Motivation: DNA barcodes are commonly used for counting and discriminating purposes in molecular and cell biology. Not every set of DNA sequences is equally suitable for this goal. There is a growing demand for more sophisticated barcode designs, with only few tools available. We prepared an R package that combines known algorithms and innovative methods for the efficient, flexible and near-optimal generation of robust barcode sets. Results: Our R-software package 'DNABarcodes' generates sets of DNA barcodes from a few basic input parameters (e.g. length, distance metric, minimum distance, chemical properties). It satisfies the specifics of most particular experimental demands in de novo design of barcodes. Additionally, the package allows analysing existing sets of DNA barcodes as well as the generation of subsets of those existing sets to improve their error correction and detection properties. 'DNABarcodes' was designed for speed, versatility, provable correctness and large set sizes. Availability and Implementation: The DNABarcodes R package is available from Bioconductor at http://bioconductor.org/packages/DNABarcodes under the GPL-2 license. Contact: tilo.buschmann@izi.fraunhofer.de. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: DNA barcodes are commonly used for counting and discriminating purposes in molecular and cell biology. Not every set of DNA sequences is equally suitable for this goal. There is a growing demand for more sophisticated barcode designs, with only few tools available. We prepared an R package that combines known algorithms and innovative methods for the efficient, flexible and near-optimal generation of robust barcode sets. Results: Our R-software package 'DNABarcodes' generates sets of DNA barcodes from a few basic input parameters (e.g. length, distance metric, minimum distance, chemical properties). It satisfies the specifics of most particular experimental demands in de novo design of barcodes. Additionally, the package allows analysing existing sets of DNA barcodes as well as the generation of subsets of those existing sets to improve their error correction and detection properties. 'DNABarcodes' was designed for speed, versatility, provable correctness and large set sizes. Availability and Implementation: The DNABarcodes R package is available from Bioconductor at http://bioconductor.org/packages/DNABarcodes under the GPL-2 license. Contact: tilo.buschmann@izi.fraunhofer.de. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Shahar Alon; Daniel R Goodwin; Anubhav Sinha; Asmamaw T Wassie; Fei Chen; George M Church; Adam H Marblestone; Edward S Boyden; Evan R Daugharthy; Yosuke Bando; Atsushi Kajita; Andrew G Xue; Karl Marrett; Robert Prior; Yi Cui; Andrew C Payne; Chun-Chen Yao; Ho-Jun Suk; Ru Wang; Chih-Chieh Jay Yu; Paul Tillberg; Paul Reginato; Nikita Pak; Songlei Liu; Sukanya Punthambaker; Eswar P R Iyer; Richie E Kohman; Jeremy A Miller; Ed S Lein; Ana Lako; Nicole Cullen; Scott Rodig; Karla Helvie; Daniel L Abravanel; Nikhil Wagle; Bruce E Johnson; Johanna Klughammer; Michal Slyper; Julia Waldman; Judit Jané-Valbuena; Orit Rozenblatt-Rosen; Aviv Regev Journal: Science Date: 2021-01-29 Impact factor: 47.728
Authors: Miwa Takahashi; Joseph D DiBattista; Simon Jarman; Stephen J Newman; Corey B Wakefield; Euan S Harvey; Michael Bunce Journal: Sci Rep Date: 2020-03-09 Impact factor: 4.379