The SILVA ribosomal RNA gene database project has introduced significant improvements in data processing and web-based tools. The database, available at http://www.arb-silva.de, provides up-to-date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from Bacteria, Archaea, and Eukaryota. The release 111 (July 2012) includes over 3.5 million small subunit (SSU) and 288,717 large subunit (LSU) rRNA gene sequences. Key enhancements include advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation, and optimized browsing, searching, and downloading features. The curated SILVA taxonomy and non-redundant datasets are ideal for high-throughput classification in next-generation sequencing (NGS) approaches. The database is structured into Parc and Ref datasets, with the latter containing high-quality nearly full-length sequences. The introduction of hidden Markov model-based rRNA gene prediction and refined quality control criteria ensures reliable sequence information. The SILVA website offers core database access features, online tools, and extensive documentation, facilitating sequence analysis, phylogenetic reconstructions, and manual curation. The project's commitment to enhancing usability and supporting high-throughput analysis pipelines is expected to grow in importance with the increasing volume of rRNA gene data.The SILVA ribosomal RNA gene database project has introduced significant improvements in data processing and web-based tools. The database, available at http://www.arb-silva.de, provides up-to-date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from Bacteria, Archaea, and Eukaryota. The release 111 (July 2012) includes over 3.5 million small subunit (SSU) and 288,717 large subunit (LSU) rRNA gene sequences. Key enhancements include advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation, and optimized browsing, searching, and downloading features. The curated SILVA taxonomy and non-redundant datasets are ideal for high-throughput classification in next-generation sequencing (NGS) approaches. The database is structured into Parc and Ref datasets, with the latter containing high-quality nearly full-length sequences. The introduction of hidden Markov model-based rRNA gene prediction and refined quality control criteria ensures reliable sequence information. The SILVA website offers core database access features, online tools, and extensive documentation, facilitating sequence analysis, phylogenetic reconstructions, and manual curation. The project's commitment to enhancing usability and supporting high-throughput analysis pipelines is expected to grow in importance with the increasing volume of rRNA gene data.