SPANNER: taxonomic assignment of sequences using pyramid matching of similarity profiles

摘要

Background: Homology-based taxonomic assignment is impeded by differences between the unassigned read and reference database, forcing a rank-specific classification to the closest (and possibly incorrect) reference lineage. This assignment may be correct only to a general rank (e. g. order) and incorrect below that rank (e. g. family and genus). Algorithms like LCA avoid this by varying the predicted taxonomic rank based on matches to a set of taxonomic references. LCA and related approaches can be conservative, especially if best matches are taxonomically widespread because of events such as lateral gene transfer (LGT). Results: Our extension to LCA called SPANNER (similarity profile annotater) uses the set of best homology matches (the LCA Profile) for a given sequence and compares this profile with a set of profiles inferred from taxonomic reference organisms. SPANNER provides an assignment that is less sensitive to LGT and other confounding phenomena. In a series of trials on real and artificial datasets, SPANNER outperformed LCA-style algorithms in terms of taxonomic precision and outperformed best BLAST at certain levels of taxonomic novelty in the dataset. We identify examples where LCA made an overly conservative prediction, but SPANNER produced a more precise and correct prediction. Conclusions: By using profiles of homology matches to represent patterns of genomic similarity that arise because of vertical and lateral inheritance, SPANNER offers an effective compromise between taxonomic assignment based on best BLAST scores, and the conservative approach of LCA and similar approaches.