SpliceGrapher is a Python package for creating splice graphs from RNA-Seq
data, guided by gene models and EST data (when available).
Starting with version 0.2.2 we introduced SpliceGrapherXT that can convert
splice graph predictions into transcript predictions.
What does it do?
SpliceGrapher takes gene models and RNA-Seq alignments as input and
predicts splice graphs as output. It accepts gene models in either
GTF or GFF3 format and alignments in the popular SAM format.
As an option, users may include EST alignments in PSL format
from tools such as BLAT or GMAP.
- Accepts alignments in SAM format, so you can use your favorite read mapping and spliced alignment tools.
SpliceGrapher has been tested with alignments from BWA, Bowtie, PASS, Tophat and MapSplice.
- Accurate spliced-alignment filtering using SVM classifiers that recognize
splice junction sequence features. We provide highly accurate classifiers
for over 100 eukaryotic species.
- Generates alternative splicing statistics for any collection of splice graphs.
- Visualization of splice graphs, splice junctions, and read depth
- Use our pipelines or construct your own using SpliceGrapher’s Python modules.
Splice graph prediction for a gene in the plant A. thaliana.
Shown from top to bottom are the original gene model,
the splice junctions used to make the prediction
and the read coverage across the gene.
To reference SpliceGrapherXT, please use the following citations:
M. F. Rogers, J. Thomas, A. S. N. Reddy, A. Ben-Hur. SpliceGrapher: Detecting patterns of alternative splicing from RNA-seq data in the context of gene models and EST data
. Genome Biology, Vol. 13, 2012.
M. F. Rogers, C. Boucher, A. Ben-Hur. SpliceGrapherXT: from splice graphs to transcripts using RNA-Seq. ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM-BCB) 2013.