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A Non-Parametric Peak Calling Algorithm for DamID-Seq

Author

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  • Renhua Li
  • Leonie U Hempel
  • Tingbo Jiang

Abstract

Protein—DNA interactions play a significant role in gene regulation and expression. In order to identify transcription factor binding sites (TFBS) of double sex (DSX)—an important transcription factor in sex determination, we applied the DNA adenine methylation identification (DamID) technology to the fat body tissue of Drosophila, followed by deep sequencing (DamID-Seq). One feature of DamID-Seq data is that induced adenine methylation signals are not assured to be symmetrically distributed at TFBS, which renders the existing peak calling algorithms for ChIP-Seq, including SPP and MACS, inappropriate for DamID-Seq data. This challenged us to develop a new algorithm for peak calling. A challenge in peaking calling based on sequence data is estimating the averaged behavior of background signals. We applied a bootstrap resampling method to short sequence reads in the control (Dam only). After data quality check and mapping reads to a reference genome, the peaking calling procedure compromises the following steps: 1) reads resampling; 2) reads scaling (normalization) and computing signal-to-noise fold changes; 3) filtering; 4) Calling peaks based on a statistically significant threshold. This is a non-parametric method for peak calling (NPPC). We also used irreproducible discovery rate (IDR) analysis, as well as ChIP-Seq data to compare the peaks called by the NPPC. We identified approximately 6,000 peaks for DSX, which point to 1,225 genes related to the fat body tissue difference between female and male Drosophila. Statistical evidence from IDR analysis indicated that these peaks are reproducible across biological replicates. In addition, these peaks are comparable to those identified by use of ChIP-Seq on S2 cells, in terms of peak number, location, and peaks width.

Suggested Citation

  • Renhua Li & Leonie U Hempel & Tingbo Jiang, 2015. "A Non-Parametric Peak Calling Algorithm for DamID-Seq," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-12, March.
  • Handle: RePEc:plo:pone00:0117415
    DOI: 10.1371/journal.pone.0117415
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    1. Fomin, Fedor V. & Fraigniaud, Pierre & Golovach, Petr A., 2022. "Present-biased optimization," Mathematical Social Sciences, Elsevier, vol. 119(C), pages 56-67.
    2. Carlos V. G. C. Lima & Dieter Rautenbach & Uéverton S. Souza & Jayme L. Szwarcfiter, 2022. "On the computational complexity of the bipartizing matching problem," Annals of Operations Research, Springer, vol. 316(2), pages 1235-1256, September.
    3. Juho Lauri & Sourav Dutta & Marco Grassia & Deepak Ajwani, 2023. "Learning fine-grained search space pruning and heuristics for combinatorial optimization," Journal of Heuristics, Springer, vol. 29(2), pages 313-347, June.
    4. Hans L. Bodlaender & Josse Dobben de Bruyn & Dion Gijswijt & Harry Smit, 2022. "Constructing tree decompositions of graphs with bounded gonality," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2681-2699, November.
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    7. Lucci, Mauro & Nasini, Graciela & Severín, Daniel, 2024. "Solving the List Coloring Problem through a branch-and-price algorithm," European Journal of Operational Research, Elsevier, vol. 315(3), pages 899-912.
    8. Matthias Bentert & René van Bevern & André Nichterlein & Rolf Niedermeier & Pavel V. Smirnov, 2022. "Parameterized Algorithms for Power-Efficiently Connecting Wireless Sensor Networks: Theory and Experiments," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 55-75, January.
    9. Niels Lindner & Julian Reisch, 2022. "An analysis of the parameterized complexity of periodic timetabling," Journal of Scheduling, Springer, vol. 25(2), pages 157-176, April.
    10. Van Bang Le & Sheng-Lung Peng, 2018. "On the complete width and edge clique cover problems," Journal of Combinatorial Optimization, Springer, vol. 36(2), pages 532-548, August.
    11. Klaus Heeger & Danny Hermelin & George B. Mertzios & Hendrik Molter & Rolf Niedermeier & Dvir Shabtay, 2023. "Equitable scheduling on a single machine," Journal of Scheduling, Springer, vol. 26(2), pages 209-225, April.
    12. Goharshady, Amir Kafshdar & Mohammadi, Fatemeh, 2020. "An efficient algorithm for computing network reliability in small treewidth," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    13. Édouard Bonnet & Sergio Cabello, 2021. "The complexity of mixed-connectivity," Annals of Operations Research, Springer, vol. 307(1), pages 25-35, December.
    14. Duv{s}an Knop & v{S}imon Schierreich, 2023. "Host Community Respecting Refugee Housing," Papers 2302.13997, arXiv.org, revised Mar 2023.

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