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+ | <p>All our software tools as well as their descriptions can be found <a | ||
+ | href="https://github.com/igemsoftware2019/iGemMarburg2019">on our github.</a> | ||
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Latest revision as of 16:46, 8 December 2019
S O F T W A R E
Our Software Tools
All our software tools as well as their descriptions can be found on our github.
aNSo - artificial Neutral integration Site option
This folder contains a Python script to find artificial Neutral integration Site options in a genome. Furthermore, it contains the input data used to find aNSo in the genome of Synechococcus elongatus UTEX 2973, such as the genome sequenced by Yu et al., 2015 as a FASTA file and a GenBank (gbk) file comprising all genes and TSS with their positions identified in a transcriptomics study by Tan et al., 2018, and the generated results for S. elongatus. To run this script Python 3 is required, using the modules SeqIO from Bio, pandas, re and os.
Growth Curve Model
Model utilizes scikit learn and uses polynomial regression to predict doubling times. The number of parameters given to the script to train is unlimited, there is no train test split or crossvalidation implemented, but it can be easily added.
Terminator Model
Kinetics and thermodynamic data for an already filtered list of natural intrinsic terminators of S. elongatus UTEX 2973. The data was mostly generated using MFOLD and KineFold.
GUIDE and Cloning Protocols
An easy to use graphical user interface for the overview and execution of OT-2 protocols. It was created in C sharp for the BioHackathon2019 organised by iGEM Vilnius. This packages also includes some prewritten OT-2 protocols. Please keep in mind that this is an alpha version and therefore might contain bugs.
Promega Plasmid Purification Protocols
The iGEM team Marburg 2019 automated Plasmid Purification for Opentrons OT-2 using the Wizard® MagneSil® Plasmid Purification System. This folder contains one Python script and one Jupyter Notebook to run the Plasmid Purification with a p300 Single-Channel Electronic Pipette for up to six samples and another Python script and Jupyter Notebook for the use of a p300 8-Channel Electronic Pipette for up to 48 samples. The user just has to change the variable "amount" to the number of samples for the Single Pipette or the number of used columns of a 96 Deep Well Plate for the 8-Channel Pipette.
Marburg Colony Identification Neural Network (MCoINN)
In the AI folder you can find the AI which is used to detect colonies in images.