Improving discrimination of essential genes by modeling local insertion frequencies in transposon mutagenesis data Conference Paper uri icon

abstract

  • 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.

name of conference

  • BCB'13: ACM-BCB2013

published proceedings

  • Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

author list (cited authors)

  • DeJesus, M. A., & Ioerger, T. R.

citation count

  • 0

complete list of authors

  • DeJesus, Michael A||Ioerger, Thomas R

editor list (cited editors)

  • Gao, J.

publication date

  • September 2013

publisher

  • ACM  Publisher