Genome sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2,951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease-causing variants, as well as common LoF variants in non-essential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes, and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.

Antimicrobial resistance (AMR) is a serious threat to global public health, but obtaining representative data on AMR for healthy human populations is difficult. Here, we use metagenomic analysis of untreated sewage to characterize the bacterial resistome from 79 sites in 60 countries. We find systematic differences in abundance and diversity of AMR genes between Europe/North-America/Oceania and Africa/Asia/South-America. Antimicrobial use data and bacterial taxonomy only explains a minor part of the AMR variation that we observe. We find no evidence for cross-selection between antimicrobial classes, or for effect of air travel between sites. However, AMR gene abundance strongly correlates with socio-economic, health and environmental factors, which we use to predict AMR gene abundances in all countries in the world. Our findings suggest that global AMR gene diversity and abundance vary by region, and that improving sanitation and health could potentially limit the global burden of AMR. We propose metagenomic analysis of sewage as an ethically acceptable and economically feasible approach for continuous global surveillance and prediction of AMR.

This paper presents standards and best practices for reporting genome sequences of uncultivated viruses.Supplementary informationThe online version of this article (doi:10.1038/nbt.4306) contains supplementary material, which is available to authorized users.

Effective use of the human and mouse genomes requires reliable identification of genes and their products. Although multiple public resources provide annotation, different methods are used that can result in similar but not identical representation of genes, transcripts, and proteins. The collaborative consensus coding sequence (CCDS) project tracks identical protein annotations on the reference mouse and human genomes with a stable identifier (CCDS ID), and ensures that they are consistently represented on the NCBI, Ensembl, and UCSC Genome Browsers. Importantly, the project coordinates on manually reviewing inconsistent protein annotations between sites, as well as annotations for which new evidence suggests a revision is needed, to progressively converge on a complete protein-coding set for the human and mouse reference genomes, while maintaining a high standard of reliability and biological accuracy. To date, the project has identified 20,159 human and 17,707 mouse consensus coding regions from 17,052 human and 16,893 mouse genes. Three evaluation methods indicate that the entries in the CCDS set are highly likely to represent real proteins, more so than annotations from contributing groups not included in CCDS. The CCDS database thus centralizes the function of identifying well-supported, identically-annotated, protein-coding regions.[Supplemental material is available online at www.genome.org. Data sets and documentation are available in the CCDS database at http://www.ncbi.nlm.nih.gov/CCDS.]One key goal of genome projects is to identify and accurately annotate all protein-coding genes. The resulting annotations add functional context to the sequence data and make it easier to traverse to other rich sources of gene and protein information. Accurately annotating known genes, identifying novel genes, and tracking annotations over time are complex processes that are best achieved through a combination of large-scale computational analyses and expert curation. These methods must (1) process repetitive sequences in multiple categories including retrotransposons, segmental duplications, and paralogs; (2) process variation including copy number variation (CNV) (Feuk et al. 2006) and microsatellites; (3) distinguish functional genes and alleles from pseudogenes; (4) define alternate splice products; and (5) avoid erroneous interpretation based on experimental error.