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Nucleic Acids Res. 2019 Dec 19. pii: gkz1173. doi: 10.1093/nar/gkz1173. [Epub ahead of print]

Identification and characterization of occult human-specific LINE-1 insertions using long-read sequencing technology.

Author information

1
Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.
2
Department of Human Genetics, University of Michigan Medical School, 1241 East Catherine Street, Ann Arbor, MI 48109, USA.
3
Molecular and Behavioral Neuroscience Institute, University of Michigan Medical School, 109 Zina Pitcher Place, Ann Arbor, MI 48109, USA.
4
Department of Internal Medicine, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA.

Abstract

Long Interspersed Element-1 (LINE-1) retrotransposition contributes to inter- and intra-individual genetic variation and occasionally can lead to human genetic disorders. Various strategies have been developed to identify human-specific LINE-1 (L1Hs) insertions from short-read whole genome sequencing (WGS) data; however, they have limitations in detecting insertions in complex repetitive genomic regions. Here, we developed a computational tool (PALMER) and used it to identify 203 non-reference L1Hs insertions in the NA12878 benchmark genome. Using PacBio long-read sequencing data, we identified L1Hs insertions that were absent in previous short-read studies (90/203). Approximately 81% (73/90) of the L1Hs insertions reside within endogenous LINE-1 sequences in the reference assembly and the analysis of unique breakpoint junction sequences revealed 63% (57/90) of these L1Hs insertions could be genotyped in 1000 Genomes Project sequences. Moreover, we observed that amplification biases encountered in single-cell WGS experiments led to a wide variation in L1Hs insertion detection rates between four individual NA12878 cells; under-amplification limited detection to 32% (65/203) of insertions, whereas over-amplification increased false positive calls. In sum, these data indicate that L1Hs insertions are often missed using standard short-read sequencing approaches and long-read sequencing approaches can significantly improve the detection of L1Hs insertions present in individual genomes.

PMID:
31853540
DOI:
10.1093/nar/gkz1173

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