Title |
Identification of host-microbe interaction factors in the genomes of soft rot-associated pathogens Dickeya dadantii 3937 and Pectobacterium carotovorum WPP14 with supervised machine learning
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Published in |
BMC Genomics, June 2014
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DOI | 10.1186/1471-2164-15-508 |
Pubmed ID | |
Authors |
Bing Ma, Amy O Charkowski, Jeremy D Glasner, Nicole T Perna |
Abstract |
A wealth of genome sequences has provided thousands of genes of unknown function, but identification of functions for the large numbers of hypothetical genes in phytopathogens remains a challenge that impacts all research on plant-microbe interactions. Decades of research on the molecular basis of pathogenesis focused on a limited number of factors associated with long-known host-microbe interaction systems, providing limited direction into this challenge. Computational approaches to identify virulence genes often rely on two strategies: searching for sequence similarity to known host-microbe interaction factors from other organisms, and identifying islands of genes that discriminate between pathogens of one type and closely related non-pathogens or pathogens of a different type. The former is limited to known genes, excluding vast collections of genes of unknown function found in every genome. The latter lacks specificity, since many genes in genomic islands have little to do with host-interaction. |
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United States | 3 | 50% |
China | 1 | 17% |
Canada | 1 | 17% |
Unknown | 1 | 17% |
Demographic breakdown
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Mendeley readers
Geographical breakdown
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South Africa | 1 | 2% |
Portugal | 1 | 2% |
United Kingdom | 1 | 2% |
India | 1 | 2% |
Unknown | 59 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 13 | 20% |
Student > Master | 7 | 11% |
Student > Postgraduate | 6 | 9% |
Student > Bachelor | 3 | 5% |
Other | 10 | 15% |
Unknown | 8 | 12% |
Readers by discipline | Count | As % |
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Business, Management and Accounting | 2 | 3% |
Other | 10 | 15% |
Unknown | 10 | 15% |