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RNA Sequencing

RNA Sequencing. Peter Tsai Bioinformatics Institute, University of Auckland. What is RNA- seq ?. Study of transcriptomes Identify known genes , exons, splicing events, ncRNA , miRNA Novel genes or transcripts Abundances of transcripts ( quantitive expression )

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RNA Sequencing

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  1. RNA Sequencing Peter Tsai Bioinformatics Institute, University of Auckland

  2. What is RNA-seq? • Study of transcriptomes • Identify known genes, exons, splicing events, ncRNA, miRNA • Novel genes or transcripts • Abundances of transcripts (quantitive expression) • Differential expressed transcripts between different conditions • Reconstructing transcriptome.

  3. General workflow Raw data QC Map to reference genome De novo transcriptomeassembly Require downstream annotation Estimate abundance Normalisation Differential expression analysis

  4. Quality checks and mapping • Use FastQC, SolexQA • Trim off low quality region, keep only proper-paired reads • Most QC software assume normality, but in RNA-seq data you will probably see none-normality • You might see some duplicated reads, its probably due to highly expressed gene. • Specific reference mapping tool that can map across splice junctions between exons, i.e. Tophat • Specific de novo transcriptome assembly software for reconstruction of transcriptomes from RNA-seqdata, i.e. Trinity

  5. Expression value in RNA-seq The total number of reads mapped to a gene/transcript (Count data or raw counts or digital gene expression) Complexity of using simple counts • Sequencing depth: the higher the sequencing depth, the higher the counts • Gene length: Counts are proportional to the length of the gene times mRNA expression level • Counts distribution: difference on how counts are distributed among samples.

  6. Normalisation • RPKM (Mortazavi et al, 2008) • Reads Per Kilobaseof exon model per Million mapped reads • FPKM (Mortazavi et al, 2010) • Fragments Per Kilobase of exon model per Million mapped reads • Paired-end RNA-Seq experiments produce two reads per fragment, but that doesn't necessarily mean that both reads will be mappable.

  7. Data exploration Replicate 2 Replicate 1

  8. Up-regulated Down-regulated

  9. ERCC spike-in control • Set of external RNA transcripts with known concentration. • Dynamic range and lower limit of detection • Fold-change response • Internal control, in order to measure against defined performance criteria

  10. Dynamic range and lower limit of detection • The dynamic range can be measured as the difference between the highest and lowest concentration. • Measure of sensitivity, and it is defined as the lowest molar amount of ERCC transcript detected in each sample

  11. Fold-change response

  12. How much library depth is needed for RNA-seq? • Depends on a number of factors • Biological questions • Complexity of the organism • Types of analysis • Types of RNA, miRNA, lncRNA. • Literature search for similar work • Pilot experiment

  13. Summary • Have 3 or more biological replicates • Analysis your data with different normalisation methods • Perform data exploration • Use a standard spike-in as internal control • Validation with qPCR

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