[[Pythonバイオ]] [[Pythonバイオ/ツール]]~ &counter(); &lastmod();~ *分析ツール [#e7c82741] -[[Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation (Nature Biotechnology volume 28, pages 511–515 (2010)):https://www.nature.com/articles/nbt.1621]] -[[Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms(PubMed):https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146043/]] -[[Third-generation sequencing fireworks at Marco Island (Nature Biotechnology volume 28, pages 426–428 (2010)):https://www.nature.com/articles/nbt0510-426]] -[[Advancing RNA-Seq analysis (Nature Biotechnology volume 28, pages 421–423 (2010)):https://www.nature.com/articles/nbt0510-421]] -[[RNA-Seqの数理―生成モデルによる発現量推定：アーカイブ - Wolfeyes Bioinformatics beta:http://yagays.github.io/blog/2013/07/14/mathematical-models-for-transcriptome-quantification-0/]] -[[Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3334321/pdf/nihms-366741.pdf]] -[[BaseSpace で行う RNA-seq 入門 < TopHat/Cufflinks編> 2015_ilmn_webinar_basespace-rna_session12.pdf:https://jp.illumina.com/content/dam/illumina-marketing/apac/japan/documents/pdf/2015_ilmn_webinar_basespace-rna_session12.pdf]] -[[RNA-Seq｜次世代シーケンサー お悩みカウンセリング(2013):http://www.mss.co.jp/businessfield/bioinformatics/ngs/pop_rna-seq.html]] -[[(ERANGE) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Method 2008:https://www.ncbi.nlm.nih.gov/pubmed/18516045]] -[[(ERANGE) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Method 2008 (ResearchGate copy):https://www.researchgate.net/publication/5331349_Mapping_and_quantifying_mammalian_transcriptomes_by_RNA-Seq/download]] ------------------------ **Cuffdiff (Cufflinks) [#sb90c59c] cufflinksでエラーが出る。[[HISAT2-StringTie-Cuffdiff error: BAM record error: found spliced alignment without XS attribute:https://www.biostars.org/p/222849/]]（正しくは？） 対策は、HISAT2でパラメータ--dta-cufflinksを指定すること ***cufflinks/cuffdiff [#ed355856] -[[cufflinks:https://github.com/cole-trapnell-lab/cufflinks]] ^[[Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks:https://www.nature.com/articles/nprot.2012.016]] -[[binary download page:http://cole-trapnell-lab.github.io/cufflinks/install/]] cuffdiffを使ってみる cuffdiffを使ってみる 2019-06-29 再度やり直す必要ありそう。まずcufflinksで集計が必要かも。要チェック。 -[[Cufflinks | RNA-seq を利用した遺伝子発現解析:https://bi.biopapyrus.jp/rnaseq/expression/cufflinks.html]] あらかじめ用意したsort済みのbamファイルSRR4535xx.tagged.sorted.bam とマップs288c_3.gffに対して $cuffdiff -p 32 -o ./results1 ../s288c_e.gff -L batch,chemo \ SRR453566.tagged.sorted.bam,SRR453567.tagged.sorted.bam,SRR453568.tagged.sorted.bam \ SRR453569.tagged.sorted.bam,SRR453570.tagged.sorted.bam,SRR453571.tagged.sorted.bam ラベル付け-Lの都合で、ラベル２種類batch,chemoに対して、入力ファイルSRR453566,67,68をbatchに、SRR453569,70,71をchemoに割り当てた（はず）。コンマの前後は空白なし（あるとコマンド解釈で引数の切れ目と思ってしまうので） この-Lと入力ファイルの区切りによって、後の解析パターン（ソースの分類）が決まるので要注意。 Warning: Could not connect to update server to verify current version. Please check at the Cufflinks website (http://cufflinks.cbcb.umd.edu). [10:38:24] Loading reference annotation. [10:38:25] Inspecting maps and determining fragment length distributions. [10:40:50] Modeling fragment count overdispersion. [10:42:08] Modeling fragment count overdispersion. > Map Properties: > Normalized Map Mass: 3618635.35 > Raw Map Mass: 3746966.57 > Fragment Length Distribution: Empirical (learned) > Estimated Mean: 209.34 > Estimated Std Dev: 99.53 > Map Properties: > Normalized Map Mass: 3618635.35 > Raw Map Mass: 5136465.50 > Fragment Length Distribution: Empirical (learned) > Estimated Mean: 213.53 > Estimated Std Dev: 96.07 > Map Properties: > Normalized Map Mass: 3618635.35 > Raw Map Mass: 3780062.80 > Fragment Length Distribution: Empirical (learned) > Estimated Mean: 206.94 > Estimated Std Dev: 94.02 > Map Properties: > Normalized Map Mass: 3618635.35 > Raw Map Mass: 2502112.93 > Fragment Length Distribution: Empirical (learned) > Estimated Mean: 202.52 > Estimated Std Dev: 96.05 > Map Properties: > Normalized Map Mass: 3618635.35 > Raw Map Mass: 3127907.67 > Fragment Length Distribution: Empirical (learned) > Estimated Mean: 211.12 > Estimated Std Dev: 94.18 > Map Properties: > Normalized Map Mass: 3618635.35 > Raw Map Mass: 3898613.50 > Fragment Length Distribution: Empirical (learned) > Estimated Mean: 213.42 > Estimated Std Dev: 98.72 [10:43:25] Calculating preliminary abundance estimates [10:43:25] Testing for differential expression and regulation in locus. > Processed 6278 loci. [*************************] 100% Performed 5732 isoform-level transcription difference tests Performed 0 tss-level transcription difference tests Performed 5732 gene-level transcription difference tests Performed 0 CDS-level transcription difference tests Performed 0 splicing tests Performed 0 promoter preference tests Performing 0 relative CDS output tests Writing isoform-level FPKM tracking Writing TSS group-level FPKM tracking Writing gene-level FPKM tracking Writing CDS-level FPKM tracking Writing isoform-level count tracking Writing TSS group-level count tracking Writing gene-level count tracking Writing CDS-level count tracking Writing isoform-level read group tracking Writing TSS group-level read group tracking Writing gene-level read group tracking Writing CDS-level read group tracking Writing read group info Writing run info $ cd results1 $ ls bias_params.info cds_exp.diff genes.read_group_tracking promoters.diff tss_groups.count_tracking cds.count_tracking cuffData.db isoform_exp.diff read_groups.info tss_groups.fpkm_tracking cds.diff gene_exp.diff isoforms.count_tracking run.info tss_groups.read_group_tracking cds.fpkm_tracking genes.count_tracking isoforms.fpkm_tracking splicing.diff var_model.info cds.read_group_tracking genes.fpkm_tracking isoforms.read_group_tracking tss_group_exp.diff この例では（CDSが書いていないので？）gene_exp.diffとisoforms_exp.diffが結果になっている。 gene_exp.diffを見ると #ref(SRR453566-cuffdiff-gene-exp-diff.png); isoforms_exp.diffを見ると #ref(SRR453566-cuffdiff-isoforms-exp-diff.png); 元論文~ [[Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks (Nat Protoc. ; 7(3): 562–578):https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3334321/pdf/nihms-366741.pdf]]~ によると、この結果は[[cummeRbund:https://www.bioconductor.org/packages/release/bioc/html/cummeRbund.html]]によって可視化できる。~ 手順はたとえば --[[CummeRbundで遺伝子発現のプロットを遺伝子ごとに並べる - Qiita:https://qiita.com/yuifu/items/cc9730f7e3a1277f6c50]] --[[cummeRbund-example-workflow.pdf:https://www.bioconductor.org/packages/release/bioc/vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf]] 後半が未完成で途中までしかない！！ --[[cummeRbund-manual.pdf:https://www.bioconductor.org/packages/release/bioc/vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf]] マニュアル風だが手順も多少意識している？ **cummeRbundを使う [#c8ee86ea] -[[Bioconductor - cummeRbund::https://www.bioconductor.org/packages/release/bioc/html/cummeRbund.html]] -[[CummeRbund: Visualization and Exploration of Cufflinks High-throughput Sequencing Data:https://www.bioconductor.org/packages/release/bioc/vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf]] ここに出ているプロセスに従って以下実験する。 -[[cummeRbund Example Workflow:https://www.bioconductor.org/packages/release/bioc/vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf]] これは書きかけの状態のようで、後半が空白。 -[[リファレンスマニュアル:https://www.bioconductor.org/packages/release/bioc/manuals/cummeRbund/man/cummeRbund.pdf]] cummeRbundの入力はcuffDiffの結果で、cummeRbandでの解析のためにcuffDiffでラベル付けをしてあること。そのラベルを元に、比較を取ったりする。 データルート SRR453566.tagged.sorted.bam SRR453567.tagged.sorted.bam ... SRR453571.tagged.sorted.bam results1 bias_params.info cds_exp.diff genes.read_group_tracking promoters.diff tss_groups.count_tracking cds.count_tracking isoform_exp.diff read_groups.info tss_groups.fpkm_tracking cds.diff gene_exp.diff isoforms.count_tracking run.info tss_groups.read_group_tracking cds.fpkm_tracking genes.count_tracking isoforms.fpkm_tracking splicing.diff var_model.info cds.read_group_tracking genes.fpkm_tracking isoforms.read_group_tracking tss_group_exp.diff ↑ このレベルをRのワーキングディレクトリにしておく。 Rを起動。 > library(cummeRbund) プログラムcummRbundをロード > cuff<-readCufflinks("results1") ディレクトリ results1 からデータ読込んでデータベースに書き込む (注) > cuff <- readCufflinks('results1', gtfFile='../s288c_e.gff')はgenome=が無いとダメ。genome=に書ける値が不明 A character string indicating to which genome build the .gtf annotations belong (e.g. ’hg19’ or ’mm9’) > cuff CuffSet instance with: 2 samples 6445 genes 6445 isoforms 0 TSS 0 CDS 0 promoters 0 splicing 0 relCDS > disp<-dispersionPlot(genes(cuff)) > disp > dens<-csDensity(genes(cuff)) > dens > b<-csBoxplot(genes(cuff)) > b > sm<-csScatterMatrix(genes(cuff)) > sm > s<-csScatter(genes(cuff),"chemo","batch",smooth=T) chemoとbatchでの散布図 > s > dend<-csDendro(genes(cuff)) > dend > m<-MAplot(genes(cuff),"chemo","batch") > m > v<-csVolcanoMatrix(genes(cuff)) > v > mySigGeneIds<-getSig(cuff,alpha=0.05,level='genes') > lengthh(mySigGeneIds) > sigGenes<-getGenes(cuff,sigGeneIds) sigGenes CuffGeneSet instance for 3900 genes Slots: annotation fpkm repFpkm diff count isoforms CuffFeatureSet instance of size 3900 TSS CuffFeatureSet instance of size 0 CDS CuffFeatureSet instance of size 0 promoters CuffFeatureSet instance of size 3900 splicing CuffFeatureSet instance of size 0 relCDS CuffFeatureSet instance of size 3900 > head(annotation(genes(cuff))) gene_id class_code nearest_ref_id gene_short_name 1 gene_0001 <NA> <NA> PAU8 2 gene_0002 <NA> <NA> YAL067W-A 3 gene_0003 <NA> <NA> SEO1 4 gene_0004 <NA> <NA> YAL065C 5 gene_0005 <NA> <NA> YAL064W-B 6 gene_0006 <NA> <NA> TDA8 locus length coverage feature_id isoform_id seqnames 1 NC_001133.9:1806-2169 NA NA NA <NA> <NA> 2 NC_001133.9:2479-2707 NA NA NA <NA> <NA> 3 NC_001133.9:7234-9016 NA NA NA <NA> <NA> 4 NC_001133.9:11564-11951 NA NA NA <NA> <NA> 5 NC_001133.9:12045-12426 NA NA NA <NA> <NA> 6 NC_001133.9:13362-13743 NA NA NA <NA> <NA> source type start end score strand frame 1 <NA> NA NA NA NA <NA> <NA> 2 <NA> NA NA NA NA <NA> <NA> 3 <NA> NA NA NA NA <NA> <NA> 4 <NA> NA NA NA NA <NA> <NA> 5 <NA> NA NA NA NA <NA> <NA> 6 <NA> NA NA NA NA <NA> <NA> > head(fpkm(genes(cuff))) > head(count(genes(cuff))) > head(fpkm(isoforms(cuff))) > head(diffData(genes(cuff))) > head(fpkmMatrix(genes(cuff))) 興味あるgene(集合)を指定して分析する > myGeneIds <- c("gene_0010", "gene_0010", "gene_0011", "gene_0012", "gene_0013", "gene_0014", "gene_0015", "gene_0016", "gene_0017", "gene_0018", "gene_0019") 興味のあるgeneidをリストしたもの。あらかじめ作ってあるとする。ここは仮の例。 > myGenes <- getGenes(cuff,myGeneIds) > myGenes CuffGeneSet instance for 10 genes Slots: annotation fpkm repFpkm diff count isoforms CuffFeatureSet instance of size 10 TSS CuffFeatureSet instance of size 0 CDS CuffFeatureSet instance of size 0 promoters CuffFeatureSet instance of size 10 splicing CuffFeatureSet instance of size 0 relCDS CuffFeatureSet instance of size 10 > myGeneset.pluri<-getGenes(cuff,myGeneIds,sampleIdList=c("batch")) > h<-csHeatmap(myGenes,cluster='both') > h > b<-expressionBarplot(myGenes) > b > s<-csScatter(myGenes,"chemo","batch",smooth=T) > s > v<-csVolcano(myGenes,"chemo","batch") > v > ih<-csHeatmap(isoforms(myGenes),cluster='both',labRow=F) > ih > th<-csHeatmap(TSS(myGenes),cluster='both',labRow=F) > th > den<-csDendro(myGenes) > den １つだけにする > myGeneId <- c("gene_0010") 1つだけにする > myGene<-getGene(cuff,myGeneId) > head(fpkm(myGene)) 同じように使える > gl<-expressionPlot(myGene) > gl > gl.iso.rep<-expressionPlot(isoforms(myGene),replicates=T) > gb<-expressionBarplot(myGene) > gb > igb<-expressionBarplot(isoforms(myGene),replicates=T) > gp<-csPie(myGene,level="isoforms") > gp > mySigMat<-sigMatrix(cuff,level='genes',alpha=0.05) > mySigMat > mySigGeneIds<-getSig(cuff,alpha=0.05,level='genes') > head(mySigGeneIds) > chemo_vs_batch.sigIsoformIds<-getSig(cuff,x='chemo',y='batch',alpha=0.05,level='isoforms') > myDistHeat<-csDistHeat(genes(cuff)) > myDistHeat > myRepDistHeat<-csDistHeat(genes(cuff),replicates=T) > myRepDistHeat > genes.PCA<-PCAplot(genes(cuff),"PC1","PC2") > genes.PCA > genes.MDS<-MDSplot(genes(cuff)) cmdscale(d, eig = TRUE, k = 2) でエラー: 'k' must be in {1, 2, .. n - 1} MDS(multi-dimensional scaling)は次元数3以上 クラスタ化 > ic<-csCluster(myGenes,k=4) > ic > ic$cluster gene_0010 gene_0011 gene_0013 gene_0014 gene_0015 gene_0016 gene_0017 1 1 2 2 3 3 4 gene_0018 gene_0019 3 3 > icp<-csClusterPlot(ic) > icp 類似geneの検出 > mySimilar<-findSimilar(cuff,myGeneId,n=20) > mySimilar.expression<-expressionPlot(mySimilar,logMode=T,showErrorbars=F) > mySimilar.expression > myProfile<-c(500,0,400) > mySimilar2<-findSimilar(cuff,myProfile,n=10) > mySimilar2.expression<-expressionPlot(mySimilar2,logMode=T,showErrorbars=F) > mySimilar2.expression > head(annotation(genes(cuff))) gene_id class_code nearest_ref_id gene_short_name 1 gene_0001 <NA> <NA> PAU8 2 gene_0002 <NA> <NA> YAL067W-A 3 gene_0003 <NA> <NA> SEO1 4 gene_0004 <NA> <NA> YAL065C 5 gene_0005 <NA> <NA> YAL064W-B 6 gene_0006 <NA> <NA> TDA8 locus length coverage feature_id isoform_id seqnames 1 NC_001133.9:1806-2169 NA NA NA <NA> <NA> 2 NC_001133.9:2479-2707 NA NA NA <NA> <NA> 3 NC_001133.9:7234-9016 NA NA NA <NA> <NA> 4 NC_001133.9:11564-11951 NA NA NA <NA> <NA> 5 NC_001133.9:12045-12426 NA NA NA <NA> <NA> 6 NC_001133.9:13362-13743 NA NA NA <NA> <NA> source type start end score strand frame 1 <NA> NA NA NA NA <NA> <NA> 2 <NA> NA NA NA NA <NA> <NA> 3 <NA> NA NA NA NA <NA> <NA> 4 <NA> NA NA NA NA <NA> <NA> 5 <NA> NA NA NA NA <NA> <NA> 6 <NA> NA NA NA NA <NA> <NA> 〜〜〜〜〜〜〜〜〜 **もう一度勉強 [#y2c99830] その前に -NIGのパイプラインサービス [[NGSアプリケーション Pipline-Menu-wiki:http://cell-innovation.nig.ac.jp/maser/Applications/applications_top.html]]、 [[提供全パイプラインリスト All-Pipelines:http://cell-innovation.nig.ac.jp/maser_cgi/cip-pl_list_violin_ja.cgi]]、と [[RNA-seqのパイプライン:http://cell-innovation.nig.ac.jp/maser/Applications/RNA-seq.html]] 参照 -主 [[Tuxedo Genome Guided Transcriptome Assembly Workshop · trinityrnaseq/RNASeq_Trinity_Tuxedo_Workshop Wiki:https://github.com/trinityrnaseq/RNASeq_Trinity_Tuxedo_Workshop/wiki/Tuxedo-Genome-Guided-Transcriptome-Assembly-Workshop]] -追加 --[[RNA seq 其の２ マッピングから統計処理まで - macでインフォマティクス:http://kazumaxneo.hatenablog.com/entry/2017/03/27/141521]]~ --[[RNA seq 其の３ ヒートマップ作成 - macでインフォマティクス:http://kazumaxneo.hatenablog.com/entry/2017/03/29/171543]] -追加 [[RNA-seq Analysis With R/Bioconductor - The Cat Way:http://catway.jp/lecture/tohoku-u/NGS-R-Bioconductor-2nd.html]] -おまけ --Filgen.jpのスライド[[NGSデータ解析入門Webセミナー:https://filgen.jp/Product/BioScience21-software/CLC/NGS_Web_Seminar_slide_20181126.pdf]] スライドp17,18に解析の全体像あり --中岡慎治 [[遺伝子発現解析入門:http://coop-math.ism.ac.jp/files/216/%E8%B3%87%E6%96%99_nakaoka.pdf]] スライド13以降がRNA-seq 少し古いか？ --アメリエフ R N A - s e q [[20150805and28_yamaguchi.pdf https://biosciencedbc.jp/gadget/human/20150805and28_yamaguchi.pdf]]
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