SpliceRover is a prediction tool which can be used for donor and acceptor splice site prediction, in both human and arabidopsis. By making use of convolutional neural networks, we achieve state-of-the-art accuracy.
All implementation details can be found in our publication at .
The input for this tool is required to be in a fasta format. A
minimum sequence length of
398 is needed for the human
models, and one of
402 for the arabidopsis models. The
maximum length is limited at
All four available models are trained on datasets described in the aforementioned publication.
The human donors & acceptors models are trained on the GWH dataset
as described in Sonnenburg et al (2007), with a
pos:neg sub sampling of
sequence length is
The arabidopsis donors & acceptors models are trained on the
arabidopsis dataset as described in Degroeve et al (2005),
retaining the original
pos:neg ratio from the publication.
The sequence length is
TISRover is a prediction tool for predicting translation initiation sites in human. By making use of convolutional neural networks, we achieve state-of-the-art accuracy
All implementation details can be found in our publication.
Predictions will be made for sequences of length
which is therefore the minimum length required. The maximum length is
The model is trained on the CCDS dataset (excluding chromosome 21), as
described in the aforementioned publication. It predicts the
probability for a positive classification of an
triplet, based on
60 nucleotides upstream and
140 nucleotides downstream.