Dataset Information

Uncovering the co-evolutionary network among prokaryotic genes.

ABSTRACT:

Motivation

Correlated events of gains and losses enable inference of co-evolution relations. The reconstruction of the co-evolutionary interactions network in prokaryotic species may elucidate functional associations among genes.

Results

We developed a novel probabilistic methodology for the detection of co-evolutionary interactions between pairs of genes. Using this method we inferred the co-evolutionary network among 4593 Clusters of Orthologous Genes (COGs). The number of co-evolutionary interactions substantially differed among COGs. Over 40% were found to co-evolve with at least one partner. We partitioned the network of co-evolutionary relations into clusters and uncovered multiple modular assemblies of genes with clearly defined functions. Finally, we measured the extent to which co-evolutionary relations coincide with other cellular relations such as genomic proximity, gene fusion propensity, co-expression, protein-protein interactions and metabolic connections. Our results show that co-evolutionary relations only partially overlap with these other types of networks. Our results suggest that the inferred co-evolutionary network in prokaryotes is highly informative towards revealing functional relations among genes, often showing signals that cannot be extracted from other network types.

Availability and implementation

Available under GPL license as open source.

Contact

talp@post.tau.ac.il.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Cohen O

PROVIDER: S-EPMC3436823 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Publications

Uncovering the co-evolutionary network among prokaryotic genes.

Cohen Ofir O Ashkenazy Haim H Burstein David D Pupko Tal T

Bioinformatics (Oxford, England) 20120901 18

<h4>Motivation</h4>Correlated events of gains and losses enable inference of co-evolution relations. The reconstruction of the co-evolutionary interactions network in prokaryotic species may elucidate functional associations among genes.<h4>Results</h4>We developed a novel probabilistic methodology for the detection of co-evolutionary interactions between pairs of genes. Using this method we inferred the co-evolutionary network among 4593 Clusters of Orthologous Genes (COGs). The number of co-ev ...[more]

PMID: 22962457

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Project description:BackgroundAcidity is a major contributor to fruit quality. Several organic acids are present in apple fruit, but malic acid is predominant and determines fruit acidity. The trait is largely controlled by the Malic acid (Ma) locus, underpinning which Ma1 that putatively encodes a vacuolar aluminum-activated malate transporter1 (ALMT1)-like protein is a strong candidate gene. We hypothesize that fruit acidity is governed by a gene network in which Ma1 is key member. The goal of this study is to identify the gene network and the potential mechanisms through which the network operates.ResultsGuided by Ma1, we analyzed the transcriptomes of mature fruit of contrasting acidity from six apple accessions of genotype Ma_ (MaMa or Mama) and four of mama using RNA-seq and identified 1301 fruit acidity associated genes, among which 18 were most significant acidity genes (MSAGs). Network inferring using weighted gene co-expression network analysis (WGCNA) revealed five co-expression gene network modules of significant (P < 0.001) correlation with malate. Of these, the Ma1 containing module (Turquoise) of 336 genes showed the highest correlation (0.79). We also identified 12 intramodular hub genes from each of the five modules and 18 enriched gene ontology (GO) terms and MapMan sub-bines, including two GO terms (GO:0015979 and GO:0009765) and two MapMap sub-bins (1.3.4 and 1.1.1.1) related to photosynthesis in module Turquoise. Using Lemon-Tree algorithms, we identified 12 regulator genes of probabilistic scores 35.5-81.0, including MDP0000525602 (a LLR receptor kinase), MDP0000319170 (an IQD2-like CaM binding protein) and MDP0000190273 (an EIN3-like transcription factor) of greater interest for being one of the 18 MSAGs or one of the 12 intramodular hub genes in Turquoise, and/or a regulator to the cluster containing Ma1.ConclusionsThe most relevant finding of this study is the identification of the MSAGs, intramodular hub genes, enriched photosynthesis related processes, and regulator genes in a WGCNA module Turquoise that not only encompasses Ma1 but also shows the highest modular correlation with acidity. Overall, this study provides important insight into the Ma1-mediated gene network controlling acidity in mature apple fruit of diverse genetic background.

Dataset Information

Uncovering the co-evolutionary network among prokaryotic genes.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Uncovering the co-evolutionary network among prokaryotic genes.

Similar Datasets

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