RNA Alternative Splicing and Structures
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1 RNA Alternative Splicing and Structures Tools and Applications Fang Zhaoyuan Wang Zefeng Lab, PICB
2 Outline Alternative splicing analyses from RNA seq data MISO rmats RNA secondary structure analyses RNAfold prediction Comparative prediction
3 Review & Warm up Connect to server Putty, SecureShell, ssh File upload/download Filezilla Review of variable & commands string= welcome to PICB! ; echo $string cd; cd. ; cd.. ; cd ; cd ls; wc; less, more + file; cat + file Pipe ( ): ls wc; ls less top: see your program; top p; kill CTRL + C
4 Load Environmental Configuration 1) 确定配置文件存在 ls /picb/course2016/data/splicing/profile 2) 加载 source /picb/course2016/data/splicing/profile
5 Copy all the scripts to be used 1) 进入个人主目录 cd 或者 cd /picb/course2016/users/yourname 2) 复制全部脚本 cp /picb/course2016/data/splicing/scripts/*. 或者 cp /picb/course2016/data/splicing/scripts/*./
6 Basics Next generation sequencing File formats
7 Next Generation DNA sequencing
8
9
10 File formats fasta: DNA/protein sequence fastq: raw reads sam/bam: genome mapping format bai: bam index bed: genomic intervals wiggle/bigwig: dense continuous data...
11 fastq
12 1 flow cell = 8 lanes = 8*100 tiles
13 sam/bam
14 Free Try cd /picb/course2016/data/splicing/data less 231ESRP.25K.rep 1.R1.fastq samtools view 231ESRP.25K.rep 1.bam less
15 Splicing analyses MISO rmats
16 MISO Single end vs. Paired end Comparison of two samples Filtering Annotation Sashimi plot
17 Single end vs. Paired end
18 Comparison of two samples Null hypothesis: δ = PSI(S1) PSI(S2) = 0 Alternative hypothesis: δ 0 Bayes Factor: How much is the data support H1 over H0? The bigger BF is, the more likely H1 is true
19 Some features of MISO Take advantage of constitutive reads Handle paired end insert sizes Computation is intensive and estimate is often approximate Using Bayesian posterior distribution PSI confidence intervals can be large
20 Demo: single end bash miso_demo1.sh /picb/course2016/data/splicing/data/s1_1.sorted.bam 90 miso_demo1
21 Look at miso parameters python $miso run $SE_events $bam output dir ${outpath}/se_events event type SE read len $read_len p $np
22 Demo result
23 Demo: paired end # control sample bash miso_demo2.sh /picb/course2016/data/splicing/data/s1.bam 90 miso_demo2_s1 # test sample bash miso_demo2.sh /picb/course2016/data/splicing/data/s2.bam 90 miso_demo2_s2
24 Look at miso parameters python $miso run $SE_events $bam output dir ${outpath}/se_events event type SE read len $read_len paired end $ins_mean $ins_sdev p $np
25 Take a look at result
26 Demo: two sample comparison 运行 bash miso_compare.sh compare_miso=/picb/extprog/src/misopy/misopy 0.5.3/misopy/compare_miso.py ctrl=/picb/course2016/data/splicing/result/miso_de mo2_s1/se_events treat=/picb/course2016/data/splicing/result/miso_d emo2_s2/se_events python $compare_miso compare samples $ctrl $treat miso_s1_vs_s2
27 output less miso_s1_vs_s2/se_events_vs_se_events/bayes factors/se_events_vs_se_events.miso_bf
28 Demo: filtering python $filter_events filter $bf num inc 1 num exc 1 num sum inc exc 10 delta psi 0.20 bayes factor 10 output dir $output num total N: Number of total reads aligning to any isoform (or to both isoforms) has to be greater than or equal to N. num inc N: Number of inclusion reads (i.e. reads supporting the first isoform) has to be greater than or equal to N. num exc N: Number of exclusion reads (i.e. reads supporting the second isoform) has to be greater than or equal to N. num sum inc exc N: The sum of inclusion and exclusion reads has to be greater than or equal to N. delta psi P: The absolute Δ Ψ value must be greater than or equal to P (where P is in [0, 1]). bayes factor N: The Bayes factor must be greater than or equal to N. apply both: By default, the above filters are required to be true in only one of the samples. To apply in both samples, use this option.
29 Run filtering bash miso_filter.sh Console log: Filtering /picb/rnasys/fangzhaoyuan/course2016/miso_s1_vs_s2/se_e vents_vs_se_events/bayes factors/se_events_vs_se_events.miso_bf into /picb/rnasys/fangzhaoyuan/course2016/miso_s1_vs_s2/se_e vents_vs_se_events/bayes factors/se_events_vs_se_events.miso_bf.filtered 11/433 events pass the filter (2.54 percent).
30 Filtered result less miso_s1_vs_s2/se_events_vs_se_events/bayes factors/se_events_vs_se_events.miso_bf.filtered
31 Demo: annotation bash miso_annotate.sh 或者 annotation=/picb/course2016/data/splicing/annotati on/miso_event_gene_hg19.txt type=se bf=miso_s1_vs_s2/se_events_vs_se_events/bayes factors/se_events_vs_se_events.miso_bf.filtered output=miso_s1_vs_s2/se_events_vs_se_events/bay es factors/se_events_vs_se_events.miso_bf.filtered.ann otated python miso_annotate.py $annotation $type $bf $output
32 Output
33 Demo: sashimi plot bash sashimi_demo.sh 或者 sashimi=/picb/course2016/program/install/misopy 0.5.3/misopy/sashimi_plot/sashimi_plot.py SE_events=/picb/course2016/data/splicing/annotation/SE_ events_chr12 python $sashimi plot event $SE_events sashimi_plot_settings.txt output dir sashimi_result
34 output
35 Free try Different parameters Different thresholds in filtering Plot additional events
36 rmats 1) Suit for biological replicates 2) Unpaired or paired designs 3) User defined differential criteria
37 Effective lengths of Inc/Skip isoforms Inc isoform Skip isoform Using junctions only: L(I) = 2*(j r+1) L(S) = 1*(j r+1) Inc isoform Skip isoform Using juncitons+target exons: L(I) = 2*(j r+1) + (e r+1) L(S) = 2*(j r+1)
38 Five AS types and PSI estimation
39 Splicing changes between two groups Null hypothesis: Δψi = ψi1 ψi2 c Alternative hypothesis: Δψi > c
40 Paired design
41 Demo: unpaired replicates rmats=/picb/course2016/program/install/rmats.3.2.5/rnase q MATS.py splicing=/picb/course2016/data/splicing python $rmats b1 $splicing/data/231esrp.25k.rep 1.bam, $splicing/data/231esrp.25k.rep 2.bam b2 $splicing/data/231ev.25k.rep 1.bam, single; paired $splicing/data/231ev.25k.rep 2.bam gtf $splicing/annotation/test.gtf o rmats_demo_real t paired len 50 a 8 c analysis U novelss 1 keeptemp U: unpaired; P: paired Anchor length The threshold c of difference Read length (default: 1) (default: ) Run: bash rmats_demo.sh
42 Demo output MATS_output/SE.MATS.JunctionCountOnly.txt MATS_output/SE.MATS.ReadsOnTargetAndJunctionCounts.txt With exons, can identify more significant events
43 Free try Modify thresholds Single end vs. paired end Remove/Add replicates Library types: LibType Default is unstranded (fr unstranded). Use fr firststrand or fr secondstrand for strand specific data. Do not run too many programs, because the demo server is not very powerful
44 RNA secondary structures Computational prediction Single sequence analysis Comparative analysis Experimental analyses SHAPE, etc.
45 Demo: single sequences bash struct_demo1.sh 或者 relplot=/picb/extprog/install/viennarna 1.8.4/share/ViennaRNA/bin/relplot.pl RNAfold p < rna_set1.fa $relplot rna01_ss.ps rna01_dp.ps > rna01_rss.ps
46 Demo Output: Yeast trna phe rna02_trna_rss.ps
47 Output rna03_rrna_rss.ps rna04_malat1_rss.ps
48 Demo: comparative analysis bash struct_demo2.sh 或者 data=/picb/course2016/data/splicing/data RNAalifold p aln color < $data/rna_set2.clu
49 output alirna.ps
50 Free try Different RNA sequences Any other RNAs
51 Summary & Acknowledgement Summary RNA seq data based splicing analyses (MISO, rmats) Sequence based RNA secondary structure prediction (RNAfold, RNAstructure, etc.) Acknowledgement Fan Xiaojuan (Wang lab) Yang Qin (Yang lab) Fan Yan & IT
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