I have more than 500 Datasets of RNA-sequencing data (both FASTA and FASTQ format) and I'd like to analyze gene expression and differentially expressed genes.
files with FASTA format are in my PC (windows OS) and with FASTQ format are imported to the galaxy website (usegalaxy . eu).
I'm not familiar with gene expression analysis (GEA) and recently installed R and I'm working with Bioconductor packages (like DESq2,edgeR, biobase and etc..) to learn how to use them for GEA. IDK how much, but it seems it takes a long time to learn and use them.
Here my question is could anyone let me know what is the best and fast way for GEA.
is R the best software for GEA, is yes, is any simple tutorial for GEA by R?
Regarding the huge mass of RNA-seq data and my pc may not be able to analyze them, is there any software on Galaxy website for GEA.
Any guide is warmly apriciated
Hi Ali,
first of all, I would suggest starting with a smaller dataset when learning how to analyse RNA-seq data. This will reduce a lot of headache and will give you an easier time bug-fixing and testing stuff.
Second, if you are serious about doing bioinformatics you will need to switch to Linux/MacOS at some point. You can keep Windows and install a virtual maschine running Linux to circumvent this. You will also need to dive deeper into bioinformatics to understand and build on what you are analysing (which is all possible but this goes beyond the scope of what you want to read here).
So to make it short, you can use galaxy and R for GE analysis of RNA-seq data. As you already have your data in galaxy, you can use "salmon quant" to quasi-map and quantify transcript levels (input are FQ files from one sample and a reference cdna you need to provide). Then you can use the salmon output to run DESeq2 and analyse differential gene expression between conditions (you need to specify you used salmon for mapping and provide a transcript ID to gene ID mapping file). Depending on what organism you are working with, it should be rather easy to find the cdna reference (just google) and a transcript ID to gene ID mapping file (you can use biomart, also just google).
Hope this helps!
Alexander
Hi Ali,
I highly recommend what Alexander suggested. You may also find a quick overview of the RNA-Seq pipeline here.
https://www.bioconductor.org/help/course-materials/2016/CSAMA/lect-07-modern-rnaseq/ModernRNAseqAnalysis.pdf
Analyzing bulk RNA-Seq data mostly depends on the biological question which is being asked but most of the time the question is to identify the genes which are expressed differently (statistically ) between two or more groups.
There are a few key steps in order to process the fastq files
1) quality control of reads (Fastqc can be used)
2) you may want to trim the adapters (Trim Galore). If you're using some soft-clipping-based aligner (STAR, Tophat2) you may skip this step since the aligner can take care of that.
3) choose an aligner (In case you have access to a cluster you may want to use STAR otherwise, using Salmon or Kallisto is recommended)
3) build the index file based on the genome/transcriptome of interest for the corresponding aligner.
4) Align reads to the transcriptome which generates bam/sam files.
5) quantify the abundance of the genes (features), STAR has a parameter which does this task. You can also use FeatureCounts as an alternative. RSEM is a very popular method that is highly recommended. If you're using Salmon or Kallisto, the output already performed this step. You can also run tximport on bam files generated by the Salmon or Kallisto.
6) Correcting for potential batch effect (Combat can be utilized)
7) Calling for differentially expressed genes which mostly depend on your condition and the assumption. You may want to use DESeq2 if you're using raw counts. Limma and EdgeR are two other methods that are also recommended. If you're using Kallisto you may want to use sleuth for DEG step.
8) You can perform pathway analysis using ClusterProfiler.
As you gain experience in analyzing RNA-Seq data, you will learn more about choosing the right tool and building the right pipeline.
Dear Alexander Neumann , Amir Vajdi
Thank you so much for your helpful answers.
I don't have 2 group to analyze Differentially Expressed genes.
I used SRA files produced at a project of several tissues of Turkey (meleagris gallopavo) and I previously predicted several highly conserved miRNAs in ( plus the number of any of them at any dataset) using birds' miRNAs available at mirbase.org. I'm going to analyze gene expression at those datasets to find any relationship (Correlation) between miRNAs and gene expression.
I installed Linux ubuntu along with windows, but I'm new to it.
Dear Amir Vajdi
Quality control and trimming adaptors have done before using data to predict miRNAs. I follow the steps you advised.
I know it may take a long time cause I'm new to GE analysis.
Thank you again ♥
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