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The existence of over- and under-represented sequence motifs in genomes provides evidence of selective evolutionary pressures on biological mechanisms such as transcription, translation, ligand-substrate binding, and host immunity. In order to accurately identify motifs and other genome-scale patterns of interest, it is essential to be able to generate accurate null models that are appropriate for the sequences under study.
(Liu et al., 2016) NullSeq: A Tool for Generating Random Coding Sequences with Desired Amino Acid and GC Contents. PLoS Comput Biol.