MOTIVATION: Two key steps in the analysis of uncultured viruses recovered from metagenomes are the taxonomic classification of the viral sequences and the identification of putative host(s). Both steps rely mainly on the assignment of viral proteins to orthologs in cultivated viruses. Viral Protein Families (VPFs) can be used for the robust identification of new viral sequences in large metagenomics datasets. Despite the importance of VPF information for viral discovery, VPFs have not yet been explored for determining viral taxonomy and host targets. RESULTS: In this work, we classified the set of VPFs from the IMG/VR database and developed VPF-Class. VPF-Class is a tool that automates the taxonomic classification and host prediction of viral contigs based on the assignment of their proteins to a set of classified VPFs. Applying VPF-Class on 731K uncultivated virus contigs from the IMG/VR database, we were able to classify 363K contigs at the genus level and predict the host of over 461K contigs. In the RefSeq database, VPF-class reported an accuracy of nearly 100% to classify dsDNA, ssDNA and retroviruses, at the genus level, considering a membership ratio and a confidence score of 0.2. The accuracy in host prediction was 86.4%, also at the genus level, considering a membership ratio of 0.3 and a confidence score of 0.5. And, in the prophages dataset, the accuracy in host prediction was 86% considering a membership ratio of 0.6 and a confidence score of 0.8. Moreover, from the Global Ocean Virome dataset, over 817K viral contigs out of 1 million were classified. AVAILABILITY AND IMPLEMENTATION: The implementation of VPF-Class can be downloaded from https://github.com/biocom-uib/vpf-tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The influence of sea ice thickness brightness temperatures and ice concentrations retrieved from passive microwave observations is quantified, using horizontally homogeneous sea ice thickness retrievals from ESA's SMOS sensor observations at high incidence angles. Brightness temperatures are influenced by thickness below 18 cm (89GHz) and 50 cm (1.4 GHz). Ice concentration retrievals reduced by ice thickness below 0.17 m and 0.33 m, with higher frequency algorithms being less influenced.

Eleven algorithms for the passive-microwave measurement of sea-ice were implemented and inter-compared. Daily, monthly and annual Arctic sea-ice concentration, area and extent were calculated by the algorithms using daily microwave brightness temperatures from SMMR, SSM/I, and SSMIS for the period 1979-2012. The differences between the 11 sea-ice concentration estimates-structural uncertainties-were quantified and analyzed spatially and seasonally. The algorithms differ in annual sea-ice area by 0.0-1.3 million km2and in extent by 0.0-0.6 million km2. Linear trends for 34- and 21-year periods were calculated and compared for sea-ice concentration, area and extent. Low-frequency algorithms obtained annual Arctic sea-ice area decrease of 0.534-0.573 million km2per decade (0.439-0.491 million km2per decade for the extent) over the period 1979 to 2012, and a decrease of 0.866-0.975 million km2per decade (0.767-0.812 million km2per decade for the extent) for the 1992-2012 period. High-frequency algorithms obtained a decrease of 0.766-0.978 million km2per decade in the area and 0.758-0.814 million km2per decade in the extent over the period 1992-2012. Results for all the algorithms have close agreement on the strength of the negative trend in Arctic sea-ice area and extent, but are individually biased from the mean. The algorithms' ensemble mean and standard deviation in sea-ice concentration, describing part of the uncertainty, are presented to provide users with more insight into the uncertainties and potential biases of sea-ice concentration data.

MOTIVATION: Shotgun sequencing generates large numbers of short DNA reads from either an isolated organism or, in the case of metagenomics projects, from the aggregate genome of a microbial community. These reads are then assembled based on overlapping sequences into larger, contiguous sequences (contigs). The feasibility of assembly and the coverage achieved (reads per nucleotide or distinct sequence of nucleotides) depend on several factors: the number of reads sequenced, the read length and the relative abundances of their source genomes in the microbial community. A low coverage suggests that most of the genomic DNA in the sample has not been sequenced, but it is often difficult to estimate either the extent of the uncaptured diversity or the amount of additional sequencing that would be most efficacious. In this work, we regard a metagenome as a population of DNA fragments (bins), each of which may be covered by one or more reads. We employ a gamma distribution to model this bin population due to its flexibility and ease of use. When a gamma approximation can be found that adequately fits the data, we may estimate the number of bins that were not sequenced and that could potentially be revealed by additional sequencing. We evaluated the performance of this model using simulated metagenomes and demonstrate its applicability on three recent metagenomic datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: A rapidly increasing number of microbial genomes are sequenced by organizations worldwide and are eventually included into various public genome data resources. The quality of the annotations depends largely on the original dataset providers, with erroneous or incomplete annotations often carried over into the public resources and difficult to correct. RESULTS: We have developed an Expert Review (ER) version of the Integrated Microbial Genomes (IMG) system, with the goal of supporting systematic and efficient revision of microbial genome annotations. IMG ER provides tools for the review and curation of annotations of both new and publicly available microbial genomes within IMG's rich integrated genome framework. New genome datasets are included into IMG ER prior to their public release either with their native annotations or with annotations generated by IMG ER's annotation pipeline. IMG ER tools allow addressing annotation problems detected with IMG's comparative analysis tools, such as genes missed by gene prediction pipelines or genes without an associated function. Over the past year, IMG ER was used for improving the annotations of about 150 microbial genomes.