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Hardware requirement for RNA-seq
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20 months ago

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

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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.

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23 days ago
genomax 68k
United States

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.

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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.

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17 months ago

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.

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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 :)

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16 months ago
lakigigar • 220
United States

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/