An analysis of the validity and utility of the proximon proposition.

The utilization of metagenomic functional interactions represents a key technique for metagenomic functional annotation efforts. By definition, metagenomic operons represent such interactions, but many operon predictions protocols rely on information about orthology and/or gene function that is frequently unavailable for metagenomic genes. Recently, the concept of the metagenomic proximon was proposed for use in metagenomic scenarios where supplemental information is sparse.

MetaProx: the database of metagenomic proximons.

MetaProx is the database of metagenomic proximons: a searchable repository of proximon objects conceived with two specific goals. The first objective is to accelerate research involving metagenomic functional interactions by providing a database of metagenomic operon candidates. Proximons represent a special subset of directons (series of contiguous co-directional genes) where each member gene is in close proximity to its neighbours with respect to intergenic distance.

Evaluation of a pooled strategy for high-throughput sequencing of cosmid clones from metagenomic libraries.

High-throughput sequencing methods have been instrumental in the growing field of metagenomics, with technological improvements enabling greater throughput at decreased costs. Nonetheless, the economy of high-throughput sequencing cannot be fully leveraged in the subdiscipline of functional metagenomics. In this area of research, environmental DNA is typically cloned to generate large-insert libraries from which individual clones are isolated, based on specific activities of interest.

Gene coexpression as Hebbian learning in prokaryotic genomes.

Biological interaction networks represent a powerful tool for characterizing intracellular functional relationships, such as transcriptional regulation and protein interactions. Although artificial neural networks are routinely employed for a broad range of applications across computational biology, their underlying connectionist basis has not been extensively applied to modeling biological interaction networks. In particular, the Hopfield network offers nonlinear dynamics that represent the minimization of a system energy function through temporally distinct rewiring events.

Metagenomic guilt by association: an operonic perspective.

Next-generation sequencing projects continue to drive a vast accumulation of metagenomic sequence data. Given the growth rate of this data, automated approaches to functional annotation are indispensable and a cornerstone heuristic of many computational protocols is the concept of guilt by association. The guilt by association paradigm has been heavily exploited by genomic context methods that offer functional predictions that are complementary to homology-based annotations, thereby offering a means to extend functional annotation.

Metagenomic annotation networks: construction and applications.

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.

Beyond the bounds of orthology: functional inference from metagenomic context.

The effectiveness of the computational inference of function by genomic context is bounded by the diversity of known microbial genomes. Although metagenomes offer access to previously inaccessible organisms, their fragmentary nature prevents the conventional establishment of orthologous relationships required for reliably predicting functional interactions. We introduce a protocol for the prediction of functional interactions using data sources without information about orthologous relationships.

Differential direct coding: a compression algorithm for nucleotide sequence data.

While modern hardware can provide vast amounts of inexpensive storage for biological databases, the compression of nucleotide sequence data is still of paramount importance in order to facilitate fast search and retrieval operations through a reduction in disk traffic. This issue becomes even more important in light of the recent increase of very large data sets, such as metagenomes. In this article, I propose the Differential Direct Coding algorithm, a general-purpose nucleotide compression protocol that can differentiate between sequence data and auxiliary data by supporting the inclusion of supplementary symbols that are not members of the set of expected nucleotide bases, thereby offering reconciliation between sequence-specific and general-purpose compression strategies.