deep learning-inspired explainable sequence model for transcription initiation

Code for the data processing and analyses present on the manuscript: "Cellular energy regulates mRNA translation and degradation in a codon-specific manner".

Draw RNA secondary structures in python.

Neural network regression model of translation elongation rate, with DP algorithm to optimize fast coding sequences under model

Predicting ribosome stalling using ROSE.

CodonTransformer: The ultimate tool for codon optimization, optimizing DNA sequences for heterologous protein expression across 164 species.

Repository for MolFormer

bpRNA: Large-scale Annotation and Analysis of RNA Secondary Structure

Code repository of "Deep generative design of RNA family sequences"

Evolutionary Scale Modeling (esm): Pretrained language models for proteins

Efficient evolution from protein language models

Computationally Restoring the Potency of a Clinical Antibody

Protein language model trained on coding DNA

Open source code for AlphaFold.

This package contains deep learning models and related scripts for RoseTTAFold

gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences.

CodonBert: a BERT-based architecture tailored for codon optimization using the cross-attention mechanism.

🧬 Generative modeling of regulatory DNA sequences with diffusion probabilistic models 💨

Rational Oligonucleotide design Calculated with Kinetic parameter for Enhanced in vitro Transcription

Genome-wide Efficient Mixed Model Association

Various algorithms for analysing genomics data

Module and script to remove contamination in assembled genomes before submission to ncbi

Repository for mRNA Paper and CodonBERT publication.