A Go package for engineering organisms.
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Updated
Oct 21, 2024 - Go
A Go package for engineering organisms.
Basecalling, alignment, assembly and deconvolution of Sanger Chromatogram trace files
The resources I always recommend to new synthetic biologists.
A lua package for designing DNA.
This repo focuses on supervised and self-supervised bio-sequence representation learning
Blog on the computational side of synthetic biology and DNA manufacturing
A Metaculus human judgment forecasting tournament established to generate forecasts and scenarios pertaining to the use of human genetic and reproductive technologies for treatment and enhancement.
I watched a video on genetic engineering and thought it was really cool. I decided to make some simple tools to work with DNA sequences for fun.
This repository provides simple bioinformatics tools for reading FASTQ and FASTA files, building k-mer overlap graphs for genome assembly, and performing pattern matching using edit distance for sequence alignment tasks
A kinetic model of CRISPR-Cas target recognition.
A curated list of awesome resources for genetic engineering.
Python repository for anyone interested in Synthetic Biology, Laboratory Automation, robots and Genetic Engineering.
🔬 BioSynth is an innovative tool designed to simulate and visualize protein synthesis from DNA sequences. It integrates modern bioinformatics methods to provide a detailed understanding of the relationship between DNA sequences and their corresponding protein structures.
StreptoCAD is an open-source toolbox that automates the design of genome engineering strategies in Streptomyces. It supports CRISPR-based workflows, generating primers, guide sequences, and plasmid simulations to accelerate genetic engineering and natural product discovery.
Future For Earth
SIRIUS is a synthetic biology tool that, given a peptide sequence, designs an array of maximally divergent gene sequences
The personal blog of Larry Rodriguez
A library to read, manipulate and write various genetic sequencing formats.
This project utilizes a series of algorithms, adversarial agents, and simulation tools to study and generate new multicellular organisms; a user interface allows human guidance to tailor desired results.
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