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Marie: Overview of the RNAseq. What can RNAseq tell us/What should we do when we write a manuscript. slides
Matthew: RNAseq lab part mechanism, experimental design etc.slides
Domniki: How to upload data to ENA. slides
Objective: Conduct comparative RNA sequencing analyis between fish brain and liver datasets
We will learn:
A. How to conduct “cleaning” of the RNAseq data.
B. How to quantify the gene expression in each sample.
C. How to conduct downstream analysis to gain biological insights.
Useful materials:
https://training.galaxyproject.org/training-material/topics/introduction/
Go to https://usegalaxy.no/ ,
Galaxy is a web platform with various software for genome analyses.
You should be able to log in with “Feide” information at galaxy.no (NMBU ID and password)
If you are not NMBU employee, you can use these for free: https://usegalaxy.eu/ or https://usegalaxy.org/
On Galaxy
- Get RNAseq data (paired end, liver and brain, three samples each)
- Get reference transcriptome: Salmo salar version 3.1
- fastp: trimming low-quality reads
- Kallisto: quantify gene expression from the RNAseq data and reference
- tximport: summarize transcripts into genes
On iDEP
- Upload gene expression table
- Quality check
- Plot genes of interest
- Differentially expressed gene
- Gene ontology analysis
There are three major public repository for genomics data ENA (Europe), NCBI (America) DDBJ (Japan) – which are regularly synchronized.
This time, we will analyze brain and liver transcriptome datasets from the following study: “Multi-tissue transcriptome profiling of North American derived Atlantic salmon”
Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6146974/
Dataset: https://www.ebi.ac.uk/ena/browser/view/PRJNA470665
Click “upload data” on the left top - and
Select Paste/Fetch data and copy/paste the URL of the datasets.
Choose local files — upload data from your computer.
As it takes hours to analyze the real (gigabytes) data, I made a miniature datasets for each samples.
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139945_1_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139945_2_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139950_1_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139950_2_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139969_1_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139969_2_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139971_1_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139971_2_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139973_1_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139973_2_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139947_1_mini.fastq.gz
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139947_2_mini.fastq.gz
http://ftp.ensembl.org/pub/release-106/fasta/salmo_salar/cdna/Salmo_salar.Ssal_v3.1.cdna.all.fa.gz
(If it does not work well, download it on your computer and upload it to Galaxy afterword)
If you need reference sequences of other species for your study, go to https://www.ensembl.org/index.html
and “select a species” in the all genomes tub.
Go to “Gene annotation”, Download FASTA files and cDNA sequence.
# Real brain samples (two files per sample)
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/005/SRR7139945/SRR7139945_1.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/005/SRR7139945/SRR7139945_2.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/007/SRR7139947/SRR7139947_1.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/007/SRR7139947/SRR7139947_2.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/000/SRR7139950/SRR7139950_1.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/000/SRR7139950/SRR7139950_2.fastq.gz
# Real liver samoles (two files per sample)
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/009/SRR7139969/SRR7139969_1.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/009/SRR7139969/SRR7139969_2.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/001/SRR7139971/SRR7139971_1.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/001/SRR7139971/SRR7139971_2.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/003/SRR7139973/SRR7139973_1.fastq.gz
ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR713/003/SRR7139973/SRR7139973_2.fastq.gz
for file in *.fastq.gz; do zcat < $file | head -100000 | gzip > {$file}_mini.fastq.gz; done
What is Paired-End Sequencing?
As each sample has two files (paired-end), we make a “collection of paired datasets” to handle many files conveniently.
fastp is a tool designed to provide fast all-in-one preprocessing for FASTQ files.
Features
Search for a tool “fastp” in the left window (Tools) and put the input files we gathered in the 3-1.
Let’s take a look on how much/what kind of reads were removed.
kallisto is a program for quantifying abundances of transcripts from RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data, kallisto can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and kallisto is therefore not only fast, but also as accurate as existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks kallisto significantly outperforms existing tools.
If it is taking too long, here are the output files. The output files are six in total, transcript * transcript counts tables.
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139945_mini.Kallisto.tabular
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139947_mini.Kallisto.tabular
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139969_mini.Kallisto.tabular
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139971_mini.Kallisto.tabular
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139973_mini.Kallisto.tabular
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/SRR7139950_mini.Kallisto.tabular
Upload a transcript-gene table to Galaxy.
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/transcript_gene_ssav3.txt
(This table can be obtained at Ensemnbl BiomaRt for many species.)
Download the results of the entire RNAseq analysis.
https://github.com/mariesaitou/Bio326/blob/master/docs/assets/RNAseq_for_lab_2022/materials/rnaseq_liver_brain.txt
And go to iDEP http://bioinformatics.sdstate.edu/idep/
Upload the gene expression matrix and iDEP automatically runs (it detect the species as well)
Quality check
Visualize the expression of your favorite genes
Transcriptome comparison between brain1 and brain2 samples
Transcriptome comparison between brain and liver samples
Gene Ontology Analysis of differencially expressed genes Principal component analysis -> visualize overall trend of all the gene expression.
KEGG Analysis
barplot density Volcano input_iDEP GO
sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.2 bslib_0.3.1 compiler_4.1.2 pillar_1.7.0
[5] later_1.3.0 git2r_0.29.0 jquerylib_0.1.4 tools_4.1.2
[9] getPass_0.2-2 digest_0.6.29 jsonlite_1.8.0 evaluate_0.15
[13] tibble_3.1.6 lifecycle_1.0.1 pkgconfig_2.0.3 rlang_1.0.2
[17] cli_3.2.0 rstudioapi_0.13 yaml_2.3.5 xfun_0.30
[21] fastmap_1.1.0 httr_1.4.2 stringr_1.4.0 knitr_1.37
[25] sass_0.4.0 fs_1.5.2 vctrs_0.3.8 rprojroot_2.0.2
[29] glue_1.6.2 R6_2.5.1 processx_3.5.2 fansi_1.0.2
[33] rmarkdown_2.13 callr_3.7.0 magrittr_2.0.2 whisker_0.4
[37] ps_1.6.0 promises_1.2.0.1 htmltools_0.5.2 ellipsis_0.3.2
[41] httpuv_1.6.5 utf8_1.2.2 stringi_1.7.6 crayon_1.5.0