Document Detail

TE-Tracker: systematic identification of transposition events through whole-genome resequencing.
MedLine Citation:
PMID:  25408240     Owner:  NLM     Status:  Publisher    
BackgroundTransposable elements (TEs) are DNA sequences that are able to move from their location in the genome by cutting or copying themselves to another locus. As such, they are increasingly recognized as impacting all aspects of genome function. With the dramatic reduction in cost of DNA sequencing, it is now possible to resequence whole genomes in order to systematically characterize novel TE mobilization in a particular individual. However, this task is made difficult by the inherently repetitive nature of TE sequences, which in some eukaryotes compose over half of the genome sequence. Currently, only a few software tools dedicated to the detection of TE mobilization using next-generation-sequencing are described in the literature. They often target specific TEs for which annotation is available, and are only able to identify families of closely related TEs, rather than individual elements.ResultsWe present TE-Tracker, a general and accurate computational method for the de-novo detection of germ line TE mobilization from re-sequenced genomes, as well as the identification of both their source and destination sequences. We compare our method with the two classes of existing software: specialized TE-detection tools and generic structural variant (SV) detection tools. We show that TE-Tracker, while working independently of any prior annotation, bridges the gap between these two approaches in terms of detection power. Indeed, its positive predictive value (PPV) is comparable to that of dedicated TE software while its sensitivity is typical of a generic SV detection tool. TE-Tracker demonstrates the benefit of adopting an annotation-independent, de novo approach for the detection of TE mobilization events. We use TE-Tracker to provide a comprehensive view of transposition events induced by loss of DNA methylation in Arabidopsis. TE-Tracker is freely available at show that TE-Tracker accurately detects both the source and destination of novel transposition events in re-sequenced genomes. Moreover, TE-Tracker is able to detect all potential donor sequences for a given insertion, and can identify the correct one among them. Furthermore, TE-Tracker produces significantly fewer false positives than common SV detection programs, thus greatly facilitating the detection and analysis of TE mobilization events.
Arthur Gilly; Mathilde Etcheverry; Mohammed-Amin Madoui; Julie Guy; Leandro Quadrana; Adriana Alberti; Antoine Martin; Tony Heitkam; Stefan Engelen; Karine Labadie; Jeremie Le Pen; Patrick Wincker; Vincent Colot; Jean-Marc Aury
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-11-19
Journal Detail:
Title:  BMC bioinformatics     Volume:  15     ISSN:  1471-2105     ISO Abbreviation:  BMC Bioinformatics     Publication Date:  2014 Nov 
Date Detail:
Created Date:  2014-11-19     Completed Date:  -     Revised Date:  2014-11-20    
Medline Journal Info:
Nlm Unique ID:  100965194     Medline TA:  BMC Bioinformatics     Country:  -    
Other Details:
Languages:  ENG     Pagination:  377     Citation Subset:  -    
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Previous Document:  Serum adiponectin relates to shortened overall survival in men with squamous cell esophageal cancer ...
Next Document:  Transcriptome sequencing of two wild barley (Hordeum spontaneum L.) ecotypes differentially adapted ...