Posts Tagged Crowd
If anything, then it’s the MultipleSequenceAlignment (MSA) problem (in combination with the folding problem) which defines the core of bioinformatics. At least from my perspective, since that’s from where I started out my adventures in the field. Already fold.it successfully demonstrated for protein folding that it is possible to tackle hard problems by crowd-sourcing, a.k.a. Citizen Science. After all, the pattern recognition software installed on the wetware between your ears is highly evolved and can complement pure in-silico calculations. With Phylo researchers from McGill university have taken this approach to the sequence level:
Phylo is a challenging flash game in which every puzzle completed contributes to mapping diseases within human DNA.
Although the call for CitizenScience is not entirely new, it is boosted by such developments over the internet significantly. Who said that science and fun do not go together and can only be done while wearing a labcoat and operating extremely expensive machinery (?) – quite the opposite!
Biochemist Erwin Chargaff advocated a return to science by nature-loving amateurs in the tradition of Descartes, Newton, Leibniz, Buffon, and Darwin — science dominated by “amateurship instead of money-biased technical bureaucrats”.
Now that’s some company to be proud of. And I can’t say I completely disagree, albeit I’d like to think the two are not necessarily mutually exclusive (for-the-love-of-it vs. for-profit). If you’d like to get started, check out the tutorial video below and have fun aligning!
Reference: “Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment” by Alexander Kawrykow, Gary Roumanis, Alfred Kam, Daniel Kwak, Clarence Leung, Chu Wu, Eleyine Zarour, Phylo players, Luis Sarmenta, Mathieu Blanchette and Jérôme Waldispühl (2012) PLoS ONE 7(3): e31362. doi:10.1371/journal.pone.0031362
“HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment” by Michael Remmert, Andreas Biegert, Andreas Hauser & Johannes Söding (Nature Methods 9, 173–175 (2012) doi:10.1038/nmeth.1818)
“HHblits is the first iterative method based on the pairwise comparison of profile Hidden Markov Models. In benchmarks it achieves better runtimes than other iterative sequence search methods such as PSI-BLAST or HMMER3 by using a fast prefilter based on profile-profile comparison. Furthermore, HHblits greatly improves upon PSI-BLAST and HMMER3 in terms of sensitivity/selectivity and alignment quality.”
The entire suite of programs is available for all major OSs.