Investigating the predictability of essential genes across distantly related organisms using an integrative approach
| العنوان: | Investigating the predictability of essential genes across distantly related organisms using an integrative approach |
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| المؤلفون: | Daniel J. Hassett, Long J. Lu, Lan Wei, Minlu Zhang, Ali A. Minai, Shengchang Su, Jingyuan Deng, Xiaodong Lin, Lei Deng |
| المصدر: | Nucleic Acids Research |
| بيانات النشر: | Oxford University Press, 2010. |
| سنة النشر: | 2010 |
| مصطلحات موضوعية: | 0106 biological sciences, Transferability, Genomics, Computational biology, Biology, 01 natural sciences, 03 medical and health sciences, Artificial Intelligence, 010608 biotechnology, Genetics, Escherichia coli, Predictability, Gene, Organism, 030304 developmental biology, 0303 health sciences, Training set, Genes, Essential, Acinetobacter, 030302 biochemistry & molecular biology, Computational Biology, Chromosome Mapping, Molecular Sequence Annotation, Classification, Corrigenda, Essential gene, Genes, Bacterial, Pseudomonas aeruginosa, Genome, Bacterial, Bacillus subtilis |
| الوصف: | Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs. |
| اللغة: | English |
| تدمد: | 1362-4962 0305-1048 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::571b317074aaeffa1bba314864394c31Test http://europepmc.org/articles/PMC3035443Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....571b317074aaeffa1bba314864394c31 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 13624962 03051048 |
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