The relative value of operon predictions

被引:79
作者
Brouwer, Rutger W. W.
Kuipers, Oscar P. [1 ]
van Hijum, Sacha A. F. T. [1 ,2 ]
机构
[1] Univ Groningen, Dept Mol Genet, Groningen Biomol Sci & Biotechnol Inst, NL-9751 NN Haren, Netherlands
[2] Ernst Moritz Arndt Univ Greifswald, Interfacultary Ctr Funct Genom, Greifswald, Germany
关键词
operon; computational prediction; bioinformatics;
D O I
10.1093/bib/bbn019
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
For most organisms, computational operon predictions are the only source of genome-wide operon information. Operon prediction methods described in literature are based on (a combination of) the following five criteria: (i) intergenic distance, (ii) conserved gene clusters, (iii) functional relation, (iv) sequence elements and (v) experimental evidence. The performance estimates of operon predictions reported in literature cannot directly be compared due to differences in methods and data used in these studies. Here, we survey the current status of operon prediction methods. Based on a comparison of the performance of operon predictions on Escherichia coli and Bacillus subtilis we conclude that there is still room for improvement. We expect that existing and newly generated genomics and transcriptomics data will further improve accuracy of operon prediction methods.
引用
收藏
页码:367 / 375
页数:9
相关论文
共 53 条
[1]   Gene Ontology: tool for the unification of biology [J].
Ashburner, M ;
Ball, CA ;
Blake, JA ;
Botstein, D ;
Butler, H ;
Cherry, JM ;
Davis, AP ;
Dolinski, K ;
Dwight, SS ;
Eppig, JT ;
Harris, MA ;
Hill, DP ;
Issel-Tarver, L ;
Kasarskis, A ;
Lewis, S ;
Matese, JC ;
Richardson, JE ;
Ringwald, M ;
Rubin, GM ;
Sherlock, G .
NATURE GENETICS, 2000, 25 (01) :25-29
[2]   NCBI GEO: mining tens of millions of expression profiles - database and tools update [J].
Barrett, Tanya ;
Troup, Dennis B. ;
Wilhite, Stephen E. ;
Ledoux, Pierre ;
Rudnev, Dmitry ;
Evangelista, Carlos ;
Kim, Irene F. ;
Soboleva, Alexandra ;
Tomashevsky, Maxim ;
Edgar, Ron .
NUCLEIC ACIDS RESEARCH, 2007, 35 :D760-D765
[3]   Operon prediction for sequenced bacterial Genomes without experimental information [J].
Bergman, Nicholas H. ;
Passalacqua, Karla D. ;
Hanna, Philip C. ;
Qin, Zhaohui S. .
APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 2007, 73 (03) :846-854
[4]   A Bayesian network approach to operon prediction [J].
Bockhorst, J ;
Craven, M ;
Page, D ;
Shavlik, J ;
Glasner, J .
BIOINFORMATICS, 2003, 19 (10) :1227-1235
[5]   Predicting bacterial transcription units using sequence and expression data [J].
Bockhorst, Joseph ;
Qiu, Yu ;
Glasner, Jeremy ;
Liu, Mingzhu ;
Blattner, Frederick ;
Craven, Mark .
BIOINFORMATICS, 2003, 19 :i34-i43
[6]   The operons, a criterion to compare the reliability of transcriptome analysis tools:: ICA is more reliable than ANOVA, PLS and PCA [J].
Carpentier, AS ;
Riva, A ;
Tisseur, P ;
Didier, G ;
Hénaut, A .
COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2004, 28 (01) :3-10
[7]   Transcriptome dynamics-based operon prediction and verification in Streptomyces coelicolor [J].
Charaniya, Salim ;
Mehra, Sarika ;
Lian, Wei ;
Jayapal, Karthik P. ;
Karypis, George ;
Hu, Wei-Shou .
NUCLEIC ACIDS RESEARCH, 2007, 35 (21) :7222-7236
[8]   Operon prediction by comparative genomics:: an application to the Synechococcus sp WH8102 genome [J].
Chen, X ;
Su, Z ;
Dam, P ;
Palenik, B ;
Xu, Y ;
Jiang, T .
NUCLEIC ACIDS RESEARCH, 2004, 32 (07) :2147-2157
[9]  
Chen Xin, 2004, Genome Inform, V15, P211
[10]  
Craven M, 2000, Proc Int Conf Intell Syst Mol Biol, V8, P116