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RNA Sequencing

RNA sequencing (RNA-seq) is a powerful technology used to study transcriptomes. It enables the identification of known genes, splicing events, and quantifies the expression levels of genes, including novel transcripts. This guide outlines the general workflow of RNA-seq, from data quality control to mapping, and differential expression analysis. It highlights important tools such as FastQC, Tophat, and Trinity for data analysis. Additionally, it discusses normalization techniques like RPKM and FPKM, factors affecting sequencing depth, and the importance of using biological replicates for robust results.

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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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