WARP is an alignment-free tool for ultra-fast protein homology detection. It evaluates the similarity between two proteins by computing an approximate Dynamic Time Warping score on some compressed numeric representation of the target proteins. It then evaluates the likelihood of two proteins being homologous using a Random Forest classifier. The corresponding scientific paper is corrently under review.
A python implemetation of the tool can be downloaded from our git repository.
Please consider that this is still an early version which comes as a scripted code. Its sole purpose is to provide a sketchy proof of concept about how WARP works, in relation to the method
explained in the article. We will provide soon a comprehensive and *really* usable implementation of WARP with the goal of substantially speeding up
the time required for homology detection in the daily life of structural bioinformaticians. This task will awnyway require a less theoretical approach and a
specific infrastructure that at the moment we cannot provide.
The code here is thus devoted to show a running example of the concepts described in the paper (such as the iDCTquantization) and you are free to use them and hack it as you wish.
WARP has some dependencies:
- python 2.6 or 2.7 must be installed
- scikit-learn python library
- fastdtw python library
- scipy and numpy python libraries
- a running version of PSIPRED (it is not currently used by the WARP script in the repository)
All the python libraries can be easily installed with pip.
We are grateful for bug reports.