this is my first time doing bioinformatics, so a user friendly software would really be good. Thank you.
Hi Preetha Rajendiran ,
use DESeq2 (R-package) (https://bioconductor.org/packages/release/bioc/html/DESeq2.html)
If it is your first time I can recommend Galaxy (usegalaxy.org) and going through this (and other) tutorials. There is a large Galaxy community so you can always ask more specific questions on forums.
https://galaxyproject.github.io/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html
TopHat, Cufflinks, cuffdiff for alignment and differentially expressed genes. After use cummeRbund to visualize and analyse the data.
agree. In most cases, we use tophat(or bowtie2) to mapping.
htseq-count to call reads for transcripts (gene)
DESeq2 : a R package aiming to find DEGs with corresponding p-value.
Following these steps, you will get what you want.
Don't forget to check the different biases related to within-sample and between-sample characteristics. The choice of the appropriate normalization method is crucial. For comparison between samples or conditions, within-sample biases like gene length and GC content doesn't affect the results but can slightly change the rank of DEGs. Usually "Preprocessing of raw reads >> STAR (tophat) >> samtools >> HTSeq (featureCounts) >> DESeq2 (edgeR)" is the "gold-standard" pipeline
I think HISAT-StringTie ( https://www.nature.com/articles/nprot.2016.095 ) is worthy to have a try.
You can refer this paper, it has full detail about analysis and software details.
Here is what we typically use at Northwestern University NUSeq Core. First to evaluate reads quality FastQC is used. Reads of poor quality or aligning to rRNA sequences need be filtered out. After data cleanup, reads are aligned to the reference genome using STAR. To generate read counts for each gene, htseq-count is deployed. Also as mentioned above, DESeq2 is then be used for normalization and differential expression analysis.
I always advice Galaxy public server for biologists. This is the best way to start with bioinformatics data analysis. Starting with R or any other programming language is nightmare.
start with galaxy -> Ugene -> learn from github & do programmatically
For mapping tophat and bowtie2. For analysis of gene expression, use software like Galaxy (for beginners), BioEdit, seqmonk (but first you need an alignment made by MEGA software or Bioedit), biojupies.
However, if you know R, then it is much easier and more efficient to skip all those programs and simply use R packages like GSVA, EDASeq, NOISeq. Below I'm sending a link to all R packages useful for RNAseq.
http://bioconductor.org/packages/release/BiocViews.html#___Software
Hey, I am a final year biotechnology student. I learned a software called MeV for gene expression analysis during our bioinformatics course. That software is easy to learn all by yourself.
https://www.mybiosoftware.com/mev-4-6-2-multiple-experiment-viewer.html
Follow the link in the blog post to download the software. Cheers.
Hello,
I have used the CLC software tools for RNA sequencing data to see the transcripts expression which is user friendly. There is lots of possibility such as finding the gene of interest and ROI.
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