Reza Mazloom, N Tessa Pierce-Ward, Parul Sharma, Leighton Pritchard, C Titus Brown, Boris A Vinatzer, Lenwood S Heath
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引用次数: 0
Abstract
As a central organizing principle of biology, bacteria and archaea are classified into a hierarchical structure across taxonomic ranks from kingdom to subspecies. Traditionally, this organization was based on observable characteristics of form and chemistry but recently, bacterial taxonomy has been robustly quantified using comparisons of sequenced genomes, as exemplified in the Genome Taxonomy Database (GTDB). Such genome-based taxonomies resolve genomes down to genera and species and are useful in many contexts yet lack the flexibility and resolution of a fine-grained approach. The Life Identification Number (LIN) approach is a common, quantitative framework to tie existing (and future) bacterial taxonomies together, increase the resolution of genome-based discrimination of taxa, and extend taxonomic identification below the species level in a principled way. Utilizing LINgroup as an organizational concept helps resolve some of the confusion and unforeseen negative effects resulting from nomenclature changes of microorganisms that are closely related by overall genomic similarity (often due to genome-based reclassification). Our experimental results demonstrate the value of LINs and LINgroups in mapping between taxonomies, translating between different nomenclatures, and integrating them into a single taxonomic framework. They also reveal the robustness of LIN assignment to hyper-parameter changes when considering within-species taxonomic groups.
期刊介绍:
IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system