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Welcome to SeqAcademy!

SeqAcademy

An easy-to-use, all-in-one jupyter notebook tutorial for the RNA-Seq and ChIP-Seq pipeline

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Contents

What is SeqAcademy?

Who is SeqAcademy for?

What does SeqAcademy teach?

How do I get started?

Authors

What is SeqAcademy?

SeqAcademy is a user-friendly jupyter notebook-based educational pipeline for RNA-Seq and epigenomic data analysis.

RNA-Seq and ChIP-Seq experiments generate large amounts of data and rely on pipelines for efficient analysis. However, existing tools perform specific portions of the pipeline or offer a complete pipeline solution for the advanced programmer.

SeqAcademy addresses these problems by providing an easy to use tutorial that outlines the complete RNA-Seq and ChIP-Seq analysis workflow and requires no prior programming experience.

Who is SeqAcademy for?

SeqAcademy is for students and researchers with little to no bioinformatics experience interested in hands-on bioinformatics tutorials. Anyone will feel comfortable analyzing epigenomic and RNA-Seq data using this simple educational tool.

What does SeqAcademy teach?

This tutorial works using HISAT2 aligner to align sample reads to a reference.

Then it performs MultiQC to extract quality control information from the aligned reads.

It uses quantification methods (such as salmon for RNA-Seq and peak-calling for ChIP-Seq) to quantify expression and determine protein-binding.

The output is analyzed (differential gene expression for RNA-Seq and peak analysis for ChIP-seq), and the results are visualized.

The model organism for this project is Yeast i.e. Saccharomyces cerevisiae. For RNA-Seq, yeast data between euploid and aneuoploid conditions will be compared. For ChIP-Seq, yeast data between 3AT-treated and untreated conditions will be compared.

How do I get started?

Go to the main page to view the instructions to start the tutorial in jupyter notebook or view the jupyter notebook tutorial online.

Authors

  • Syed Hussain Ather (shussainather [at] gmail.com) (http://hussainather.com)
  • Olaitan Awe (laitanawe [at] gmail.com)
  • TJ Butler (tjbutler003 [at] gmail.com)
  • Tamiru Denka (tamiru.dank [at] nih.gov)
  • Stephen Semick (stephen.semick [at] libd.org)
  • Wanhu Tang (tangw2 [at] niaid.nih.gov)
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