This preprint is under consideration at Algorithms for Molecular Biology. Preprints are preliminary reports that have not undergone peer review. They should not be considered conclusive, used to inform clinical practice, or referenced by the media as validated information.
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Context-Aware Seeds for Read Mapping
Carl Kingsford
Carnegie Mellon University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0118-5516
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 mappings 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.
Keywords
Read mapping, Seeds, Error tolerance, Seed and extend
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CURRENT STATUS: Under Revision
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Posted 18 Dec, 2019
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On 23 Jan, 2020
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On 22 Dec, 2019
Reviewer #1 agreed
On 19 Dec, 2019
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This preprint is under consideration at Algorithms for Molecular Biology. Preprints are preliminary reports that have not undergone peer review. They should not be considered conclusive, used to inform clinical practice, or referenced by the media as validated information.
Learn more about In Review
Context-Aware Seeds for Read Mapping
Carl Kingsford
Carnegie Mellon University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-0118-5516
Prescreen
This preprint passed our Prescreen
Ethical approvals present
Financial disclosures and conflicts of interest declared
Relevant permissions obtained
Prescreen
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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 mappings 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.
Figures
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Full Text
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.
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