Software

Use description

ChatGPT

We used ChatGPT to enhance the clarity and accuracy of our written content by correcting grammar and punctuation, particularly for our Wiki texts. Additionally, the tool supported us in debugging code throughout the development of our project.

Perplexity AI

We used Perplexity A Ito assist in our brainstorming sessions and to identify relevant literature on key topics related to our project.

AlphaFold

We used AlphaFold to generate a structural prediction of Cas13, which we then utilized to create visual representations for presentations and other project materials.

Canva

We utilized Canva to design presentations and organize graphics for the Wiki. Our primary use of Canva involved arranging elements and adding simple components like arrows and text. The core graphics, however, were created by our team’s talented graphic designer, Kasia Urbanelis.

SnapGene

We used SnapGene to design, edit, and visualize vector maps and our biological parts throughout the project.

Benchling

We used Benchling to maintain our laboratory notebooks throughout the course of our project.

Microsoft Excel

We used Excel extensively for analyzing SHERLOCK fluorescence readout data throughout our project. Additionally, we created several plots in Excel, which are featured on our Wiki.

Inkscape

We used Inkscape to design our team logo and create various graphics featured on our Wiki.

Gimp

We used GIMP to assist in designing our team logo and creating graphics for the Wiki.

TinkerCAD

We used TinkerCAD to design models of our 3D-printed components for our hardware.

AutoCAD

We used AutoCAD to refine our models of the 3D-printed components for our hardware.

PrusaSlicer

We used PrusaSlicer to generate files for 3D printing our hardware components.

Mathjax

We used MathJax to incorporate mathematical equations into our Wiki.

OpenCV

We used the Python OpenCV library for image processing of our fluorescence readout images while designing our PrymChip software.

NumPy

We used the Python NumPy library to perform calculations in the code for our PrymChip software.

Scikit-learn

We used the Python library Scikit-learn to analyze data in our PrymChip software, establishing a relationship between green fluorescence intensity and known concentrations.

Scipy

We used the Python Scipy library for performing data analysis throughout our project.

Pandas

We used the Python Pandas library for performing data analysis throughout our project.

Maplotlib

We used the Python library Matplotlib for generating multiple plots that have been included in the Wiki.

BUSCO

We used BUSCO to perform analyses on the Prymnesium parvum genome data.

BLAST+

We used BLAST+ to perform analyses on the Prymnesium parvum genome data.

Plotly

We used the Python library Plotly to generate multiple plots that have been included in the Wiki.

MEGA

We used MEGA to analyze Prymnesium parvum genome data.

Visual Studio Code

We used VSC as a code editor throughout our project.