Experimental & Computational Techniques Underlying RNA-Seq

Models, Inference & Algorithms (MIA) is a new Broad initiative to support learning and collaboration across the interface of biology and mathematics / statistics / machine learning / computer science.

Primer: Experimental and computational techniques underlying RNA-seq

Speaker – Adrian Veres – Harvard Sys Bio, HST

Abstract:  We will provide an overview of the experimental and computational steps involved in RNA-seq for both bulk and single-cell experiments. We will begin with a brief review of Illumina short-read sequencing by synthesis; continue to describing the molecular biology used in preparing RNA-seq libraries; and discuss quality trimming, read alignment, transcript quantification and normalization of gene expression measures. We will conclude with a discussion of techniques commonly leveraged in single-cell RNA-Seq: linear pre-amplification, unique molecular identifiers (UMI/RMTs) and 3’-barcode counting. Throughout the primer, we will mention potential sources of bias that can be introduced at each step and why they occur.

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