MetaRon
DeePhage
MetaRon | DeePhage | |
---|---|---|
1 | 1 | |
6 | 19 | |
- | - | |
1.8 | 3.9 | |
about 2 years ago | 6 months ago | |
Python | MATLAB | |
GNU General Public License v3.0 or later | GNU Lesser General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MetaRon
-
Bad tools that NEED improvement
MetaRon is a tool for metagenomic operon prediction that was published in BMC Genomics in 2020. As far as I can tell, no one has been able to get it to work, and the authors are unresponsive on GitHub. If I knew any python, I would take a crack at fixing it.
DeePhage
-
Bad tools that NEED improvement
Deephage and PPR-meta: both by the same group. They require MATLAB which makes them tricky on an HPC or cloud system. They both say that if you need to run on several samples concurrently, you must clone the tool to a new directory for each(!). Likely due to temporary files being written to the working directory. Entirely unscalable in that case.
What are some alternatives?
RNN-VirSeeker - This is a deep learning method for identification of viral contigs with short length from metagenomic data.
PPR-Meta - A tool for identifying phages and plasmids from metagenomic fragments using deep learning
metaGEM - :gem: An easy-to-use workflow for generating context specific genome-scale metabolic models and predicting metabolic interactions within microbial communities directly from metagenomic data
VirusSeeker-Virome - VirusSeeker is a set of fully automated and modular software package designed for mining sequence data to identify sequences of microbial origin.
virnet - VirNet: A deep attention model for viral reads identification
PhaMers - A bioinformatic tool for identifying bacteriophages using machine learning and k-mers