FOCUS, an innovative and agile composition based using non-negative least squares to profile and report abundant organisms present in me-tagenomic samples and their relative abundance without sequence length dependencies. The program was tested with simulated and real metagenomes, and the results show that our approach predicts the organisms in random communities.

Availability and implementation: The code implemented in Python can be found here and a web-sever at http://edwards.sdsu.edu/FOCUS.

Dependencies: Jellyfish, Numpy, and Scipy.

Cite FOCUS

Silva, G. G. Z., D. A. Cuevas, B. E. Dutilh, and R. A. Edwards, 2014: FOCUS: an alignment-free model to identify organisms in metagenomes using non-negative least squares. PeerJ, 2, e425,doi:10.7717/peerj.425.

Features

  • Metagenomes
  • Richness
  • Evenness

Project Samples

Genera-level taxonomy classification sorted by FOCUS prediction for the metagenome from a diseased human oral cavity using FOCUS, MetaPhlAn, MG-RAST, PhymnBL, RAIphy, Taxy, and FOCUS (mean). Error bars represent the standard deviation uncertainty in tested metagenome Scalability test using different sub-sets of the human oral cavity under disease metagenome using FOCUS, MetaPhlAn, MG-RAST, PhymnBL, RAIphy, Taxy Genera-level taxonomy classification sorted by FOCUS prediction for the metagenome from a health human oral cavity using FOCUS, MetaPhlAn, MG-RAST, PhymnBL, RAIphy, Taxy, and FOCUS (mean). Error bars show the standard deviation uncertainty for the real metagenome. Genera-level taxonomy classification for the SimShort dataset using FOCUS, PhymnBL, RAIphy, and FOCUS (mean). Class-level taxonomy classification for the SimHC dataset using FOCUS, PhymnBL, RAIphy, and FOCUS (mean) Workflow of the FOCUS program

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Registered

2013-06-29
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