Introduction

Transposable elements (TEs) are conventionally identified in eukaryotic genomes by alignment to consensus element sequences. Using this approach, about half of the human genome has been previously identified as TEs and low-complexity repeats. We recently developed a highly sensitive alternative de novo strategy, P-clouds, that instead searches for clusters of high-abundance oligonucleotides that are related in sequence space (oligo "clouds"). We show here that P-clouds predicts >840 Mbp of additional repetitive sequences in the human genome, thus suggesting that 66%-69% of the human genome is repetitive or repeat-derived. To investigate this remarkable difference, we conducted detailed analyses of the ability of both P-clouds and a commonly used conventional approach, RepeatMasker (RM), to detect different sized fragments of the highly abundant human Alu and MIR SINEs. RM can have surprisingly low sensitivity for even moderately long fragments, in contrast to P-clouds, which has good sensitivity down to small fragment sizes (∼25 bp). Although short fragments have a high intrinsic probability of being false positives, we performed a probabilistic annotation that reflects this fact. We further developed "element-specific" P-clouds (ESPs) to identify novel Alu and MIR SINE elements, and using it we identified ∼100 Mb of previously unannotated human elements. ESP estimates of new MIR sequences are in good agreement with RM-based predictions of the amount that RM missed. These results highlight the need for combined, probabilistic genome annotation approaches and suggest that the human genome consists of substantially more repetitive sequence than previously believed.

Publications

  1. Repetitive elements may comprise over two-thirds of the human genome.
    Cite this
    de Koning AP, Gu W, Castoe TA, Batzer MA, Pollock DD, 2011-12-01 - PLoS genetics
  2. Identification of repeat structure in large genomes using repeat probability clouds.
    Cite this
    Gu W, Castoe TA, Hedges DJ, Batzer MA, Pollock DD, 2008-09-01 - Analytical biochemistry

Credits

  1. A P Jason de Koning
    Developer

    Department of Biochemistry and Molecular Genetics, School of Medicine, United States of America

  2. Wanjun Gu
    Developer

  3. Todd A Castoe
    Developer

  4. Mark A Batzer
    Developer

  5. David D Pollock
    Investigator

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Summary
AccessionBT006917
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByDavid D Pollock