MIA: Rafael Irizarry, Overcoming bias in high-throughput data; Adrian Veres, RNA-seq

Models, Inference and Algorithms
Broad Institute
September 28, 2016

MIA Meeting: https://youtu.be/8RIKZJ3zXqg?t=3331

Rafael Irizarry
Dana-Farber Cancer Institute
Harvard School Public Health

Overcoming Bias and Batch Effects in High-Throughput Data

Abstract: The unprecedented advance in digital technology during the second half of the 20th century has produced a measurement revolution that is transforming science. In the life sciences, data analysis is now part of practically every research project. Genomics, in particular, is being driven by new measurement technologies that permit us to observe certain molecular entities for the first time. These observations are leading to discoveries analogous to identifying microorganisms and other breakthroughs permitted by the invention of the microscope. An examples of this are the many application of next generation sequencing. Biases, systematic errors and unexpected variability are common in biological data. Failure to discover these problems often leads to flawed analyses and false discoveries. As datasets become larger, the potential of these biases to appear to be significant actually increases. In this talk I will describe several examples of these challenges using very specific examples from gene expression microarrays, RNA-seq, and single-cell assays. I will describe data science solution to these problems.

MIA Primer:

Adrian Veres
Harvard Sys Bio, HST

Primer: Experimental and computational techniques underlying RNA-seq

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.

For more information on the Broad institute and Models, inference and Algorithms visit: http://www.broadinstitute.org/mia


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