When biologists study proteins, DNA, or other biological molecules that are represented in the computer as sequences, they rely on known information but also must predict missing data. Given that reality, major challenges exist to having accurate results.
At the University of Arizona, computer scientists have spent years developing improved computer software to aid biologists in obtaining more accurate analyses. The aim is to remove the guessing game involved in tuning parameter values, while also improving the ability to obtain the best sequence alignment without having all information available.
The new technique automatically tunes parameters and improves accuracy by 27 percent using an approach the team has termed "parameter advising." With parameter advising, the software frees biologists from having to rely on the default settings in the software tools they use for sequence analysis. The newly developed software model is able to quickly analyze those settings, along with information about the sequence provided by the biologist, to swiftly detect the best parameters.
The underlying approach is remarkably general, and the system will be available as open source software for widespread use – not merely for those scientists doing biological sequence alignment.
The algorithm does not randomly try parameter values, but carefully chooses the next value to use, learning in a short period of time the best values far quicker than a human possibly could. The general software system will ultimately lead to improved speed and accuracy for scientific models outside the field of biology – which could provide a tremendous boost to scientific research.
