This page describes SplAdder - A tool for augmenting a given annotation based on evidence from RNA-Seq alignment data.
SplAdder is the short form of Splice Adder and describes a tool that augments a given annotation of a genome based on evidence from RNA-Seq alignment data.
Following a straightforward approach, SplAdder transforms a given annotation into a splicing graph representation encoding all annotated transcripts and adds new vertices (exons) and edges (introns) to the graph. Different confidence levels are available for adaptation of sensitivity.
The development of SplAdder started in Matlab and has been recently moved to Python. Currently, both implementations are available, implementing the very same algorithm. Future developments will focus on the Python version.
All source code is available at GitHub. Improvements are made constantly and any feedback is very welcome.
We have started a WIKI to collect information on how to run SplAdder and collect answers to frequently asked questions.
The Matlab implementation requires Matlab to run. Unfortunately, Octave is not supported, as SplAdder makes intensive use of low level HDF5 functions that are not available in Octave (yet).
The Python implementation relies on a few standard packages that are part of most python package managers:
SplAdder comes with a set of useful visualization functions that can be used to get an overview of complex splicing behavior and put RNA-Seq samples into the context of the splicing graph.
An example script showing how to run SplAdder is part of the source code package. The data used in this example will be automatically downloaded upon invokation of the script but is also available for download on our server.
We generated evaluation data as described in the SplAdder manuscript. The data is available for download from our ftp server at this location.
A more detailed overview of the data is provided here:
|Alignments of the simulated data||Alignments|
|Annotation files used for simulation||Annotations|
|Simulated read data||Simulations|
|Results of event prediction comparison||Predictions|
|Results of differential testing evaluation||Difftest|