Identifying the inserted locus of randomly integrated expression plasmids by whole-genome sequencing of Aspergillus strains.

Whole-genome sequencing was conducted on two Aspergillus oryzae strains used for the manufacturing of food enzymes, Acrylaway® and Shearzyme®, with the aim of identifying the inserted locus of randomly integrated expression plasmid and obtaining flanking sequences for safety assessment. Illumina paired-end sequencing was employed, and the obtained reads were mapped to two references: the public genome sequence of Aspergillus oryzae RIB40 and the in-house sequence of the used expression plasmid. Introducing the concept of linking-reads, one locus for each was successfully identified as the integrated site. In the case of Acrylaway®, the obtained sequences suggested that the expression plasmid had been integrated as multiple copies in tandem form. In the case of Shearzyme®, however, information on one edge of the insert was missing, which required extra polymerase chain reaction (PCR) cloning for safety assessment. A 4-kb deletion was detected at the integrated site. There was also evidence of rearrangement occurring in Shearzyme® strain.

AftrRAD: a pipeline for accurate and efficient de novo assembly of RADseq data.

An increase in studies using restriction site-associated DNA sequencing (RADseq) methods has led to a need for both the development and assessment of novel bioinformatic tools that aid in the generation and analysis of these data. Here, we report the availability of AftrRAD, a bioinformatic pipeline that efficiently assembles and genotypes RADseq data, and outputs these data in various formats for downstream analyses. We use simulated and experimental data sets to evaluate AftrRAD's ability to perform accurate de novo assembly of loci, and we compare its performance with two other commonly used programs, stacks and pyrad. We demonstrate that AftrRAD is able to accurately assemble loci, while accounting for indel variation among alleles, in a more computationally efficient manner than currently available programs. AftrRAD run times are not strongly affected by the number of samples in the data set, making this program a useful tool when multicore systems are not available for parallel processing, or when data sets include large numbers of samples.