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Software

  • CellRepo: Cloud-based version control system for digital twins in bioengineering https://cellrepo.herokuapp.com/ (L. Hobbs);
  • BakodoA Ruby gem that is capable of calculating DNA barcodes and their digital equivalents for use in CellRepo  (L. Hobbs);
  • olibrary: Scripts to build origami domain level model from topology files (B. Shirt-Ediss);
  • orenderScripts to render scaffold route and staple routes for 2D origamis using Kamada Kawai layout algorithm (B. Shirt-Ediss);
  • ofold: Origami folding model, developed in collaboration with Dr. Juan Elezgaray (Bordeaux University) (B. Shirt-Ediss);
  • revnanoOrigami design reverse engineering from sequences (B. Shirt-Ediss);
  • stackxy: DNA data stack with X and Y bricks (stochastic model, to be published with paper) (B. Shirt-Ediss);
  • stackwDeterministic chemistry model for DNA stack with no washing (COPASI+python) (B. Shirt-Ediss);
  • stack-uvkinetics: Scripts to turn UV Vis data to k rate constants for DNA data stack proj (B. Shirt-Ediss);
  • circadian: Cyanobacteria circadian clock models. Master student Zac Rubin (2018) helped development (B. Shirt-Ediss);
  • onepot: Multi-objective selection of origami scaffold sequences using “well folding” heuristics. (B. Shirt-Ediss);
  • ROTC system to identify genes associated with specific stress conditions in bacteria (David Markham, Anil Wipat);
  • mcSTACKa CTMC-based model checker for DNA stack system (Bowen Li);
  • https://bitbucket.org/JordanConnolly/dna-origami-dataset/src/master/ (Jordon Conolly)
  • Source Code bioinformatics paper https://github.com/chang88ye/NIHBA (Shouyong Jiang);
  • Codes used in paper ‘Computational strategies for the identification of transcriptional biomarker panels to sense cellular growth states in Bacillus subtilis‘; https://github.com/neverbehym/transcriptional-biomarkers-subtiliscreated byWe designed a set of computational strategies to identify a few key genes indicative of cellular growth state in bacteria via data mining on bacteria condition-dependent transcriptomes. It can (i) construct a transcriptional landscape capturing the transcriptome shifts under conditions where similar transcriptional states were positioned close; (ii) discover clusters of samples to represent distinct transcriptional states which correlates with cellular growth state in the landscape; (iii) identify reduced sets of biomarker genes that can pinpoint the cluster in the landscape.(Yiming Huang);