RestrictionDigest: A powerful Perl module for simulating genomic restriction digests

• Jinpeng Wang ^[1] ; Li Li ^[1] ; Haigang Qi ^[1] ; Xuedi Du ^[1] ; Guofan Zhang ^[1]
  1. [1] Chinese Academy of Sciences

     Chinese Academy of Sciences

     China

     
• Localización: Electronic Journal of Biotechnology, ISSN-e 0717-3458, Vol. 19, Nº. 3, 2016, págs. 36-42
• Idioma: inglés
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• Resumen

  • Background: Reduced-representation sequencing technology is widely used in genotyping for its economical and efficient features. A popular way to construct the reduced-representation sequencing libraries is to digest the genomic DNA with restriction enzymes. A key factor of this method is to determine the restriction enzyme(s). But there are few computer programs which can evaluate the usability of restriction enzymes in reduced-representation sequencing. SimRAD is an R package which can simulate the digestion of DNA sequence by restriction enzymes and return enzyme loci number as well as fragment number. But for linkage mapping analysis, enzyme loci distribution is also an important factor to evaluate the enzyme. For phylogenetic studies, comparison of the enzyme performance across multiple genomes is important. It is strongly needed to develop a simulation tool to implement these functions. Results: Here, we introduce a Perl module named RestrictionDigest with more functions and improved performance. It can analyze multiple genomes at one run and generate concise comparison of enzyme performance across the genomes. It can simulate single-enzyme digestion, double-enzyme digestion and size selection process and generate comprehensive information of the simulation including enzyme loci number, fragment number, sequences of the fragments, positions of restriction sites on the genome, the coverage of digested fragments on different genome regions and detailed fragment length distribution. Conclusions: RestrictionDigest is an easy-to-use Perl module with flexible parameter settings. With the help of the information produced by the module, researchers can easily determine the most appropriate enzymes to construct the reduced-representation libraries to meet their experimental requirements.