Biostar Beta. Not for public use.
Question: Hardware requirement for RNA-seq
1
Entering edit mode

Hi all,

I'm new to RNA-seq and am curious about what the minimal hardware requirements are for processing. I'd prefer to work with a Mac desktop if possible

Details: Organism- mouse

aligning against reference not de novo

diff expression analysis using R

Entering edit mode
0

Thanks for the answer geno, and for the links AT.

ADD REPLYlink 11 months ago
Antonio.Aubry
• 10
Entering edit mode
0

If an answer was helpful you should upvote it, if the answer resolved your question you should mark it as accepted.
Upvote|Bookmark|Accept

ADD REPLYlink 11 months ago
WouterDeCoster
39k
Entering edit mode
0

If quantifying gene expression and performing differential expression analysis, the requirements are very small, depending on the method. See the small experiment Bioinformatics on a Rock64. However, I don't know if the same machine is sufficient to perform all steps, as the post doesn't make it clear if the human transcriptome index was built by the same Rock64, or built externally by a more powerful computer.

ADD REPLYlink 11 months ago
h.mon
25k
3
Entering edit mode

Would mainly depend on aligner you choose to use.

bwa may be the one with the lightest (~6-7 G free RAM) requirement. Other aligners will need significantly more ~30G.

Consider using salmon instead of a regular aligner (https://salmon.readthedocs.io/en/latest/index.html ) as an alternative.

That should cover the rest of the analyses.

ADD COMMENTlink 11 months ago genomax 68k
Entering edit mode
0

I would typically run TopHat2 with 8 GB of RAM and 4 cores on a cluster (and I ran STAR with similar run time a few times, even though I think it tends to use more RAM when run on a computer that has extra RAM available). However, that is with 50 bp Single-End reads: if you have paired-end reads (and want to focus more on splicing and/or mutation calling), you may need additional resources and/or time (but that also means you'll need to do a genome alignment).

I guess this really relates to Brett's answer suggesting considering use of a compute cluster, but I thought I should also say something here.

ADD REPLYlink 11 months ago
Charles Warden
6.8k
3
Entering edit mode

Kallisto is really similar to salmon for "pseudo alignment", I ran it with about 8GB of RAM on human samples. I think the most demanding thing was building the RNA index, which you can get "pre-built" if you look around and probably could be run with a lot less. STAR is also really popular, but on the cusp of what is doable without moving to specialized hardware. When I played with it I could do human samples with about 32GB of RAM on a desktop after a lot of coaxing. You could move to AWS or university clusters at that point, which may be something you might be interested in anyways.

ADD COMMENTlink 11 months ago brett.vanderwerff • 100
Entering edit mode
1

Running a transcription quantification (like Salmon or kallisto) on a local computer shouldn't be a problem.

If you had a MiSeq/MiniSeq/iSeq experiments with a lower number of total reads in a SE 50 bp polyA library (say 5-10M reads per sample), you might even be able to do a genome alignment in serial (assuming you have a 2-group comparison with triplicates) on a computer with 8 GB of RAM and 2-4 cores within one day. In that case, I do think there are benefits to being able to visualize your alignment. However, most people would probably use a cluster for genome alignments (and probably have more reads from a NextSeq/HiSeq). So, learning to submit jobs on a cluster is also a very useful skill :)

ADD REPLYlink 11 months ago
Charles Warden
6.8k
0
Entering edit mode

You are right that salmon is similar to kallisto, however kallisto is faster, a relevant matter if one is working on a Mac desktop. https://liorpachter.wordpress.com/2017/08/02/how-not-to-perform-a-differential-expression-analysis-or-science/

ADD COMMENTlink 11 months ago lakigigar • 220

Login before adding your answer.

Similar Posts
Loading Similar Posts
Powered by the version 2.0