Phage Tools & Resources
We are collecting a list of phage bioinformatics tools and resources.
Showing 5 tools
PATRIC is the Bacterial Bioinformatics Resource Center, an information system designed to support the biomedical research community’s work on bacterial infectious diseases via integration of vital pathogen information with rich data and analysis tools. PATRIC sharpens and hones the scope of available bacterial phylogenomic data from numerous sources specifically for the bacterial research community, in order to save biologists time and effort when conducting comparative analyses. The freely available PATRIC platform provides an interface for biologists to discover data and information and conduct comprehensive comparative genomics and other analyses in a one-stop shop
Web Apollo is the first instantaneous, collaborative genomic annotation editor available on the web. One of the natural consequences following from current advances in sequencing technology is that there are more and more researchers sequencing new genomes. These researchers require tools to describe the functional features of their newly sequenced genomes. With Web Apollo researchers can use any of the common browsers (for example, Chrome or Firefox) to jointly analyze and precisely describe the features of a genome in real time, whether they are in the same room or working from opposite sides of the world.
Read more here:
Apollo: Democratizing genome annotation
Web Apollo: a web-based genomic annotation editing platform
Galaxy (Center for Phage Technology)
Galaxy is an open source, web-based platform for data intensive biomedical research.
The above URL links to the Center for Phage Technology’s instance of Galaxy.
To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for any biological sequence data. We demonstrate on DNA sequences how Selene allows researchers to easily train a published architecture on new data, develop and evaluate a new architecture, and use a trained model to answer biological questions of interest.
Read the full paper here: https://www.nature.com/articles/s41592-019-0360-8
CyVerse provides life scientists with powerful computational infrastructure to handle huge datasets and complex analyses, thus enabling data-driven discovery. Our extensible platforms provide data storage, bioinformatics tools, image analyses, cloud services, APIs, and more.
CyVerse is funded by the National Science Foundation’s Directorate for Biological Sciences. We are a dynamic virtual organization led by the University of Arizona to fulfill a broad mission that spans our partner institutions: Texas Advanced Computing Center, and Cold Spring Harbor Laboratory.