The bioinformatics sector has gone through many ups and downs. While “pure” informatics companies are not quite as common as they used to be, information-based science is becoming an increasing part of the discovery process. However, over the years, I am not sure that the algorithms and methods that drive informatics have taken that next step. To some extent, we are still running many of the same algorithms and doing some of the same tasks we were doing at the turn of the century. Some of the more interesting advances have been made by companies like SciTegic and InforSense, which have created environments for data pipelining and application integration. It would be nice if the data and applications took the kind of leap that those suites provide to workflow development. I have no idea how good the technology from this company is, but it is one of the few times that I have seen a potentially exciting new approach to mining and integrating all the wonderful biological information out there. The word that caught my attention was “intelligence”, a word too often mis-used (and may be here as well), but much needed in the informatics sector.
Technorati Tags: Bioinformatics, Science
2 Comments
Speaking of data pipelines and workflow management. Taverna a project initiated by a group at EBI comes to my mind.
Taverna includes a suite of tools to connect to a variety of biological data sources and build customized workflows. Look at http://taverna.sourceforge.net/
That said, A fact about bioinformatics is also true about a lot of “wet” biological research: A lot of effort is spent in creating new tools and developing innovative approaches to generating and looking at an unprecedented wealth of infomation.
By “looking at “, I mean just systematizing the information and making it comprehensible to an everyday researcher. Or in the case of tools , making it possible to do old things quicker and easier and in a high-throughput manner.
These attempts and approaches are indeed necessary and are the first baby steps , and in my opinion are natural in any emerging field/technology.
The problem lies in the refusal to admit that almost all of these are just that “emerging technologies”. In the biological world (sadly) they will remain so for a very long period of time. And a lot of this boils down to a fact that we are now in the genomic era . The post genomic era is something we will get to hopefully several 10s of years down the line.
In this postgenomic era we will be able to do all the wonderfull buzzwords that are thrown around today as being almsot possible: Personalized medicine, Stem cell therapeutics , Gene therapy and a number of words that we have almost been at the brink of since the 1980s.
So I only hope that the vast number of smart computational scientists and para-biologically trained “bioinformaticians” who rushed into the “field” of bioinformatics hoping for a quick fix or a single algorithm to solve all of mankinds ills, stay put and patiently toil until we all collectively learn how to deal with the incredible complexity that is our body and hopefully eventualy survive till we enter the real postgenomic era.
HJ
I completely agree with your statement on the post-genomic era. Contrary to what many would like us to believe, the post-genomic era is still some years away, and at best is only just beginning to take shape. It is at least a decade before the first true “post-genomic” drugs will start percolating through the clinical trial process. That said, in some parts of industry, there are some rather creative people who are trying to make practical use of all the genomic information available, and as the FDA starts looking increasingly at biomarkers as surrogate endpoints, the value of informatics will only increase. It’s at the academic end that many of my frustrations lie. A somewhat related example is CASP. Since CASP4, the field of protein structure prediction has not really made any significant advances for quality structure prediction and almost none in the prediction of the structures of large complexes.