Table 7 Deep learning packages and resources
From: A review of deep learning applications in human genomics using next-generation sequencing data
Resource Name | Category | Application | Date created | Link | Free/paid |
|---|---|---|---|---|---|
Libraries | |||||
Janggua | Python package | facilitates deep learning in the context of genomics | 2020 | Free | |
ExPectoa | Python-based repository | Contains code for predicting expression effects of human genome variants ab initio from sequence | 2018 | Free | |
Selenea | PyTorch-based Library | A library for biological sequence data training and model architecture development | 2019 | Free | |
Pysstera | TensorFlow-based Library | Used for learning sequence and structure motifs In biological sequences using convolutional neural networks | 2018 | Free | |
Kipoia | Python package | Kipoi is an API and a repository of ready-to-use trained models for genomics | 2019 | Free | |
Compute platform | |||||
Google Colaboratory (Colab) | PnP GPUs | Colab allows anybody to write and execute arbitrary python code through the browser, and is especially well suited to machine learning, data analysis and education | 2017 | Free | |
IBM Cloud | Cloud service | Cloud computing platform; Design complex neural networks, then experiment at scale to deploy optimised learning models within IBM Watson Studio | 2011 | Free tier Cost tier | |
Google CloudML | PnP GPUs | For extreme scalability in the long run | 2008 | Paid | |
Vertex AI | AI platform | Google Cloud’s new unified ML platform | 2021 |  | |
Amazon EC2 | Cloud service | A website facility which delivers secure, scalable compute power in the cloud | 2006 | Free Paid | |