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Fast Computation and Applications of Genome Mappability

Author

Listed:
  • Thomas Derrien
  • Jordi Estellé
  • Santiago Marco Sola
  • David G Knowles
  • Emanuele Raineri
  • Roderic Guigó
  • Paolo Ribeca

Abstract

We present a fast mapping-based algorithm to compute the mappability of each region of a reference genome up to a specified number of mismatches. Knowing the mappability of a genome is crucial for the interpretation of massively parallel sequencing experiments. We investigate the properties of the mappability of eukaryotic DNA/RNA both as a whole and at the level of the gene family, providing for various organisms tracks which allow the mappability information to be visually explored. In addition, we show that mappability varies greatly between species and gene classes. Finally, we suggest several practical applications where mappability can be used to refine the analysis of high-throughput sequencing data (SNP calling, gene expression quantification and paired-end experiments). This work highlights mappability as an important concept which deserves to be taken into full account, in particular when massively parallel sequencing technologies are employed. The GEM mappability program belongs to the GEM (GEnome Multitool) suite of programs, which can be freely downloaded for any use from its website (http://gemlibrary.sourceforge.net).

Suggested Citation

  • Thomas Derrien & Jordi Estellé & Santiago Marco Sola & David G Knowles & Emanuele Raineri & Roderic Guigó & Paolo Ribeca, 2012. "Fast Computation and Applications of Genome Mappability," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-16, January.
    • Handle: RePEc:plo:pone00:0030377
    DOI: 10.1371/journal.pone.0030377
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    Cited by:

    1. Kiran Krishnamachari & Dylan Lu & Alexander Swift-Scott & Anuar Yeraliyev & Kayla Lee & Weitai Huang & Sim Ngak Leng & Anders Jacobsen Skanderup, 2022. "Accurate somatic variant detection using weakly supervised deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    2. Michael DeGiorgio & Zachary A Szpiech, 2022. "A spatially aware likelihood test to detect sweeps from haplotype distributions," PLOS Genetics, Public Library of Science, vol. 18(4), pages 1-37, April.

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