SeqAcademy

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

Ready to get going? Jump to "How do I use SeqAcademy?" and get started!

Keywords: RNA-Seq, ChIP-Seq, alignment, differential gene expression, peak-calling, education, tutorial, pipeline

What is SeqAcademy?

SeqAcademy is a user-friendly 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.

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.

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

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 use SeqAcademy?

1. Identify and open the terminal emulator program on your computer. Mac and Linux systems come with Terminal installed, and Windows systems come with Console. If there isn't one installed, download one online.

2. Type pwd and press enter. This command shows what your current working directory is. Typing commands and pressing enter will be the primary way of running commands in this tutorial. Type ls to display which directories and files are in this current directory.

3. If you'd like to use the tutorial in this current working directory, skip to step 5. Otherwise, you may make a new directory or move to another one. To make a new directory, run mkdir DIRECTORY in which DIRECTORY is the name of the directory you'd like to make. To move to another directory, run cd DIRECTORY in which DIRECTORY is the name of the DIRETORY you'd like to move to. To move up a directory, run cd ...

4. Given the disk space and RAM requirements, it's likely you'll want to use a virtual machine for this tutorial. To connect to a virtual machine, make sure you use your own domain name or IP address.

If you know the hostname you'd like to connect to, run ssh -L PORTNUMBER:localhost:PORTNUMBER USERNAME@HOSTNAME in which PORTNUMBER is a chosen unique identifable number, USERNAME is your username, and HOSTNAME is your hostname.

If you know the IP you'd like to connect to, run ssh -L PORTNUMBER:localhost:PORTNUMBER USERNAME@IP in which IP is the IP address of the machine you wish to connect to.

5. Download anaconda (https://www.anaconda.com/download/) and git (https://git-scm.com/downloads).

6. Run git clone https://github.com/NCBI-Hackathons/seqacademy.git to clone the directory such that you can download the tutorial. This will download a folder called seqacademy.

7. Before running any programs, we'll make sure that each software is installed correctly. This tutorial uses Bioconda (https://bioconda.github.io/). Bioconda is a channel for the conda package manager specializing in bioinformatics software. The available packages are listed here: https://bioconda.github.io/recipes.html#recipes.

You will need to add the bioconda channel as well as the other channels bioconda depends on. It is important to add them in this order so that the priority is set correctly (that is, bioconda is highest priority).

The conda-forge channel contains many general-purpose packages not already found in the defaults channel. The r channel is only included due to backward compatibility. It is not mandatory, but without the r channel packages compiled against R 3.3.1 might not work.

This tutorial uses cells written in python and unix to perform its analyses. Lines that are written in unix are prefixed by an exclamation point.

Select the following cell and run it. To run a cell, select the cell, click "Cell" the upper taskbar, and select "Run Cells". Or click the cell and press shift + enter. Alternatively, the contents of any cell may be copy+pasted into the terminal emulator to run.

The --add channels is an option that is supplied to the command ot tell it to add certain channels that you specify. The way it is written, the channels "defaults", "conda-forge", and "bioconda", would be added in that order.

Run the following three lines in command line in the following order:

conda config --add channels defaults conda config --add channels conda-forge conda config --add channels bioconda

In this tutorial we will create an environment named "tutorial" and install the packages in there. Environments offer ways of installing packages in specific environments so they can be managed and run for different specifications. You can create, export, list, remove and update environments that have different versions of Python and/or packages installed in them. Switching or moving between environments is called activating the environment. You can also share an environment file.

This command will create an environment "tutorial" in which to install the packages used in this tutorial.

Run the following commands to create the environment. The -n flag specifies the name of the environment to create (which is called "tutorial") and the list of packages following the name are the packages that will be installed in the "tutorial" environment. This will most likely take 10-15 minutes.

conda create -n tutorial jupyter hisat2 multiqc macs2 bioconductor-deseq2 bioconductor-genomicdatacommons bioconductor-rtracklayer bioconductor-dupradar matplotlib ggplot samtools bioconductor-rsamtools bedtools htseq --yes

Then activate the environment with the following command:

For Mac and Linux

source activate tutorial

or

conda activate tutorial

For Windows

activate tutorial

Begin the tutorial

8. Follow the instructions in rnaseq.md or chipseq.md for the corresponding tutorial.

RNA-Seq

The following data presents the RNA-Seq data used in this tutorial. This tutorial observes RNA-Seq data of aneuploidy in Yeast (source: https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP106028).

BioSample	Experiment	MBases	MBytes	Run	SRA_Sample	Sample_Name	karyotype	replicate	Assay_Type	AvgSpotLen	BioProject	Center_Name	Consent	DATASTORE_filetype	DATASTORE_provider	InsertSize	Instrument	LibraryLayout	LibrarySelection	LibrarySource	LoadDate	Organism	Platform	ReleaseDate	SRA_Study	source_name	strain
SAMN06859211	SRX2775581	1632	575	SRR5494627	SRS2158877	GSM2595338	Aneuploid	First	RNA-Seq	51	PRJNA385090	GEO	public	sra	ncbi	0	Illumina HiSeq 2500	SINGLE	cDNA	TRANSCRIPTOMIC	2017-05-02	Saccharomyces cerevisiae	ILLUMINA	2017-09-12	SRP106028	Yeast cells	S288c
SAMN06859210	SRX2775582	940	331	SRR5494628	SRS2158878	GSM2595339	Aneuploid	Second	RNA-Seq	51	PRJNA385090	GEO	public	sra	ncbi	0	Illumina HiSeq 2500	SINGLE	cDNA	TRANSCRIPTOMIC	2017-05-02	Saccharomyces cerevisiae	ILLUMINA	2017-09-12	SRP106028	Yeast cells	S288c
SAMN06859209	SRX2775583	1195	421	SRR5494629	SRS2158879	GSM2595340	Aneuploid	Third	RNA-Seq	51	PRJNA385090	GEO	public	sra	ncbi	0	Illumina HiSeq 2500	SINGLE	cDNA	TRANSCRIPTOMIC	2017-05-02	Saccharomyces cerevisiae	ILLUMINA	2017-09-12	SRP106028	Yeast cells	S288c
SAMN06859208	SRX2775584	815	288	SRR5494630	SRS2158880	GSM2595341	Euploid	First	RNA-Seq	51	PRJNA385090	GEO	public	sra	ncbi	0	Illumina HiSeq 2500	SINGLE	cDNA	TRANSCRIPTOMIC	2017-05-02	Saccharomyces cerevisiae	ILLUMINA	2017-09-12	SRP106028	Yeast cells	S288c
SAMN06859207	SRX2775585	946	333	SRR5494631	SRS2158881	GSM2595342	Euploid	Second	RNA-Seq	51	PRJNA385090	GEO	public	sra	ncbi	0	Illumina HiSeq 2500	SINGLE	cDNA	TRANSCRIPTOMIC	2017-05-02	Saccharomyces cerevisiae	ILLUMINA	2017-09-12	SRP106028	Yeast cells	S288c
SAMN06859206	SRX2775586	1152	407	SRR5494632	SRS2158882	GSM2595343	Euploid	Third	RNA-Seq	51	PRJNA385090	GEO	public	sra	ncbi	0	Illumina HiSeq 2500	SINGLE	cDNA	TRANSCRIPTOMIC	2017-05-02	Saccharomyces cerevisiae	ILLUMINA	2017-09-12	SRP106028	Yeast cells	S288c

Principal component analysis (PCA) suggests gene expression for euploid yeast samples (haploid) clusters distinctly from that of the aneuploid yeast samples (diploid chromosome X).The first two PCs account for ~70% of the variance in expressed genes). Data provided by Mulla et al. (https://elifesciences.org/articles/27991).

A volcano plot of differentially expressed genes between euploid yeast colonies versus aneuploid yeast colonies. The x-axis represents the difference in gene expression between the conditions. False discovery rate (FDR), a method for controlling for multiple testing, is along the y-axis. Each point represents a tested gene (N=3,926). Red points are those reaching genome-wide significance (at FDR<0.05, N=663), whereas grey points are genes not reaching statistical significance (FDR>0.05, N=3,263). Data provided by Mulla et al. (https://elifesciences.org/articles/27991).

The relative enrichment of chrX for differentially expressed genes suggests the downstream results of this processing pipeline are consistent with biological expectations. The RNA-seq experiment was performed on yeast colonies with an extra chromosome X. Data provided by Mulla et al. (https://elifesciences.org/articles/27991).

ChIP-Seq

The following data presents the ChIP-Seq data used in this tutorial. This tutorial observes ChIP-Seq data of induction by 3-AT in Yeast (source: https://www.ncbi.nlm.nih.gov/Traces/study/?acc=SRP132584).

AvgSpotLen	BioSample	Experiment	MBases	MBytes	Run	SRA_Sample	Sample_Name	genotype	source_name	strain	Assay_Type	BioProject	Center_Name	Consent	DATASTORE_filetype	DATASTORE_provider	InsertSize	Instrument	LibraryLayout	LibrarySelection	LibrarySource	LoadDate	Organism	Platform	ReleaseDate	SRA_Study
148	SAMN08513506	SRX3677830	8816	3690	SRR6703656	SRS2938492	GSM2991004	MATa ade2-1 can1-100 leu2-3,112 trp1-1 ura3-1	Untreated	YDC111	RNA-Seq	PRJNA433659	GEO	public	sra	ncbi	0	NextSeq 500	PAIRED	cDNA	TRANSCRIPTOMIC	2018-02-09	Saccharomyces cerevisiae	ILLUMINA	2018-02-27	SRP132584
148	SAMN08513513	SRX3677835	9614	4022	SRR6703661	SRS2938497	GSM2991009	MATa ade2-1 can1-100 leu2-3,112 trp1-1 ura3-1	3AT-treated for 40 min	YDC111	RNA-Seq	PRJNA433659	GEO	public	sra	ncbi	0	NextSeq 500	PAIRED	cDNA	TRANSCRIPTOMIC	2018-02-09	Saccharomyces cerevisiae	ILLUMINA	2018-02-27	SRP132584
150	SAMN08513512	SRX3677836	6049	2749	SRR6703662	SRS2938498	GSM2991010	MATa ade2-1 can1-100 leu2-3,112 trp1-1 ura3-1	Untreated	YDC111	RNA-Seq	PRJNA433659	GEO	public	sra	ncbi	0	NextSeq 500	PAIRED	cDNA	TRANSCRIPTOMIC	2018-02-09	Saccharomyces cerevisiae	ILLUMINA	2018-02-27	SRP132584
150	SAMN08513511	SRX3677837	6918	3140	SRR6703663	SRS2938499	GSM2991011	MATa ade2-1 can1-100 leu2-3,112 trp1-1 ura3-1	3AT-treated for 40 min	YDC111	RNA-Seq	PRJNA433659	GEO	public	sra	ncbi	0	NextSeq 500	PAIRED	cDNA	TRANSCRIPTOMIC	2018-02-09	Saccharomyces cerevisiae	ILLUMINA	2018-02-27	SRP132584

Distribution of intersected peaks across the yeast genome. This IGV screenshot shows in the bottom row the intersected peaks between the two treatment conditions of the yeast samples. The matching genes with each intersected peak can be analyzed.

Authors

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