Hans‐Peter Klenk

Actinobacteria are Gram-positive bacteria with high G+C DNA content that constitute one of the largest bacterial phyla, and they are ubiquitously distributed in both aquatic and terrestrial ecosystems. Many Actinobacteria have a mycelial lifestyle and undergo complex morphological differentiation. They also have an extensive secondary metabolism and produce about two-thirds of all naturally derived antibiotics in current clinical use, as well as many anticancer, anthelmintic, and antifungal compounds. Consequently, these bacteria are of major importance for biotechnology, medicine, and agriculture. Actinobacteria play diverse roles in their associations with various higher organisms, since their members have adopted different lifestyles, and the phylum includes pathogens (notably, species of Corynebacterium, Mycobacterium, Nocardia, Propionibacterium, and Tropheryma), soil inhabitants (e.g., Micromonospora and Streptomyces species), plant commensals (e.g., Frankia spp.), and gastrointestinal commensals (Bifidobacterium spp.). Actinobacteria also play an important role as symbionts and as pathogens in plant-associated microbial communities. This review presents an update on the biology of this important bacterial phylum.

The complete 1.66-megabase pair genome sequence of an autotrophic archaeon, Methanococcus jannaschii, and its 58- and 16-kilobase pair extrachromosomal elements have been determined by whole-genome random sequencing. A total of 1738 predicted protein-coding genes were identified; however, only a minority of these (38 percent) could be assigned a putative cellular role with high confidence. Although the majority of genes related to energy production, cell division, and metabolism in M. jannaschii are most similar to those found in Bacteria, most of the genes involved in transcription, translation, and replication in M. jannaschii are more similar to those found in Eukaryotes.

The pragmatic species concept for Bacteria and Archaea is ultimately based on DNA-DNA hybridization (DDH). While enabling the taxonomist, in principle, to obtain an estimate of the overall similarity between the genomes of two strains, this technique is tedious and error-prone and cannot be used to incrementally build up a comparative database. Recent technological progress in the area of genome sequencing calls for bioinformatics methods to replace the wet-lab DDH by in-silico genome-to-genome comparison. Here we investigate state-of-the-art methods for inferring whole-genome distances in their ability to mimic DDH. Algorithms to efficiently determine high-scoring segment pairs or maximally unique matches perform well as a basis of inferring intergenomic distances. The examined distance functions, which are able to cope with heavily reduced genomes and repetitive sequence regions, outperform previously described ones regarding the correlation with and error ratios in emulating DDH. Simulation of incompletely sequenced genomes indicates that some distance formulas are very robust against missing fractions of genomic information. Digitally derived genome-to-genome distances show a better correlation with 16S rRNA gene sequence distances than DDH values. The future perspectives of genome-informed taxonomy are discussed, and the investigated methods are made available as a web service for genome-based species delineation.

The bacterial and archaeal genomes that have been sequenced to date were chosen for sequencing based mainly on their physiology, which is fine but has resulted in a distinct phylogenetic bias. An alternative approach has been taken in the Genomic Encyclopedia of Bacteria and Archaea (GEBA) project, which advocates choosing genomes based on the organism's phylogenetic position, with the aim filling in the gaps in sequencing along on bacterial and archaeal branches of the tree of life. The value of this approach has been demonstrated by a pilot study of the genome sequences of 56 culturable species selected to maximize phylogenetic coverage. Analysis of the sequences provides insights into phylogenetics, protein function and genome annotation. There are now nearly 1,000 completed bacterial and archaeal genomes available, but as most of them were chosen for sequencing on the basis of their physiology, the data are limited by a highly biased phylogenetic distribution. To explore the value added by choosing microbial genomes for sequencing on the basis of their evolutionary relationships, the genomes of 56 species of Bacteria and Archaea selected to maximize phylogenetic coverage are now sequenced and analysed. Sequencing of bacterial and archaeal genomes has revolutionized our understanding of the many roles played by microorganisms1. There are now nearly 1,000 completed bacterial and archaeal genomes available2, most of which were chosen for sequencing on the basis of their physiology. As a result, the perspective provided by the currently available genomes is limited by a highly biased phylogenetic distribution3,4,5. To explore the value added by choosing microbial genomes for sequencing on the basis of their evolutionary relationships, we have sequenced and analysed the genomes of 56 culturable species of Bacteria and Archaea selected to maximize phylogenetic coverage. Analysis of these genomes demonstrated pronounced benefits (compared to an equivalent set of genomes randomly selected from the existing database) in diverse areas including the reconstruction of phylogenetic history, the discovery of new protein families and biological properties, and the prediction of functions for known genes from other organisms. Our results strongly support the need for systematic ‘phylogenomic’ efforts to compile a phylogeny-driven ‘Genomic Encyclopedia of Bacteria and Archaea’ in order to derive maximum knowledge from existing microbial genome data as well as from genome sequences to come.

The Critical Assessment of Metagenome Interpretation (CAMI) community initiative presents results from its first challenge, a rigorous benchmarking of software for metagenome assembly, binning and taxonomic profiling. Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.