Counting of oligomers in sequences generated by markov chains for DNA motif discovery.
By means of the technique of the imbedded Markov chain, an efficient algorithm is proposed to exactly calculate first, second moments of word counts and the probability for a word to occur at least once in random texts generated by a Markov chain. A generating function is introduced directly from the imbedded Markov chain to derive asymptotic approximations for the problem. Two Z-scores, one based on the number of sequences with hits and the other on the total number of word hits in a set of sequences, are examined for discovery of motifs on a set of promoter sequences extracted from A. thaliana genome. Source code is available at http://www.itp.ac.cn/zheng/oligo.c.
Shan
Gao
G
Beijing Genomics Institute, Shenzhen (BGI-SZ), Shenzhen 518083, China.
Zheng
Wei-Mou
WM
D000465:Algorithms; D001483:Base Sequence; D016384:Consensus Sequence / Q000235:genetics*; D004247:DNA / Q000235:genetics*; D003627:Data Interpretation, Statistical; D008390:Markov Chains; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
D004247:DNA