Including the cost of personnel, lab equipment, consumables, sequencing, and everything that makes your lab able to produce and publish science, what proportions of these costs (in percentage) is related to computing (computers, accounts, storage...) not including personnel ?
This kind of depends on how you classify the salaries of people whose job is computational analysis/pipeline building/etc. But here, I'd estimate 50% for all computational costs including said personnel.
I've never been a PI so I can only guess, but on the labs I worked ranged from probably less than 2% (basically, a server and a CLC license) to something akin to 20-30% (a cluster with 7 nodes, one of then with 512Gb memory, NFS storage server, lots of SAS disks; but also did lots of wet lab work, bought Illumina sequencers, etc) to close to 100% on a "pure" bioinformatics lab.
This is a tricky calculation to do well. For us, though, storage has come out to be much more challenging and expensive than compute, both in terms of personnel and in terms of infrastructure.
The cost of computing is very little here I would say. We (i.e. the lab for which I'm bioinfomatician) have bought a server (cost about 3-4000£, it was a bargain I think), each group member has a mac laptop and/or a mac desktop, and we have a bunch of external disk drives (nothing fancy). The heavy duty jobs are done on our institute cluster. Storage and archiving is as well provided by our institute.
To put things a bit in perspective, in the last 3-4 years I went through about 3000 fastq file (~35 billion sequences) in ~1000 libraries, plus a bunch of microarrays (not much compared to sequencing).
This is an excellent question, but unfortunately the best answer I can come up with is 'it depends'.
Even if the overall use cases remain constant (i.e. the type of research you're doing and the scale of the data), it will depend what your existing resources are and if they are sufficient. For example, here at Indiana we have access to excellent University-wide HPC resources (e.g. http://rt.uits.iu.edu/bigred2/), but our group has standalone linux VMs and high memory machines that are housed in our CS building. This gives us added flexibility and speed (no queue times) in our research.
However, once those (capital) costs are allocated, the rest of the spend is relatively small (storage, maintenance, support). So overall my (very) rough estimate would be something on the order of 5% of one's research budget, but possibly higher if a significant upgrade is immediately needed.