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Random DNA sequence generation software tools | Synthetic biology

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.

Source text:
(Liu et al., 2016) NullSeq: A Tool for Generating Random Coding Sequences with Desired Amino Acid and GC Contents. PLoS Comput Biol.

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NullSeq
Generates unbiased random sequences with pre-specified amino acid and GC content, which has been developed into a python package. NullSeq method allows users to obtain maximally unbiased random sequences that are subject to GC usage and primary amino acid sequence constraints. Furthermore, this approach can easily be expanded to create unbiased random sequences that incorporate more complicated constraints such as individual nucleotide usage or even di-nucleotide frequencies. The ability to generate correctly specified null models will allow researchers to accurately identify sequence motifs which will lead to a better understanding of biological processes as well as more effective engineering of biological systems.
CutFree
Identifies the most diverse randomer that is free of recognition sites for any set of restriction enzymes. CutFree converts the problem of blocking restriction sites into a mixed-integer linear program (MILP) that returns a site-free randomer of maximum diversity. It can be applied to block a randomer of any length from containing arbitrary sequences. This tool ensures large numbers of barcode sequences and is compatible with restriction enzyme assembly methods like Golden Gate cloning.
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