only platform that actively incorporates homologous small (SSU) as well as large (LSU) subunit sequences from all three domains of life, the Bacteria, Archaea (16S/23S) and Eukarya (18S/28S). All projects provide web-based software tools for the alignment and classiﬁca- tion of sequences as well as probe match functionalities. Downloading of sequences is provided in various formats including the commonly used FASTA and GenBank ﬁle formats. Additionally, greengenes provides ARB compa- tible datasets, but only for nearly full length sequences (>1250 bases) of Bacteria and Archaea. An increasing awareness and motivation to catalogue and protect the biodiversity on Earth using molecular techniques demands comprehensive knowledge databases spanning all three domains of life. Furthermore, a majority of the sequences available is derived from cultivation independent biodiversity surveys, which rely on rapid pattern- or clone-based approaches that often generate partial rRNA sequences. To conserve this suboptimal information especially for diversity studies, state of the art databases need to retain partial sequences. To compensate for the limited phylogenetic resolution of the SSU rRNA (13,14) the two fold larger LSU rRNA should now also be included in the rRNA approach (6). Especially for Eukaryotes the highly variable regions in the LSU rRNA are already commonly used for species discrimination (15). Triggered by a new capacity for cheap and rapid sequencing, there is a steady ﬂow of approximately 10 000 rRNA sequences per month into the public databases of the International Nucleotide Sequence Database Collaboration (http://www.insdc.org). To make full use of these data for reliable phylogenetic reconstruc- tions and biodiversity analysis careful inspection of each sequence and alignment is necessary. To support the users with this task, standardized procedures to assign a deﬁned set of contextual information to each sequence must be established. Uniﬁed quality control mechanisms are urgently needed to intuitively ﬂag potential problems with each sequence. The recent introduction of accelerated and less expensive sequencing technologies, such as pyrosequencing (16), and their successful application for a census of marine microbial diversity (17), further substantiates the need for comprehensive quality con- trolled databases for comparisons. Such databases provide a stable framework enabling biologists to transfer the copious data into reliable biological knowledge. The SILVA database project is designed to satisfy the request for comprehensive quality controlled and aligned rRNA datasets. It is intended to provide a central knowledge resource to alleviate users of the time consuming manual curation process. MATERIALS AND METHODS Sequence data The SILVA release cycle and numbering corresponds to that of the EMBL database, a member of the Inter- national Nucleotide Sequence Database Collaboration (http://www.insdc.org). Thus, the ribosomal RNA sequences used to build version 91 of the SILVA Nucleic Acids Research, 2007, Vol. 35, No. 21 7189 databases, which is referred to in this paper, were retrieved from release 91 (June 2007) of EMBL. A complex combination of keywords including all permutations of 16S/18S, 23S/28S, SSU, LSU, ribosomal and RNA was used to retrieve a comprehensive subset of all available small and large subunit ribosomal RNA sequences. All candidate rRNA sequences extracted from the EMBL database were stored locally in a relational database system (MySQL). The speciﬁcity of the SILVA databases for rRNA is assured by the subsequent processing of the primary sequence information. The source database providing the seed alignment, required for the incremental alignment process, included a representative set of 51 601 aligned rRNA sequences from Bacteria, Archaea and Eukarya with 46 000 alignment positions. The SSU alignment positions are currently kept identical with the ssu_jan04.arb database which has oﬃcially been released by the ARB project (http:// www.arb-home.de) in 2004. For the large subunit RNA databases, an in-house, aligned database was used as the seed. It encompasses a representative set of 2868 sequences from all three domains (150 000 alignment positions). Since the quality of the ﬁnal datasets critically depends on the quality of the seed alignments both datasets were iteratively cross-checked by expert curators during data- base build-up. Within this process, all sequences that could not be unambiguously aligned were removed from the seed. Quality checks Every imported SSU and LSU sequence had to pass a multi-stage quality inspection. Sequences were rejected if they were shorter than 300 unaligned nucleotides, if they were composed of more than 2% of ambiguities or more than 2% homopolymeric stretches longer than four bases, which means only bases exceeding homotetramers are counted, or if they had more than 5% identity to vector sequences. The identity was checked by querying a database of commonly used vector sequences, based on the EMVEC (http://www.ebi.ac.uk/blastall/vectors.html) and UniVec (http://www.ncbi.nlm.nih.gov/VecScreen/ VecScreen.html) databases using the blastn tool. All thresholds to reject sequences were deﬁned based on statistical analysis of the retrieved SSU and LSU sequences. Each sequence in the SILVA databases carries the percentages of ambiguities, homopolymers, and vector contamination. Additionally, a summary ‘sequence qual- ity’ score is calculated according to the following formula, where Sq = sequence quality, A = % ambiguities, H = % homopolymers and V = % vector identity: Sq ¼ 1 ðA=AmaxÞ þ ðH=HmaxÞ þ ðV=VmaxÞ 3 100 This score represents the mean of the three individual parameters, such that 100 is the best possible value. All sequences that passed the quality thresholds were auto- matically aligned against the seed alignment using the new SILVA INcremental Aligner (SINA).