Transcription of Practical Guide to Interpreting RNA-seq Data
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Practical Guide to Interpreting RNA-seq Data
Practical Guide to Interpreting RNA-seq DataSkyler Kuhn1,2 Mayank Tandon1,21. CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, NCI2. Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer ResearchOverviewI. Experimental DesignHypothesis-drivenOverview of Best PracticeII. Quality-controlPre- and post- alignment QC metricsInterpretationIII. PipelineFastQ Files -> Counts matrixReproducibility 1IV. Downstream AnalysisPrincipal Components Analysis (PCA)Differential ExpressionPathway AnalysisV. Advanced VisualizationsGroup comparisonsAlternative Splicing EventsPathway Diagrams Design: Overview Hypothesis-drivenAddresses a well thought-out quantifiable questionConsiderations: Library Construction: mRNA versus total RNAS ingle-end versus Paired-end SequencingSequencing Depth: quantifying gene-level or transcript-level expressionNumber of Replicates: statistical-power and ability drop a bad sampleReducing Batch Effects Design: Library Construction Total RNA contains high-levels of ribosomal RNA (rRNA): 80%mRNApoly(A) selection ~ standard profiling for gene expressionLow RIN may results in 3 biasTotal RNArRNA depletionmRNA + non-coding RNA species (lncRNA)Prokaryotic samples Design: Sequencing Depth mRNA: poly(A)-selectionRecommended Sequencing Depth: 10
QC Metric Guidelines mRNA total RNA RNA Type(s) Coding Coding + non-coding RIN > 8 [low RIN = 3’ bias] > 8 Single-end vs Paired-end Paired-end Paired-end Recommended Sequencing Depth 10-20M PE reads 25-60M PE reads FastQC Q30 > 70% Q30 > 70% Percent Aligned to Reference > 70% > 65% Million Reads Aligned Reference > 7M PE reads (or > 14M reads) > …
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