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ariadne [ 320 ] quantifies as events from short read sequences by testing the directionality of reads and connecting potential as events based on paired-end reads and ab initio gene prediction. unlike isbetch, ariadne can only detect known as events because of the lack of paired-end data. also, ariadne quantifies the relative abundance of known isoforms from rna-seq and alternative isoform transcripts from rna-seq, which are the two most commonly detected isoforms from a single gene. furthermore, ariadne can also generate multiple types of outputs including novel as events, the alternative isoform expression level for each isoform, the regulation score of the detected event, and the relative isoform expression level for each sample. the maximum coverage of detection and precision of detected as event is limited to 10, where the precision of the detection and the coverage of the detection are two parameters to assess as events [ 320 ]. as ariadne is based on a machine-learning method, this algorithm can detect novel as events or predict the as events by combining data from various rna-seq experiments [ 320 ].
diffnets [ 321 ] is a graph-based method to identify the as events from short reads, which connects isoforms with paired-end reads and clusters as events. it uses a global network to simulate as events and then uses local network to refine its prediction. however, current implementations of diffnets can only identify known as events and cannot detect novel as events. the major drawback of diffnets is that it has low precision and detects only a small number of known as events in a large-scale rna-seq dataset due to the potential variation between rna-seq datasets [ 321 ].
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