RIBRA-an error-tolerant algorithm for the NMR backbone assignment problem

Kuen Pin Wu*, Jia Ming Chang, Jun Bo Chen, Chi Fon Chang, Wen Jin Wu, Tai Huang Huang, Ting Yi Sung, Wen Lian Hsu


研究成果: 雜誌貢獻會議論文同行評審

2 引文 斯高帕斯(Scopus)


We develop an iterative relaxation algorithm, called RIBRA, for NMR protein backbone assignment. RIBRA applies nearest neighbor and weighted maximum independent set algorithms to solve the problem. To deal with noisy NMR spectral data, RIBRA is executed in an iterative fashion based on the quality of spectral peaks. We first produce spin system pairs using the spectral data without missing peaks, then the data group with one missing peak, and finally, the data group with two missing peaks. We test RIBRA on two real NMR datasets: hb-SBD and hbLBD, and perfect BMRB data (with 902 proteins) and four synthetic BMRB data which simulate four kinds of errors. The accuracy of RIBRA on hbSBD and hbLBD are 91.4% and 83.6%, respectively. The average accuracy of RIBRA on perfect BMRB datasets is 98.28%, and 98.28%, 95.61%, 98.16% and 96.28% on four kinds of synthetic datasets, respectively.

頁(從 - 到)103-117
期刊Lecture Notes in Bioinformatics (Subseries of Lecture Notes in Computer Science)
出版狀態已發佈 - 2005
事件9th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2005 - Cambridge, MA, 美国
持續時間: 2005 5月 142005 5月 18

ASJC Scopus subject areas

  • 理論電腦科學
  • 一般電腦科學


深入研究「RIBRA-an error-tolerant algorithm for the NMR backbone assignment problem」主題。共同形成了獨特的指紋。