AMI stands for Amazon Machine Image, which is a pre-configured virtual machine image used to create EC2 instances in AWS. An AMI contains all the information needed to launch an EC2 instance, including the operating system, application server, and any other software required to run an application.
There are several AMIs available on AWS that are optimized for bioinformatics workloads. Here are some popular options:
- Bio-Linux: Bio-Linux is a Debian-based operating system that is pre-configured with a range of bioinformatics tools and databases. It includes over 500 bioinformatics packages, making it a popular choice for researchers and bioinformaticians.
- Amazon Machine Learning AMI: The Amazon Machine Learning AMI is a pre-configured environment that includes several bioinformatics tools and frameworks, including TensorFlow, Theano, and Caffe. It also includes tools for data processing and visualization, making it a good choice for machine learning and data science workloads in bioinformatics.
- Deep Learning AMI (Ubuntu): The Deep Learning AMI is a pre-configured environment for deep learning and AI workloads. It includes several popular deep learning frameworks, including TensorFlow, PyTorch, and MXNet, as well as several bioinformatics tools and databases.
- Illumina BaseSpace Sequence Hub: The Illumina BaseSpace Sequence Hub is a cloud-based analysis platform that is optimized for next-generation sequencing (NGS) data. It includes several bioinformatics tools and workflows, and is designed to handle large-scale NGS data analysis.
- DNAnexus Apollo: DNAnexus Apollo is a cloud-based bioinformatics platform that includes several tools and workflows for NGS data analysis. It supports several popular sequencing platforms and provides a range of analysis tools and pipelines.
These are just a few of the many AMIs available on AWS for bioinformatics workloads. It’s important to choose an AMI that meets your specific requirements and provides the tools and frameworks you need for your research or application.