MGC: A metagenomic gene caller

BMC Bioinformatics

Volumn 14, Issue SUPPL9, 2013, Pages

  El Allali, Achraf ^a   Rose, John R ^a  

^a University of South Carolina   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLETE GENOMES; MACHINE LEARNING APPROACHES; OPEN READING FRAME; OVERALL ACCURACIES; PREDICTION ALGORITHMS; SINGLE MODEL APPROACH; TRAINING MODEL; TRANSLATION INITIATION SITE;

ALGORITHMS; LEARNING SYSTEMS; SEPARATION;

GENES;

ALGORITHM; ARCHAEAL GENOME; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACTERIAL GENOME; BIOLOGY; CODON; COMPARATIVE STUDY; DNA BASE COMPOSITION; METAGENOMICS; METHODOLOGY; NUCLEOTIDE SEQUENCE; OPEN READING FRAME; PROCEDURES;

ALGORITHMS; BASE COMPOSITION; BASE SEQUENCE; CODON; COMPUTATIONAL BIOLOGY; GENOME, ARCHAEAL; GENOME, BACTERIAL; METAGENOMICS; NEURAL NETWORKS (COMPUTER); OPEN READING FRAMES;

CODON;

EID: 84887145957     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-S9-S6     Document Type: Article

References (22)

1. Hoff KJ, Lingner T, Meinicke P, Tech M. Orphelia: predicting genes in metagenomic sequencing reads. Nucleic acids research 2009, 37(Web Server):W101-5. 10.1093/nar/gkp327, 2703946, 19429689., http://www.ncbi.nlm.nih.gov/pubmed/19429689
2. Allali AE, Rose JR. MIM: A Species Independent Approach for Classifying Coding and Non-Coding DNA Sequences in Bacterial and Archaeal Genomes. Engineering and Technology 2010, 411-418.
3. Chaisson MJ, Pevzner PA. Short read fragment assembly of bacterial genomes. Genome Research 2008, 18(2):324-330. 10.1101/gr.7088808, 2203630, 18083777.
4. Butler J, MacCallum I, Kleber M, Shlyakhter IA, Belmonte MK, Lander ES, Nusbaum C, Jaffe DB. ALLPATHS: De novo assembly of whole-genome shotgun microreads. Genome Research 2008, 18(5):810-820. 10.1101/gr.7337908, 2336810, 18340039., http://www.ncbi.nlm.nih.gov/pubmed/18340039
5. Noguchi H, Park J, Takagi T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Research 2006, 34(19):5623-5630. 10.1093/nar/gkl723, 1636498, 17028096., http://www.ncbi.nlm.nih.gov/pubmed/17028096
6. Borodovsky M, Mills R, Besemer J, Lomsadze A. Prokaryotic gene prediction using GeneMark and GeneMark.hmm. Current protocols in bioinformatics editoral board Andreas D Baxevanis et al 2003, Chapter 4:Unit4.5., http://www.ncbi.nlm.nih.gov/pubmed/18428700
7. Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Research 2010, 38(20):e191. 10.1093/nar/gkq747, 2978382, 20805240.
8. Chan PK, Stolfo SJ. A comparative evaluation of voting and meta-learning on partitioned data. Proc 12th International Conference on Machine Learning 1995, 90-98. Morgan Kaufmann., http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.47.7713
9. Singer GA, Hickey DA. Nucleotide bias causes a genomewide bias in the amino acid composition of proteins. Molecular Biology and Evolution 2000, 17(11):1581-1588. 10.1093/oxfordjournals.molbev.a026257, 11070046., http://www.ncbi.nlm.nih.gov/pubmed/11070046
10. Lightfield J, Fram NR, Ely B. Across Bacterial Phyla, Distantly-Related Genomes with Similar Genomic GC Content Have Similar Patterns of Amino Acid Usage. PLoS ONE 2011, 6(3):12. 3053387, 21423704.
11. Oliver JL, Marín A. A relationship between GC content and coding-sequence length. Journal of Molecular Evolution 1996, 43(3):216-223. 10.1007/BF02338829, 8703087.
12. Misawa K, Kikuno RF. Relationship between amino acid composition and gene expression in the mouse genome. BMC research notes 2011, 4:20. 10.1186/1756-0500-4-20, 3038927, 21272306.
13. Wilkins MR, Pasquali C, Appel RD, Ou K, Golaz O, Sanchez JC, Yan JX, Gooley AA, Hughes G, Humphery-Smith I, Williams KL, Hochstrasser DF. From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Biotechnology NY 1996, 14:61-65. 10.1038/nbt0196-61, 9636313.
14. Hobohm U, Sander C. A sequence property approach to searching protein databases. Journal of Molecular Biology 1995, 251(3):390-399. 10.1006/jmbi.1995.0442, 7650738.
15. Guruprasad K, Reddy BV, Pandit MW. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Engineering 1990, 4(2):155-161. 10.1093/protein/4.2.155, 2075190.
16. Merkl R. A survey of codon and amino acid frequency bias in microbial genomes focusing on translational efficiency. Journal of Molecular Evolution 2003, 57(4):453-466. 10.1007/s00239-003-2499-1, 14708578.
17. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank: update. Nucleic Acids Research 2004, 32(Database):D23-D26. 308779, 14681350., http://www.ncbi.nlm.nih.gov/pubmed/14681350
18. Hoff KJ, Tech M, Lingner T, Daniel R, Morgenstern B, Meinicke P. Gene prediction in metagenomic fragments: a large scale machine learning approach. BMC bioinformatics 2008, 9:217. 10.1186/1471-2105-9-217, 2409338, 18442389., http://www.ncbi.nlm.nih.gov/pubmed/18442389
19. MacKay DJC. A Practical Bayesian Framework for Backpropagation Networks. Neural Computation 1992, 4(3):448-472., http://www.mitpressjournals.org/doi/abs/10.1162/neco.1992.4.3.448
20. Nabney I. NETLAB: algorithms for pattern recognition (Google eBook) 2002, Springer., http://www.springer.com/computer/ai/book/978-1-85233-440-6
21. Thomas T, Gilbert J, Meyer F. Metagenomics - a guide from sampling to data analysis. Microbial Informatics and Experimentation 2012, 2:3. 10.1186/2042-5783-2-3, 3351745, 22587947., http://www.microbialinformaticsj.com/content/2/1/3
22. Hansen JV, Krogh A. A general method for combining predictors tested on protein secondary structure prediction. Artificial Neural Networks in Medicine and Biology 2000, 259-264., http://link.springer.com/chapter/10.1007%2F978-1-4471-0513-8_39