Since most of bioinformatics are into predictions, let us see how the community predicts this bit.
In five years time, how would the bioinformatics landscape be and what will probably be the main focus(es) in bioinformatics i.e the hottest areas in bioinformatics?
Perhaps you're looking for daring and bold predictions, but I tend to see lots of incremental progress, especially on the following fronts:
If by "hottest", you mean "number of employees", I think that there will be large number of openings for Masters-level (or lower) bioinformatics staff. These are the folks who will handle routine munging of huge data sets at most sequencing centers. At the present, a lot of that is still handled by either PhDs or grad students. As tools and standards get entrenched, though, you'll see more and more offloaded to technical staff.
There's bound to be a lot of movement in the health informatics field, building tools that can take in your personal genome sequence and spit out useful medical advice (in a format that's useful to both patients and clinicians). This involves not only genomics skills, but also mining of the medical literature and building useful and searchable databases.
Though systems biology has been muted a little as the hype wears off, it's poised to undergo a huge leap forward. With high-throughput data from tens or hundreds of thousands of cells, our models of how the cell works at a network or pathway level are only going to improve.
Other things that will be in demand:
Database and other "big data" skills - how are you going to store and access data from millions of genomes? We're talking petabytes of information here.
Visualization - the larger the data gets, the less we're able to really wrap our heads around it. A few good pictures can often tell us more than a million lines of data.
Truly interdisciplinary scientists. Not CS people who picked up a little bit of biology, or Bio majors who hack a little perl. We're going to see the first generation of scientists who have really been trained to straddle the boundary between the two. They're going to be well-poised to not only do solid research on their own, but be the lynchpins of successful collaborations.
Edit: It occurs to me that if you asked me where I saw the state of genomics in 5 years, or the state of cancer research, I'd have a lot more to say. I just think that the basic skillsets that bioinformaticians use today aren't likely to change tremendously. They'll just get bigger, more parallel, and more in demand.
I still don't see bioinformatics as a discipline per se, more of an adjunct to the business of biological research. That's my perspective and I'm sure people would disagree ;)
Like Chris I think systems biology is going to be the driver for bioinformatics in the next few years, followed by synthetic biology soon after. These are disciplines which are built on and cannot function without the essential bioinformatics component.
The thing is that I don't feel that traditional bioinformatics problems are solved per se. Network analysis, modelling of systems and especially data integration are currently active research topics that I don't see going away any time soon. I think the later depends very much on the 'big data' approaches that Chris also touches on.
I can only add a ++ to both points about visualisation and personal genomics.
Daniel: IMHO every applied branch of science / technology is facing this 'discipline' issue. Bioinformatics is more of a fusion of many applied science and technology streams. Also, I would kindly disagree: if you "say systems biology will drive bioinformatics". IMHO bioinformatics is already contributing to systems biology projects. I would say in the next five years, bioinformatics will drive various domains of biology (including systems biology). After all, the beauty of the scientific question is hidden in the deluge of data and bioinformatics will be the way to uncover it.
Khader, I see your point entirely. I may have phrased the second paragraph in terms of employment. Of course bioinformatics is contributing to systems biology - I tried to emphasise it was a critical component, but of all the current bio disciplines systems biology is the most reliant on interdisciplinary efforts. I would argue that that is a combination of bioinformatics, computing science, statistics and biology. I'd still further argue that these all intersect, but perhaps in some ways remain distinct.
Some excellent answers already to a question I hear frequently. However the question is usually asked to provide the questioner direction on their future career. I used to answer with the 'trend de jour': genomics/transcriptomics/systems biology/synthetic biology/genomics. However in essence the answer should be the same: namely, what do you like doing?
Analysis [any technique esp high throughput]: learn stats, databases and scripting. Also knowing your biology is key and I mean not just the biology of the technique but general biology relating to the disease, trait or structure studied. This is perhaps the biggest field and the most overlooked. Even a basic amount of stats will improve a CV of a candidate looking for work in this area.
Modelling [ systems biology/ population genetics/ ...] Mathematics - inc matlab/octave/R + scripting. Biology stuff as relevant as before
Making stuff - software: [any discipline] learn several languages and understand which libraries will make best use of your dev time. Do not assume that java or perl will be the best language to use for every occasion (mea maxima culpa). What biology to learn will depend on that lab you are in. The lab nearest the cutting edge is best. Howerver the best labs (or company division) may have a project manager to tell you what to do. If you want to be a project manager:- well you should already know the area you are in (I know this is not always the case .. I just feel it should be)
Making stuff - bio-engineering: Call it synthetic biology or what have you. A new field in relation to bioblocks and systems biology, but still in essence the same disciple as the bioreactors/transgenic animals/GM crops of the past 20 years. Creating and or combining bioblocks could be exciting: depending on the project, what luck it has, and its commercial application. Obviously knowing some bench science (bio and/or chem) may help here in addition to at least some basic systems biology depending on your fit to the team/lab that you are in.
Many will see themselves as some of the above. The lack of pigeon holes is a good thing and in general if you are good at several things there is usually room in a group/division to accommodate this (but perhaps more so in academia than industry).
A field that's really going to start to become very relevant is the integration of different sequencing datasets. For example, integrating ChIP-Seq RNA-Seq and genome sequencing to better understand the interplay between transcription, splicing, histone modifications and so forth. As the cost of "next generation" sequencing continues to drop there will be more and more of this data piling up and there will be a need for people who understand not only the biology but the techniques necessary to combine the analysis of all of this data. This is on top of all the necessary support staff to facilitate this kind of analyiss.
I never implied that it would be confined to just system's biology. There are a lot of different areas that bioinformatics will excel in. And I agree with Chris Miller that things will get more and more exciting as people with truly integrated educations begin to move into the field.
GenomeWeb has just (April 2011) released a supplement to its monthly magazine Genome Technology entitled Bioinformatics Trends. The stories are culled from recent issues of BioInform. I cannot find it on-line and so give you here the topics in the supplement - volume 1 - along with links to relevant info (when I could find it):
Next-Gen Sequence Analysis Broad Institute says new algorithm improves de novo sequence assembly US, European teams launch challenges to improve genome assembly; Sanger, CRG kick off RGASP 3 with lens trained on RNA-seq mapping; Baylor's Milosavljevic on software for detecting CNVs in short-read data
Cloud Computing DNAnexus adds variant analysis tool amid mounting interest in cloud computing; Stanford team says cloud an affordable option for translational bioinformatics
Data Integration for Translational Research Coriell, OSU personalized medicine study mixes genetic risk info with EMRs; New database merges -omics, clinical data to personalize cancer treatment; Harvard's Boguski outlines vision for clinical genomics informatics
Storage and Data Management Biogemma invests in informatics to tackle challenges of plant genomics; Startup targets ag-bio researchers for genomic data management system
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version 2.3.6
edited for bump ;) I think it is good to review this, also we are now half-way into the 5-year forecast period. Time for a reality check?
Perfect question for community wiki I feel.
my first question at biostar seems to be pretty famous ...