New genes in old sequence: a strategy for finding genes in the bacterial genome.
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Amino Acid Sequence↗
Biomedical subjects
M Borodovsky
Publications and source records attributed to M Borodovsky.
Characteristic features of the nucleotide sequences of yeast mitochondrial ribosomal protein genes as analyzed by computer program GeneMark.
The nucleotide sequence data for yeast mitochondrial ribosomal protein (MRP) genes were analyzed by the computer program GeneMark which predicts the presence of likely genes in sequence data by calculating statistical biases in the appearance of consecutive nucleotides. The program uses a set of standard sequence data for this calculation. We used this program for the analysis of yeast nucleotide sequence data containing MRP genes, hoping to obtain information as to whether they share features in common that are different from other yeast genes. Sequence data sets for ordinary yeast genes and for 27 known MRP genes were used. The MRP genes were nicely predicted as likely genes regardless of the data sets used, whereas other yeast genes were predicted to be likely genes only when the data set for ordinary yeast genes was used. The assembled sequence data for chromosomes II, III, VIII and XI as well as the segmented data for chromosome V were analyzed in a similar manner. In addition to the known MRP genes, eleven ORF's were predicted to be likely MRP genes. Thus, the method seems very powerful in analyzing genes of heterologous origins.
Base Sequence↗
Recognition of genes in DNA sequence with ambiguities.
The search for genes in a newly sequenced DNA is a well known problem. Among other factors, the gene-searching process is hampered by a number of ambiguities which may remain unresolved experimentally for a long time. A computer method that is able to predict genes in a DNA sequence containing ambiguities has been developed, based on the non-homogeneous Markov chain technique. The reliability of the method has been tested using a set of sequences generated by a Monte-Carlo procedure and a set of 425 E. coli sequences with ambiguities introduced artificially.
DNA↗
First and second moment of counts of words in random texts generated by Markov chains.
An exact expression for the variance of random frequency that a given word has in text generated by a Markov chain is presented. The result is applied to periodic Markov chains, which describe the protein-coding DNA sequences better than simple Markov chains. A new solution to the problem of word overlap is proposed. It was found that the expected frequency and overlapping properties determine most of the variance. The expectation and variance of counts for triplets are compared with experimental counts in Escherichia coli coding sequences.
Algorithms↗