Classification and taxonomy articles from across Nature Portfolio

A sampling-based manifold learning method is proposed to study the cluster structure of high-dimensional data. Its applicability and scalability have been verified in single-cell data analysis and anomaly detection in electrocardiogram signals.

This Genome Watch highlights the advantages and limitations of k-mer approaches versus standard alignment-based methods for analysing metagenomic data, assisting microbial species identification, microbial abundance estimation and their application in pathogen genomic epidemiology.

This Genome Watch article highlights recent findings that expand the known diversity of methanogenic archaea and the metagenomic evidence that led to their identification and cultivation.

Inspired by active learning approaches, we have developed a computational method that selects minimal gene sets capable of reliably identifying cell-types and transcriptional states in large sets of single-cell RNA-sequencing data. As the procedure focuses computational resources on poorly classified cells, active support vector machine (ActiveSVM) scales to data sets with over one million cells.

This month’s Genome Watch highlights the systematic discovery of defence systems, paving the way to decode novel genetic functions and further our understanding of microbial warfare.

A suite of new enzymes reveals more on how Nature breaks down plant-based polysaccharides and how these enzymes might be harnessed in the utilization of plant-based biomass.

Improvements to the Genome Taxonomy Database provide a complete bacterial and archaeal taxonomy.