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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 . 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  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:  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  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.