Using genome-context data to identify specific types of functional associations in pathway/genome databases

Abstract

We have developed two new genome-context-based algorithms. Algorithm 1 extends our previous algorithm for identifying missing enzymes in predicted metabolic pathways (pathway holes) to use genome-context features. The new algorithm has significantly improved scope because it can now be applied to pathway reactions to which sequence similarity methods cannot be applied due to an absence of known sequences for enzymes catalyzing the reaction in other organisms. The new method identifies at least one known enzyme in the top ten hits for 58% of EcoCyc reactions that lack enzyme sequences in other organisms. Surprisingly, the addition of genome-context features does not improve the accuracy of the algorithm when sequences for the enzyme do exist in other organisms. Algorithm 2 uses genome-context methods to predict three distinct types of functional relationships between pairs of proteins: pairs that occur in the same protein complex, the same pathway, or the same operon. This algorithm performs with varying degrees of accuracy on each type of relationship, and performs best in predicting pathway and protein complex relationships.

Linking Genome-Scale Metabolic Modeling and Genome Annotation

“…PHFiller-GC (Pathway Hole Filler – Genome Context) extends on the PHFiller algorithm to use a context-specific prediction of genes for orphan reactions based on shared pathways, shared operons between proteins, shared proteins in a complex, and regulatory interactions [44]. …”

MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks

“…Missing reactions are obtained either from other species [22,23,26-28], or via computational chemistry methodologies that enumerate possible metabolic routes [29], aiming to identify a minimal number of missing reactions to fulfill the required objective. A specific approach for network reconstruction that is based on the concept of elementary flux modes [30] was previously applied to successfully recover missing network reactions [16,31,32]. Another gap-filling approach that integrates some of these principles has been recently used to reconstruct 130 genome-scale metabolic network models of various bacteria [22].…”

“…While most of the above gap-filling methods rely strictly on metabolic flux analysis and do not utilize functional genomics data to guide the search for missing reactions, computational methods that aim to address the second challenge of gene-reaction assignment do rely intensively on functional genomics data. Specifically, several methods predict gene assignment based on genomic data, utilizing principles such as conserved chromosomal proximity [31,33,34] and similarity in phylogenetic profiles with neighboring genes in the same pathway [32,35,36]. Others rely on an additional array of functional genomics data, including gene co-expression and protein-protein interactions [37-43].…”

Experimental evidence validating the computational inference of functional associations from gene fusion events: a critical survey

“…Using gold-standard data sets compiled from the emerging large-scale functional genomics and proteomics experiments, an increasingly wider range of reference genomes and a mixture of variants and parameters [10, 11], these ‘genome-aware’ sequence analysis methods and in particular gene cluster/fusion detection, have yielded an impressive level of performance and accuracy [12]. …”