1998/01/01 by Steven L. Salzberg, Arthur L. Delcher, Simon Kasif +1 · 5 citations
Biochemistry, Genetics and Molecular Biology · #Genomics and Phylogenetic Studies #RNA and protein synthesis mechanisms #Machine Learning in Bioinformatics
paper · pdf · doi:10.1093/nar/26.2.544
openalex publication_date 1998/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
This paper describes a new system, GLIMMER, for finding genes in microbial genomes. In a series of tests on Haemophilus influenzae , Helicobacter pylori and other complete microbial genomes, this system has proven to be very accurate at locating virtually all the genes in these sequences, outperforming previous methods. A conservative estimate based on experiments on H.pylori and H. influenzae is that the system finds >97% of all genes. GLIMMER uses interpolated Markov models (IMMs) as a framework for capturing dependencies between nearby nucleotides in a DNA sequence. An IMM-based method makes predictions based on a variable context; i.e., a variable-length oligomer in a DNA sequence. The context used by GLIMMER changes depending on the local composition of the sequence. As a result, GLIMMER is more flexible and more powerful than fixed-order Markov methods, which have previously been the primary content-based technique for finding genes in microbial DNA.