Journal article
Benchmarking of gene prediction programs for metagenomic data
Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2010, pp 6190-6193
2010
PMID: 21097156
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This manuscript presents the most rigorous benchmarking of gene annotation algorithms for metagenomic datasets to date. We compare three different programs: GeneMark, MetaGeneAnnotator (MGA) and Orphelia. The comparisons are based on their performances over simulated fragments from one hundred species of diverse lineages. We defined four different types of fragments; two types come from the inter- and intra-coding regions and the other types are from the gene edges. Hoff et al. used only 12 species in their comparison; therefore, their sample is too small to represent an environmental sample. Also, no predecessors has separately examined fragments that contain gene edges as opposed to intra-coding regions. General observations in our results are that performances of all these programs improve as we increase the length of the fragment. On the other hand, intra-coding fragments of our data show low annotation error in all of the programs if compared to the gene edge fragments. Overall, we found an upper-bound performance by combining all the methods.
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Details
- Title
- Benchmarking of gene prediction programs for metagenomic data
- Creators
- Non Yok - Drexel University, Electrical and Computer Engineering Department, 3141 Chestnut Street, PA 19104, USA. ng39@drexel.eduGail Rosen
- Publication Details
- Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), v 2010, pp 6190-6193
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE); United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000287964006147
- Scopus ID
- 2-s2.0-78650812650
- Other Identifier
- 1424441242; 9781424441242; 991014877903904721
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- Web of Science research areas
- Engineering, Biomedical
- Engineering, Electrical & Electronic