A short introduction to the core concepts of transcriptomics, which is the systematic measurement of all transcripts. I will start with quickly explaining the relation between genes (DNA) and transcripts (RNAs), then cover the two main technologies (DNA microarrays and RNA sequencing) including important steps in the computational analysis, important differences in experimental sample preprocessing (poly(A) selection vs. rRNA depletion), and finally statistical analysis to identify differentially expressed genes or transcripts.

0:00 Introduction: gene transcription, definition of transcriptomics, and overview of the presentation
1:09 Microarrays: cDNA / two-channel arrays, competitive hybridization, Affymetrix GeneChips / one-channel arrays, and non-linear normalization
3:05 RNA sequencing: read mapping (HISAT2 / STAR), transcript assembly (StringTie), read count matrix (HTSeq), and normalized metrics (RPKM)
4:32 Sample preprocessing: most RNA is ribosomal, polyA selection of transcripts, rRNA depletion, ncRNAs, and total RNA-seq
5:58 Statistical testing: differential expression, statistics for microarrays (normal distribution), and statistics for RNA-Seq data (negative binomial distribution)

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