SSPred is a splice site prediction toolchain which incorporated a statistical model of splicing signals built based on the entropy density profile (EDP) method, weight array method (WAM), and kappa test, and a model of splicing regulatory elements developed by an unsupervised self-learning method to detect motifs associated with splicing regulatory elements.
dSSpred is developed for the prediction of donor splice sites in eukaryotic species. It is based on all possible first order di-nucleotide dependencies that exist in the signal region at the exon-intron boundary.
PASSion uses the mapped read in a pair as anchor and then uses a high resolution algorithm, pattern growth, to remap the proximal and distal fragments of the unmapped read to a local region of the reference indicated by the mate. It is capable of identifying both known and novel canonical and non-canonical junctions with SNP or sequencing error tolerance.
Yanju Zhang (Leiden University Medical Center, The Netherlands): y.zhang AT lumc.nl
GeneSplicer is a fast, flexible system for detecting splice sites in the genomic DNA of various eukaryotes. The system has been trained and tested successfully on Plasmodium falciparum (malaria), Arabidopsis thaliana, human, Drosophila, and rice . Training data sets for human and Arabidopsis thaliana are included.