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    Education: Course on the analysis of single cell RNA-seq data
    Submitted by Carlo Pecoraro; posted on Wednesday, July 11, 2018

    February 25 – March 1, 2019
    FU University in Berlin
    www.physalia-courses.org/cour[...]se18/

    OVERVIEW

    In recent years single-cell RNA-seq (scRNA-seq) has become widely used for transcriptome analysis in many areas of biology. In contrast to bulk RNA-seq, scRNA-seq provides quantitative measurements of the expression of every gene in a single cell. However, to analyze scRNA-seq data, novel methods are required and some of the underlying assumptions for the methods developed for bulk RNA-seq experiments are no longer valid. In this course we will cover all steps of the scRNA-seq processing, starting from the raw reads coming off the sequencer. The course includes common analysis strategies, using state-of-the-art methods and we also discuss the central biological questions that can be addressed using scRNA-seq.

    FORMAT

    The course will be delivered over the course of five days. Each day will include a lecture and laboratory component. The lecture will introduce the topics of discussion and the laboratory sessions will be focused on practical hands-on analysis of scRNA-seq data. These sessions will involve a combination of both mirroring exercises with the instructor to demonstrate a skill as well as applying these skills on your own to complete individual exercises. After and during each exercise, interpretation of results will be discussed as a group. Computing will be done using a combination of tools installed on the attendees laptop computer and web resources accessed via web browser.

    TARGETED AUDIENCE & ASSUMED BACKGROUND

    This course is aimed at researchers and technical workers who are or will be analyzing scRNA-seq data. The material is suitable both for experimentalists who want to learn more about data-analysis as well as computational biologists who want to learn about scRNASeq methods. Examples demonstrated in this course can be applied to any experimental protocol or biological system.

    REQUIREMENTS

    • Working knowledge of unix (managing files, running programs)
    • Programming experience in R (writing a function, basic I/O operations, variable types, using packages)
    • Bioconductor experience is a plus.
    • Familiarity with NGS data and its analyses (using alignment and quantification tools for bulk sequencing data)

    INSTRUCTORS

    Dr. Ayshwarya Subramanian (Harvard School of Public Health, Broad Institute, Dana-Farber Cancer Institute, US)
    Dr. Dana Silverbush (Harvard School of Public Health, Broad Institute, Dana-Farber Cancer Institute, US)
    Dr. Ehsan Habibi (Harvard School of Public Health, Broad Institute, Dana-Farber Cancer Institute, US)

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