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Second, enumeration of all paths defined by a splicing graph is often not tractable.
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The following RNA-Seq based methods build on quantification approaches but identify additional transcripts by enumerating all potential transcripts from a splicing graph.
By imposing a k-mer coverage cutoff, each component becomes a splicing graph that specifies the alternatively spliced variants of a gene.
For each gene, all splice sites of exons reported by sim4 alignments were integrated into a data structure called a splicing graph [ 27].
These most often decompose the alignments into introns and exons, build a splicing graph data structure [ 10] and traverse the graph to find combinations of introns and exons that are best supported by the underlying data under some scoring scheme.
In order to generate a splicing graph from a set of transcripts for a given gene, we thereby developed the Alternative Splicing Graph Server (ASGS) [ 8].
After these adjustments, we consider each resulting component as a splicing graph that specifies the alternative splicing paths of one gene.
Max hits denotes the maximum number of different genes that have BLAST hits to a splicing graph.
Scripture (Guttman et al., 2010) enumerates all potential transcripts from a splicing graph and reports them in the result file.
In Figure 3A, we consider a splicing graph encoding three exons skips leading to eight possible transcripts.
The only possible connections available in a splicing graph are distinct-distinct, distinct-variant, variant-distinct and variant-variant.
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