JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns.Alternative pre-mRNA splicing (AS) greatly diversifies metazoan transcriptomes and proteomes and is crucial for gene regulation.  To address this critical bioinformatics problem, we developed a method called the junction usage model (JUM) that uses a bottom-up approach to identify, analyze, and quantitate global AS profiles without any prior transcriptome annotations.  JUM accurately reports global AS changes in terms of the five conventional AS patterns and an additional "composite" category composed of inseparable combinations of conventional patterns.  JUM stringently classifies the difficult and disease-relevant pattern of intron retention (IR), reducing the false positive rate of IR detection commonly seen in other annotation-based methods to near-negligible rates.  When analyzing AS in RNA samples derived from Drosophila heads, human tumors, and human cell lines bearing cancer-associated splicing factor mutations, JUM consistently identified approximately twice the number of novel AS events missed by other methods.  Computational simulations showed JUM exhibits a 1.2 to 4.8 times higher true positive rate at a fixed cutoff of 5% false discovery rate.  In summary, JUM provides a framework and improved method that removes the necessity for transcriptome annotations and enables the detection, analysis, and quantification of AS patterns in complex metazoan transcriptomes with superior accuracy.
JUM
JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns.Alternative pre-mRNA splicing (AS) greatly diversifies metazoan transcriptomes and proteomes and is crucial for gene regulation.  To address this critical bioinformatics problem, we developed a method called the junction usage model (JUM) that uses a bottom-up approach to identify, analyze, and quantitate global AS profiles without any prior transcriptome annotations.  JUM accurately reports global AS changes in terms of the five conventional AS patterns and an additional "composite" category composed of inseparable combinations of conventional patterns.  JUM stringently classifies the difficult and disease-relevant pattern of intron retention (IR), reducing the false positive rate of IR detection commonly seen in other annotation-based methods to near-negligible rates.  When analyzing AS in RNA samples derived from Drosophila heads, human tumors, and human cell lines bearing cancer-associated splicing factor mutations, JUM consistently identified approximately twice the number of novel AS events missed by other methods.  Computational simulations showed JUM exhibits a 1.2 to 4.8 times higher true positive rate at a fixed cutoff of 5% false discovery rate.  In summary, JUM provides a framework and improved method that removes the necessity for transcriptome annotations and enables the detection, analysis, and quantification of AS patterns in complex metazoan transcriptomes with superior accuracy.
JUM
JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns.Alternative pre-mRNA splicing (AS) greatly diversifies metazoan transcriptomes and proteomes and is crucial for gene regulation.  To address this critical bioinformatics problem, we developed a method called the junction usage model (JUM) that uses a bottom-up approach to identify, analyze, and quantitate global AS profiles without any prior transcriptome annotations.  JUM accurately reports global AS changes in terms of the five conventional AS patterns and an additional "composite" category composed of inseparable combinations of conventional patterns.  JUM stringently classifies the difficult and disease-relevant pattern of intron retention (IR), reducing the false positive rate of IR detection commonly seen in other annotation-based methods to near-negligible rates.  When analyzing AS in RNA samples derived from Drosophila heads, human tumors, and human cell lines bearing cancer-associated splicing factor mutations, JUM consistently identified approximately twice the number of novel AS events missed by other methods.  Computational simulations showed JUM exhibits a 1.2 to 4.8 times higher true positive rate at a fixed cutoff of 5% false discovery rate.  In summary, JUM provides a framework and improved method that removes the necessity for transcriptome annotations and enables the detection, analysis, and quantification of AS patterns in complex metazoan transcriptomes with superior accuracy.
JUM