Improving discrimination of essential genes by modeling local insertion frequencies in transposon mutagenesis data
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Transposon mutagenesis experiments enable the identification of essential genes in bacteria. Deep-sequencing of mutant libraries provides a large amount of high-resolution data on essentiality. Statistical methods developed to analyze this data have traditionally assumed that the probability of ob- serving a transposon insertion is the same across the genome. This assumption, however, is inconsistent with the observed insertion frequencies from transposon mutant libraries of M. tuberculosis. Copyright 2007 by the Association for Computing Machinery.
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Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics