Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from ∼1,700 novel and known microbial genomes, as well as ∼600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results.Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

Background The population of Atlantic cod ( Gadus morhua ), also known as Northeast Arctic cod, migrating Atlantic cod, or simply “skrei,” lives mainly in the Barents Sea and Svalbard waters and migrates in annual cycles to the Norwegian coast in order to spawn eggs during late winter. It is the world’s largest population of Atlantic cod, and the population is distinct from the Norwegian coastal cod (or “fjord” cod). Despite the biological, economic, and cultural importance of migrating Atlantic cod, current knowledge on the associated microbiota is very limited. Using shotgun metagenomics and metaproteomics approaches, we present here the gut microbiota, metagenome-assembled genomes (MAGs) of the most abundant bacterial species, DNA-based functional profile, and the metaproteome of Atlantic cod specimens caught at a spawning area in an open ocean outside of Tromsø, Norway. Results Our analyses identified 268 bacterial families in DNA isolated from feces of 6 individual migrating Atlantic cod. The most abundant family was Vibrionaceae (52%; 83% if unclassified reads are excluded), with Photobacterium (genus) representing the vast majority. The recovery of metagenome-assembled genomes provided further details and suggests that several closely related Photobacterium strains from the Photobacterium phosphoreum clade are the most abundant. A genomic-based functional profiling showed that the most abundant functional subsystems are “Carbohydrates”; “Amino Acids and Derivatives”; “Protein Metabolism”; “Cofactors, Vitamins, Prosthetic, Groups, and Pigments”; and “DNA Metabolism,” which is in agreement with other studies of gut microbiomes of marine organisms. Finally, the MS-based metaproteomic dataset revealed that the functional category “Protein Metabolism” is highly overrepresented (3×) when compared to the genome-based functional profile, which shows that ribosomal proteins are rich in the bacterial cytosol. Conclusion We present here the first study of bacterial diversity of the gut of migrating Atlantic cod using shotgun sequencing and metagenome-assembled genomes (MAGs). The most abundant bacteria belong to the Photobacterium genus ( Vibrionaceae family). We also constructed functional profiles of the gut microbiome. These may be used in future studies as a platform for mining of commercially interesting cold-active enzymes. Electronic supplementary material The online version of this article (10.1186/s40168-019-0681-y) contains supplementary material, which is available to authorized users.

A new inter‐governmental research infrastructure, ELIXIR, aims to unify bioinformatics resources and life science data across Europe, thereby facilitating their mining and (re‐)use.

Understanding fish‐microbial relationships may be of great value for fish producers as fish growth, development and welfare are influenced by the microbial community associated with the rearing systems and fish surfaces. Accurate methods to generate and analyze these microbial communities would be an important tool to help improve understanding of microbial effects in the industry. In this study, we performed taxonomic classification and determination of operational taxonomic units on Atlantic salmon microbiota by taking advantage of full‐length 16S rRNA gene sequences. Skin mucus was dominated by the genera Flavobacterium and Psychrobacter. Intestinal samples were dominated by the genera Carnobacterium, Aeromonas, Mycoplasma and by sequences assigned to the order Clostridiales. Applying Sanger sequencing on the full‐length bacterial 16S rRNA gene from the pool of 46 isolates obtained in this study showed a clear assignment of the PacBio full‐length bacterial 16S rRNA gene sequences down to the genus level. One of the bottlenecks in comparing microbial profiles is that different studies use different 16S rRNA gene regions. Comparisons of sequence assignments between full‐length and in silico derived variable 16S rRNA gene regions showed different microbial profiles with variable effects between phylogenetic groups and taxonomic ranks.