Minimum entropy decomposition: Unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences

Abstract

Molecular microbial ecology investigations often employ large marker gene datasets, for example, ribosomal RNAs, to represent the occurrence of single-cell genomes in microbial communities. Massively parallel DNA sequencing technologies enable extensive surveys of marker gene libraries that sometimes include nearly identical sequences. Computational approaches that rely on pairwise sequence alignments for similarity assessment and de novo clustering with de facto similarity thresholds to partition high-throughput sequencing datasets constrain fine-scale resolution descriptions of microbial communities. Minimum Entropy Decomposition (MED) provides a computationally efficient means to partition marker gene datasets into ‘MED nodes', which represent homogeneous operational taxonomic units. By employing Shannon entropy, MED uses only the information-rich nucleotide positions across reads and iteratively partitions large datasets while omitting stochastic variation. When applied to analyses of microbiomes from two deep-sea cryptic sponges Hexadella dedritifera and Hexadella cf. dedritifera, MED resolved a key Gammaproteobacteria cluster into multiple MED nodes that are specific to different sponges, and revealed that these closely related sympatric sponge species maintain distinct microbial communities. MED analysis of a previously published human oral microbiome dataset also revealed that taxa separated by less than 1% sequence variation distributed to distinct niches in the oral cavity. The information theory-guided decomposition process behind the MED algorithm enables sensitive discrimination of closely related organisms in marker gene amplicon datasets without relying on extensive computational heuristics and user supervision.

Disease Progression and Resolution in Rodent Models of Clostridium difficile Infection and Impact of Antitoxin Antibodies and Vancomycin

“…Sequencing was carried out using Illumina HiSeq v3 in 2-bp-by-300-bp paired-end read mode. Sequences were partitioned into operational taxonomic units (OTU), each representing a distinct cluster with significant sequence divergence from each other cluster (496 OTUs for cecum samples and 420 OTUs for fecal samples) (25). Taxonomic information was assigned to OTUs to the highest degree of specificity allowed by the data (ranging from kingdom to species) (26,27).…”

Exact sequence variants should replace operational taxonomic units in marker gene data analysis

“…Recently, new methods have been developed that resolve sequence variants (SVs) from Illumina-scale amplicon data without imposing the arbitrary dissimilarity thresholds that define molecular OTUs (Eren et al, 2013;Tikhonov et al, 2015;Eren et al, 2015;Callahan et al, 2016a;Callahan et al, 2016b;Edgar, 2016). SV methods infer the biological sequences in the sample prior to the introduction of amplification and sequencing errors, and distinguish sequence variants differing by as little as one nucleotide.…”

New Insights into Microbial Ecology through Subtle Nucleotide Variation

“…The use of 97% sequence similarity threshold emerged as a de facto standard, and has successfully reduced the impact of erroneous OTUs on diversity estimations. However, the computational convenience this arbitrary threshold offers has been at the expense of accurate ecological inference, as 3% OTUs are often phylogenetically mixed, and inconsistent (Koeppel and Wu, 2013;Eren et al, 2014;Nguyen et al, 2016).…”