| Class | Novel AS | Detection region | Comments |
---|---|---|---|---|
DiffSplice | IR | Any type | ASM | Assembles transcriptome based on graph theory. Does not rely on annotation but does not use annotation either. The goodness of ASM is questionable. Generally low AUC. Performs poorly when detecting SE events. |
Cufflinks | IR | Any type | Gene | Assembled transcripts merge with annotation to provide a more confident reference. Is least affected by incomplete annotation. Model is designed for pair-end data. Performs better for medium read depth than both low and high read depth. Performs better when detecting A3SS and A5SS events than other types of AS events. Computationally slow, but allows parallelization. |
DEXSeq | CB | Only SE | Exon | Uses a generalized linear NB model. Achieves the highest AUC in many cases using accurate annotation. However, incomplete annotation can impose considerable problems for it. Poor FDR control. |
MATS | CB | NS | AS event | Uses a Bayesian model. Solely based on junction reads. Can not detect complex AS events. Annotates splicing events with corresponding event types. Good FDR control in many simulation studies. Performs the best for real data. |
rDiff-param | CB | NS | Gene | Conservative with default settings. Good FDR control but low AUC in many cases. Computationally fast. |
SplicingCompass | CB | Only SE | Gene | Compares geometry angles of read count vectors. Generally poor FDR control and Medium AUC. Performs well when detecting SE events. |
DSGseq | CB | Only SE | Gene | No p-value reported. Generally medium AUC. Performs well when detecting IR events and when using incomplete annotation. Computationally fast. |
SeqGSEA | CB | Only SE | Gene | Integrates DE analysis with DS analysis. Generally high AUC. Requires a sample size around 5 to claim significance at a reasonable FDR level, i.e. F D R=0.05. Computation time increases dramatically as permutation times increases. |