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- W19421310 abstract "Random sequences are very useful in bioinformatics. According to a very powerful paradigm, they represent the “background noise” from which the actual biological information must differentiate. For example, random sequences are widely used in order to detect overand under-represented motifs, or to determine whether scores of pairwise alignments are relevant. Analytic approaches do exist for these kinds of problems. But, for the most complex cases, experimental approach (i.e. in silico generation of random sequences) is still necessary. Random sequences must obey to a model that takes into account some relevant properties of actual sequences. Usually, the model is based on statistical parameters only: the numbers of occurrences of oligomers in a given biological sequence lead to the parameters of a Markovian model. Our aim is to enrich these classical models by adding structural parameters. Comparing biological sequences to such more “realistic” random sequences may help to discover more refined biological properties. We present a methodology and a software, GenRGenS, for generating genomic sequences and structures, which obey both to statistical and syntaxic (grammatical) parameters. In section 2, we briefly describe an algorithm which generates random words of a context-free language according to a given probability distribution. In section 3, we illustrate the method by applying it to basic RNA secondary structures, using the GenRGenS software." @default.
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- W19421310 date "2003-01-01" @default.
- W19421310 modified "2023-09-27" @default.
- W19421310 title "Random generation of structured genomic sequences" @default.
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