TP RNA-seq : Differential expression analysis

Transcription

1 TP RNA-seq : Differential expression analysis

2 Overview of RNA-seq analysis Fusion transcripts detection Differential expresssion Gene level RNA-seq Transcript level Transcripts and isoforms detection 2

3 Overview of RNA-seq analysis Fusion transcripts detection Differential expresssion Gene level RNA-seq Transcript level Transcripts and isoforms detection 3

4 Input data 8 fastq files : RNA-seq paired-end Illumina 2 x 75 pb Ex : Hct116_rep1_R1.fastq cell line replicate pair 1 fasta file : reference genome sequence chr22.fasta 1 gtf file : reference genome annotation chr22.gtf 4

5 Data import Go to the web page : Go to «Shared Data» and click on «Data Librairies» Then click on «[FORMATION] Input Data» and select the folder «Roscoff», then validate by clicking on «GO», at the bottom of the page 5

6 Data import Return at the home page by clicking on «Analyze data» Rename the history, click on «Unnamed history» then type a new name and press enter 6

7 Steps of analysis Quality control Mapping Quantification Differential analysis Formation NGS & Cancer - Analyses RNA-Seq novembre 2014

8 Steps of analysis Quality control Mapping Quantification Differential analysis Formation NGS & Cancer - Analyses RNA-Seq novembre 2014

9 Steps of analysis Quality control FastQC FASTQ Trimmer Mapping Quantification Differential analysis Formation NGS & Cancer - Analyses RNA-Seq novembre 2014

10 Quality control In the left panel, type «fastqc» in the search tool, then click on «FastQC:Read QC» Select «Gm12878_rep1_R1.fastq» sample and click on «Execute» 10

11 Quality control Run the tool again by clicking on the rerun icon, and select «Gm12878_rep1_R2.fastq» sample To view the results, click on the eye on the dataset 11

12 Quality control Encoding quality 12

13 Quality control The end of the sequence is generally of poor quality Trimming 13

14 Fastq trimming Type «fastq trim» on the search tool, then select the «FASTQ Quality Trimmer» tool. Galaxy is don't aware of the quality of the fastq The datatype of the fastq need to be changed manually 14

15 Change datatype To change the dataype, click on the pencil icon on the dataset Then click on «Dataype», search the fastqsanger format and validate by clicking on «Save» Repeat this operation for all the fastq 15

16 Fastq trimming Use the «FASTQ Quality Trimmer» tool on Gm12878_rep1_R1.fastq 16

17 Fastq trimming Repeat this operation for Gm12878_rep1_R2.fastq 17

18 Fastq trimming Rename the outputs of fastq trimming, click on the pencil icon, type a new name and don't forget to save. 18

19 Quality control You can check the result of trimming by running «FastQC:Read QC» on the trimmed fastq. 19

20 Quality control Before trimming After trimming 20

21 Steps of analysis Quality control Mapping Tophat2 Quantification Differential analysis Formation NGS & Cancer - Analyses RNA-Seq novembre 2014

22 Mapping Which mapper? (Fonseca N A et al. Bioinformatics 2012;28: ) 22

23 Mapping Tophat2 : - Use Bowtie2 - splice aware - optimised for 75 pb reads or longer - several options : not all include in galaxy - different mode (ex : tophat-fusion) - tophat+tophat citations - alternatives : STAR, RUM, Mapsplice (Trapnell C et al. Bioinformatics 2009 ;25: ) 23

24 Mapping In the left panel in the section «NGS : RNA-seq Fusion», click on «Tophat2» 24

25 Mapping You can run an other job on the dataset even if is not terminated We use the tool «flagstat» to have statistics on the mapping 25

26 Mapping Tophat outputs : - bed files : coordinates of splice junctions, insertions or deletions - bam file : accepted_hits mapped reads BAM = binary alignment format Bam-to-Sam SAM = sequence alignment format 26

27 Mapping Header SAM file ID:TopHat SO:coordinate LN: VN: CL:/bioinfo/local/build/tophat /tophat -p 4 -r 200 [ ] Read 616L7AAXX_HWUSI:2:41:19518: chr M = GTGCCTGGCTGACTTATTGGCATTTCTAACAGAGAAGAAGAGAAAGTAAGCAACTTGGAAAACATTTTTGAGGAT GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGBGGGGEGGGGFGGGFGGGGFFGFGGGEFDGGGGGGGGFAGEGG AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:75 YT:Z:UU XP:Z:chr M 213 Mandatory fields 616L7AAXX_HWUSI:2:41:19518: Read sequence AGAACTCCCGTGAGACTGAAGGTAGGCAGTGAAGCAAATGTTTGCATTCTTGTGTGGCTCTGATTAGCATCAGGA Read quality DFFDF=FEFFGGGGGGFGGGGGGGGEGGGFGGGGGGGGGGGGGGFGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG Optionnal fields chr AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:75 50 YT:Z:UU 75M = XP:Z:chr M NH:i:1 NH:i:1 27

28 Mapping SAM file : Read 616L7AAXX_HWUSI:2:41:19518: chr M = GTGCCTGGCTGACTTATTGGCATTTCTAACAGAGAAGAAGAGAAAGTAAGCAACTTGGAAAACATTTTTGAGGAT GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGBGGGGEGGGGFGGGFGGGGFFGFGGGEFDGGGGGGGGFAGEGG AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:75 YT:Z:UU XP:Z:chr M L7AAXX_HWUSI:2:41:19518: chr M = AGAACTCCCGTGAGACTGAAGGTAGGCAGTGAAGCAAATGTTTGCATTCTTGTGTGGCTCTGATTAGCATCAGGA DFFDF=FEFFGGGGGGFGGGGGGGGEGGGFGGGGGGGGGGGGGGFGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG AS:i:0 XN:i:0 XM:i:0 XO:i:0 XG:i:0 NM:i:0 MD:Z:75 YT:Z:UU XP:Z:chr M -213 NH:i:1 NH:i:1 Flags Explain SAM flags : 28

29 Mapping Flagstat output : Rename the «accepted_hits» dataset 29

30 Mapping Visualization with IGV - download the bam file : click on the disk icon - download the bam_index : click on the arrow then select «Download bam_index» 30

31 Mapping - Open IGV and click on «File», then «Load from file», select the bam file, click on open 31

32 Mapping - To view alignment, you can specify a gene of your choice, here we use LGALS1 as example 32

33 Steps of analysis Quality control Mapping Quantification HTSeq-count Differential analysis 33

34 Quantification Search the tool «htseq-count», change the stranded option to «No» and click on «Execute» 34

35 Quantification HTSeq-count : Counting reads in features - input : BAM file GTF file reference annotation - feature type and ID Attribute : chr20 protein_coding gene_name "ZNF366"; chr20 protein_coding gene_name "ZNF366"; chr20 protein_coding gene_name "ZNF366"; chr20 protein_coding gene_name "ZNF366"; chr20 protein_coding gene_name "ZNF366"; exon gene_id "ENSBTAG "; transcript_id "ENSBTAT "; CDS gene_id "ENSBTAG "; transcript_id "ENSBTAT "; exon gene_id "ENSBTAG "; transcript_id "ENSBTAT "; CDS gene_id "ENSBTAG "; transcript_id "ENSBTAT "; exon gene_id "ENSBTAG "; transcript_id "ENSBTAT "; - counting mode : union 35

36 Quantification 36

37 Steps of analysis Quality control Mapping Workflow Quantification Differential analysis 37

38 Workflow Rename the htseq-count output Click on the wheel of History panel, then click on «Extract Workflow» 38

39 Workflow Uncheck the unusued datasets, then click on «Create Workflow» 39

40 Workflow Click on «Workflow» on the top menu Click on the Workflow that have been created, and select «Edit» 40

41 Workflow You can view the schema of the Workflow, move the datasets to organize the workflow like you want 41

42 Workflow Change the name of the output files 42

43 Workflow Click on the wheel of the Workflow panel and select «Save» then «Run» Now, we can run the workflow on the 3 others samples : Gm12878_rep2, Hct116_rep1 and Hct116_rep2 43

44 Workflow Click on the pages to enable list Select the R1 of sample Select the R2 of sample 44

45 Steps of analysis Quality control Mapping Quantification Differential analysis DESeq 45

46 Differential analysis Search and select the «DESeq» tool 46

47 Differential analysis 47

48 Differential analysis DESeq : differential expression analysis for sequence count data - at gene level transcripts, exons levels : DEXSeq - comparison of 2 conditions complex designs (ex : Time-series) : DESeq2 - alernatives : edger, limma DESeq manual : /DESeq/inst/doc/DESeq.pdf 48

49 Differential analysis Steps of analysis with DESeq : - Normalization of expression : comparable expression - Variance estimation : by the negative binomial distribution - Inference : calling of differential expression Choice of parameters : - mode : explication of dispersion - method : impact of design - type : fonction to estimate dispersion 49

50 Differential analysis Outputs of deseq : - html file : figures to understand and verify the experiment - 2 tabular files : results for significantly over-expressed genes and sub-expressed genes. id = gene identification basemean = normalized mean counts foldchange = mean ratios pval = p value padj = adjusted p value 50

51 Differential analysis 51

52 Differential analysis 52

53 Differential analysis Differentially expressed genes Low counts Not differentially expressed genes (Ex : on the entire genome) 53

54 Differential analysis 54

55 Differential analysis 55

56 Differential analysis - Red line : estimated dispersion - Black dots : observed dispersion 56

57 Differential analysis Overexpression cond1 MA-plot : Mean Average Plot Overexpression Cond2 Significant genes Not significant genes FoldChange = 1 Genes expression 57

58 Differential analysis Volcano Plot -/+ Inf : not expressed genes in one condition Significance Significant genes Not significant genes Overexpression cond2 Overexpression cond1 58