Here are some books, courses, and software you may find helpful on your bioinformatics journey Books to change your perspective For building a great career Cal Newport is a theoretical computer science professor, and his passion project for many years has been figuring of what makes people happy in their careers. He wrote a book called Deep Work: Rules for Focused Success in a Distracted World, that I found really resonated with me. The idea of deep work is something that I have applied a lot in my own work, and it really drove me to set better boundaries and protect some time every week and every day to actually get better at what I do, and not just put out email fires constantly. I also highly recommend reading Cal Newport’s first book on this topic, So Good They Can’t Ignore You: Why Skills Trump Passion in the Quest for Work You Love which describes how getting really damn good at what you do gives you a better life than just trying to “find your passion.” This then becomes the reason to pursue Deep Work for yourself because it’s the best way to build those skills that will buy you the power of control over your working life. I just zoomed through both these books in one weekend, and I find it easy to relate to Cal Newport when he talks about trying to succeed at graduate school and figuring out how to build a great career. This is a huge contrast to the way most people online talk about “finding your passion.” I find that I become passionate about something after getting really good at it and understanding it on a deeper level.
This certainly happened with bioinformatics, and it even happened on a smaller scale with multiple school projects. I gave a presentation to my climate change class in college about electric cars back in 2010, so I liked Tesla before it was cool 😉 #hipster. I’ve been crazy about electric cars ever since that project. I vowed that my next car would be electric, so since I’m still poor (no Tesla money yet!), I now drive a cute little nerdy-looking Nissan Leaf.
Anyway, you can build passion by learning and getting really good at things, and if you want to learn more about why deep work is more important than finding that elusive passion, go read Cal Newport’s books! Bioinformatics Courses These are some of the online courses I recommend you check out. Different people love and hate the same course, so feel free to skip around and just take a peak at what you find interesting. Bioinformatics is a huge field, so you don’t have to love all of its subfields, but this should give you a taste of a few different perspectives:. from Johns Hopkins on Coursera. Within that specialization I especially recommend Ben Langmead’s course using Python for working with DNA sequences. Ben is the creator of Bowtie who first figured out that the Burrows Wheeler Transform could be used to create an index of the genome for fast, efficient genome alignments, which made a big difference in the field.
For machine learning, on Coursera is a classic that I highly recommend. I went through this and did all of the programming assignments in MatLab because I had just finished Linear Algebra. If you are not completely comfortable with linear algebra, skip the programming assignments and just watch the lectures. For learning hard-core algorithms in bioinformatics, check out. Software A fantastic text editor that will color your code for multiple languages, run Python scripts, and let you manipulate data on multiple rows at once. It’s kind of magical. This neat little program (for Mac) is an upgrade for your clipboard, allowing you to copy multiple things and paste them again.
This is such a time-saver and lets you remember that piece of code you copied but accidentally copied over it with something else, not you can just go down the list and find it again. ITerm is a replacement for the terminal on Mac. I switched to it after some frustration with creating multiple tabs and windows in the terminal, and I have never gone back (except to make the first ) Recording setup: 1.
Audio: Blue Raspberry Microphone This sounds much clearer than my laptop’s or iPhone’s microphones. Video: my iPhone 6 It turns out iPhones have pretty decent cameras.
And I can plug the Blue Raspberry microphone straight into the iPhone. Tripod from AmazonBasics 4. Screen recording: QuickTime Player (comes with Mac computers) This works fantastically. There are paid screen recorders out there, but I haven’t had any problems just using QuickTime.
It’s simple, high-quality, and makes circles whenever you click, so viewers can see what you are doing. Video editing: iMovie (comes with Mac computers) Pretty easy to use, tons of tutorials out there. I only ran into one oddity to look out for: If you want a high-resolution video, the very first footage or photo you add to a movie must have that high resolution. For some reason it sets the maximum resolution by the first piece of media you add, even if you remove it later, and there is no way to change it without starting over. Now I just have to be careful to check that the resolution is high right after adding the first media to the project.
♦ 10k wrote: I am planning computing needs for our group for the 2-3 years to come and came across a difficult decision when having to decide which kind of computer to choose. I am thinking of a system with 100Go of RAM, Xeon class processors or equivalent (up to 16 threads). The thing should be able to boot under both Windows and Linux. I could go for a Lenovo ThinkStation or something of the kind, but I am evaluating the potential of getting a Mac Pro. EDIT: Depending on how we do things, I may end up buying two computers.
One computer would be used by lab members to perform routine analyses that they cannot run on their laptops and for which the standard desktops that we have available can't do the job because of too low RAM or processing power. As a result, the easiness of the user interface has some importance. The other would be used by me for my daily work. I'm not thinking about high end servers, rather something to serve as middle-ground between student laptops and weak lab desktops on one hand and servers and super-computers on the other end. DISCLAIMER: I am a Linux lover and am very sceptical about Macs. On the other side, I am also very open to seeing what advantages there are to Macs. Here is where you come in.
I would really like to have your opinion about the following:. Are you using a Mac as a main computer for bioinformatic work?. What positive points do you see about using a mac for bioinformatics?. What negative points do you see about using a mac for bioinformatics?.
Ease of installing software (talking about bioinfo software here)?. Freedom to hack quick solutions for custom problems?. Whatever opinion you want to share about this question. Why should I (or not) think of getting Macs for our lab? My goal is definitely NOT to turn this into a flame war. It would be nice if you could keep it about facts. (Please also go to and vote to say what operating system you are using for bioinformatics.).
I think some people are confused about what you want an answer for. Are you asking whether or not Macs are a good buy for 'middle tier' workstations that users will have on their desktops?
Or are you asking if a mac is a good choice for a (single) compute server that will service the needs of the people in your lab when they don't want (can't) get access to a larger (presumably linux) compute cluster? I think you are asking for recommendations on the latter, but it seems like some answers are addressing the former. If it is a multi-user system the interface shouldn't matter. It is probably best to set it up so that users remote connect to their accounts and run jobs in the same way they would a server or computer cluster, but probably without the job scheduler.
Although that may be a good idea just for management depending on the number of users. Otherwise they should be trained in making sure there are no other big analyses running that theirs would interfere with, to talk to other lab members before running jobs that will run for a very long time, etc. ♦ 6.9k wrote: I used Mac for 5 years (IT only allowed Windows or Mac for researchers, I hate windows), it was the first time I was using Mac as my main OS.
I found really easy to work everyday with it, but my Mac was used only to code, write papers and do som basic R plots/analysis. The real job was performed in several Linux clusters with high scalability, sometimes I prepare and tested my pipelines in the Mac, but never run a full work (occasionally it had a hard drive fail twice, TimeMachine was my salvation). If you are thinking in buying a high-end server for bioinformatics, you have no choice: Linux.
1 I have to agree with this. I was a linux and windows user until 2 years agoand only used my macbook for travel and home entertainment. But, now I have switched over in the office and am very happy with my mac pro for office productivity, analysis and interpretation work (IGV, browsers, etc) R coding, some light software/database development, web development. I also use it for simple tests of many informatics tools and for developing educational materials.
Its great because it is a jack of all trades. But, for any kind of actual data processing (if you have NGS data) you will have to go onto a linux server. Installing bioinformatics software seems to have gotten dramatically better on mac in recent years.
Basic tools like, tuxedo suite, etc install without problems. But, you will still run into the occasional mac specific issues.
♦ 31k wrote: I do not have a desktop. I do most things, including entertainment, daily work and coding of course, on my mac laptop. I think mac achieves a good balance between a sophisticated unix interface and ease of use. However, as you are using Mac as a workstation but not its user interface, you will not benefit from Mac. I would recommend Linux instead. Stata 13 mac serial number.
Firstly, rpm/deb is still more convenient and more robust, IMO, than macports and homebrew. Secondly, a few tools are harder to compile and install on Mac. If someone have not ported such tools to Mac, you will have a hard time.
Thirdly, I more like the development tools on Linux, most importantly valgrind and linux perf. For these reasons, I still have a linux in VM on my mac laptop. ♦ 15k wrote: Are you using a Mac as a main computer for bioinformatic work? For running tasks, I connect to a server which is not a mac. For local use where most jobs are not extremely intensive, I use a linux laptop. However, I can easily use my personal macbook pro and actually prefer it.
What positive points do you see about using a mac for bioinformatics? Most linux packages should work on a mac.
OSX is actually a pretty nice OS. There are software on osx that isn't on linux or hard to install on linux (Adobe illustrator for example). What negative points do you see about using a mac for bioinformatics? Installing packages can be a pain if you have never done it. Ease of installing software (talking about bioinfo software here)? Central repositories for installing stuff seems to be very transient.
I think right now the popular option is using Homebrew. But it seems like new installation software comes up every couple of years. Freedom to hack quick solutions for custom problems? You can still run your python/perl/whatever scripts with no problem. So depends on what you mean by hacking I guess. Whatever opinion you want to share about this question I would not use a mac for your middle - heavy server needs. But I would recommend a mac as a portable laptop for light work loads.
Why should I (or not) think of getting Macs for our lab? Don't get it as the server for your lab. I think it's only really good as a personal laptop. The Mac Pro might turn out great, but I would hold off on it and wait for more user testing.
1.8k wrote: I regularly use my Macbook Pro for running quick one-off tasks, as others mentioned for any serious work I still connect to a linux box. Ease of installing software (talking about bioinfo software here)? Try, it gives you easy access to a huge number of software. It now has a special section for scientific software.
You gain access to this section by: brew tap homebrew/science For a complete list of software under this section see. Installing software then becomes as simple as: brew install samtools Finally contributing new 'formulas' is very easy and well documented.
Give it a try if the software you are interested is not there. ♦ 26k wrote: Are you using a Mac as a main computer for bioinformatic work? I use a few Macs for script and software development. I use the lab's Linux cluster for launching larger analyses. What positive points do you see about using a mac for bioinformatics?
Unlike most handbuilt Linux desktop options, the hardware and software work well together, so I don't constantly need to be a sys admin like I did with Linux. Wireless networking just works. Plugging in a laptop to do presentations just works. Power management settings just work. The OS X operating system is less fragile to kernel and hardware changes and is easier to fix on my own, when such rare problems arise. OS X can run useful (if still commercial) visualization and productivity tools with easy access to a UNIX terminal, which Linux still cannot do (generally speaking).
Many native GUI and command-line tools are available for doing source code control on OS X, so I don't lose any abilities there. What negative points do you see about using a mac for bioinformatics? A lot of open source gets distributed as RPM packages or similar, while package systems for OS X are a bit less friendly, in that regard. Some open source GUI apps are X11 based, which not only makes them look ugly but introduces a whole separate set of key shortcuts to learn, just for Linux-y apps. This also makes integration difficult — copying and pasting between native OS X apps is a nice feature, but is difficult between native apps and X11-ized apps. If you're running a computational cluster, then Linux is a winner here, for the simple fact that the stripped-down hardware you need for that application is cheaper per node. You don't need a Mac Pro's super-duper graphics adapters, for instance, unless you are running custom analyses on fancy GPUs.
On the other hand, now that Thunderbolt shows up as an Ethernet adapter in Mavericks, I'd be interested to see if it gets used as a very fast interconnect for cluster work (a cheaper alternative to Infiniband, for instance). Ease of installing software (talking about bioinfo software here)? Depends on the software. Most of it can be compiled easily but you'll need to install Xcode, at a minimum, which used to be a PITA to download (used to need an Apple Developer Center account and navigate Apple's ADC site maze) but it is now pretty easy (OS X App Store + Apple ID + one or two clicks). Just about everything else that you can't compile on your own is something you can manage with macports or homebrew. On the subject of macports and homebrew, pick one or the other, not both. They will step on each others toes as far as managing dependencies.
I prefer macports, but I suspect it is largely a matter of how much you like (or dislike) Ruby, if you want to try homebrew. Freedom to hack quick solutions for custom problems? Decently modern versions of Ruby, Python, Perl etc. Come pre-installed. So long as your sys admin has given your account admin rights on your Mac, you can compile and install whatever else you need through Apple's Xcode/clang, GNU's gcc and macports or homebrew. Whatever opinion you want to share about this question As a political matter, I love Linux's openness and customizability, but as a daily-use operating system, its interface just isn't very well engineered (just one person's opinion) and I suspect that no amount of catch-up work to imitate commercial operating systems will make that aspect of it as easy and reliable to use as OS X. Tools which other staff use for writing and managing papers (Word, Endnote, etc.) just aren't available, and open source and online 'equivalents' do not quite make the grade, IMO.
Managing document citations in a shared Google Doc is simply a non-starter, for instance. Of course, I'm less amenable to making compromises because I'm a pretty lazy human being. If your time is free and you love the OSS movement and you're willing to put in the work, you can do a lot of these things in Linux. Why should I (or not) think of getting Macs for our lab? I suspect it will reduce your administration costs, at the up-front expense of slightly more expensive hardware and the cost of commercial software. You'd need to balance that against the productivity gains from having your staff be able to collaborate together and share data more effectively.
Compared with Linux, this is probably a plus mark in favor of OS X and Windows, generally speaking. On the other hand, you're going to spend a bit more up front. Six of one, half dozen of another. You'll need to decide what your priorities are with this investment, I suspect. 340 wrote:. Yes I do, and I can use it for some heavier lifting as I have 32Gs of RAM and a new iMAC. I like Pages or Word; I do use LaTex but not everyone I work with does, so when I need to use a simpler text editor I prefer Pages or Word to LibreOffice.
Also, you can do non-analysis type stuff much easier on a Mac. It's got decent customer support. You will pay more to get less in terms of hardware. I don't like the inactive memory, it used to ballon out of control until I figure out how to manage it. Also, maybe because of how I use it, I find the OS takes up a fair bit of the memory and compute power because of all it's little optimizations it tries to do, and sometimes I find this annoying for parallelization.
The fact that Macs use bashprofile as opposed to bashrc will cause some minor annoyances until you fix it. I have found it to be pretty easy.
I needed to install both XCode and homebrew, however, and once homebrew was installed it was easy to get any other libraries I needed to install software. Also, if someone is new to linux environments, MACs allow for nice baby steps.
I think so. I would get MACs if you have a very diverse lab (people with different skill sets), if you want the computers to do more than just analysis, and if you have money. They're pretty user friendly, the Linux backend allows you do to what you would normally do on a Linux system (with some differences) if you're an advance user and it will hold your hand if you're not. But you will pay more for less, and in terms on analysis I find there isn't anything I can do on a Mac that I can't do on a Linux machine. I also find Ubuntu, while not quite the same as a OS 10.9, to be pretty good at hand holding too. ♦ 3.3k wrote: I am someone who used OSX for my main work laptop since 2009, while getting other work done in parallel on Linux systems in the lab. A little less than a year ago I was faced with the cost of upgrading my dated Mac and buying a new one, or going entirely with Linux.
Looking at the price gap between the closed environment of Mac vs Linux, I made the decision to go with a Thinkpad into which I shoved extra RAM and put on my favorite Linux distro. There have been some definite pluses and minuses, and I do not think there is one easy answer here. On the plus side, all of the development issues that I had working in the Mac environment have gone away. Although Perl, Python, Ruby, and R are all found on Mac, I never was able to install catalyst, some Ruby gems wouldn't install properly (I know this sounds vague.but boy, catalyst was a time sink getting it to run on a Mac.as in, it never worked.), and bioconductor, I gave up trying to install it on my mac in 2010 or so.
(although others here may have a different experience.partly is my laziness about upgrading my version of OSX). But now on Linux I can do both tool development and data analysis on the same laptop, and I am quite happy with that. For years on my Mac I used open-office/Libreoffice, and zotero for reference management for writing scientific papers, so that hasn't changed. You do need to plan well when contributing to papers with non-open source software users, but I just convert whatever to.doc before sending it to collaborators. I've been very happy though with this system, and I don't think my collaborators can tell the difference. There is no Photoshop that will run on Linux, and the gimp is.just.different when it comes to making publication-ready images.
So that part is a bit rocky and a learning curve, but not too bad. Now for the downsides since I made the switch - Java apps can be a pain with Linux. For example, just today I spent too much time trying to get compatibility with a common web-based screen-sharing video conferencing tool.and ended up jumping over to my old Mac to get the job done. Adobe reader just won't work on my distro either. Also, wifi connectivity is a real downside, as in, this laptop with this Linux distro just cannot find public wifi reliably.
I took the Linux laptop to a recent conference, and was not able to connect anywhere, while my Mac would have done the job nicely. That is a big downside, although I never have that problem when I stick to work or home. These are the user interface downsides Alex Reynolds points out too.
Plugging that Linux laptop in to a vga projector to give a talk is always an exercise in anxiety.will it find the display? In OSX these things do just work.although you pay more for it.
Yet, the bottom line is whenever I want to comfortably get any work done at the command-line or ssh into anything, or run something in R, I'm very happy I have the Linux laptop, since all of THAT just works, and it wasn't reliably my experience on a Mac. Why not find a way to use both? ♦ 4.9k wrote: I think I'm echoing what Dan Gaston, JC, Heng, and perhaps others, are saying here, but to be very blunt: If you have the money to buy two machines, I'd say you should buy yourself a nice macbook (pro or air) for your own work and buy an even nicer 'server/HPC grade' linux machine for the lab. I've been doing all of my work since forever on macs and have relied on big-iron linux machines for things that need a lot of wall time (short read alignment, variant calling, etc). Like JC said, prepping your pipeline code on the mac and getting it to run on your big-iron-linux machine is rather straightforward. 2 I haven't really run into these problems, but then again I don't really use sed or awk much - most all of my text munging is done in another scripting language (either Python or R).
Like Alex suggested, GNU versions of these userland tools are also available to you by either compiling them yourself, or (likely preferably) through a package manager. As far as package managers go, homebrew (not macports anymore (sorry Alex;-)) seems to be 'the chosen one' these days, especially after you add the science 'tap' via brew tap homebrew/science, as suggested by Carlos B. ♦ 45k wrote: As a Server I think it is maybe the hardware that is the limiting factor here, because the MacPro is more like a powerful graphics workstation than a number-crunching - server. I think it is good to use if you want that kind of stylish workstation to put under or on your desk as it is (is it loud?), and do some video editing, 3/4D rendering and protein modeling, and other graphically demanding visualizations, but following I have my doubts about its usefulness as a general a bioinformatics server. 'Configurable to 32GB (four 8GB) or 64GB (four 16GB)', that's less than your required 100Gig. Possibly overpowered graphics if you don't need it.
If you want to use GPU-based acceleration make sure that the HW and OS are supported by the software you want to use. It doesn't fit in a rack (obviously), but you might have to have a rack full of other equipment (storage, backup, UPS, all other machines). Its form factor doesn't seem to stack well. Your tech department might not be amused over this either, if you want to put it in the common server room.
Internal storage is max 1TB. Are you prepared to buy and attach external thunderbolt storage?
Or can it be integrated with your existing storage and backup solution, or are you prepared to build that up from scratch (see also point 3)?. Is it compatible with the cluster solution you have to use?. Is all the software you want to use available or can it be compiled? (That's the primary question for any OS). The price (if you end up installing Linux on it then it will just be a very expensive linux box) As a Laptop. I need stuff that just works, I don't want to waste time and money on figuring out that the power management or network doesn't work with this specific chip in the latest laptop I bought, but windows is not an option. ♦ 7.1k wrote: I'll echo what a lot of people seem to be saying and just iterate from my own experience: Are you using a Mac as a main computer for bioinformatic work?
I have two computers at my desk. One is a large workstation (theoretically multi-user) but I am the only bioinformatician on our project, so it is all mine. 50GB of RAM, 8 cores, a few TB of storage. That does all the heavy lifting tasks (mapping of exome sequencing data, variant calling, analyses, etc). But I also have a Macnbook air in front of me that I am using constantly. That's where I do all my writing, most of my coding, my research, and actually look at most of the data. I generate reports intended for bench molecular biologists and clinicians so having access to Microsoft office products is pretty key for ease of use within the team.
And I still say those programs are far better than LibreOffice, etc. What positive points do you see about using a mac for bioinformatics? It's a UNIX system. It has a few quirks (but so do all unix/linux distros) but you have the command line, you can build software from source, and most people make OSX binaries. While I am very comfortable in a Linux environment the fact that OSX largely 'just works' and doesn't require much fiddling and tinkering leaves me more time to actually do research. What negative points do you see about using a mac for bioinformatics?
For heavy lifting computation the equivalent machines do tend to be far more expensive than a Linux workstation. Sometimes you run in to some problems getting some software compiled and built for the OS, but I find that pretty rare. Ease of installing software (talking about bioinfo software here)? Usually just as easy or easier than on Linux. Vastly superior to Windows.
Freedom to hack quick solutions for custom problems? Again, about the same as Linux, way easier than Windows.
Can still do all the same scripting, shell work, etc than I can on any UNIX system. Why should I (or not) think of getting Macs for our lab? I wouldn't get it for your workhorse workstation/server needs. A Mac Pro is way more expensive than the equivalent workstation.
I wouldn't even go with a name brand solution myself. Find one of your local computer builders who deal with enterprise solution and see what they can do for you. We have done that for our needs in the past and they were cheaper and way better service. On-site service with long warranties for no additional fees, etc. If you can;t find anywhere good then go towards an IBM, Dell, etc.
They tend to be easy to deal with as well.
Homebrew-bioinformatics-linux The too hard basket for Linux bioinformatics brew formulae! Is the software packaging system for Mac OS X that Apple should have written. Is a fork of Homebrew for Linux.
One of the best features of Brew is that a user without root privileges can install packages in their own home directory. Packages are described by formulae which are simple to write, and based on Ruby. Formulae are kept in taps. There are lots of good taps.
The one I use a lot is. However to get into an offical tap like that one, a tool needs to be capable of being compiled and/or run on Mac OS X. I use Linuxbrew for my servers, and some of our tools are too hard to compile for OS X or simply only exist as Linux binaries. So that's why I started this project. If you have brew installed, you can enable this repository like this: brew tap tseemann/homebrew-bioinformatics-linux You will also need the main homebrew-science tap: brew tap homebrew/science Then you can install any formulae I have written, for example: brew install blast-legacy brew install figtree If you are on OS X, some of these formulae may work, but some won't. I'm happy to accept pull requests for new packages, but if it would not take too much effort to work on OS X too, then I will suggest you contribute it to instead.
This repository is really just a 'too hard basket' for Linux bioinformatics tools. But, if possible, I would like formulae here to ultimately be migrated to.
♦ 10k wrote: I am planning computing needs for our group for the 2-3 years to come and came across a difficult decision when having to decide which kind of computer to choose. I am thinking of a system with 100Go of RAM, Xeon class processors or equivalent (up to 16 threads). The thing should be able to boot under both Windows and Linux. I could go for a Lenovo ThinkStation or something of the kind, but I am evaluating the potential of getting a Mac Pro. EDIT: Depending on how we do things, I may end up buying two computers. One computer would be used by lab members to perform routine analyses that they cannot run on their laptops and for which the standard desktops that we have available can't do the job because of too low RAM or processing power.
Finance Software For Mac
![Bioinformatics Bioinformatics](/uploads/1/2/3/9/123954941/629591285.jpg)
As a result, the easiness of the user interface has some importance. The other would be used by me for my daily work. I'm not thinking about high end servers, rather something to serve as middle-ground between student laptops and weak lab desktops on one hand and servers and super-computers on the other end. DISCLAIMER: I am a Linux lover and am very sceptical about Macs. On the other side, I am also very open to seeing what advantages there are to Macs. Here is where you come in.
I would really like to have your opinion about the following:. Are you using a Mac as a main computer for bioinformatic work?. What positive points do you see about using a mac for bioinformatics?. What negative points do you see about using a mac for bioinformatics?. Ease of installing software (talking about bioinfo software here)?. Freedom to hack quick solutions for custom problems?. Whatever opinion you want to share about this question.
Why should I (or not) think of getting Macs for our lab? My goal is definitely NOT to turn this into a flame war. It would be nice if you could keep it about facts. (Please also go to and vote to say what operating system you are using for bioinformatics.).
I think some people are confused about what you want an answer for. Are you asking whether or not Macs are a good buy for 'middle tier' workstations that users will have on their desktops? Or are you asking if a mac is a good choice for a (single) compute server that will service the needs of the people in your lab when they don't want (can't) get access to a larger (presumably linux) compute cluster? I think you are asking for recommendations on the latter, but it seems like some answers are addressing the former. If it is a multi-user system the interface shouldn't matter. It is probably best to set it up so that users remote connect to their accounts and run jobs in the same way they would a server or computer cluster, but probably without the job scheduler. Although that may be a good idea just for management depending on the number of users.
Otherwise they should be trained in making sure there are no other big analyses running that theirs would interfere with, to talk to other lab members before running jobs that will run for a very long time, etc. ♦ 6.9k wrote: I used Mac for 5 years (IT only allowed Windows or Mac for researchers, I hate windows), it was the first time I was using Mac as my main OS. I found really easy to work everyday with it, but my Mac was used only to code, write papers and do som basic R plots/analysis.
The real job was performed in several Linux clusters with high scalability, sometimes I prepare and tested my pipelines in the Mac, but never run a full work (occasionally it had a hard drive fail twice, TimeMachine was my salvation). If you are thinking in buying a high-end server for bioinformatics, you have no choice: Linux. 1 I have to agree with this. I was a linux and windows user until 2 years agoand only used my macbook for travel and home entertainment. But, now I have switched over in the office and am very happy with my mac pro for office productivity, analysis and interpretation work (IGV, browsers, etc) R coding, some light software/database development, web development.
I also use it for simple tests of many informatics tools and for developing educational materials. Its great because it is a jack of all trades. But, for any kind of actual data processing (if you have NGS data) you will have to go onto a linux server. Installing bioinformatics software seems to have gotten dramatically better on mac in recent years. Basic tools like, tuxedo suite, etc install without problems. But, you will still run into the occasional mac specific issues.
♦ 31k wrote: I do not have a desktop. I do most things, including entertainment, daily work and coding of course, on my mac laptop. I think mac achieves a good balance between a sophisticated unix interface and ease of use.
However, as you are using Mac as a workstation but not its user interface, you will not benefit from Mac. I would recommend Linux instead. Firstly, rpm/deb is still more convenient and more robust, IMO, than macports and homebrew. Secondly, a few tools are harder to compile and install on Mac.
If someone have not ported such tools to Mac, you will have a hard time. Thirdly, I more like the development tools on Linux, most importantly valgrind and linux perf. For these reasons, I still have a linux in VM on my mac laptop. ♦ 15k wrote: Are you using a Mac as a main computer for bioinformatic work?
For running tasks, I connect to a server which is not a mac. For local use where most jobs are not extremely intensive, I use a linux laptop. However, I can easily use my personal macbook pro and actually prefer it. What positive points do you see about using a mac for bioinformatics? Most linux packages should work on a mac.
OSX is actually a pretty nice OS. There are software on osx that isn't on linux or hard to install on linux (Adobe illustrator for example). What negative points do you see about using a mac for bioinformatics? Installing packages can be a pain if you have never done it. Ease of installing software (talking about bioinfo software here)? Central repositories for installing stuff seems to be very transient.
I think right now the popular option is using Homebrew. But it seems like new installation software comes up every couple of years. Freedom to hack quick solutions for custom problems? You can still run your python/perl/whatever scripts with no problem. So depends on what you mean by hacking I guess. Whatever opinion you want to share about this question I would not use a mac for your middle - heavy server needs.
But I would recommend a mac as a portable laptop for light work loads. Why should I (or not) think of getting Macs for our lab? Don't get it as the server for your lab. I think it's only really good as a personal laptop. The Mac Pro might turn out great, but I would hold off on it and wait for more user testing.
1.8k wrote: I regularly use my Macbook Pro for running quick one-off tasks, as others mentioned for any serious work I still connect to a linux box. Ease of installing software (talking about bioinfo software here)?
Try, it gives you easy access to a huge number of software. It now has a special section for scientific software. You gain access to this section by: brew tap homebrew/science For a complete list of software under this section see. Installing software then becomes as simple as: brew install samtools Finally contributing new 'formulas' is very easy and well documented. Give it a try if the software you are interested is not there. ♦ 26k wrote: Are you using a Mac as a main computer for bioinformatic work? I use a few Macs for script and software development.
I use the lab's Linux cluster for launching larger analyses. What positive points do you see about using a mac for bioinformatics?
Unlike most handbuilt Linux desktop options, the hardware and software work well together, so I don't constantly need to be a sys admin like I did with Linux. Wireless networking just works. Plugging in a laptop to do presentations just works. Power management settings just work. The OS X operating system is less fragile to kernel and hardware changes and is easier to fix on my own, when such rare problems arise. OS X can run useful (if still commercial) visualization and productivity tools with easy access to a UNIX terminal, which Linux still cannot do (generally speaking). Many native GUI and command-line tools are available for doing source code control on OS X, so I don't lose any abilities there.
What negative points do you see about using a mac for bioinformatics? A lot of open source gets distributed as RPM packages or similar, while package systems for OS X are a bit less friendly, in that regard. Some open source GUI apps are X11 based, which not only makes them look ugly but introduces a whole separate set of key shortcuts to learn, just for Linux-y apps. This also makes integration difficult — copying and pasting between native OS X apps is a nice feature, but is difficult between native apps and X11-ized apps.
If you're running a computational cluster, then Linux is a winner here, for the simple fact that the stripped-down hardware you need for that application is cheaper per node. You don't need a Mac Pro's super-duper graphics adapters, for instance, unless you are running custom analyses on fancy GPUs. On the other hand, now that Thunderbolt shows up as an Ethernet adapter in Mavericks, I'd be interested to see if it gets used as a very fast interconnect for cluster work (a cheaper alternative to Infiniband, for instance).
Ease of installing software (talking about bioinfo software here)? Depends on the software. Most of it can be compiled easily but you'll need to install Xcode, at a minimum, which used to be a PITA to download (used to need an Apple Developer Center account and navigate Apple's ADC site maze) but it is now pretty easy (OS X App Store + Apple ID + one or two clicks). Just about everything else that you can't compile on your own is something you can manage with macports or homebrew. On the subject of macports and homebrew, pick one or the other, not both. They will step on each others toes as far as managing dependencies.
I prefer macports, but I suspect it is largely a matter of how much you like (or dislike) Ruby, if you want to try homebrew. Freedom to hack quick solutions for custom problems? Decently modern versions of Ruby, Python, Perl etc. Come pre-installed. So long as your sys admin has given your account admin rights on your Mac, you can compile and install whatever else you need through Apple's Xcode/clang, GNU's gcc and macports or homebrew. Whatever opinion you want to share about this question As a political matter, I love Linux's openness and customizability, but as a daily-use operating system, its interface just isn't very well engineered (just one person's opinion) and I suspect that no amount of catch-up work to imitate commercial operating systems will make that aspect of it as easy and reliable to use as OS X. Tools which other staff use for writing and managing papers (Word, Endnote, etc.) just aren't available, and open source and online 'equivalents' do not quite make the grade, IMO.
Managing document citations in a shared Google Doc is simply a non-starter, for instance. Of course, I'm less amenable to making compromises because I'm a pretty lazy human being. If your time is free and you love the OSS movement and you're willing to put in the work, you can do a lot of these things in Linux. Why should I (or not) think of getting Macs for our lab?
I suspect it will reduce your administration costs, at the up-front expense of slightly more expensive hardware and the cost of commercial software. You'd need to balance that against the productivity gains from having your staff be able to collaborate together and share data more effectively. Compared with Linux, this is probably a plus mark in favor of OS X and Windows, generally speaking. On the other hand, you're going to spend a bit more up front. Six of one, half dozen of another. You'll need to decide what your priorities are with this investment, I suspect.
340 wrote:. Yes I do, and I can use it for some heavier lifting as I have 32Gs of RAM and a new iMAC. I like Pages or Word; I do use LaTex but not everyone I work with does, so when I need to use a simpler text editor I prefer Pages or Word to LibreOffice. Also, you can do non-analysis type stuff much easier on a Mac. It's got decent customer support.
You will pay more to get less in terms of hardware. I don't like the inactive memory, it used to ballon out of control until I figure out how to manage it. Also, maybe because of how I use it, I find the OS takes up a fair bit of the memory and compute power because of all it's little optimizations it tries to do, and sometimes I find this annoying for parallelization.
The fact that Macs use bashprofile as opposed to bashrc will cause some minor annoyances until you fix it. I have found it to be pretty easy.
I needed to install both XCode and homebrew, however, and once homebrew was installed it was easy to get any other libraries I needed to install software. Also, if someone is new to linux environments, MACs allow for nice baby steps. I think so. I would get MACs if you have a very diverse lab (people with different skill sets), if you want the computers to do more than just analysis, and if you have money. They're pretty user friendly, the Linux backend allows you do to what you would normally do on a Linux system (with some differences) if you're an advance user and it will hold your hand if you're not.
But you will pay more for less, and in terms on analysis I find there isn't anything I can do on a Mac that I can't do on a Linux machine. I also find Ubuntu, while not quite the same as a OS 10.9, to be pretty good at hand holding too. ♦ 3.3k wrote: I am someone who used OSX for my main work laptop since 2009, while getting other work done in parallel on Linux systems in the lab. A little less than a year ago I was faced with the cost of upgrading my dated Mac and buying a new one, or going entirely with Linux. Looking at the price gap between the closed environment of Mac vs Linux, I made the decision to go with a Thinkpad into which I shoved extra RAM and put on my favorite Linux distro. There have been some definite pluses and minuses, and I do not think there is one easy answer here. On the plus side, all of the development issues that I had working in the Mac environment have gone away.
Although Perl, Python, Ruby, and R are all found on Mac, I never was able to install catalyst, some Ruby gems wouldn't install properly (I know this sounds vague.but boy, catalyst was a time sink getting it to run on a Mac.as in, it never worked.), and bioconductor, I gave up trying to install it on my mac in 2010 or so. (although others here may have a different experience.partly is my laziness about upgrading my version of OSX). But now on Linux I can do both tool development and data analysis on the same laptop, and I am quite happy with that. For years on my Mac I used open-office/Libreoffice, and zotero for reference management for writing scientific papers, so that hasn't changed. You do need to plan well when contributing to papers with non-open source software users, but I just convert whatever to.doc before sending it to collaborators. I've been very happy though with this system, and I don't think my collaborators can tell the difference.
There is no Photoshop that will run on Linux, and the gimp is.just.different when it comes to making publication-ready images. So that part is a bit rocky and a learning curve, but not too bad.
Now for the downsides since I made the switch - Java apps can be a pain with Linux. For example, just today I spent too much time trying to get compatibility with a common web-based screen-sharing video conferencing tool.and ended up jumping over to my old Mac to get the job done. Adobe reader just won't work on my distro either. Also, wifi connectivity is a real downside, as in, this laptop with this Linux distro just cannot find public wifi reliably.
I took the Linux laptop to a recent conference, and was not able to connect anywhere, while my Mac would have done the job nicely. That is a big downside, although I never have that problem when I stick to work or home. These are the user interface downsides Alex Reynolds points out too.
Plugging that Linux laptop in to a vga projector to give a talk is always an exercise in anxiety.will it find the display? In OSX these things do just work.although you pay more for it. Yet, the bottom line is whenever I want to comfortably get any work done at the command-line or ssh into anything, or run something in R, I'm very happy I have the Linux laptop, since all of THAT just works, and it wasn't reliably my experience on a Mac. Why not find a way to use both? ♦ 4.9k wrote: I think I'm echoing what Dan Gaston, JC, Heng, and perhaps others, are saying here, but to be very blunt: If you have the money to buy two machines, I'd say you should buy yourself a nice macbook (pro or air) for your own work and buy an even nicer 'server/HPC grade' linux machine for the lab. I've been doing all of my work since forever on macs and have relied on big-iron linux machines for things that need a lot of wall time (short read alignment, variant calling, etc). Like JC said, prepping your pipeline code on the mac and getting it to run on your big-iron-linux machine is rather straightforward.
2 I haven't really run into these problems, but then again I don't really use sed or awk much - most all of my text munging is done in another scripting language (either Python or R). Like Alex suggested, GNU versions of these userland tools are also available to you by either compiling them yourself, or (likely preferably) through a package manager. As far as package managers go, homebrew (not macports anymore (sorry Alex;-)) seems to be 'the chosen one' these days, especially after you add the science 'tap' via brew tap homebrew/science, as suggested by Carlos B. ♦ 45k wrote: As a Server I think it is maybe the hardware that is the limiting factor here, because the MacPro is more like a powerful graphics workstation than a number-crunching - server.
I think it is good to use if you want that kind of stylish workstation to put under or on your desk as it is (is it loud?), and do some video editing, 3/4D rendering and protein modeling, and other graphically demanding visualizations, but following I have my doubts about its usefulness as a general a bioinformatics server. 'Configurable to 32GB (four 8GB) or 64GB (four 16GB)', that's less than your required 100Gig.
Possibly overpowered graphics if you don't need it. If you want to use GPU-based acceleration make sure that the HW and OS are supported by the software you want to use. It doesn't fit in a rack (obviously), but you might have to have a rack full of other equipment (storage, backup, UPS, all other machines). Its form factor doesn't seem to stack well.
Your tech department might not be amused over this either, if you want to put it in the common server room. Internal storage is max 1TB. Are you prepared to buy and attach external thunderbolt storage? Or can it be integrated with your existing storage and backup solution, or are you prepared to build that up from scratch (see also point 3)?. Is it compatible with the cluster solution you have to use?. Is all the software you want to use available or can it be compiled? (That's the primary question for any OS).
The price (if you end up installing Linux on it then it will just be a very expensive linux box) As a Laptop. I need stuff that just works, I don't want to waste time and money on figuring out that the power management or network doesn't work with this specific chip in the latest laptop I bought, but windows is not an option.
♦ 7.1k wrote: I'll echo what a lot of people seem to be saying and just iterate from my own experience: Are you using a Mac as a main computer for bioinformatic work? I have two computers at my desk. One is a large workstation (theoretically multi-user) but I am the only bioinformatician on our project, so it is all mine. 50GB of RAM, 8 cores, a few TB of storage. That does all the heavy lifting tasks (mapping of exome sequencing data, variant calling, analyses, etc).
But I also have a Macnbook air in front of me that I am using constantly. That's where I do all my writing, most of my coding, my research, and actually look at most of the data. I generate reports intended for bench molecular biologists and clinicians so having access to Microsoft office products is pretty key for ease of use within the team.
And I still say those programs are far better than LibreOffice, etc. What positive points do you see about using a mac for bioinformatics? It's a UNIX system. It has a few quirks (but so do all unix/linux distros) but you have the command line, you can build software from source, and most people make OSX binaries. While I am very comfortable in a Linux environment the fact that OSX largely 'just works' and doesn't require much fiddling and tinkering leaves me more time to actually do research. What negative points do you see about using a mac for bioinformatics?
For heavy lifting computation the equivalent machines do tend to be far more expensive than a Linux workstation. Sometimes you run in to some problems getting some software compiled and built for the OS, but I find that pretty rare. Ease of installing software (talking about bioinfo software here)? Usually just as easy or easier than on Linux.
Vastly superior to Windows. Freedom to hack quick solutions for custom problems?
Again, about the same as Linux, way easier than Windows. Can still do all the same scripting, shell work, etc than I can on any UNIX system. Why should I (or not) think of getting Macs for our lab? I wouldn't get it for your workhorse workstation/server needs. A Mac Pro is way more expensive than the equivalent workstation.
I wouldn't even go with a name brand solution myself. Find one of your local computer builders who deal with enterprise solution and see what they can do for you. We have done that for our needs in the past and they were cheaper and way better service. On-site service with long warranties for no additional fees, etc. If you can;t find anywhere good then go towards an IBM, Dell, etc.
They tend to be easy to deal with as well.
Kind of a loaded question. Bioinformatics analysis can range from just using existing software to developing your own software. So depending what you want to accomplish will result in a different answer. However, Linux is probably your best bet because the majority of bioinformatics software needs to be compiled from source, and was likely developed on a Linux compatible platform. My current setup is a Windows OS with Linux running in. This way I get good hardware support (which Linux still somewhat lacks) and the ability to use a plethora of software programs I need for my research.
For bioinfo, Linux is good, but it's likely that you could need some software to do data analysis. For example, in my case I had to improve vector graphics images produced via a script by means of adobe illustrator. In that case, a full linux setup would have been bad for me.
For servers and running programs (mostly perl script, as the bioinfo community likes perl) I strongly endorse Linux. Any other Unix is either obsolete or is going to be very soon. Therefore, my suggestion is Linux on the server, and another Unix on your laptop, so to have better compatibility. Since the only Unix solution nice for desktop is MacOSX, that's the answer to your question.
I've noticed a big shift to MacBooks recently. At Conferences recently MacBooks have overtaken linux or windows laptops. The advantage of a mac laptop is that you can do all the linux stuff on the laptop in the shell using fink or similar if by chance some package isn't available. And you can actually use the laptop to do other non-core work stuff. Windows applications are there using Parallels or VMFusion - plus with features like unity you don't need to run a full Windows VM. On the desktop side, I would probably still recommend linux.
This is because if you developing any kind of server infrastructure then the deployment environment is invariably linux as well. Red Hat on server is popular in academic environments. However, this is not as big a deal as it used to be and I see a lot of mac based desktops as well. I also note an increase in the number of people who just use their laptops for development. Almost all bioinformatic software is available for linux (and therefore usually works with Mac).
I've yet to find a program for bioinformatics unavailable for linux - its almost all open-source. The major advantage of the Mac is superior software for presentations and programs like Illustrator. LaTeX is also well supported on the mac for writing manuscripts and Word works natively for sharing documents with non-geeks.
Scripting languages like perl and python (the lingua franca of bioinformatics) are well supported on the mac - and it should be remembered that there are many versions of linux. The same script may require some work to get running on different versions of linux. Most students and postdocs in biology labs use Macs and OS/X. It is the predominant platform. Typically bioinformatics types are more comp sci oriented however they still must interface with the biologists and bioengineers, who often are not much into comp sci. Count the number of laptops running which O/S at a bioinformatics conference and the most popular will be OS/X. Many bioinformatics tools are written in Perl or Java, with Python becoming more popular.
Because these are POSIX-oriented languages with numerous 3rd party libraries written for POSIX operating systems, choosing Windows would be a not-so-great choice. However: most of the commercial applications for instrument control such as biorobotics are written primarily for Windows, because those vendors choose the most popular corporate platform (typically ignoring the complaints of the biologists). Unix systems are classically popular for science, so I suggest Linux especially if you plan using FORTRAN. You could try Windows but the Windows API is not exactly programmer-firendly and C# (while it is very fast for most applications) is perhaps too slow for scientific calculations. F# is really great for science but I'm not sure if it is fast enough for complex computations. I guess you could also use C, STL and maybe some plaftorm-independent GUI toolkit like Qt or wxWidgets. This way, you won't need to worry about the platform (that much).
Have a look at Linux!! It is not only an operating system, but a software project produced and maintained by a community of people, in all its parts (from the kernel to the various programs). If you use Linux for a long time (one or two years) you will learn how to live and contribute to a community of people, and this can be very useful for a scientist. From the technical point of view, Linux is a free clone of an Unix system, and this means that it has a good support to work at the command line (bash), along with a lot of tools to manage flat files (sed, awk, grep), which will enable you to execute operations on text files without having to open them directly.
Moreover, it has good tools that allows you to manage the programs installed in your system, and to download and install new programs with a click. It may lack some advanced proprietary programs, but on the other side you have a lot of free tools with good documentation. I have been using Linux for 4 years now and I don't miss other operating systems at all. In bioinformatics and similar domains, you choose among toolsets, which usually involve multiple host/guest/target OSes. The OS you work in depends primarily on where/how you work.
I've worked in a number of different work settings from pure Linux to 'pure' Windows (hel-lo Cygwin). I've never worked in Mac OS X, but know those who do exclusively as well. If you're going to develop FOSS apps for wide distribution, then Linux, ANSI C, Perl/Python, Apache/MySQL (i.e. LAMP stack) is the way to go.
Besides, there are similar 'WAMP' stacks for Windows and using Cygwin someone can compile and use many tools developed in Linux. As far as I know, many Linux apps can be built/run on Mac OS X as well. For data analysis/visualization, popular commercial and even many OSS tools run in Windows with either native or Java versions. So maybe the best setup would be a native Windows XP machine and xhosted access to a real/virtual instance of Linux. I did a course in bioinformatics at University and we used a variety of tools on Windows, Linux, Solaris and some web based ones. I think the short answer is you'll need access to any of the above. Though you should be able to cope with just windows or linux.
It just limits you to the tools available on that platform. In my experience (which is cursory) the tools for bioinformatics are usually written in perl, or java, and I think more recently Python, so thats mostly platform independant. There are some tools which will be written in c so to use them you will need to have whatver platform it was built for available, or find a build for whatever your chosen platform.