Generating Random Genomic Sequences and Structures with GenRGenS

Random sequences can be used to extract relevant information from biological sequences. The random sequences represent the “background noise” from which it is possible to differentiate the real biological information. For example, random sequences are widely used to detect over-represented and under-represented motifs, or to determine whether the scores of pairwise alignments are relevant. Analytic approaches exist for solving these kinds of problems although for the most complex cases, an experimental approach (i.e. the computer generation of random sequences) is still necessary. Some programs are already currently available for generating random sequences, such as HmmerEmit and Corrupt in the GCG package [Butler, 1998], Seq-Gen [Rambaut and Grassly, 1997] and RandSeq [Gasteiger et al., 2003], Shufflet [Coward, 1999]. However, until now, there has been no software package that can integrate several statistical and syntaxical models of random sequences and combine them. This is the purpose of GenRGenS.