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Tools Optimized for Operating System Environments
It seems that almost all well used NGS software used for assemblies and etc... are well adapted for the general Linux community, but in most cases they're adapted for Debian, Ubuntu, or Red Hat builds. We've become well acquainted with many of the software tools under Ubuntu, but unfortunately our machines are limited in memory or CPU capabilities. We have some machines of decent capability operating under the Solaris and SLES OS environments, but many of the packages fail to compile and are plagued with incompatibilities. It would be great to have some hints toward overcoming some of the compiling, or library hurdles taken to get some of these software packages working on the less used OS environments. Some compile issues have also been with the chipsets in use such as ia64 vs x86, and others with OS releases. In general, it would be great if prerequisites and success stories can be set for many of these packages regarding chipsets, OS, compile libraries and packages.
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This is a problem you'll have with most of the software available (not only for bionformatics)... BTW, I have some packages built on Solaris, Nexenta and FreeBSD, I may help in porting, if you like.
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Sometimes developers focus mainly in getting the tool up and running on the traditional OS and hardware, that's linux (The windows of this field
) and x86.
I have to say there are many tools out there that follow very well the typical open source building process and benefit from this approach. When the tool presents issues with a particular environment and the user contacts the community, authors, developers and users work together to make the tool run on that particular environment.-drdComment
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Running Ubuntu as a virtual machine on your higher spec'd machines might be an answer.Originally posted by grlazo View Post
Another is to rent machines via Amazon or equivalent if you really can't afford to invest in hardware for your NGS analysis.
However, you might find you need less CPU and memory than you think. 8Gb of RAM and 8 cores is adequate for many tasks, and machines like this can be built for under $1000.Comment
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I agree with the OP that this issue is valuable enough to capture the tricks of the trade somewhere. A few examples could be:
- compiling R optimized for the cpu and OS, whether on linux or OSX -- the speed can be improved substantially over the general-purpose default Ubuntu R package if compiled with the proper, and/or math libraries (ATLAS-tuned BLAS, Lapack, etc.)
- compiling tools with one of various flavors of MPI.
- use of different GCC versions for compiles.
- use of OpenCL/CUDA/ATI libraries in place of standard libraries -- I'm still curious whether their math libraries could substitute, for example, in R compiles above. And of course, I don't know the improvement/impact.
Are you proposing something specific? I.e. a wiki, or a Github?Comment
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...06-18-2026, 07:11 AM -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM
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