vcontact

Viral Contig Automatic Clustering and Taxonomy (legacy version)

https://github.com/geeklhem/vcontact

Science Score: 31.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 8 DOI reference(s) in README
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (4.2%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Viral Contig Automatic Clustering and Taxonomy (legacy version)

Basic Info
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 4
  • Releases: 0
Created about 12 years ago · Last pushed over 8 years ago
Metadata Files
Readme License Citation

README

# This is a very old version of vContact that was written during my master degree ! 

It does not reflect the current version of vContact and should **NOT** be used in production.
I keep this repository for **historic purpose only**, since I am no more involved in the development of vContact.

vConTACT lives here now: 
>    Bolduc B, Jang HB, Doulcier G, You Z, Roux S, Sullivan MB. (2017) 
>    vConTACT: an iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria. PeerJ 5:e3243 
>    https://doi.org/10.7717/peerj.3243 

The documentation to use vContact within CyVerse can be found here:
- Preparing data: dx.doi.org/10.17504/protocols.io.gwdbxa6
- Running: dx.doi.org/10.17504/protocols.io.gwcbxaw

Owner

  • Name: Guilhem Doulcier
  • Login: geeklhem
  • Kind: user
  • Location: Paris (France)

Citation (CITATION)

If you use this software please cite:

Automatic taxonomic affiliation via “Guilt by contig association” in viral metagenomic data.
Guilhem Doulcier,
Master degree internship report,
2014.

GitHub Events

Total
Last Year

Dependencies

setup.py pypi
  • biopython ==1.63
  • networkx ==1.8.1
  • numpy ==1.8.0
  • pandas ==0.13.1
  • scikit-learn ==0.14.1
  • scipy ==0.13.3