Computational Prediction of Operons in Synechococcus sp. WH8102
スポンサーリンク
概要
- 論文の詳細を見る
We computationally predict operons in the <I>Synechococcus sp</I>. WH8102 genome based on three types of genomic data: intergenic distances, COG gene functions and phylogenetic profiles. In the proposed method, we first estimate a log-likelihood distribution for each type of genomic data, and then fuse these distribution information by a perceptron to discriminate pairs of genes within operons (WO pairs) from those across transcription unit borders (TUB pairs). Computational experiments demonstrated that WO pairs tend to have shorter intergenic distances, a higher probability being in the same COG functional categories and more similar phylogenetic profiles than TUB pairs, indicating their powerful capabilities for operon prediction. By testing the method on 236 known operons of <I>Escherichia coli</I> K12, an overall accuracy of 83. 8% is obtained by joint learning from multiple types of genomic data, whereas individual information source yields accuracies of 80.4%, 74.4%, and 70.6% respectively.<BR>We have applied this new approach, in conjunction with our previous comparative genome analysis-based approach, to predict 556 (putative) operons in WH8102. All predicted data are available at (http://www. cs. ucr. edu/xin/operons. htm) for public use.
- 日本バイオインフォマティクス学会の論文
日本バイオインフォマティクス学会 | 論文
- Performance Improvement in Protein N-Myristoyl Classification by BONSAI with Insignificant Indexing Symbol
- A combined pathway to simulate CDK-dependent phosphorylation and ARF-dependent stabilization for p53 transcriptional activity
- A versatile petri net based architecture for modeling and simulation of complex biological processes
- XML documentation of biopathways and their simulations in Genomic Object Net
- Prediction of debacle points for robustness of biological pathways by using recurrent neural networks