的定量表达分析软件有：Kallisto, Salmon, RSEM or StringTie等
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IsoformSwitchAnalyzeR包可基于上述转录本定量结果，分析同一基因在对照/实验条件下(normal/disease)是否会表达不同比例的转录本。
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如上图所示，IsoformSwitchAnalyzeR分析流程主要分为两大部分：(1) identify isoform switch[基础分析]; (2) predict potential functional consequences of the identified isoform switches[进阶分析]
2、基础分析#
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library(IsoformSwitchAnalyzeR)
library(tidyverse)
library(data.table)
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2.1 初步创建switchAnalyzeRlist对象#
2.1.1 导入转录本定量数据#
使用的是IsoformSwitchAnalyzeR的示例数据，为salmon软件的定量结果。
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#2组，各两个样本
list.files("data/salmon",
recursive = T, full.names = T)
# [1] "data/salmon/hESC_0/quant.sf.gz"
# [2] "data/salmon/hESC_1/quant.sf.gz"
# [3] "data/salmon/iPS_0/quant.sf.gz"
# [4] "data/salmon/iPS_1/quant.sf.gz"
fread("data/salmon/hESC_0/quant.sf.gz") %>% head
#包含5列，分别为转录本id， 转录本长度， 有效长度，TPM标准化，原始count
# Name Length EffectiveLength TPM NumReads
# 1: TCONS_00000001 1652 1652 0.000 0.00000
# 2: TCONS_00000002 1488 1488 0.000 0.00000
# 3: TCONS_00000003 1595 1595 0.000 0.00000
# 4: TCONS_00000006 78 78 1377990.000 4.67384
# 5: TCONS_00000007 2750 2750 329.487 589.15400
# 6: TCONS_00000008 4369 4369 358.067 1050.07000
#importIsoformExpression会自动识别salmon的定量结果，储存到list对象里
salmonQuant <- importIsoformExpression(
parentDir = "data/salmon"
)
salmonQuant$abundance %>% head(3)
# isoform_id hESC_0 hESC_1 iPS_0 iPS_1
# 1 TCONS_00000001 0 0.000000 0.000000 4.659597
# 2 TCONS_00000002 0 1.564879 5.504247 2.818824
# 3 TCONS_00000003 0 0.000000 0.000000 0.000000
salmonQuant$counts %>% head(3)
# isoform_id hESC_0 hESC_1 iPS_0 iPS_1
# 1 TCONS_00000001 0 0.0000000 0.00000 18.13313
# 2 TCONS_00000002 0 0.1116201 21.10248 10.96964
# 3 TCONS_00000003 0 0.0000000 0.00000 0.00000
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2.1.2 样本分组信息#
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myDesign <- data.frame(
sampleID = c("hESC_0", "hESC_1", "iPS_0", "iPS_1"),
condition = c("hESC", "hESC", "iPS", "iPS"))
# sampleID condition
# 1 hESC_0 hESC
# 2 hESC_1 hESC
# 3 iPS_0 iPS
# 4 iPS_1 iPS
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2.1.3 其它注释信息#
（1）gtf文件: 转录本的外显子组成以及对应基因#
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fread("data/example.gtf.gz") %>% head(2)
# V1 V2 V3 V4 V5 V6 V7 V8
# 1: chr1 cufflinks exon 11874 12227 . + .
# 2: chr1 cufflinks exon 11874 12227 . + .
# V9
# 1: transcript_id "TCONS_00000001"; gene_id "XLOC_000001";
# 2: transcript_id "TCONS_00000002"; gene_id "XLOC_000001";
fread("data/example.gtf.gz") %>%
dplyr::count(V3)
# V3 n
# 1: CDS 9612
# 2: exon 10929
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（2）fasta文件：转录本的核苷酸序列#
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fread("data/example_isoform_nt.fasta") %>% head()
# >TCONS_00000001
# 1: CTTGCCGTCAGCCTTTTCTTTGACCTCTTCTTTCTGTTCATGTGTATTTGCTGTCTCTTAGCCCAGACTTCCCGTGTCCT
# 2: TTCCACCGGGCCTTTGAGAGGTCACAGGGTCTTGATGCTGTGGTCTTCATCTGCAGGTGTCTGACTTCCAGCAACTGCTG
# 3: GCCTGTGCCAGGGTGCAAGCTGAGCACTGGAGTGGAGTTTTCCTGTGGAGAGGAGCCATGCCTAGAGTGGGATGGGCCAT
# 4: TGTTCATCTTCTGGCCCCTGTTGTCTGCATGTAACTTAATACCACAACCAGGCATAGGGGAAAGATTGGAGGAAAGATGA
# 5: GTGAGAGCATCAACTTCTCTCACAACCTAGGCCAGTGTGTGGTGATGCCAGGCATGCCCTTCCCCAGCATCAGGTCTCCA
# 6: GAGCTGCAGAAGACGACGGCCGACTTGGATCACACTCTTGTGAGTGTCCCCAGTGTTGCAGAGGCAGGGCCATCAGGCAC
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2.1.4 创建switchAnalyzeRlist对象#
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sar1 <- importRdata(
isoformCountMatrix = salmonQuant$counts,
isoformRepExpression = salmonQuant$abundance,
designMatrix = myDesign,
isoformExonAnnoation = "data/example.gtf.gz",
isoformNtFasta = "data/example_isoform_nt.fasta"
)
sar1 %>% length()
# 11
names(sar1)
# [1] "isoformFeatures" "exons" "conditions"
# [4] "designMatrix" "sourceId" "isoformCountMatrix"
# [7] "isoformRepExpression" "runInfo" "orfAnalysis"
# [10] "isoformRepIF" "ntSequence"
sar1$isoformFeatures %>% head(2)
# iso_ref/gene_ref : 新的唯一编号，不重复
# gene_overall_mean/iso_overall_mean : 基因/转录本在所有样本的平均表达量
# IF_overall(isoform fraction=isoform_exp / gene_exp): 在所有样本中，特定基因表达该转录本的比例
# iso_ref gene_ref isoform_id gene_id condition_1 condition_2
# 280 isoComp_00000001 geneComp_00000001 TCONS_00000316 AADACL3 hESC iPS
# 281 isoComp_00000002 geneComp_00000001 TCONS_00000317 AADACL3 hESC iPS
# gene_name gene_biotype iso_biotype gene_overall_mean gene_value_1 gene_value_2
# 280 AADACL3 NA NA 25.37693 32.66891 18.08494
# 281 AADACL3 NA NA 25.37693 32.66891 18.08494
# gene_stderr_1 gene_stderr_2 gene_log2_fold_change gene_q_value iso_overall_mean
# 280 10.1063 6.337321 -0.8527734 NA 3.704839
# 281 10.1063 6.337321 -0.8527734 NA 21.672089
# iso_value_1 iso_value_2 iso_stderr_1 iso_stderr_2 iso_log2_fold_change iso_q_value
# 280 4.846923 2.562755 3.823911 2.562511 -0.9167289 NA
# 281 27.821989 15.522189 6.282387 3.774811 -0.8414829 NA
# IF_overall IF1 IF2 dIF isoform_switch_q_value gene_switch_q_value PTC
# 280 0.114475 0.124 0.10495 -0.01905 NA NA FALSE
# 281 0.885525 0.876 0.89505 0.01905 NA NA FALSE
sar1$isoformFeatures %>%
dplyr::filter(gene_id=="AADACL3") %>%
dplyr::select(isoform_id, gene_id, dplyr::contains("IF"))
# isoform_id gene_id IF_overall IF1 IF2 dIF
# 1 TCONS_00000316 AADACL3 0.114475 0.124 0.10495 -0.01905
# 2 TCONS_00000317 AADACL3 0.885525 0.876 0.89505 0.0190
#ORF转录本开放阅读框注释
sar1$orfAnalysis %>% head(2)
# orfTransciptStart/orfTransciptEnd 基于转录本核苷酸序列(剪切后)的起始位置
# orfStartGenomic/orfEndGenomic 基于基因组坐标(剪切前)的起始位置
# isoform_id orfTransciptStart orfTransciptEnd orfTransciptLength
# 1 TCONS_00000001 1402 1629 228
# 2 TCONS_00000002 317 715 399
# orfStarExon orfEndExon orfStartGenomic orfEndGenomic
# 1 3 3 14159 14386
# 2 1 3 12190 13636
# stopDistanceToLastJunction stopIndex PTC orf_origin
# 1 -1163 3 FALSE Annotation
# 2 -231 3 FALSE Annotation
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2.2 过滤#
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sar2 <- preFilter(sar1)
#默认过滤标准
# geneExpressionCutoff = 1 : 根据gene_overall_mean列过滤低表达基因
# isoformExpressionCutoff = 0 : 根据iso_overall_mean列过滤低表达基因
# IFcutoff=0.01: 根据IF_overall过滤
# dIFcutoff = 0.1: 根据dIF过滤（绝对值）
# removeSingleIsoformGenes = TRUE: 删除只有一个转录本的基因
dim(sar1)
# [1] 1060 30
dim(sar2)
# [1] 760 30
sar2
# This switchAnalyzeRlist list contains:
# 760 isoforms from 208 genes
# 1 comparison from 2 conditions (in total 4 samples)
#
# Feature analyzed:
# [1] "ORFs, ntSequence"
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sar3 <- isoformSwitchTestDEXSeq(sar2)
sar3$isoformFeatures %>% head(2)
# 如下，主要增加了isoform_switch_q_value与gene_switch_q_value两列信息
# iso_ref gene_ref isoform_id gene_id condition_1
# 557 isoComp_00000004 geneComp_00000003 TCONS_00003880 ACAP3 hESC
# 558 isoComp_00000005 geneComp_00000003 TCONS_00003881 ACAP3 hESC
# condition_2 gene_name gene_biotype iso_biotype gene_overall_mean
# 557 iPS ACAP3 NA NA 910.5955
# 558 iPS ACAP3 NA NA 910.5955
# gene_value_1 gene_value_2 gene_stderr_1 gene_stderr_2
# 557 1771.599 49.59168 377.02 15.68552
# 558 1771.599 49.59168 377.02 15.68552
# gene_log2_fold_change gene_q_value iso_overall_mean iso_value_1
# 557 -5.158528 NA 241.0513 478.7646
# 558 -5.158528 NA 186.6521 365.0107
# iso_value_2 iso_stderr_1 iso_stderr_2 iso_log2_fold_change
# 557 3.337951 175.44243 3.337951 -7.159924
# 558 8.293521 89.61604 1.997000 -5.458111
# iso_q_value IF_overall IF1 IF2 dIF
# 557 NA 0.156075 0.26100 0.05115 -0.20985
# 558 NA 0.188100 0.20455 0.17165 -0.03290
# isoform_switch_q_value gene_switch_q_value PTC
# 557 0.2601812 0.02341811 FALSE
# 558 0.7079831 0.02341811 FALSE
#如下结果gene_switch_q_value即为isoform_switch_q_value的最小值
sar3$isoformFeatures %>%
dplyr::filter(gene_id=="ACAP3") %>%
dplyr::select(isoform_id, gene_id,dIF,
dplyr::contains("switch"))
# isoform_id gene_id dIF isoform_switch_q_value gene_switch_q_value
# 1 TCONS_00003880 ACAP3 -0.20985 0.26018123 0.02341811
# 2 TCONS_00003881 ACAP3 -0.03290 0.70798307 0.02341811
# 3 TCONS_00003882 ACAP3 0.29825 0.02341811 0.02341811
# 4 TCONS_00003883 ACAP3 -0.05555 0.58385549 0.02341811
switchPlot(sar3, gene = 'ACAP3',
condition1="hESC",
condition2="iPS")
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#四个子图
switchPlotGeneExp(sar3, gene = 'ACAP3',
condition1="hESC",
condition2="iPS")
switchPlotIsoExp(sar3, gene = 'ACAP3',
condition1="hESC",
condition2="iPS")
switchPlotIsoUsage(sar3, gene = 'ACAP3',
condition1="hESC",
condition2="iPS")
switchPlotTranscript(sar3, gene = 'ACAP3',
condition1="hESC",
condition2="iPS")
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sar3$isoformSwitchAnalysis %>% head()
# iso_ref gene_ref isoform_id condition_1 condition_2 dIF pvalue
# 1 isoComp_00000922 geneComp_00000294 TCONS_00000007 hESC iPS 0.01615 8.645971e-01
# 2 isoComp_00000923 geneComp_00000294 TCONS_00000008 hESC iPS -0.25885 4.859083e-02
# 3 isoComp_00000924 geneComp_00000294 TCONS_00000009 hESC iPS 0.24275 7.547031e-05
# 4 isoComp_00000929 geneComp_00000298 TCONS_00000017 hESC iPS 0.08785 7.690961e-01
# 5 isoComp_00000930 geneComp_00000298 TCONS_00000018 hESC iPS -0.47220 6.113540e-14
# 6 isoComp_00000931 geneComp_00000298 TCONS_00000019 hESC iPS 0.23965 2.119346e-03
# padj IF1 IF2
# 1 9.665421e-01 0.51665 0.53280
# 2 1.464787e-01 0.48320 0.22435
# 3 7.536969e-04 0.00015 0.24290
# 4 9.355274e-01 0.00000 0.08785
# 5 3.815868e-12 0.47220 0.00000
# 6 1.250306e-02 0.00000 0.23965
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2.4.1 Top Switches–gene#
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sar.ge_df=extractTopSwitches(
sar3,
filterForConsequences = FALSE,
n = NA,
extractGenes = TRUE, # when FALSE isoforms are returned
sortByQvals = TRUE
)
dim(sar.ge_df)
# [1] 68 7
head(sar.ge_df)
# gene_ref gene_id gene_name condition_1 condition_2 gene_switch_q_value Rank
# 1 geneComp_00000088 ENO1 ENO1 hESC iPS 2.429932e-90 1
# 4 geneComp_00000324 XLOC_001217 <NA> hESC iPS 1.464063e-24 2
# 5 geneComp_00000171 NBL1 NBL1 hESC iPS 1.039215e-21 3
# 7 geneComp_00000053 CASP9 CASP9 hESC iPS 5.232094e-18 4
# 10 geneComp_00000282 UBR4 UBR4 hESC iPS 1.597415e-17 5
# 14 geneComp_00000072 CROCC CROCC hESC iPS 1.752050e-17 6
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sar.iso_df=extractTopSwitches(
sar3,
filterForConsequences = FALSE,
n = NA,
extractGenes = FALSE, # when FALSE isoforms are returned
sortByQvals = TRUE
)
dim(sar.iso_df)
# [1] 121 12
head(sar.iso_df)
# iso_ref gene_ref isoform_id gene_id gene_name condition_1 condition_2 IF1
# 1 isoComp_00000297 geneComp_00000088 TCONS_00004087 ENO1 ENO1 hESC iPS 0.157
# 2 isoComp_00000985 geneComp_00000324 TCONS_00003819 XLOC_001217 <NA> hESC iPS 0.084
# 3 isoComp_00000551 geneComp_00000171 TCONS_00000463 NBL1 NBL1 hESC iPS 0.112
# 4 isoComp_00000549 geneComp_00000171 TCONS_00000460 NBL1 NBL1 hESC iPS 0.881
# 5 isoComp_00000152 geneComp_00000053 TCONS_00004180 CASP9 CASP9 hESC iPS 0.474
# 6 isoComp_00000242 geneComp_00000072 TCONS_00000420 CROCC CROCC hESC iPS 0.683
# IF2 dIF isoform_switch_q_value Rank
# 1 0.000 -0.157 2.429932e-90 1
# 2 0.434 0.350 1.464063e-24 2
# 3 1.000 0.888 1.039215e-21 3
# 4 0.000 -0.881 1.194327e-19 4
# 5 0.000 -0.474 5.232094e-18 5
# 6 0.173 -0.510 1.752050e-17 6
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2.4.3 火山图#
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ggplot(data=sar3$isoformFeatures, aes(x=dIF, y=-log10(isoform_switch_q_value))) +
geom_point(
aes( color=abs(dIF) > 0.1 & isoform_switch_q_value < 0.05 ), # default cutoff
size=1
) +
geom_hline(yintercept = -log10(0.05), linetype='dashed') + # default cutoff
geom_vline(xintercept = c(-0.1, 0.1), linetype='dashed') + # default cutoff
scale_color_manual('Signficant\nIsoform Switch', values = c('black','red')) +
labs(x='dIF', y='-Log10 ( Isoform Switch Q Value )') +
theme_bw()
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3、进阶分析#
对于不同组样本的某一基因，存在其中一种isoform相对上调，另一种isoform相对下调的情况。可以对isoform的特点进行分析，从而进一步分析isoformswitch的影响与意义。
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sar3
# This switchAnalyzeRlist list contains:
# 312 isoforms from 68 genes
# 1 comparison from 2 conditions (in total 4 samples)
#
# Switching features:
# Comparison Isoforms Switches Genes
# 1 hESC vs iPS 121 95 68
#
# Feature analyzed:
# [1] "Isoform Switch Identification, ORFs, ntSequence"
names(sar3)
# [1] "isoformFeatures" "exons" "conditions" "designMatrix"
# [5] "sourceId" "isoformCountMatrix" "isoformRepExpression" "runInfo"
# [9] "orfAnalysis" "isoformRepIF" "ntSequence"
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3.1 可变剪切Alternative Splicing#
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sar4 <- analyzeAlternativeSplicing(sar3)
sar4$AlternativeSplicingAnalysis %>% head(2)
# isoform_id ES ES_genomic_start ES_genomic_end MEE MEE_genomic_start MEE_genomic_end MES
# 1 TCONS_00003880 0 <NA> <NA> 0 <NA> <NA> 0
# 2 TCONS_00003881 1 1230098 1230196 0 <NA> <NA> 0
# MES_genomic_start MES_genomic_end IR IR_genomic_start IR_genomic_end A5 A5_genomic_start A5_genomic_end
# 1 <NA> <NA> 0 <NA> <NA> 0 <NA> <NA>
# 2 <NA> <NA> 0 <NA> <NA> 0 <NA> <NA>
# A3 A3_genomic_start A3_genomic_end ATSS
# 1 0 <NA> <NA> 1
# 2 0 <NA> <NA> 1
# ATSS_genomic_start
# 1 1234725;1235211;1235353;1235538;1235889;1237368;1238302;1238542;1239466;1243149;1244822
# 2 1243149;1244822
# ATSS_genomic_end ATTS
# 1 1234736;1235285;1235448;1235582;1236072;1237426;1238355;1238661;1241309;1243269;1244989 0
# 2 1243269;1244989 0
# ATTS_genomic_start ATTS_genomic_end
# 1 <NA> <NA>
# 2 <NA> <NA>
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extractSplicingSummary(sar4)
# 一个significant isoform如果发生了A3事件，如果其dIF>0,则iPS相对hESC used more;反之used less
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extractSplicingEnrichment(sar4)
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extractSplicingGenomeWide(sar4)
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sar5 <- extractSequence(sar4,
writeToFile=TRUE,
pathToOutput = 'data/output')
list.files("data/output/")
# [1] "isoformSwitchAnalyzeR_isoform_AA.fasta"
# [2] "isoformSwitchAnalyzeR_isoform_nt.fasta"
sar5$ntSequence %>% head(3)
#DNAStringSet object of length 6:
# width seq names
# [1] 2750 GGGTCTCCCTCTGTTG...CCCACGCGGACAGAG TCONS_00000007
# [2] 4369 TTACTGTTGATTGTGA...AAAATATCGCCCACG TCONS_00000008
# [3] 4272 TTACTGTTGATTGTGA...AAAATATCGCCCACG TCONS_00000009
sar5$aaSequence %>% head(3)
#AAStringSet object of length 3:
# width seq names
# [1] 389 MLLPPGSLSRPRTFSS...QAQLLPHSGPFRPNS TCONS_00000007
# [2] 389 MLLPPGSLSRPRTFSS...QAQLLPHSGPFRPNS TCONS_00000008
# [3] 389 MLLPPGSLSRPRTFSS...QAQLLPHSGPFRPNS TCONS_00000009
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- CPAT : The Coding-Potential Assessment Tool which is a tool for predicting whether an isoform is coding or not. (_nt.fasta)
- CPC2 : The Coding Potential Calculator 2 which is a tool for predicting whether an isoform is coding or not. (_nt.fasta)
- Pfam : Prediction of protein domains (_AA.fasta)
- SignalP : Prediction of Signal Peptides(_AA.fasta)
- IUPred2A: Predicts Intrinsically Disordered Regions (IDR) and Intrinsically Disordered Binding Regions (IDBR)(_AA.fasta)
- NetSurfP-2 : Prediction of Intrinsically Disordered Regions (IDR) (_AA.fasta)
上传核苷酸/氨基酸序列到相应网站，下载预测的结果，再导入到switchAnalyzeRlist对象中
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sar6 <- analyzeCPAT(
switchAnalyzeRlist = sar5,
pathToCPATresultFile = "data/external/cpat_results.txt",
codingCutoff = 0.725,
removeNoncodinORFs = TRUE
)
sar6 <- analyzePFAM(
switchAnalyzeRlist = sar6,
pathToPFAMresultFile = "data/external/pfam_results.txt"
)
sar6 <- analyzeSignalP(
switchAnalyzeRlist = sar6,
pathToSignalPresultFile = "data/external/signalP_results.txt"
)
sar6 <- analyzeIUPred2A(
switchAnalyzeRlist = sar6,
pathToIUPred2AresultFile = "data/external/iupred2a_result.txt.gz"
)
sar7 <- analyzeSwitchConsequences(sar6)
length(sar7)
# [1] 18
names(sar7)
# [1] "isoformFeatures" "exons"
# [3] "conditions" "designMatrix"
# [5] "sourceId" "isoformCountMatrix"
# [7] "isoformRepExpression" "runInfo"
# [9] "orfAnalysis" "isoformRepIF"
# [11] "ntSequence" "isoformSwitchAnalysis"
# [13] "AlternativeSplicingAnalysis" "aaSequence"
# [15] "domainAnalysis" "signalPeptideAnalysis"
# [17] "idrAnalysis" "switchConsequence"
sar7$switchConsequence %>%
dplyr::filter(isoformsDifferent=="TRUE") %>% head()
# gene_ref gene_id gene_name condition_1 condition_2 isoformUpregulated
# 1 geneComp_00000003 ACAP3 ACAP3 hESC iPS TCONS_00003882
# 2 geneComp_00000004 ACOT7 ACOT7 hESC iPS TCONS_00004035
# 3 geneComp_00000004 ACOT7 ACOT7 hESC iPS TCONS_00004035
# 4 geneComp_00000004 ACOT7 ACOT7 hESC iPS TCONS_00004035
# 5 geneComp_00000004 ACOT7 ACOT7 hESC iPS TCONS_00004035
# 6 geneComp_00000004 ACOT7 ACOT7 hESC iPS TCONS_00004036
# isoformDownregulated iso_ref_up iso_ref_down featureCompared
# 1 TCONS_00003880 isoComp_00000006 isoComp_00000004 ORF_seq_similarity
# 2 TCONS_00004039 isoComp_00000010 isoComp_00000014 ORF_seq_similarity
# 3 TCONS_00004039 isoComp_00000010 isoComp_00000014 NMD_status
# 4 TCONS_00004039 isoComp_00000010 isoComp_00000014 domains_identified
# 5 TCONS_00004039 isoComp_00000010 isoComp_00000014 IDR_identified
# 6 TCONS_00004039 isoComp_00000011 isoComp_00000014 NMD_status
# isoformsDifferent switchConsequence
# 1 TRUE ORF is longer
# 2 TRUE ORF is longer
# 3 TRUE NMD insensitive
# 4 TRUE Domain gain
# 5 TRUE IDR gain
# 6 TRUE NMD insensitive
extractConsequenceSummary(sar7)
|
1
|
extractConsequenceEnrichment(sar7)
|
1
2
3
|
switchPlot(sar7, gene = 'ACAP3',
condition1="hESC",
condition2="iPS")
|
