Best Bioinformatics Software for GitHub
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Compare the Top Bioinformatics Software that integrates with GitHub as of June 2025
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This a list of Bioinformatics software that integrates with GitHub. Use the filters on the left to add additional filters for products that have integrations with GitHub. View the products that work with GitHub in the table below.
What is Bioinformatics Software for GitHub?
Bioinformatics software is a type of software designed to analyze biological data. It can be used for processes such as gene sequencing, analyzing DNA structure, or modeling protein interactions. Many bioinformatics software programs are available and offer various tools and features, depending on the type of analysis required. These programs are mostly built using high-level programming language that is accessible to both scientists and researchers with expertise in the field. Compare and read user reviews of the best Bioinformatics software for GitHub currently available using the table below. This list is updated regularly.
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Elucidata Polly
Elucidata
Harness the power of biomedical data with Polly. The Polly Platform helps to scale batch jobs, workflows, coding environments and visualization applications. Polly allows resource pooling and provides optimal resource allocation based on your usage requirements and makes use of spot instances whenever possible. All this leads to optimization, efficiency, faster response time and lower costs for the resources. Get access to a dashboard to monitor resource usage and cost real time and minimize overhead of resource management by your IT team. Version control is integral to Polly’s infrastructure. Polly ensures version control for your workflows and analyses through a combination of dockers and interactive notebooks. We have built a mechanism that allows the data, code and the environment co-exist. This coupled with data storage on the cloud and the ability to share projects ensures reproducibility of every analysis you perform. -
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Evo 2
Arc Institute
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It utilizes a frontier deep learning architecture to model biological sequences at single-nucleotide resolution, achieving near-linear scaling of compute and memory relative to context length. Trained with 40 billion parameters and a 1 megabase context length, Evo 2 processes over 9 trillion nucleotides from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function prediction across multiple biological modalities, including DNA, RNA, and proteins, and to generate novel sequences with plausible genomic architecture. The model's capabilities have been demonstrated in tasks such as designing functional CRISPR systems and predicting disease-causing mutations in human genes. Evo 2 is publicly accessible via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework.
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