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

By A. M. Eren, Hilary G. Morrison, Pamela J Lescault, Julie Reveillaud, Joseph H Vineis, Mitchell L Sogin. The ISME Journal, 2014

539 citations recorded by the source corpus.

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.

Subfields: Biotechnology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0a2b7701fb57b76da0738d56856b5e83aa983744

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Citations in the corpus

Exact sequence variants should replace operational taxonomic units in marker-gene data analysis (2017, methodology): “…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).”
Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns (2017, unknown): “We omitted classic OTU methods and MED (10), given the benchmarks described in reference 6.”
Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics (2016, background): “Importantly, higher resolution analytical tools have been published that overcome some of the limitations associated with clustering algorithms (Eren et al., 2013, 2014; Tikhonov et al., 2015).”
Mangrove lagoons of the Great Barrier Reef support coral populations persisting under extreme environmental conditions (2019, methodology): “Sequence quality control was conducted as part of the SymPortal pipeline using Mothur 1.39.5 (Schloss et al. 2009), the BLAST+ suite of executables (Camacho et al. 2009) and minimum entropy decomposition (Eren et al. 2015).”
Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research (2017, methodology): “The output numbers (always 37 or 54) and identities of the OTUs were consistent between each replicate of a given variable region. species present in the mock community.”
Environmental DNA metabarcoding: Transforming how we survey animal and plant communities (2017, unknown): “…Armbrust, 2010), probabilistic taxonomic placement (e.g., PROTAX (Somervuo, Koskela, Pennanen, Henrik Nilsson, & Ovaskainen, 2016; Somervuo et al., 2017), minimum entropy decomposition (e.g., oligotyping, Eren et al., 2015), MEGAN (Huson, Auch, Qi, & Schuster, 2007) and ecotag (Boyer et al., 2016).”