This is the accurate splicer (asp) program accompanying the paper 
"Accurate Splice Site Prediction Using Support Vector Machines"
by Soeren Sonnenburg, Gabriele Schweikert, Petra Philips,
Jonas Behr and Gunnar Raetsch [1].


ASP PROGRAM REQUIREMENTS:

Asp requires a working python (2.4 or later) installation with numpy
(version 1.0 or later) and the shogun toolbox (version 0.7.3 or later)
- which is available from http://www.shogun-toolbox.org for Linux, MacOSX,
cygwin/win32. If you are running Debian GNU Linux, shogun 0.7.3 is available in
debian unstable http://packages.debian.org/unstable/science/shogun-python-modular.

ASP PROGRAM RUNNING TIME AND MEMORY REQUIREMENTS:

Asp requires about 100M of memory for short sequences. Memory requirements
don't grow much (a additional linear term w.r.t. the length of the input
sequence). On first run with a new model (see --model option below),
asp will load and decompress the .bz2 compressed model file and store it
as a python native pickle dump, which increases startup times a lot.
Due to the optimizations in [2] splice form prediction (layer 1) times
won't change much for many/long sequences. 

ASP PROGRAM USAGE:

./asp fasta_file.fa

This will read all entries in the .fa file and print a .gff file with the
predictions for each of the entries to stdout. One may optionally specify the
start and stop of the transcript via --start <basenum> / --stop <basenum> and
the model via --model one of worm, fly, cress, fish, human.
<basenum> is zero based.


REFERENCES:

[1] S. Sonnenburg, G. Schweikert, P. Philips, J. Behr and Gunnar Raetsch,
	Accurate Splice Site Prediction, BMC Bioinformatics, Special Issue from NIPS workshop on
	New Problems and Methods in Computational Biology Whistler, Canada, 18 December 2006},
	December, 2007, BMC Bioinformatics,8:(Suppl. 10):S7

[2]	Sonnenburg, S, Rätsch, G, Schäfer, C, Schölkopf, B. Large Scale Multiple
	Kernel Learning. Journal of Machine Learning Research,7:1531-1565,
	July 2006, K.Bennett and E.P.-Hernandez Editors.
