SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive resource for up-to-date quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. SILVA provides a manually curated taxonomy for all three domains of life, based on representative phylogenetic trees for the small- and large-subunit rRNA genes. This article describes the improvements the SILVA taxonomy has undergone in the last 3 years. Specifically we are focusing on the curation process, the various resources used for curation and the comparison of the SILVA taxonomy with Greengenes and RDP-II taxonomies. Our comparisons not only revealed a reasonable overlap between the taxa names, but also points to significant differences in both names and numbers of taxa between the three resources.

Publicly available sequence databases of the small subunit ribosomal RNA gene, also known as 16S rRNA in bacteria and archaea, are growing rapidly, and the number of entries currently exceeds 4 million. However, a unified classification and nomenclature framework for all bacteria and archaea does not yet exist. In this Analysis article, we propose rational taxonomic boundaries for high taxa of bacteria and archaea on the basis of 16S rRNA gene sequence identities and suggest a rationale for the circumscription of uncultured taxa that is compatible with the taxonomy of cultured bacteria and archaea. Our analyses show that only nearly complete 16S rRNA sequences give accurate measures of taxonomic diversity. In addition, our analyses suggest that most of the 16S rRNA sequences of the high taxa will be discovered in environmental surveys by the end of the current decade.

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SILVA (lat. forest) is a comprehensive web resource, providing services around up to date, high-quality datasets of aligned ribosomal RNA gene (rDNA) sequences from the Bacteria, Archaea, and Eukaryota domains. SILVA dates back to the year 1991 when Dr. Wolfgang Ludwig from the Technical University Munich started the integrated software workbench ARB (lat. tree) to support high-quality phylogenetic inference and taxonomy based on the SSU and LSU rDNA marker genes. At that time, the ARB project maintained both, the sequence reference datasets and the software package for data analysis. In 2005, with the massive increase of DNA sequence data, the maintenance of the software system ARB and the corresponding rRNA databases SILVA was split between Munich and the Microbial Genomics and Bioinformatics Research Group in Bremen. ARB has been continuously developed to include new features and improve the usability of the workbench. Thousands of users worldwide appreciate the seamless integration of common analysis tools under a central graphical user interface, in combination with its versatility. The first SILVA release was deployed in February 2007 based on the EMBL-EBI/ENA release 89. Since then, full SILVA releases offering the database content in various flavours are published at least annually, complemented by intermediate web-releases where only the SILVA web dataset is updated. SILVA is the only rDNA database project worldwide where special emphasis is given to the consistent naming of clades of uncultivated (environmental) sequences, where no validly described cultivated representatives are available. Also exclusive for SILVA is the maintenance of both comprehensive aligned 16S/18S rDNA and 23S/28S rDNA sequence datasets. Furthermore, the SILVA alignments and trees were designed to include Eukaryota, another unique feature among rDNA databases. With the termination of the European Ribosomal RNA Database Project in 2007, the SILVA database has become the authoritative rDNA database project for Europe. The application spectrum of ARB and SILVA ranges from biodiversity analysis, medical diagnostics, to biotechnology and quality control for academia and industry.

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This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

Determining which microbial taxa are out there, where they live, and what they are doing is a driving approach in marine microbial ecology. The importance of these questions is underlined by concerted, large-scale, and global ocean sampling initiatives, for example the International Census of Marine Microbes, Ocean Sampling Day, or Tara Oceans. Given decades of effort, we know that the large majority of marine Bacteria and Archaea belong to about a dozen phyla. In addition to the classically culturable Bacteria and Archaea, at least 50 “clades,” at different taxonomic depths, exist. These account for the majority of marine microbial diversity, but there is still an underexplored and less abundant portion remaining. We refer to these hitherto unrecognized clades as unknown, as their boundaries, names, and classifications are not available. In this work, we were able to characterize up to 92 of these unknown clades found within the bacterial and archaeal phylogenetic diversity currently reported for marine water column environments. We mined the SILVA 16S rRNA gene datasets for sequences originating from the marine water column. Instead of the usual subjective taxa delineation and nomenclature methods, we applied the candidate taxonomic unit (CTU) circumscription system, along with a standardized nomenclature to the sequences in newly constructed phylogenetic trees. With this new phylogenetic and taxonomic framework, we performed an analysis of ICoMM rRNA gene amplicon datasets to gain insights into the global distribution of the new marine clades, their ecology, biogeography, and interaction with oceanographic variables. Most of the new clades we identified were interspersed by known taxa with cultivated members, whose genome sequences are available. This result encouraged us to perform metabolic predictions for the novel marine clades using the PICRUSt approach. Our work also provides an update on the taxonomy of several phyla and widely known marine clades as our CTU approach breaks down these randomly lumped clades into smaller objectively calculated subgroups. Finally, all taxa were classified and named following standards compatible with the Bacteriological Code rules, enhancing their digitization, and comparability with future microbial ecological and taxonomy studies.