 Hi, I'm Peter Rogan, and I'm here with John Mccockey and Ben Shirley from Western University in Ontario, and these are my co-authors on the paper, Prediction of Mutant mRNA Splice Isoforms by Information Theory-Based Exxon Definition. mRNA processing mutations are responsible for a wide range of human diseases. Information theory-based software tools have been useful in interpreting non-coding sequence variation within functional splice sequence elements such as splice sites and is capable of distinguishing null from partially functional alleles. Information content is defined as the number of choices needed to describe a sequence pattern using a logarithmic scale. A set of either donor or acceptor splice junction recognition sites are aligned and the frequency of bases at each position are determined. The information content of a sequence is known as its RI, which measures binding affinity in bits, where one bit change represents a minimum two-fold decrease in binding affinity. The automatic splice site analysis server, or ASSA, was developed to allow users to determine changes in RI values that result from mutations at splice sites and splicing regulatory elements. Today, we are introducing the automatic splice site and exon definition analysis server, or ACETA, which performs all of the ASSA server's functions but also predicts molecular phenotypes based on changes in RI total. RI total is the total information content of an exon and is the sum of the RI values for its donor and acceptor splice sites adjusted for the self-information of the distance separating these sites, the gap sprisal. Differences between total exon information contents are predictive of the relative abundance of these exons in distinct processed mRNAs. The equation allows any number of sites to be considered in exon definition. However, the transcriptome-wide distribution between each additional site and the constitutive splice site that it impacts has to be determined. This enabled us to develop exon definition models that incorporate regulatory as well as constitutive splice sites, as I will describe later. Here is an example BRCA1 mutation, found in the plus one position of exon 20's natural donor. As you would expect from a plus one donor mutation, the natural site is abolished. There are two predicted cryptic exons, one 87 and another 133 nucleotides downstream from the affected donor site. The 87 nucleotide splice form, which is much stronger and ranked first, has been detected in wet lab experiments, along with exon skipping. Acida also gives the user a visual representation of the predicted splice forms, as well as a 3D plot which visually illustrates the difference in the predictive relative abundance between splice forms before and after the mutation. Splicing enhancers and repressors can also have a direct impact on splicing. The ASA featured weight matrices SF2ASF, SRP40, and SC35, and we've recently added SRP55 and HRNPH2ACIDA. By default, the exon definition feature of ACIDA does not take these splicing elements into account, though you can activate SF2ASF and SC35 in the options menu. As this option can skew your results if no splicing regulatory element is involved, we suggest activating this feature only if you suspect one has an effect. SMN1 and SMN2 are two genes that are near identical, but there is a crucial nucleotide difference in an SF2ASF site which leads to an increase in exon 7 skipping. We use this feature to show that there is a 5.7-bit difference between SMN1 and SMN2, which explains the skipping. ACIDA has a high overall sensitivity and specificity for constitutive and splicing regulatory mutations. In our supplementary section of our manuscript, we have several tables that list published splicing mutations that have been annotated using wet lab techniques. ACIDA has over an 85% concordance rate with the 61 listed splicing mutations, correctly predicting the weakened natural splice site and any activated cryptic exons. Out of 35 mutations that activated a cryptic site, in supplementary table S2 and S3, 25 were ranked first, 7 more in the top 6, and only 3 were lower ranked or not predicted at all. While the ACIDA server presents results for individual mutations, then we'll introduce software that we have recently developed for information theory-based splicing mutation analysis using the genome scale data. Our lab has recently released companion software called the Shannon Human Splicing Pipeline, which analyzes splicing mutations in large next-generation sequencing data sets by information analysis. It is available through the CLC Bioworkbench and has the ability to analyze millions of mutations in just a few hours. Simply import a VCF file and run the pipeline. We will receive results as annotated tables, Manhattan style plots, as well as custom tracks. Once again, the URL for ACIDA is splice.uwo.ca. We thank you for your time, and we hope you can make use of this resource to better understand your experimental results.