Gene Recognition in Cyanobacterium Genomic Sequence Data Using the Hidden Markov Model

We have developed a hidden Markov model (HMM)to detect the protein coding regions within one megabase contiguous sequence data, registered in a database called GenBankin eight entries, of the genomeof cyanobacterium, Sgnechocystis sp. strain PCC6803.Detection of the coding regions in the database entry was performed by using HMMwhose parameters were determined by taking the statistics from the rests of the entries. This HMMhas states modeling the di-codons asld their frequencies within coding regions and those modeling its base contents in the intergenic regions. Results of the cross--validation showed that the HMMrecognized 92.1% of coding regions assigned in sequence annotation. In addition, it suggested 9.t potential new coding regions whose length are longer than 90 bases. The recognition accuracy calculated at the level of individual bases was 90.7% for the coding regions and 88.1% for the intergenic regions. This corresponds to a correlation coefficient for coding region recogni...

• Type: Conference
• Year: 1996
• Where: ISMB
• Authors: Tetsushi Yada, Makoto Hirosawa
• Keywords: Coding Regions, Computational Biology, Intergenic Regions, ISMB 1996, Protein Coding Regions