Literature DB >> 22561113

Prediction of operon-like gene clusters in the Arabidopsis thaliana genome based on co-expression analysis of neighboring genes.

Masayoshi Wada1, Hiroki Takahashi, Md Altaf-Ul-Amin, Kensuke Nakamura, Masami Y Hirai, Daisaku Ohta, Shigehiko Kanaya.   

Abstract

Operon-like arrangements of genes occur in eukaryotes ranging from yeasts and filamentous fungi to nematodes, plants, and mammals. In plants, several examples of operon-like gene clusters involved in metabolic pathways have recently been characterized, e.g. the cyclic hydroxamic acid pathways in maize, the avenacin biosynthesis gene clusters in oat, the thalianol pathway in Arabidopsis thaliana, and the diterpenoid momilactone cluster in rice. Such operon-like gene clusters are defined by their co-regulation or neighboring positions within immediate vicinity of chromosomal regions. A comprehensive analysis of the expression of neighboring genes therefore accounts a crucial step to reveal the complete set of operon-like gene clusters within a genome. Genome-wide prediction of operon-like gene clusters should contribute to functional annotation efforts and provide novel insight into evolutionary aspects acquiring certain biological functions as well. We predicted co-expressed gene clusters by comparing the Pearson correlation coefficient of neighboring genes and randomly selected gene pairs, based on a statistical method that takes false discovery rate (FDR) into consideration for 1469 microarray gene expression datasets of A. thaliana. We estimated that A. thaliana contains 100 operon-like gene clusters in total. We predicted 34 statistically significant gene clusters consisting of 3 to 22 genes each, based on a stringent FDR threshold of 0.1. Functional relationships among genes in individual clusters were estimated by sequence similarity and functional annotation of genes. Duplicated gene pairs (determined based on BLAST with a cutoff of E<10(-5)) are included in 27 clusters. Five clusters are associated with metabolism, containing P450 genes restricted to the Brassica family and predicted to be involved in secondary metabolism. Operon-like clusters tend to include genes encoding bio-machinery associated with ribosomes, the ubiquitin/proteasome system, secondary metabolic pathways, lipid and fatty-acid metabolism, and the lipid transfer system.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 22561113     DOI: 10.1016/j.gene.2012.04.043

Source DB:  PubMed          Journal:  Gene        ISSN: 0378-1119            Impact factor:   3.688


  15 in total

Review 1.  Bioinformatics tools for the identification of gene clusters that biosynthesize specialized metabolites.

Authors:  Arvind K Chavali; Seung Y Rhee
Journal:  Brief Bioinform       Date:  2018-09-28       Impact factor: 11.622

2.  Cytokinin-overinduced transcription factors and thalianol cluster genes in CARBOXYL-TERMINAL DOMAIN PHOSPHATASE-LIKE 4-silenced Arabidopsis roots during de novo shoot organogenesis.

Authors:  Akihito Fukudome; Hisashi Koiwa
Journal:  Plant Signal Behav       Date:  2018-09-06

Review 3.  Renaissance in phytomedicines: promising implications of NGS technologies.

Authors:  Sonal Sharma; Neeta Shrivastava
Journal:  Planta       Date:  2016-03-22       Impact factor: 4.116

4.  The biosynthetic pathway of the nonsugar, high-intensity sweetener mogroside V from Siraitia grosvenorii.

Authors:  Maxim Itkin; Rachel Davidovich-Rikanati; Shahar Cohen; Vitaly Portnoy; Adi Doron-Faigenboim; Elad Oren; Shiri Freilich; Galil Tzuri; Nadine Baranes; Shmuel Shen; Marina Petreikov; Rotem Sertchook; Shifra Ben-Dor; Hugo Gottlieb; Alvaro Hernandez; David R Nelson; Harry S Paris; Yaakov Tadmor; Yosef Burger; Efraim Lewinsohn; Nurit Katzir; Arthur Schaffer
Journal:  Proc Natl Acad Sci U S A       Date:  2016-11-07       Impact factor: 11.205

5.  Production of bioactive diterpenoids in the euphorbiaceae depends on evolutionarily conserved gene clusters.

Authors:  Andrew J King; Geoffrey D Brown; Alison D Gilday; Tony R Larson; Ian A Graham
Journal:  Plant Cell       Date:  2014-08-29       Impact factor: 11.277

6.  Determinants of correlated expression of transcription factors and their target genes.

Authors:  Adam B Zaborowski; Dirk Walther
Journal:  Nucleic Acids Res       Date:  2020-11-18       Impact factor: 16.971

7.  Co-expression and co-responses: within and beyond transcription.

Authors:  Takayuki Tohge; Alisdair R Fernie
Journal:  Front Plant Sci       Date:  2012-11-08       Impact factor: 5.753

8.  The PhytoClust tool for metabolic gene clusters discovery in plant genomes.

Authors:  Nadine Töpfer; Lisa-Maria Fuchs; Asaph Aharoni
Journal:  Nucleic Acids Res       Date:  2017-07-07       Impact factor: 16.971

9.  Current challenges and future potential of tomato breeding using omics approaches.

Authors:  Miyako Kusano; Atsushi Fukushima
Journal:  Breed Sci       Date:  2013-03-01       Impact factor: 2.086

10.  A predictor for predicting Escherichia coli transcriptome and the effects of gene perturbations.

Authors:  Maurice H T Ling; Chueh Loo Poh
Journal:  BMC Bioinformatics       Date:  2014-05-13       Impact factor: 3.169

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