Pasteur deep seq analysis practical Part - 2015
- 1. Deep Seq Data Analysis Part II Christophe.antoniewski@upmc.fr http://drosophile.org Mouse Genetics January 29, 2015, 13:30– 15:00 http://fr.slideshare.net/christopheantoniewski/
- 2. The article
- 3. The method section available on line RNA isolation and library construction Both human and mouse blastomeres were prepared using identical protocols. Single blastomeres were isolated by removing the zona pellucida using acidic tyrode solution (Sigma, catalogue no. T1788), then separated by gentle mouth pipetting in a calcium-free medium. Single cells were washed twice with 1× PBS containing 0.1% BSA before placing in lysis buffer. RNA was isolated from single cells or single morula embryos and amplified as described previously14. Library construction was performed following Illumina manufacturer suggestions. Libraries were sequenced on the Illumina Hiseq2000 platform and sequencing reads that contained polyA, low quality, and adapters were pre-filtered before mapping. Filtered reads were mapped to the hg19 genome and mm9 genome using default parameters from BWA aligner29, and reads that failed to map to the genome were re-mapped to their respective mRNA sequences to capture reads that span exons. Transcriptional profiling In both human and mouse cases, data normalization was performed by transforming uniquely mapped transcript reads to RPKM30. Genes with low expression in all stages (average RPKM < 0.5) were filtered out, followed by quantile normalization. For differential expression, we compared every time point to its previous time point using default parameters in DESeq using normalized read counts. Genes were called differentially expressed if they exhibited a Benjamini and Hochberg–adjusted P value (FDR) <5% and a mean fold change of >2.
- 4. Data 1 GEO dataset accession: GSE44183 http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44183 • Take the SRP identifier at the bottom of the page: SRP018525 • Search for this identifier in EBI SRA ENA SRA Galaxy tool • Check for your experiment accession by clicking on the SRX…. links • Click on the fastq files (galaxy) links Files are uploaded in yellow datasets that show up in the current history GSM1080195: mouse oocyte 1; Mus musculus; RNA-Seq 1 ILLUMINA (Illumina HiSeq 2000) run: 16.4M spots, 3G bases, 1.9Gb downloads Accession: SRX229784 GSM1080196: mouse oocyte 2; Mus musculus; RNA-Seq 1 ILLUMINA (Illumina HiSeq 2000) run: 20.2M spots, 3.6G bases, 2.4Gb downloads Accession: SRX229785 GSM1080197: mouse pronuclei 1; Mus musculus; RNA-Seq 1 ILLUMINA (Illumina HiSeq 2000) run: 17.2M spots, 3.1G bases, 2Gb downloads Accession: SRX229786 GSM1080198: mouse pronuclei 2; Mus musculus; RNA-Seq 1 ILLUMINA (Illumina HiSeq 2000) run: 12.8M spots, 2.3G bases, 1.5Gb downloads Accession: SRX229787 GSM1080199: mouse pronuclei 3; Mus musculus; RNA-Seq 1 ILLUMINA (Illumina HiSeq 2000) run: 12.4M spots, 2.2G bases, 1.5Gb downloads Accession: SRX229788 • Register in mississippi.fr • Take an identifier : oocyte1@pasteur.fr • oocyte2@pasteur.fr • pronuclei1@pasteur.fr • pronuclei2@pasteur.fr • pronuclei3@pasteur.fr • And the same password: gsgalaxy • Click on “Analyze Data” • You are by default on an unnamed history • Name it “Datasets”
- 5. Data 2 • Click on “Share Data Data Libraries” • Click on “Public Datasets” • Click on “Mouse Pasteur” • Check boxes corresponding RefSeq_Genes_mm9.gtf, and your datasets • Click on the “Go” item • Click on “Analyze Data” • Look at the imported data sets (3 green boxes) • Look at their content (eye) • Look at their metadata (info icon) The dataset are already available from the server
- 6. Read Mapping 1. Type “fastqc” in the search field at the left-hand column 2. Click on “FastQC:Read QC reports using FastQC” 3. Select your first fastq data set 4. Run the tool 5. Select the yellow box (running tool) 6. Click on the “redo” box 7. Select your second fastq data set 8. Run the tool it will take 4-5 min max 9. Search for “bwa” in the tool search field 10. Select “Map with BWA for Illumina” 11. Lets have a look to the tool form Filtered reads were mapped to the hg19 genome and mm9 genome using default parameters from BWA aligner29, and reads that failed to map to the genome were re-mapped to their respective mRNA sequences to capture reads that span exons. 1. The procedure is not reproducible because metadata and parameters are lacking. 2. The procedure is out of date • The article has been published in 2013 • Tophat has been published in 2009, 2012 – Tophat2 in April 2013
- 7. Look at fastQC results
- 8. Read Mapping using Tophat2 See https://wiki.galaxyproject.org/Events/GCC2014/TrainingDay?action=AttachFile&do=view&target=RNA-SeqAltSlides.pdf For a nice introduction to RNA-seq analysis
- 9. Read Mapping using Tophat2 in Galaxy 1. Create a new history and name it “tophat2 alignment” 2. Copy your 2 fastq files from the previous history, as well as the RefSeq.gtf reference file 3. Rename the files and put an annotation 4. Find and fill in the tophat2 tool form 5. Run the tool 6. Select your first fastq data set 7. Run the tool 8. While it is running look at the metadata 9. Rename the datasets using the pencil box 10. Import Two other datasets 11. Re-run the Tophat2 on these datasets 12. Look at the job in the admin panel (reproducible analyses) 13. Look at the tool on the galaxy tool repository 14. Stop all running tools 15. Import the history “GS SRP018525 tophat2” 16. Visualize your reads in Trackster (1 gtf track + 1 condition mapping) 17. Optional, visualize junctions, etc… 18. Compare with another public genome browser (UCSC or Ensembl) Paired-end reads were mapped to the mm9 genome using Tophat2 the parameters ---, and the RefSeq gtf mm9 annotation as a guide.
- 10. Read Counting using featureCounts in Galaxy 1. Create a new history called “Read Counts” 2. Copy the accepted hits datasets from the “imported: GS SRP018525 tophat2” history as well as the RefSef GTF guide 3. You have now 6 datasets in the “Read Counts” history 4. Run feature count once on oocyte 1 data 5. Re-run the tool for oocyte 2 and pronuclei 1, 2, 3 6. Change the metadata of featureCount summaries 7. Iteratively paste the featureCounts outputs using the Paste two files side by side tool 8. We have a hit Table 9. Rename it FeatureCounts HIT TABLE 10. We can visualize data using chart
- 11. Differential count analysis 1. Create a new history called “Differential count analysis” 2. Copy the “FeatureCounts HIT TABLE” 3. Run “Differential_Count models using BioConductor packages” on the FeatureCounts HIT TABLE 4. Review the results 5. Yet, we did not reproduce the sup Fig. 1
- 12. DESeq Analysis 1. Let’s examine Fig.1, together with the published methods 2. The information is wrong, but we will approach the figure, trying to guess what has been really done 3. Copy the “FeatureCounts HIT TABLE” in a new history called “my DESeq approach” 4. To run the Deseq(1) package we need to reformat the HIT TABLE 5. With a text editor OR within Galaxy 1. Cut columns 2. Remove header 3. Upload new header 4. Manipulate header 5. Concatenate files 6. Run the tool “DESeq Profiling (replicates) with sample replicates” 7. Get the R code available in the public library: Rscript_for_Sup_Fig1a 8. Run the Docker Tool Factory tool with this R code to generate the figure 9. Run the tool “DESeq2 Profiling” 10. Re-run the Docker Tool Factory tool with the same R code on the DESeq2 DE analysis Transcriptional profiling In both human and mouse cases, data normalization was performed by transforming uniquely mapped transcript reads to RPKM30. Genes with low expression in all stages (average RPKM < 0.5) were filtered out, followed by quantile normalization. For differential expression, we compared every time point to its previous time point using default parameters in DESeq using normalized read counts. Genes were called differentially expressed if they exhibited a Benjamini and Hochberg–adjusted P value (FDR) <5% and a mean fold change of >2.
- 13. Optional: comparison between the tophat2 approach and the BWA approach 1. Sharing the “SRP018525 BWA” history 2. Sharing the “Comparison BWA / Tophat” visualization 3. Analyze the differences