MlrA is the only microcystin-degrading gene detected in Sphingomonas sp. MJ-PV. The gene has an extremely rare nucleotide sequence and homologous genes have not yet been discovered in the DNA database. We discovered the existence of a gene homologous to mlrA in new microcystin-degrading bacteria, MD-1 and Y2. These strains possessed mlrA homologues, and the identities of the genes of MD-1 and Y2 with the corresponding MJ-PV exceeded 98% and 84%, respectively. On the other hand, the mlrA gene was not detected in laboratory strains of the closely related Sphingomonas spp. strains employing hemi-nested polymerase chain reaction detection using two primer sets. Although the microcystin-degrading bacteria were closely related strains, they did not cluster together as the same species. We can conclude that the mlrA gene is conserved in three different bacterial species, and it is unique to microcystin degraders but not to the genus Sphingomonas.

Sequence data is now widely used to observe relationships among organisms. However, understanding structure of the qualitative data is challenging. Conventionally, the relationships are analysed using a dendrogram that estimates a tree shape. This approach has difficulty in verifying the appropriateness of the tree shape; rather, horizontal gene transfers and mating can make the shape of the relationship as networks. As a connection-free approach, principal component analysis (PCA) is used to summarize the distance matrix, which records distances between each combination of samples. However, this approach is limited regarding the treatment of information of sequence motifs; distances caused by different motifs are mixed up. This hides clues to figure out how the samples are different. As any bases may change independently, a sequence is multivariate data essentially. Hence, differences among samples and bases that contribute to the difference should be observed coincidentally. To archive this, the sequence matrix is transferred to boolean vector and directly analysed by using PCA. The effects are confirmed in diversity of Asiatic lion and human as well as environmental DNA. Resolution of samples and robustness of calculation is improved. Relationship of a direction of difference and causative nucleotides has become obvious at a glance.