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Supportive Courses for Bioinformatics?
Per Christian Henden asks: "I`m aiming for a masters degree in bioinformatics, and I`m uncertain which courses would be good to follow (and my counselor doesn't know, either). There are of course some courses that 'belong' to this degree, and I`ll take those, but I get to choose a number of additional courses. I want to ask people working in bioinformatics 'What (CS) subjects are important or especially useful in bioinformatics?' I`m planning on choosing 'Large Datasets', 'Parallel Programming', 'Image Classification', and 'Subsymbolic AI', because I think those are important, but I`m really not sure what is useful or not in real life bioinformatics." Other Ask Slashdot articles, which have touched on bioinformatics, have dealt with magazines, books and graduate schools.
I work for a Bioinformatics company. Biology Biology Biology Biology!!! We have, and they are not doing as well, several people who with more of a CS background. They don't understand what it is that they are writing programs/algorithms about. We have found it easier to take Biologists who know computers and get good work from them than to do the converse. Make sure you get a VERY good grounding in basic biology. If you don't know what the data you are looking at mean, in the biological sense, then you will make the same mistake a lot do. Just because an algorithm is cool doesn't mean it makes sense. Only by understanding the Biology can you understand the difference.
Gavin Fischer
Your first problem is asking a counselor.
Find a professor at your University doing this, or interested in this, and get his advice. Better yet, start working for him.
That's just generally good advice for anyone who wants to go to grad school in science or engineering.
subject line says it all.
No, seriously, this is a good book.
668: Neighbour of the Beast
I work for a huge Bioinformatics department at a major pharmaceutical company. One thing you need to understand is that the lion's share of Bioinformatics would probably be considered "run of the mill" stuff as far as the rest of computer science is concerned. The biggest problem in Bioinformatics is that our IT people lack sufficient understanding of the underlying biology. If I had to pick an ideal candidate for a position with us, I would look for the following (in order of importance):
Biology background that includes some of the following undergraduate courses: molecular biology, genetics, evolutionary biology, biochemistry/biophysics, virology, & microbiology
Computer background that includes many of the following: Perl, Java, relational databases, XML, Visual Basic (lots of stupid stuff uses it) & general UNIX skills
Preferably some kind of lab experience
Any background in statistics (the more the better)
What we really need are people capable of integrating large amounts of data (DNA sequences, protein structure info, gene expression results, biochemical pathways, clinical data, etc...). This stuff all needs to tie together somehow. While this doesn't require massive programming heroics, it does require an attention to the underlying science or else the wrong assumptions can be made and a total disaster created.
Now, that's not to say that people with experience in areas such as artificial intelligence, image analysis, or natural language processing wouldn't be assets, but to be honest, we're just not at that point yet. Some actual Bioinformatics companies and organizations (TIGR, Celera, Affymetrix, Rosetta, etc...) may (and probably do) have a need for these specialized skills more than us. But do yourself a favor and take as much biology as possible. It's invaluable to my "office karma score" to be able to discuss the latest on Slashdot with our programmers, and two minutes later talk about an article from Nature with our biologists.
Depending on exactly which area of bioinformatics you're looking to get into, statistics may be an important aspect and is often overlooked. Not just your basic stats 101, but understanding what hidden Markov models, Monte Carlo simulations, and Bayesian analysis are good for.
:p.
I didn't take enough stats and have no real good grasp of the above
-h3
I work for a market research firm that focuses exclusively on the bioinformatics market. With a bachelors degree in Mechanical Engineering I am the worst educated employee of the company if you don't count the President/co-owner with his bachelor's in history.
The main problem we have is that all our employees, regardless of field, need a strong scientific education (there's only 15 people and the company is profitable, so there is a lot of cross-utilization). Even the marketing folks come from a lab background, so they understand what is going on. I am their resident IT geek and in the last year I have been forced to absorb more about biology tools and techniques that I would have even dreamed of, and that's without even trying.
Pedro
----
The Insomniac Coder
I recently visited the Human Brain Project yearly conference at the NIH in Bethesda Maryland. It answers all your questions. There was one computer person giving a talk for every three researchers' talks.
The big buzzword this year was Bayseian Filtering. People were using it to model probabilities that genetic sequences would correspond to: Behavior, electcrical signals in specific cells, pathology.
Lots of people were using Java. a few more people using PHP, Python and Perl... no mention of C# or other microsoft stuff. Two projects were using Qt extensively, although they were a little disapponted with something to do with Jpeg image stuff on OS X. I got the impression that they didn't see Qt as being perfect.
They're mostly Mac people And there are a _lot_ of linux enthusiasts. I had dinner with a handfull of people who seemed like 50+ year old administrators. When the subject of Linux came up things got animated. One gentleman runs every SuSE on every desktop in his department. (using Crossover plugin for MS office)
Lots of people are putting together very large databases, and trying to model lots of complex interactions. Others were trying to standardize cross-database communication.
Henry Markram from Switzerland stole the show with a Neocortical microcircuit database. He has measured electrical signals from thousands of living brain cells, traced the cells using Neurolucida, and found corresponding factors between genetic sequences and patterns of electrical activity.
It's clear there's still a lot of excitement (and a little money) in bioinformatics. The important thing is to make sure it's being driven by science.
Almost all of the work that's being done (in this field) is being done in Academia by cheap graduate students... so the complaint is that the programmers rarely stay with the project for more than a year or two.
-Jim
Celebrate Excellence!
Not knowing how to assess significance of your results can cause you a lot of grief, either if you miss important results, or assign importance to non-signifcant results.
In the second tier, it is crucial to have a good grasp of the biology itself - and here it highly depends on what you're researching. If you deal with genetics, molecular biology is critical; the more the better.
Also, take some machine-learning courses if you can (those that deal with classifications).
The software-writing part in computational biology is less important, and as long as you feel comfortable in whatever programming language, it'll probably do. The difficult part is knowing WHAT to program, which data to use, and figuring how to extract significance out of it.