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ZENODO
Other ORP type . 2023
License: CC BY
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ZENODO
Other ORP type . 2023
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WORKSHOP: RNASeq: reads to differential genes and pathways

Authors: Samaha, Georgina; Deshpande, Nandan; Lu, Ching-Yu; Chung, Jessica; Stott, Audrey; Ip, Alex;

WORKSHOP: RNASeq: reads to differential genes and pathways

Abstract

This record includes training materials associated with the Australian BioCommons workshop 'RNASeq: reads to differential genes and pathways'. This workshop took place over two, 3 hour sessions on 11 and 12 October 2023. Event description RNA sequencing (RNAseq) is a popular and powerful technique used to understand the activity of genes. Using differential gene profiling methods, we can use RNAseq data to gain valuable insights into gene activity and identify variability in gene expression between samples to understand the molecular pathways underpinning many different traits. In this hands-on workshop, you will learn RNAseq fundamentals as you process, analyse, and interpret the results from a real RNAseq experiment on the command-line. In session one, you will convert raw sequence reads to analysis-ready count data with the nf-core/rnaseq workflow. In session two, you'll work interactively in RStudio to identify differentially expressed genes,perform functional enrichment analysis, and visualise and interpret your results using popular and best practice R packages. This workshop was delivered as a part of the Australian BioCommons Bring Your Own Data Platforms Project and will provide you with an opportunity to explore services and infrastructure built specifically for life scientists working at the command line. By the end of the workshop, you will be familiar with Pawsey's Nimbus cloud platform and be able to process your own RNAseq datasets and perform differential expression analysis on the command-line. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Lead trainers: Dr Georgina Samaha (Sydney Informatics Hub), Dr Nandan Deshpande (Sydney Informatics Hub) Facilitators: Ching-Yu Lu and Jessica Chung. Infrastructure provision: Audrey Stott (Pawsey Supercomputing Research Centre), Alex Ip (AARNet) Host: Melissa Burke, Australian BioCommons Training materials Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. Materials shared elsewhere: This workshop follows the tutorial 'Introduction to RNAseq workshop: reads to differential gene expression' developed by the Sydney Informatics Hub. https://sydney-informatics-hub.github.io/rnaseq-workshop-2023/ Additional supporting materials are available via GitHub Rstudio rnaseq container: https://github.com/Sydney-Informatics-Hub/Rstudio-rnaseq-contained/tree/main RNAseq differential expression R notebook: https://github.com/Sydney-Informatics-Hub/rna-differential-expression-Rnotebook

Related Organizations
Keywords

transcriptomics, bioinformatics, RNA-seq, RNAseq

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average