Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

Intervene: a tool for intersection and visualization of multiple genomic region and gene sets

A python wrapper for the benchling api

Python script to quickly extract promoter and terminator regions with the NCBI API. Search for the presence of individual pattern or transcription factor responsive elements with manual sequence (IUPAC) or JASPAR API.

Coronavirus genome analysis.

Deep neural networks implemented in TensorFlow & Python for predicting whether transcription factors will bind to given DNA sequences

Deep learning for identifying important motifs in DNA sequences. Exploration of the structure of the human genome by training neural network architectures (CNN, LSTM, Attention).

Bioinformatics 2020: Graph Neural Networks for DNA Sequence Classification

Python scripting library for generating designs readable by scadnano.

A python package and a set of shell commands to handle GTF files

Repository for the paper "Optimal design of stochastic DNA synthesis protocols based on generative sequence models" (Weinstein et al., AISTATS, 2022).

Pytorch implementation of DeePromoter Active sequence detection for promoter(DNA subsequence regulates transcription initiation of the gene by controlling the binding of RNA polymerase)

A DNA encryption algorithm based on a symmetric key encryption combined with a genetic algorithm

Yin, C. (2018). Encoding DNA sequences by integer Chaos Game Representation, Journal of Computational Biology

👾 Generate identicons for DNA sequences with Python

Simple Python program to perform codon optimization or heterology calculations.

Read DNA sequences from colourful Microsoft Word documents

Polyploid micro-haplotype assembly using Markov chain Monte Carlo simulation.

This repository contains the python package for Helical

This project includes Needleman-Wunsch and Smith-Waterman algorithms and their afine gap variations (Gotoh) written to work with Cython, PyPy and Numba. Numba JIT shows greater performance. For Best performance use gotoh_jit.py to get only the best score and use gotoh_jit_traceback to get the best alignment