Interpolated variable order motifs for identification of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands
Year: 2006
Type: article
Abstract: MOTIVATION: There is a growing literature on the detection of Horizontal Gene Transfer (HGT) events by means of parametric, non-comparative methods. Such approaches rely only on sequence information and utilize different low and high order indices to capture compositional deviation from the genome backbone; the superiority of the latter over the former has been shown elsewhere. However even high order k-mers may be poor estimators of HGT, when insufficient information is available, e.g. in short sliding windows. Most of the current HGT prediction methods require pre-existing annotation, which may restrict their application on newly sequenced genomes. RESULTS: We introduce a novel computational method, Interpolated Variable Order Motifs (IVOMs), which exploits compositional biases using variable order motif distributions and captures more reliably the local composition of a sequence compared with fixed-order methods. For optimal localization of the boundaries of each predicted region, a second order, two-state hidden Markov model (HMM) is implemented in a change-point detection framework. We applied the IVOM approach to the genome of Salmonella enterica serovar Typhi CT18, a well-studied prokaryote in terms of HGT events, and we show that the IVOMs outperform state-of-the-art low and high order motif methods predicting not only the already characterized Salmonella Pathogenicity Islands (SPI-1 to SPI-10) but also three novel SPIs (SPI-15, SPI-16, SPI-17) and other HGT events. AVAILABILITY: The software is available under a GPL license as a standalone application at http://www.sanger.ac.uk/Software/analysis/alien_hunter CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Source: Bioinformatics
Authors: Georgios S. Vernikos, Julian Parkhill
Institution: Wellcome Sanger Institute
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,989
Funder: Wellcome Trust
OpenAlex ID: W2108228587
