bioRxivBioinformaticsPreprint
SWIF-TE: identifying novel transposable element insertions from short read data
Claire C. MenardiD, Nathan S. CatliniD, Adrian E. PlattsiD, Yinjie QiuiD, Emma Y. RobackiD, Manisha MunasingheiD, Nathan M. SpringeriD, Emily B. JosephsiD, Candice N. HirschiD
Abstract
Transposable element (TE) insertion polymorphisms (TIPs) are TEs not in the same location between individuals. TIPs have contributed to genomic and phenomic variation but have been historically difficult to study due to their repetitive nature. Here, we describe a fast and memory-efficient tool to identify novel TIPs from short read sequences. SWIF-TE was able to identify 1,438 insertions at a precision rate of 27% using 0.10 Gb of memory and 0.82 hours of runtime from 15x resequencing data of a non-reference maize inbred. SWIF-TE is a powerful tool for studying TE variation in species with TE-rich genomes.
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