+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: not found

      Distribution-based clustering: using ecology to refine the operational taxonomic unit.

      Applied and Environmental Microbiology

      Algorithms, Base Sequence, Classification, methods, Cluster Analysis, Computational Biology, DNA Primers, genetics, Microbiota, Molecular Sequence Data, RNA, Ribosomal, 16S, Species Specificity, Real-Time Polymerase Chain Reaction, Sensitivity and Specificity, Sequence Analysis, DNA

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          16S rRNA sequencing, commonly used to survey microbial communities, begins by grouping individual reads into operational taxonomic units (OTUs). There are two major challenges in calling OTUs: identifying bacterial population boundaries and differentiating true diversity from sequencing errors. Current approaches to identifying taxonomic groups or eliminating sequencing errors rely on sequence data alone, but both of these activities could be informed by the distribution of sequences across samples. Here, we show that using the distribution of sequences across samples can help identify population boundaries even in noisy sequence data. The logic underlying our approach is that bacteria in different populations will often be highly correlated in their abundance across different samples. Conversely, 16S rRNA sequences derived from the same population, whether slightly different copies in the same organism, variation of the 16S rRNA gene within a population, or sequences generated randomly in error, will have the same underlying distribution across sampled environments. We present a simple OTU-calling algorithm (distribution-based clustering) that uses both genetic distance and the distribution of sequences across samples and demonstrate that it is more accurate than other methods at grouping reads into OTUs in a mock community. Distribution-based clustering also performs well on environmental samples: it is sensitive enough to differentiate between OTUs that differ by a single base pair yet predicts fewer overall OTUs than most other methods. The program can decrease the total number of OTUs with redundant information and improve the power of many downstream analyses to describe biologically relevant trends.

          Related collections

          Author and article information



          Comment on this article