About the project..

Invisible to the naked human eye, microorganisms play an extremely important role in several aspects of life on earth. From human health and disease, biogeochemical cycles, energy production, bioremediation to generation and maintenance of biodiversity, contribution of microorganisms cannot be ignored. Current knowledge about microbes, conservatively estimated to be less than 1% of available microbial species on earth far underscores their prevalence and significance. This is primarily due to our inability to effectively isolate and culture pure microbial cells from their natural habitats. Novel sequencing technologies have given us a new perspective by allowing us to observe microbes through the genomics lens and have drastically changed the way we think about microbial science. Equipped with cutting-edge tools and approaches such as metagenomics and single-cell genomics, the quest for the unexplored microbial world has never been larger. As we try to keep pace with the discovery of novel microbes, an additional challenge is to correctly identify and name them. The underlying concept of naming new microbial species depends on our ability to compare and connect newly discovered microbes to the known ones. Thus we need to be confident with prevalent microbial taxonomic classification before binning new ones; otherwise a cascade of errors may be initiated.

Whole-genome based Average Nucleotide Identity (gANI) is an effort to delineate species-level diversity among prokaryotic organisms in the genomic era. Microorganisms are evolving continuously and the signature of evolution is observed across its entire genome. As such, a single gene or a select group of marker genes should not be used to infer new species as it may not be a correct representation in the evolutionary landscape. Especially with the wealth of genomic information currently available, one needs to graduate from the gene-centric 16S based classification to a holistic, genome-based method. In our pipeline, gANI is calculated using a modification of the method originally proposed by Konstantinidis et al . By considering the entire genomic content of individual isolates, gANI measures the genomic distance between genome pairs, which can then be translated to infer species relationship and divergence. Clustering microbes based on gANI provides a more realistic picture about their evolutionary history. While a distinct genetic demarcation is present in certain genome groups, overlapping species boundaries can be observed for a large number of sequenced genomes.

The tool is now successfully implemented in Integrated Microbial Genomes and allows a registered user to compare two or more genomes using gANI,AF and the MiSI method.

Please cite:
Microbial species delineation using whole genome sequences. Neha J. Varghese; Supratim Mukherjee; Natalia Ivanova; Konstantinos T. Konstantinidis; Kostas Mavrommatis; Nikos C. Kyrpides; Amrita Pati.Nucleic Acids Research 2015;doi: 10.1093/nar/gkv657

Full link to article is here.

More information about the project can be found here.

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