The most important 'skill' to have is PATIENCE, just enough to avoid an early death due to bashing your head against the wall when trying to get convoluted R, C, C++, Python, Perl, etc packages to run. Half your life will be spent resolving dependencies only to find that when it all finally compiles it's useless anyway. Welcome to NGS! ;-)
But seriously, *nix sysadmin skills are *essential*. Usually some flavor of Linux, but you may have to mess around with Solaris if you're running Sun equipment. If you're moving serious quantities of data then you're going to need decent servers (we use 64-bit, 32 CPU, 130GB RAM, 4TB onboard systems with 32TB Sun Thumpers as NAS). Even having competent dedicated sysadmins on deck will not release you from having to have solid command line skills. Get a VPN set up or get familiar with SSH and SSH tunneling (for secure VNC for example), both can work well together and allow you to work happily remotely. Consider DynDNS if you have a dynamic IP at a remote site.
A 'scripting' language is essential. It doesn't matter what, but the bioinformatics community seems to prefer being stuck in the early 1990's with Perl while the rest of the world is moving on. As painful as Perl is when compared to something like Python, it's hard to argue against the depth of modules available.
Interpreted languages are getting better and better but you'll still need a compiled language at some point. I prefer C++ (with Boost) because it's slightly less painful than C and almost as fast when compiled with -O3.
Database skills are also essential. MySQL is a good compromise between efficiency and ease of use and nobody can argue with the price. Be sure to configure it to make the most of your systems (use block rather than file storage and tweak my-huge.cnf).
For statistics, R is popular with the bioinformatics community despite the fact that it's one of the most awful hacked-together, inconsistent, illegible, slow, lumps of crap in existence. Leveraging other peoples' R packages can sometimes be a time saver, but if you're really serious then pay the money and use Mathematica.
Finally, there's the important aspect of visualization that often gets forgotten until it's too late. A decent charting package is worth the money. A genome browser that slots in happily with your choice of server-side setup is also advantageous.
If you're in a team of bioinformaticists and can delegate specific roles then great. Otherwise, you're expected to be a Swiss army knife.
It also depends on which software/pipelines you plan to use for your analysis. ERANGE provides a pretty comprehensive suite of functions, and requires some basic python knowledge. There are also a lot of good libraries being developed in the R language. R is a nice option because it's quite user-friendly and provides good plotting functions. I'd also suggest having a look through the SEQanswers forums to get some direction from the active community.
Good luck.
For sure good linux knowledge since most powerful tools are running mainly via command line on linux systems.
My two cents:
If one just want to run some software or packages available for basic analysis, linux+bash should be OK;
for further analysis, perl/python or any other language and R;
for more efficient data retrieve and storage, MySQL
My answer would be a solid understanding of biology and/or molecular biology if you are involved in studies related to biology and/or disease. My most productive experiences have come when bioinformaticians are familiar with the biological system(s) at a rudimentary level and understand the biological question(s) being asked. Of course, I answer as a molecular biologist who has been involved in NexGen Sequencing. I'm not a biostatistician.
Linux shell + Perl + MySQL. Also Galaxy is emerging as a more user-friendly alternative in NGS analysis.
@ agreed statistics play an important part of NGS but
knowing some languages is important e.g. PERL and programming in R at least..........
It really depends on what type of bioinformatician you want to be. If you want to be developing tools, you should be comfortable with the basics of algorithms, data structures, maybe some software engineering in addition to a decent helping in probability and statistics. At this level you should have some skill in the other PCR... Not Polymerase Chain Reaction (though that is useful too), but Python (or Perl if you must), C/C++, and R. Comfort with the basics of Unix are also an essential.
If you want to be more of an "end-user, then technical skills with Unix, Python (or Perl) and R are essential, as well as sufficient background in probability &/or statistics.
In addition to these, MATLAB is quite useful for efficient matrix computation in sequence analysis
Statistics is a must for making sense of the results of any analysis. For me R and scripting languages and scripting the UNIX shell are very important, even if a more powerful compiled language is needed for "heavy lifting" of intense algorithms and big data that is coming along very rapidly in this area.
You will also need to be very familiar with statistics. If you are just a tool user, then basic biostats will be suficient to understand the different scores and p-values and so on. If you plan to develop new analysis tools, then you will need knowledge in algorithmics
Being a molecular biologist, I started off using Galaxy (very userfriendly and very good for even some complex analysis), and am now in the stage that I start to feel the need to learn a computer language to be able to dive deeper. Linux is my first choice....
The most important 'skill' to have is PATIENCE, just enough to avoid an early death due to bashing your head against the wall when trying to get convoluted R, C, C++, Python, Perl, etc packages to run. Half your life will be spent resolving dependencies only to find that when it all finally compiles it's useless anyway. Welcome to NGS! ;-)
But seriously, *nix sysadmin skills are *essential*. Usually some flavor of Linux, but you may have to mess around with Solaris if you're running Sun equipment. If you're moving serious quantities of data then you're going to need decent servers (we use 64-bit, 32 CPU, 130GB RAM, 4TB onboard systems with 32TB Sun Thumpers as NAS). Even having competent dedicated sysadmins on deck will not release you from having to have solid command line skills. Get a VPN set up or get familiar with SSH and SSH tunneling (for secure VNC for example), both can work well together and allow you to work happily remotely. Consider DynDNS if you have a dynamic IP at a remote site.
A 'scripting' language is essential. It doesn't matter what, but the bioinformatics community seems to prefer being stuck in the early 1990's with Perl while the rest of the world is moving on. As painful as Perl is when compared to something like Python, it's hard to argue against the depth of modules available.
Interpreted languages are getting better and better but you'll still need a compiled language at some point. I prefer C++ (with Boost) because it's slightly less painful than C and almost as fast when compiled with -O3.
Database skills are also essential. MySQL is a good compromise between efficiency and ease of use and nobody can argue with the price. Be sure to configure it to make the most of your systems (use block rather than file storage and tweak my-huge.cnf).
For statistics, R is popular with the bioinformatics community despite the fact that it's one of the most awful hacked-together, inconsistent, illegible, slow, lumps of crap in existence. Leveraging other peoples' R packages can sometimes be a time saver, but if you're really serious then pay the money and use Mathematica.
Finally, there's the important aspect of visualization that often gets forgotten until it's too late. A decent charting package is worth the money. A genome browser that slots in happily with your choice of server-side setup is also advantageous.
If you're in a team of bioinformaticists and can delegate specific roles then great. Otherwise, you're expected to be a Swiss army knife.
Considering that you will be required to parse a lot of files, I strongly suggest learning Python. It can be a time-saver to write a little Python script, even if you can use the CLI proficiently. There simply are things which are faster to write in Python rather than Bash or perl.
And yes, *nix sysadmin skills are priority. Most labs do not have a dedicated one, and anyway you will have to move around a lot in your servers, so they will come in handy.
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