Hello to everyone I am searching for a new laptop as I am starting to getting deeper into bioinformatics.
The budget is around 1000 dollars.
Also is there any reason to search for laptops with dedicated gpu instead of integrated graphics as demanding graphics aren't needed for bioinformatics.
Thanks in advance!
I have seen a lot of misinformation in this thread about Mac M1 systems. I feel the need to address these as an new answer rather than a comment.
First, I have yet to see a command line tool that did not run on M1. Perhaps those with GUIs do not.
There is no need to compile to ARM! Even with that invisible translation layer, a tool written for X64 CPU runs much-much faster on M1 than on its native processor!
In the biostar handbook I have an installation script where a single command(!) bootstraps a fully operational bioinformation environment in about 5 minutes (it all depends on your network speed actually).
Installs everything, sets proper promts, paths, conda, mamba, creates a custom environment bioinfo filled with the most common bioinformatics tools, boom, in just a single command. Runs the same way on Mac and Linux, and is my go-to script to bootstrap environments in general.
The environment comes with a doctor.pyscript that checks that the installation is complete. Everything runs on M1 without explicitly invoking the Rosetta environment. Here is the relevant output:
ialbert@yolo ~
$ conda activate bioinfo
(bioinfo)
ialbert@yolo ~
$ uname -a
Darwin yolo.local 21.5.0 Darwin Kernel Version 21.5.0: Tue Apr 26 21:08:29 PDT 2022;
root:xnu-8020.121.3~4/RELEASE_ARM64_T8101 x86_64
(bioinfo)
ialbert@yolo ~
$ ~/bin/doctor.py
# Doctor! Doctor! Give me the news.
# Checking 15 symptoms...
# bwa ... OK
# datamash ... OK
# fastqc -h ... OK
# hisat2 ... OK
# seqret -h ... OK
# featureCounts ... OK
# efetch ... OK
# esearch ... OK
# samtools ... OK
# fastq-dump ... OK
# bowtie2 ... OK
# bcftools ... OK
# seqtk ... OK
# seqkit ... OK
# bio ... OK
# You are doing well!
The installation script and the doctor were all developed in the trenches, over many years, addressing the problems people actually had, while teaching the command line to people that never used the command line before. It is robust, battle-tested and it works.
M1 is the progress in technology that we all needed. The M1 is the huge leap forward, out of the rut of incrementally better Intel CPUs at increasingly ridiculous power use and cost. This is why Apple is now worth Google+Facebook+Amazon combined! They are producing the true innovation.
Deliberately avoiding the M1 for some silly reason, is like a caveman refusing to use the wheel because the old-style of pushing and dragging still "works" and they are all "used to it".
My "desktop" machine is a VM in a data centre and I use a Raspberry Pi 4B as a thin client to connect to it. I now use my laptop mostly for Zoom meetings and either as a thin client to connect to remote compute resources or for light tasks such as prototyping code on the go. You don't need much power for a thin client. A colleague of mine even uses his mobile phone for this.
Everyone has there own preference. Personally i'd choose something with 32GB of RAM, and not woory about the processor. A faster processor will determine how Long something takes. RAM will determine whether its possible to do something or not.
That said, by personal set up is an 4 year old laptop with a 6th generation i5 and 8gb of ram, and an 8 year old desktop with an (8 year old) quad core i7 and 32gb of ram. Which is more than enough to connect to the HPC cluster or cloud machines where i do heavy lifting, and then load the data back into r for plotting.
If the price is not an issue, which one would you suggest please, MacBook Pro Retina 16-inch (2019) - Core i9 - 32GB SSD 1024 QWERTY - or MacBook air M2 ?
Air M2 all day every day, unless you plan to use the computer, almost always, in the same place.
Can i have the extra money xD?
Get an M1 based Macbook Air - the best bioinformatics platform ever invented.
It will be faster than any other laptop you could ever get.
that little thin clamshell system, with no fan, running on a battery will be faster than a Linux machine with 1000W power draw over 16 cores and 3Ghz speeds (I know because I have one)
All you've given here is some performance benchmarks on an M1 notebook. How much actual bioinformatics software have you run? I bought an M1 mini recently and haven't had a lot of time to play with it. However, I can make some observations:
A lot of things will still require Rosetta, which runs Intel binaries by translating x86 instructions to ARM instructions. It will likely take several years before most bioinformatics applications have native versions versions for Macos-ARM. That depends on each 3rd party author to port the code.
How many programs have you re-compiled for macos-arm? I haven't had a chance to do more than dabble yet, but to make the case that macos-arm is "the best bioinformatics platform" you would have to demonstrate that you have found that code flawlessly compiles on the new system. Have you done that? So far I have gotten some fairly straightforward C++ programs to compile and run natively. Some did not immediately compile with existing Makefiles. At the very least, some packages will still have to have new Makefiles.
Python - apparently, python3 does not come pre-installed, which seems minor, but really? I ended up accidentally installing python when prompted during installation of another package, which was nice, but why not just have Python?
For those considering buying a Mac-mini or desktop, but using a pre-existing monitor, be aware that you would need to get an adaptor. I first bought a DVI to HDMI adaptor, which didn't work. Then I bought a DVI to USB-C adaptor which worked fine.
Monteray seems to lock down a lot of things. For example,
joe@mymac ~ % ls -l Downloads
total 0
ls: Downloads: Operation not permitted
jue@mymac ~ % ls -ld Downloads
drwx------+ 5 joe staff 160 Oct 20 18:12 Downloads
So Downloads (along with a number of other directories, is locked down. What does the + mean? After about 45 min. of searching I found articles describing the fact that Apple tries to idiot-proof some critical directories, which I get. But not letting me even get a directory listing of a directory I own? Anyway, the solution is to go into settings and, for any application, like terminal, you need explicitly allow access. enter link description here This was also a problem when I tried to run a custom backup script. For example tar cvfp /Users/joe should create a tar archive of joe's $HOME directory. Nope. tar exits when if fails to archive these locked down directories.
I am not saying that macos-arm won't at some point be a good platform for bioinformatics work that doesn't require HPC systems. What I am saying is that for some time to come, this platform will still require a bit of work.
My advice is buy a cheap used laptop, wipe windows an install Linux. Macs simply aren't worth the high price tag.
I am surprised and incredulous by this comment. Really?
Right now, I am teaching a bioinformatics class for 30+ students, and over half of them have brand-new M1 Macs. Most of them never used the command line.
None of reported any of the problems you mentioned.
I am also using M1 for all kinds of work - I have never encountered either a locked Downloads folder nor did I ever see a command that did not work. You don't even need to set up Rosetta anymore.
Then you later mention zsh being the default shell as being a problem ... ok, now I start to get it. Is that a problem even worth mentioning? Changing the default shell once and for all is just one command away:
chsh -s /bin/bash
All done. Or, even better, you could run your scripts with bash script.sh, which I would recommend in the first place as the proper practice.
Perhaps all the other problems have similarly simple solutions that are trivially easy to solve. I may have once turned on access to Downloads folders, it might have been a click someplace - but must be so inconsequential that I don't even remember doing it.
These are all simple setup problems. I guarantee that Linux will have many more setup problems than a Mac.
I have to say your advice at the end:
just get a used laptop, wipe it and install Linux
sounds completely disconnected from reality. You are recommending the OP to get a random, used laptop that may or may not even run Linux (!), with probably a shot battery and unknown past ... IMO, researching the right laptop, in addition to the work needed to set it up will far exceed the effort required to solve the issues you list as a deterrent from M1. Plus, you end up with a used laptop with a questionable past.
For $1100, you can get a MacBook Air with 16 GB RAM, which would go a long way in supporting a wide variety of needs.
Make no mistake, the MacBook air is highly performant, the CPU performance is extraordinary, and the overall build quality, convenience, and productivity you get from a built-in UNIX system are unmatched.
Consult the benchmarks scores, MacBook Air:
https://browser.geekbench.com/v5/cpu/18353874
Compare the above to a Dell Latitude 5520 (about the same cost)
https://browser.geekbench.com/v5/cpu/18346330
the $1100 MacBook Air is faster than the top-of-the-line Dell Latitude 7430 that costs about $2000
https://browser.geekbench.com/v5/cpu/18234384
This latter will be a noisy and hot beast with spinning fans, heating up, drawing power like crazy. MacBook Air does not even need a fan ...
The ARM-based Macs are the ultimate bioinformatics laptops.
I have shopped quite a bit at Dell in the past in an educational setting. You have a limited selection, and perhaps you get big discounts on things they want to get rid of. Not the case that you pick and choose what you want, then bam 50% off. I've never seen that happen.
If you go to the dell educational site right now, you see the following, you get up to $200 off, which is a weasel statement frankly, you don't even know how much you save, but not 50%
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Sorry this isn't a specific laptop recommendation, but I believe investing in RAM over GPU is a better idea. Having abundant RAM and never accidentally using swap memory that slow down processes loading lots of data into memory (many bioinformatic processes) in general seems like the way to go.
Curious what others think about the dedicated GPU question - I think bioinformatic applications that harness the GPU are more experimental for things like NVIDIA's parabricks and do not have lots of wide-spread application, as in writing something reliant on GPU might prevent others from using your tool. If people are regularly using applications dependent, or significantly sped up with a dedicated GPU, please let me know.
Thanks a lot for the help David
right now the emphasis and interest in GPUs mostly centers on running/using certain kinds of machine learning algorithms that can exploit them.
if by 'bioinformatics' you definitely mean to include machine learning applications, then probably youd want to balance the two. if however youll divide time between NGS data analysis, R, GSEA, large file parsing, etc. as well as ML applications, go with extra RAM.
Please clarify if you intend to use this laptop as your sole computer or you will have access to other compute resources (central IT/cloud etc) for doing the heavy lifting.
If $1000 is your budget then that is what you have to work with. Unless portability is a must, you may also want to look at a desktop solution (AMD CPU's are well priced) as well.
Certain discrete ("dedicated") GPUs will allow you to do machine learning. But you'll have to adjust your budget upwards by another couple grand and it will add to delivery time.