Abstract
The derivation and comparison of biological interaction networks are vital for understanding the functional capacity and hierarchical organization of integrated microbial communities. In the current work we present metagenomic annotation networks as a novel taxonomy-free approach for understanding the functional architecture of metagenomes. Specifically, metagenomic operon predictions are exploited to derive functional interactions that are translated and categorized according to their associated functional annotations. The result is a collection of discrete networks of weighted annotation linkages. These networks are subsequently examined for the occurrence of annotation modules that portray the functional and organizational characteristics of various microbial communities. A variety of network perspectives and annotation categories are applied to recover a diverse range of modules with different degrees of annotative cohesiveness. Applications to biocatalyst discovery and human health issues are discussed, as well as the limitations of the current implementation.
Highlights
The ubiquity of next-generation sequencing projects has vastly accelerated the accumulation of metagenomic sequence data
In order to demonstrate the utility of metagenomic annotation networks, networks employing a variety of perspectives and annotation categories were constructed and compared
Metagenomic genes were parsed from downloaded raw data and used in a two-phase protocol consisting of network prediction followed by network translation
Summary
The ubiquity of next-generation sequencing projects has vastly accelerated the accumulation of metagenomic sequence data. A common goal in attempting to understand the functional capabilities of newly sequenced microbial communities involves the annotation of putative genes through the assignment of biological functions. Such functional annotation relies heavily on homology-based annotation transfer using tools such as BLAST, HMMs, and motif finding algorithms [3]. An important proportion of microorganisms do not grow under common laboratory culturing conditions [4,5] This limited spectrum of cultured microbial diversity, combined with biases in applied research interests, has yielded a skewed representation within sequence annotation databases [6].
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