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Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor (https://bioconductor.org) is a flexible, widely used, and respected collection of R packages for the statistical analysis and comprehension of many common types of high-throughput genomic data. Learning to use Bioconductor's core infrastructure, domain-specific analysis packages, and annotation resources can pose significant challenges, both to those embarking on their first significant bioinformatic analysis and to those encountering the Bioconductor ecosystem after developing considerable skill in other programming paradigms. This workshop introduces strategies for effectively training users new to Bioconductor. Topics covered include the following:
Assessing abilities: where do we begin?
Connecting with new users and refocusing experienced analysts
The central importance of reproducible research
How Bioconductor departs from common idioms in R and other languages
Practical activities with meaningful feedback
Representing informatic data: BED files and GenomicRanges
Managing cognitive load
Single cell RNA-seq differential expression workflows: how 'looking at the data' motivates subtle statistical analysis
Matching student expectations with learning objectives
Focused topics with broad appeal
Annotation resources for effective communication
Markdown documents and jupyter notebooks for guided analysis
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