7190 Nucleic Acids Research, 2007, Vol. 35, No. 21 Table 1. Description of database ﬁelds in ARB ﬁles exported from SILVA for ARB speciﬁc ﬁelds and entries ARB ﬁeld name Owned by Description User ARB ARB ARB ARB ARB User ARB User-deﬁned entry, e.g. name and date of the person who aligned the sequence Ambiguities calculated in ARB using ‘count ambiguities’ Stores the information about sequence colors Internal ARB database ID, do not change! Number of nucleotides; calculated by ARB using ‘count nucleotides’ Number of nucleotides coding for the respective rRNA gene; calculated by ‘count nucleotides gene’ Field for remarks Used by several ARB modules aligned ambig ARB_color name nuc nuc_term remark tmp Aligner To cope with the huge amount of sequence information and to minimize the workload for manual curation, a new dynamic incremental proﬁle sequence aligner (SINA) was developed. In the ﬁrst step the aligner uses the suﬃx tree concept of ARB (8) to search for up to 40 closely related sequences in the seed alignment. The reference sequences from the seed are transferred into a partial order graph as used in (18), while preserving the positional identity from the reference alignment. The sequence under investigation is then aligned to this graph using a variant of the Needleman-Wunsch algorithm (19) with aﬃne gap penal- ties and cost free overhang. The graph concept allows ‘jumping’ between the diﬀerent references to ﬁnd an optimal alignment for the diﬀerent sequence regions. This technique enables the algorithm to correctly place bases that were e.g. deleted from the closest relative, but are present in the candidate sequence and other relatives. It also eliminates the need for synthetic full length sequences in the reference alignment as introduced for the NAST aligner (20). To further improve the alignment quality a variability statistic is used to give more weight to conserved positions. Results of each step of the aligner are reported to the database and shown in the correspond- ing ﬁelds of the exported ARB ﬁle (Tables 1–3). The ‘alignment quality’ score is a measure of the similarity with the seed sequences that are taken into account for the alignment process. The score is derived from the dynamic programming score by removing the eﬀects of sequence length and positional weighting. High values (>90) mean that nearly identical sequences have been found within the seed alignment, resulting in a high likeliness for the alignment to be accurate. Low values indicate a high distance as perceived by the aligner, making the alignment task more diﬃcult and lowering the average accuracy. Due to the size of the seed alignment, low values are rather rare and ask for manual inspection of the alignment. The ‘basepair’ score is calculated from the number of bases involved in helix binding according to the secondary structure model of Gutell et al. (21) as already implemen- ted in the ARB package. Canonical and non-canonical base pairings are evaluated, weighted according to the cost model implemented in the Probe_Match (‘weighted mismatches’) tool in ARB (8). To ﬁt our uniﬁed scoring scheme, the alignment quality and the base pair score were normalized to values between 0 and 100, such that 100 represents the maximum score. After aligning, the number of successfully aligned bases was again counted and sequences with less than 300 bases within the boundaries of the respective SSU or LSU rRNA genes were discarded. Anomaly check To check for sequence anomalies, a custom version of the Pintail software (22) was used. The software was speciﬁcally adapted for batch processing by the RDP II team. By design, Pintail can only detect anomalies between two sequences. To circumvent this limitation, a pairwise comparison of all sequences in the seed against a group of 20 sequences was performed. If a majority of the comparisons was deemed anomalous, the sequences were iteratively eliminated from the seed alignment until no such sequences remained. Subsequently, all aligned sequences of the SSU database were tested against their ﬁve closest relatives within this pruned seed. The number of ‘yes’, ‘likely’ and ‘no’ reported by Pintail was counted for each sequence and transferred into the ‘Pintail quality’ value. This score was normalized between 0 and 100, such that 100 indicates the best quality and a low probability that the sequence is anomalous or chimeric. Only SSU sequences were checked for anomalies because the Pintail software is currently not designed to handle LSU sequences. Taxonomy Every sequence in the SILVA databases carries the EMBL taxonomy assignment. Where available, the greengenes and RDP taxonomies where added for comparison. The EMBL taxonomy was retrieved simultaneously with the sequence, whereas the other taxonomies have been assigned to the sequences based on accession numbers. The greengenes taxonomic outline was acquired in June 2007 from the greengenes website (http://greengenes.lbl. gov/) and the RDP Nomenclatural Taxonomy was acquired from RDP II release 9.51. At the moment, no other up to date databases containing aligned LSU sequences are available. Therefore, the only taxonomy provided with the LSU database is the taxonomy used by EMBL. Type strain information has been added to the ﬁeld ‘strain’ and is indicated by [T]. Mapping was done based on the RDP II dataset and is therefore only available for Bacteria.