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The R package otu2ot for implementing the entropy decomposition of nucleotide variation in sequence data.

Ramette A, Buttigieg PL - Front Microbiol (2014)

Bottom Line: The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework.These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible.The package and procedures are illustrated by several tutorials and examples.

View Article: PubMed Central - PubMed

Affiliation: HGF-MPG Group for Deep Sea Ecology and Technology, Max Planck Institute for Marine Microbiology Bremen, Germany.

ABSTRACT
Oligotyping is a novel, supervised computational method that classifies closely related sequences into "oligotypes" (OTs) based on subtle nucleotide variation (Eren et al., 2013). Its application to microbial datasets has helped reveal ecological patterns which are often hidden by the way sequence data are currently clustered to define operational taxonomic units (OTUs). Here, we implemented the OT entropy decomposition procedure and its unsupervised version, Minimal Entropy Decomposition (MED; Eren et al., 2014c), in the statistical programming language and environment, R. The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework. In addition, two complementary approaches are implemented: (1) An analytical method (the broken stick model) is proposed to help identify OTs of low abundance that could be generated by chance alone and (2) a one-pass profiling (OP) method, to efficiently identify those OTUs whose subsequent oligotyping would be most promising to be undertaken. These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible. The package and procedures are illustrated by several tutorials and examples.

No MeSH data available.


Entropy profile of file “HGB_0013_GXJPMPL01A3OQX.fasta” and further nucleotide composition of the position of higher Shannon entropy (position 242).
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Figure 1: Entropy profile of file “HGB_0013_GXJPMPL01A3OQX.fasta” and further nucleotide composition of the position of higher Shannon entropy (position 242).

Mentions: Using one abundant OTU (1175 sequences, 1133 positions) whose sequences are provided in file HGB_0013_GXJPMPL01A3OQX.fasta, we generated a Shannon entropy profile of the alignment and a nucleotide composition profile of the position with the highest Shannon entropy (position 242). Note that alignment gaps (−) are also considered as informative in these calculations (Figure 1; Tutorial 1). By using the sample information in each sequence header, a raw sample-by-OT compositional table was generated (Figure 2A), which can be filtered by minimum OT abundance in the table (Figure 2B) or further filtered by applying the broken stick model (BSM) rule (Figures 2C,D).


The R package otu2ot for implementing the entropy decomposition of nucleotide variation in sequence data.

Ramette A, Buttigieg PL - Front Microbiol (2014)

Entropy profile of file “HGB_0013_GXJPMPL01A3OQX.fasta” and further nucleotide composition of the position of higher Shannon entropy (position 242).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231947&req=5

Figure 1: Entropy profile of file “HGB_0013_GXJPMPL01A3OQX.fasta” and further nucleotide composition of the position of higher Shannon entropy (position 242).
Mentions: Using one abundant OTU (1175 sequences, 1133 positions) whose sequences are provided in file HGB_0013_GXJPMPL01A3OQX.fasta, we generated a Shannon entropy profile of the alignment and a nucleotide composition profile of the position with the highest Shannon entropy (position 242). Note that alignment gaps (−) are also considered as informative in these calculations (Figure 1; Tutorial 1). By using the sample information in each sequence header, a raw sample-by-OT compositional table was generated (Figure 2A), which can be filtered by minimum OT abundance in the table (Figure 2B) or further filtered by applying the broken stick model (BSM) rule (Figures 2C,D).

Bottom Line: The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework.These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible.The package and procedures are illustrated by several tutorials and examples.

View Article: PubMed Central - PubMed

Affiliation: HGF-MPG Group for Deep Sea Ecology and Technology, Max Planck Institute for Marine Microbiology Bremen, Germany.

ABSTRACT
Oligotyping is a novel, supervised computational method that classifies closely related sequences into "oligotypes" (OTs) based on subtle nucleotide variation (Eren et al., 2013). Its application to microbial datasets has helped reveal ecological patterns which are often hidden by the way sequence data are currently clustered to define operational taxonomic units (OTUs). Here, we implemented the OT entropy decomposition procedure and its unsupervised version, Minimal Entropy Decomposition (MED; Eren et al., 2014c), in the statistical programming language and environment, R. The aim of this implementation is to facilitate the integration of computational routines, interactive statistical analyses, and visualization into a single framework. In addition, two complementary approaches are implemented: (1) An analytical method (the broken stick model) is proposed to help identify OTs of low abundance that could be generated by chance alone and (2) a one-pass profiling (OP) method, to efficiently identify those OTUs whose subsequent oligotyping would be most promising to be undertaken. These enhancements are especially useful for large datasets, where a manual screening of entropy analysis results and the creation of a full set of OTs may not be feasible. The package and procedures are illustrated by several tutorials and examples.

No MeSH data available.