BACH is a gene composer software that optimizes coding sequences and calculates corresponding RiPS to illustrate their translation rates. In sequence optimization, Bach incorporates 4 distinct approaches from which users can freely choose: SYNONYMOUS SUBSTITUTION, SLOW, FAST and MATCH. In the last section of GET RIPS, users are provided with a quantitative view of the translational behavior of the input coding sequence and the four sequences produced by the four methods as well.

  "CloneAssistant 1.0" is a standalone bio-software, which can automatically design the cloning primers for users with full consideration of the sequence information of vectors and genes, cloning strategies, the principles of primer design, reading frame, position effects, and enzymatic reaction conditions. It can be used for restriction map analysis, ORF (open reading frame) finding, sequence alignment and complementary analysis, translation, restriction enzyme and universal buffer query, and isocaudamer analysis.

  BNArray is a systemized tool developed in R. It facilitates the construction of gene regulatory networks from DNA microarray data by using Bayesian network. Significant submodules of regulatory networks with high confidence are reconstructed using our extended sub-network mining algorithm for directed graphs.
  To evaluate the statistical features of generated Bayesian networks, re-sampling procedure are utilized to yield collection of candidates networks. These 1st-order network sets are used to mine dense coherent sub-networks. Additionally, BNArray can handle microarray data sets with missing data.
  BNArray package is implemented in R, an open source programming environment. It has four main function modules:
    1. Imputing missing data in microarray experiments with LLSimpute algorithm. Thus, we can input complete database for constructing Bayesian networks.
    2. Constructing Bayesian networks for gene regulation. We utilize previously implemented R package deal for learning Bayesian networks with mixed variables.
    3. Re-sampling microarray data set to produce more reliable data using Efron's Bootstrap, and then repeating procedure 2 to construct a collection of 1st-order Bayesian networks with high scores.
    4. Reconstructing significant coherent regulatory sub-networks with our extended CODENSE algorithm for directed graph from previously induced candidate Bayesian networks.
  BNArray allows users to specify their own parameters and modify the open source code to meet their individual needs.