The RIPper was used to determine the occurrence and frequency of RIP mutations in sequences that were experimentally and computationally shown to be affected by RIP.These included particular regions (ranging from 478 to 61,000 bp in size) in the genomes of L. maculans (Plissonneau et al., 2016 (link)), N. crassa (Margolin et al., 1998 (link)), Podospora anserina (Hamann, Feller & Osiewacz, 2000 (link)), Colletotrichum cereale (Crouch et al., 2008 (link)), Aspergillus fumigatus (Paris & Latgé, 2001 (link)), Aspergillus oryzae (Montiel, Lee & Archer, 2006 (link)), Magnaporthe grisea (Nakayashiki et al., 1999 (link)) and Chrysoporthe deuterocubensis (Kanzi et al., 2019 (link)). We also used the genomes of N. crassa strain OR74a and T. reesei strain QM6a, which are known to be RIP competent (Selker et al., 2003 (link); Li et al., 2017 (link)). All these sequences were obtained from the NCBI database using accession numbers GCF_000182925.2/ , GCA_002006585.1 , KT804641.1 , AF181821.1 , AJ270953.1 , DQ663509.1 , AF202956.1 , DQ327733.1 , AB024423.1 and GCF_001513825.1 .
The RIPper was used to determine whether this software was capable of identifying large regions affected by RIP in the sequences analyzed. Additionally, for the N. crassa (OR74a) genome assembly, we also generated genome-wide RIP statistics using the “RIP profile” tool. The genomic locations and RIP statistics of putative LRARs were determined using the “calculate LRAR” tool, while the proportion of individual N. crassa chromosomes affected by RIP were calculated using the “RIP sequence” tool. Also, for the T. reesei (QM6a) genome, chromosome-wide changes in RIP index values were compared to changes in GC content, as it was previously shown that centromeric regions are AT-rich due to RIP mutations (Li et al., 2017 (link)).
All chromosome- and genome-wide analyses used sliding windows of 1,000 bp and 500 bp steps. For particular genomic regions, fine-scale analyses were performed using 100 bp sliding windows with a step size of 50 bp. The genomic sequence containing the mating type region of C. deuterocubensis was analyzed using a 1,000 bp window and a 500 bp step size. The RIPper was used to generate graphs for visualizing changes in RIP index values and GC content across the length of the query sequences. This software was also used to generate RIP summary statistics.
The RIPper was used to determine whether this software was capable of identifying large regions affected by RIP in the sequences analyzed. Additionally, for the N. crassa (OR74a) genome assembly, we also generated genome-wide RIP statistics using the “RIP profile” tool. The genomic locations and RIP statistics of putative LRARs were determined using the “calculate LRAR” tool, while the proportion of individual N. crassa chromosomes affected by RIP were calculated using the “RIP sequence” tool. Also, for the T. reesei (QM6a) genome, chromosome-wide changes in RIP index values were compared to changes in GC content, as it was previously shown that centromeric regions are AT-rich due to RIP mutations (Li et al., 2017 (link)).
All chromosome- and genome-wide analyses used sliding windows of 1,000 bp and 500 bp steps. For particular genomic regions, fine-scale analyses were performed using 100 bp sliding windows with a step size of 50 bp. The genomic sequence containing the mating type region of C. deuterocubensis was analyzed using a 1,000 bp window and a 500 bp step size. The RIPper was used to generate graphs for visualizing changes in RIP index values and GC content across the length of the query sequences. This software was also used to generate RIP summary statistics.
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