Context-Aware Seeds for Read Mapping

Authors Hongyi Xin, Mingfu Shao, Carl Kingsford



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Author Details

Hongyi Xin
  • Computer Science Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
Mingfu Shao
  • Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, USA
Carl Kingsford
  • Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

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Hongyi Xin, Mingfu Shao, and Carl Kingsford. Context-Aware Seeds for Read Mapping. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 15:1-15:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.WABI.2019.15

Abstract

Motivation: Most modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t. As t grows (such as in long reads with high error rate), this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers. Results: We propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mapping but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed in the reference. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t. CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS reduces seed frequencies by up to 20.3% when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver. Availability: https://github.com/Kingsford-Group/CAS_code

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
Keywords
  • Read Mapping
  • Seed and Extend
  • Edit Distance
  • Suffix Trie

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References

  1. Alexander Dobin, Carrie A Davis, Felix Schlesinger, Jorg Drenkow, Chris Zaleski, Sonali Jha, Philippe Batut, Mark Chaisson, and Thomas R Gingeras. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1):15-21, 2013. Google Scholar
  2. Eric Dugat-Bony, Eric Peyretaillade, Nicolas Parisot, Corinne Biderre-Petit, Faouzi Jaziri, David Hill, Sébastien Rimour, and Pierre Peyret. Detecting unknown sequences with DNA microarrays: explorative probe design strategies. Environmental Microbiology, 14(2):356-371, 2012. Google Scholar
  3. Ehsan Haghshenas, Faraz Hach, S Cenk Sahinalp, and Cedric Chauve. Colormap: correcting long reads by mapping short reads. Bioinformatics, 32(17):i545-i551, 2016. Google Scholar
  4. Ehsan Haghshenas, S Cenk Sahinalp, and Faraz Hach. lordFAST: sensitive and fast alignment search tool for long noisy read sequencing data. Bioinformatics, 35(1):20-27, 2018. Google Scholar
  5. Chirag Jain, Alexander Dilthey, Sergey Koren, Srinivas Aluru, and Adam M Phillippy. A fast approximate algorithm for mapping long reads to large reference databases. In International Conference on Research in Computational Molecular Biology, pages 66-81. Springer, 2017. Google Scholar
  6. Szymon M Kiełbasa, Raymond Wan, Kengo Sato, Paul Horton, and Martin C Frith. Adaptive seeds tame genomic sequence comparison. Genome Research, 21(3):487-493, 2011. Google Scholar
  7. Gad M Landau and Uzi Vishkin. Fast parallel and serial approximate string matching. Journal of Algorithms, 10(2):157-169, 1989. Google Scholar
  8. Ben Langmead and Steven L Salzberg. Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4):357, 2012. Google Scholar
  9. Heng Li. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv, 2013. URL: http://arxiv.org/abs/1303.3997.
  10. Qingge Li, Guoyan Luan, Qiuping Guo, and Jixuan Liang. A new class of homogeneous nucleic acid probes based on specific displacement hybridization. Nucleic Acids Research, 30(2):e5-e5, 2002. Google Scholar
  11. Ngoc Hieu Tran and Xin Chen. AMAS: optimizing the partition and filtration of adaptive seeds to speed up read mapping. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(4):623-633, 2016. Google Scholar
  12. Juexiao Sherry Wang and David Yu Zhang. Simulation-guided DNA probe design for consistently ultraspecific hybridization. Nature Chemistry, 7(7):545, 2015. Google Scholar
  13. Jason L Weirather, Mariateresa de Cesare, Yunhao Wang, Paolo Piazza, Vittorio Sebastiano, Xiu-Jie Wang, David Buck, and Kin Fai Au. Comprehensive comparison of Pacific Biosciences and Oxford Nanopore Technologies and their applications to transcriptome analysis. F1000Research, 6, 2017. Google Scholar
  14. Hongyi Xin, Donghyuk Lee, Farhad Hormozdiari, Samihan Yedkar, Onur Mutlu, and Can Alkan. Accelerating read mapping with FastHASH. BMC Genomics, 14(1):S13, 2013. Google Scholar
  15. Hongyi Xin, Sunny Nahar, Richard Zhu, John Emmons, Gennady Pekhimenko, Carl Kingsford, Can Alkan, and Onur Mutlu. Optimal seed solver: optimizing seed selection in read mapping. Bioinformatics, 32(11):1632-1642, 2015. Google Scholar
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