Setup

1. Option 1 (preferred): using a cloud instance
- 1.1 Installing Google Cloud SDK
- 1.2 Installing IGV
2. Option 2: manual installation
- 2.1 Softwares and packages
- 2.2 Data files
3. Original study
- 3.1 Gene counts
- 3.2 Experimental design table

1. Option 1 (preferred): using a cloud instance

1.1 Installing Google Cloud SDK

The Google Cloud Software Development Kit (SDK) is a set of tools that allows you to connect to a Google Cloud machine from your local computer. We will assign you specific cloud machines at the beginning of the course.

Open Install the gcloud CLI in your browser.
Scroll down and select your operating system, then follow the instructions for installation.

1.2 Installing IGV

The Integrated Genome Viewer (IGV) is a genome viewer which allows you to view the alignment of your RNAseq reads to the genome.

Open IGV Downloads in your browser.
Select the installation that matches your operating system.

2. Option 2: manual installation

This is the second way to install softwares and packages. It should work but there is no guarantee that it will work since R and packages versions on your machine might be different from the software and package versions used in this lesson. Thus, the preferred way is still to use the Docker image (option 1).

2.1 Softwares and packages

Before you start.

Before the training, please make sure you have done the following:

Download and install up-to-date versions of:

R: https://cloud.r-project.org.

RStudio: http://www.rstudio.com/download.

The DESeq2 package: https://bioconductor.org/packages/release/bioc/html/DESeq2.html.

The tidyverse package: https://www.tidyverse.org/.

The EnhancedVolcano package: http://bioconductor.org/packages/release/bioc/html/EnhancedVolcano.html.

The pheatmap package: https://cran.r-project.org/web/packages/pheatmap/index.html.

The biomartr package: https://cran.r-project.org/web/packages/biomartr/index.html.

The clusterProfiler package: https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

The org.At.tair.db package: https://www.bioconductor.org/packages/release/data/annotation/html/org.At.tair.db.html.

The biomaRt package: https://bioconductor.org/packages/release/bioc/html/biomaRt.html.

The pwr package: https://cran.r-project.org/web/packages/pwr/index.html.

Read the workshop Code of Conduct to make sure this workshop stays welcoming for everybody.

Get comfortable: if you’re not in a physical workshop, be set up with two screens if possible. You will be following along in RStudio on your own computer while also following this tutorial on your own. More instructions are available on the workshop website in the Setup section.

2.2 Data files

What you need to download for the part completed in the Shell (fastq QC, alignment, counting)

Please download the necessary data files for the lesson from the Zenodo archive.

Arabidopsis_sample1/2/3/4.fq.gz: A FASTQ file containing a sample sequenced mRNA-seq reads in the FASTQ format.

AtChromosome1.fa.gz: the gzipped chromosome 1 sequence of the Arabidopsis thaliana genome in FASTA format.

ath_annotation.gff3.gz: the gzipped genome annotation of Arabidopsis thaliana for chromosome 1 in the GFF3 format. This indicates the positions of genes, their exons and 5’ or 3’ UTR on the chromosome and is used to generate the gene counts.

adapters.fasta: the Illumina adapter sequences used for read trimming using Trimmomatic.

What you need to download for the part completed in R (PCA, DEseq2, Clustering)

Please download the necessary data files for the lesson from the Zenodo archive.

Counts: A raw_counts.csv dataframe of the sample raw counts. It is a tab separated file therefore data are in tabulated separated columns.

Samples to experimental conditions: the samples_to_conditions.csv dataframe indicates the correspondence between samples and experimental conditions (e.g. control, treated).

Differentially expressed genes: differential_genes.csv dataframe contains the result of the DESeq2 analysis.

Please read the original study description below and have a look at the file preview to understand their format.

These raw_counts.csv file was obtained by running the v0.1.1 version of a RNA-Seq bioinformatic pipeline on the mRNA-Seq sequencing files from Vogel et al. (2016).

3. Original study

This RNA-seq lesson will make use of a dataset from a study on the model plant Arabidopsis thaliana inoculated with commensal leaf bacteria (Methylobacterium extorquens or Sphingomonas melonis) and infected or not with a leaf bacterial pathogen called Pseudomonas syringae. Leaf samples were collected from Arabidopsis plantlets from plants inoculated or not with commensal bacteria and infected or not with the leaf pathogen either after two days (2 dpi, dpi: days post-inoculation) or seven days (6 dpi).

All details from the study are available in Vogel et al. in 2016 and was published in New Phytologist.

3.1 Gene counts

The dimension of this table are 33,769 rows x 49 columns.

33,769 rows: one for gene and sample names and the rest for gene counts.
49 columns: one for the gene id and the rest for sample accession identifiers (from the EBI European Nucleotide Archive).

Geneid	ERR1406259	ERR1406260	ERR1406261	ERR1406262	ERR1406263	ERR1406264	ERR1406265	ERR1406266	ERR1406268	ERR1406269	ERR1406270	ERR1406271	ERR1406272	ERR1406273	ERR1406274	ERR1406275	ERR1406276	ERR1406277	ERR1406278	ERR1406279	ERR1406280	ERR1406281	ERR1406282	ERR1406284	ERR1406285	ERR1406286	ERR1406287	ERR1406288	ERR1406289	ERR1406290	ERR1406291	ERR1406292	ERR1406293	ERR1406294	ERR1406296	ERR1406297	ERR1406298	ERR1406299	ERR1406300	ERR1406301	ERR1406302	ERR1406303	ERR1406304	ERR1406305	ERR1406306	ERR1406307	ERR1406308	ERR1406309
AT1G01010	59	81	40	51	57	110	93	87	99	131	80	79	142	216	102	76	92	116	100	126	151	249	61	189	161	92	80	125	77	106	90	86	164	71	64	83	100	86	91	214	142	76	84	123	91	69	75	85
AT1G01020	365	466	440	424	393	567	397	468	465	365	382	365	595	509	323	422	325	358	415	403	498	501	441	498	409	396	472	566	422	462	504	434	717	534	408	346	757	456	443	976	517	467	533	648	457	393	538	579
AT1G03987	8	16	13	19	13	20	19	24	8	10	10	14	11	13	10	9	11	20	14	10	10	8	14	25	14	13	18	17	19	4	12	14	29	15	19	47	28	6	21	20	5	5	8	17
AT1G01030	111	200	189	164	141	389	200	175	127	186	140	189	147	193	102	101	103	128	136	120	162	229	124	177	125	136	169	197	141	217	214	180	253	161	98	152	371	219	170	566	441	99	207	220	169	117	123	183
AT1G03993	131	179	169	157	114	156	138	184	193	143	135	155	218	236	159	194	149	156	168	128	174	269	183	215	176	165	171	247	179	181	177	199	313	236	154	169	313	201	202	332	169	218	203	250	190	188	223	218
AT1G01040	1491	1617	1418	1543	1224	1635	1524	1665	1565	1566	1496	1499	2244	1881	1177	1751	1444	1631	1393	1407	1880	2311	1529	1919	1662	1537	1691	2142	1469	1733	1910	1873	3079	2179	1486	1471	2840	1891	1924	3136	1520	1901	1950	2596	1802	1851	2133	1984
AT1G01046	35	30	48	32	28	50	51	56	36	26	29	38	48	30	15	44	23	31	22	27	33	51	41	35	48	38	41	49	27	36	39	50	57	49	41	30	54	41	43	85	42	42	59	65	49	64	50	46
ath-miR838	12	11	22	18	15	21	22	24	16	12	10	15	17	16	7	20	11	14	6	11	16	17	17	15	26	12	17	13	15	12	18	25	26	25	15	15	22	20	14	37	20	20	22	27	17	21	23	23
AT1G01050	1484	1483	1237	1544	1119	1453	1280	1256	1768	1869	1709	1649	2431	1858	1195	1518	1325	2013	1645	1666	2056	2258	1530	1834	1477	1532	1609	2220	1552	1976	1706	1807	2656	1873	1329	1512	2915	1646	1983	2687	1548	1740	1632	2330	1578	1521	1970	1977

… many more lines …

3.2 Experimental design table

dpi: days post-inoculation.

sample	growth	infected	dpi
ERR1406259	MgCl2	mock	2
ERR1406271	MgCl2	mock	2
ERR1406282	MgCl2	mock	2
ERR1406294	MgCl2	mock	2
ERR1406305	MgCl2	mock	7
ERR1406306	MgCl2	mock	7
ERR1406307	MgCl2	mock	7
ERR1406308	MgCl2	mock	7
ERR1406260	MgCl2	Pseudomonas_syringae_DC3000	2
ERR1406261	MgCl2	Pseudomonas_syringae_DC3000	2
ERR1406262	MgCl2	Pseudomonas_syringae_DC3000	2
ERR1406309	MgCl2	Pseudomonas_syringae_DC3000	2
ERR1406263	MgCl2	Pseudomonas_syringae_DC3000	7
ERR1406264	MgCl2	Pseudomonas_syringae_DC3000	7
ERR1406265	MgCl2	Pseudomonas_syringae_DC3000	7
ERR1406266	MgCl2	Pseudomonas_syringae_DC3000	7
ERR1406287	Methylobacterium_extorquens_PA1	mock	2
ERR1406288	Methylobacterium_extorquens_PA1	mock	2
ERR1406289	Methylobacterium_extorquens_PA1	mock	2
ERR1406290	Methylobacterium_extorquens_PA1	mock	2
ERR1406291	Methylobacterium_extorquens_PA1	mock	7
ERR1406292	Methylobacterium_extorquens_PA1	mock	7
ERR1406293	Methylobacterium_extorquens_PA1	mock	7
ERR1406296	Methylobacterium_extorquens_PA1	mock	7
ERR1406297	Methylobacterium_extorquens_PA1	Pseudomonas_syringae_DC3000	2
ERR1406298	Methylobacterium_extorquens_PA1	Pseudomonas_syringae_DC3000	2
ERR1406299	Methylobacterium_extorquens_PA1	Pseudomonas_syringae_DC3000	2
ERR1406300	Methylobacterium_extorquens_PA1	Pseudomonas_syringae_DC3000	2
ERR1406301	Methylobacterium_extorquens_PA1	Pseudomonas_syringae_DC3000	7
ERR1406302	Methylobacterium_extorquens_PA1	Pseudomonas_syringae_DC3000	7
ERR1406303	Methylobacterium_extorquens_PA1	Pseudomonas_syringae_DC3000	7
ERR1406304	Methylobacterium_extorquens_PA1	Pseudomonas_syringae_DC3000	7
ERR1406268	Sphingomonas_melonis_Fr1	mock	2
ERR1406269	Sphingomonas_melonis_Fr1	mock	2
ERR1406270	Sphingomonas_melonis_Fr1	mock	2
ERR1406272	Sphingomonas_melonis_Fr1	mock	2
ERR1406273	Sphingomonas_melonis_Fr1	mock	7
ERR1406274	Sphingomonas_melonis_Fr1	mock	7
ERR1406275	Sphingomonas_melonis_Fr1	mock	7
ERR1406276	Sphingomonas_melonis_Fr1	mock	7
ERR1406277	Sphingomonas_melonis_Fr1	Pseudomonas_syringae_DC3000	2
ERR1406278	Sphingomonas_melonis_Fr1	Pseudomonas_syringae_DC3000	2
ERR1406279	Sphingomonas_melonis_Fr1	Pseudomonas_syringae_DC3000	2
ERR1406280	Sphingomonas_melonis_Fr1	Pseudomonas_syringae_DC3000	2
ERR1406281	Sphingomonas_melonis_Fr1	Pseudomonas_syringae_DC3000	7
ERR1406284	Sphingomonas_melonis_Fr1	Pseudomonas_syringae_DC3000	7
ERR1406285	Sphingomonas_melonis_Fr1	Pseudomonas_syringae_DC3000	7
ERR1406286	Sphingomonas_melonis_Fr1	Pseudomonas_syringae_DC3000	7

Introduction to RNA-seq: Setup