From sequencing data to gene functions: co-functional network approaches

Abstract

Advanced high-throughput sequencing technology accumulated massive amount of genomics and transcriptomics data in the public databases. Due to the high technical accessibility, DNA and RNA sequencing have huge potential for the study of gene functions in most species including animals and crops. A proven analytic platform to convert sequencing data to gene functional information is co-functional network. Because all genes exert their functions through interactions with others, network analysis is a legitimate way to study gene functions. The workflow of network-based functional study is composed of three steps: (i) inferencing co-functional links, (ii) evaluating and integrating the links into genome-scale networks, and (iii) generating functional hypotheses from the networks. Co-functional links can be inferred from DNA sequencing data by using phylogenetic profiling, gene neighborhood, domain profiling, associalogs, and co-expression analysis from RNA sequencing data. The inferred links are then evaluated and integrated into a genome-scale network with aid from gold-standard co-functional links. Functional hypotheses can be generated from the network based on (i) network connectivity, (ii) network propagation, and (iii) subnetwork analysis. The functional analysis pipeline described here requires only sequencing data which can be readily available for most species by next-generation sequencing technology. Therefore, co-functional networks will greatly potentiate the use of the sequencing data for the study of genetics in any cellular organism.

TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions

“…Our results showed that TRRUST v2 not only provides information extracted from the literatures but can also generate novel functional hypotheses to study the transcriptional regulation involved in human diseases. Generation of functional hypothesis using co-functional networks have increased the popularity of such networks, and various network-based algorithms have been developed ( 21 ). Algorithms also need to be developed based on regulatory gene networks to identify key transcriptional regulators associated with human diseases.…”

The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma

“…In addition, we have concentrated on direct protein-protein interactions only. Inclusion of other types of interaction has the potential to increase this coverage of the Hymenolepis microstoma somatic proteome; for example, 'regulog' networks link orthologs of regulatory interactions [78] and 'associalog' networks link proteins/genes based on any type of interaction: physical, genetic, regulatory and other types of functional association [50,79,80]. However, these…”

The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma

“…Inclusion of other types of interaction has the potential to increase this coverage of the Hymenolepis microstoma proteome. For example, regulogs networks link orthologs of regulatory interactions [Yu et al, 2004] and associalog networks link proteins/genes based on any type of interaction: physical, genetic, regulatory and other types of functional association [Kim et al, 2013, Lee et al, 2008, Shim et al, 2017]. However, these approaches generally come at the cost of more noise from false positives [Kim et al, 2013, Lee et al, 2008].…”