Van Wyk, StephanieHarrison, Christopher H.Wingfield, Brenda D.De Vos, LieschenVan der Merwe, Nicolaas Albertus (Albie)Steenkamp, Emma Theodora2019-11-192019-11-192019-08Van Wyk, S., Harrison, C.H., Wingfield, B.D. et al. 2019, 'The RIPper, a web-based tool for genome-wide quantification of Repeat-Induced Point (RIP) mutations', PeerJ, vol. 7, art. e7447, pp. 1-18.2167-8359 (online)10.7717/peerj.7447http://hdl.handle.net/2263/72341Supplement 1. Total percentage of nucleic acid sequence affected by RIP Bar charts summarizing the total proportion of simulated nucleic acid sequences (1 Mbp) that constitutes RIP mutations. Calculated using different RIP parameters 1,2 for a given GC content range 3. 1 RIP product index value cut-off: 1.1, 1.15 1.2, and 1.25 2 RIP substrate index cut-off: 0.75, 0.8, 0.85, and 0.9 3 The average GC content (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%) of 1Mbp simulated nucleic acid sequences consists 100 replicates of randomly generated data.Supplement 2. Genome-wide RIP index values of Neurospora crassa Example of results using the RIP Genome tool of The RIPper for the genome-wide RIP analyses of the Neurospora crassa (Strain:OR74a; assembly number: GCA_000182925.2; accessed through the National Centre for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov)) genome assembly. The results also includes the average GC content calculated per window. RIP product index values above 1.1, RIP substrate index values below 0.9, and RIP composite index values above 0 indicate RIP affected windows (Selker, 1990; Selker et al., 2003; Lewis et al., 2009; Margolin et al., 1998).Supplement 3. RIP statistics (RIP product and substrate cut-off values) calculated for nine datasets that were simulated based on GC content datasets. Highlighted cells illustrate the instances where the average RIP affected proportion (%) of dataset is greater than 1%.Supplement 4. Genome-wide RIP statistics of control organisms investigated using different RIP index cut-off values. Values highlighted in blue indicate a larger proportion of the overall recorded genome-wide RIP of negative control organisms, due to less stringent RIP substrate index value parameters.Supplement 5. Alternate Fig. 2 with colors adjusted for accessibilitySupplement 6. Alternate Fig. 4 with colors adjusted for accessibility.Supplement 7. Alternate Fig. 5 with colors adjusted for accessibility.BACKGROUND. The RIPper (http://theripper.hawk.rocks) is a set of web-based tools designed for analyses of Repeat-Induced Point (RIP) mutations in the genome sequences of Ascomycota. The RIP pathway is a fungal genome defense mechanism that is aimed at identifying repeated and duplicated motifs, into which it then introduces cytosine to thymine transition mutations. RIP thus serves to deactivate and counteract the deleterious consequences of selfish or mobile DNA elements in fungal genomes. The occurrence, genetic context and frequency of RIP mutations are widely used to assess the activity of this pathway in genomic regions of interest. Here, we present a bioinformatics tool that is specifically fashioned to automate the investigation of changes in RIP product and substrate nucleotide frequencies in fungal genomes. RESULTS. We demonstrated the ability of The RIPper to detect the occurrence and extent of RIP mutations in known RIP affected sequences. Specifically, a sliding window approach was used to perform genome-wide RIP analysis on the genome assembly of Neurospora crassa. Additionally, fine-scale analysis with The RIPper showed that gene regions and transposable element sequences, previously determined to be affected by RIP, were indeed characterized by high frequencies of RIP mutations. Data generated using this software further showed that large proportions of the N. crassa genome constitutes RIP mutations with extensively affected regions displaying reduced GC content. The RIPper was further useful for investigating and visualizing changes in RIP mutations across the length of sequences of interest, allowing for fine-scale analyses. CONCLUSION. This software identified RIP targeted genomic regions and provided RIP statistics for an entire genome assembly, including the genomic proportion affected by RIP. Here, we present The RIPper as an efficient tool for genome-wide RIP analyses.en© 2019 van Wyk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.RIP profileLarge RIP affected regionsFine-scale RIP analysesGenome-wide quantificationWeb-based toolRepeat-induced point (RIP)RIPperArticle