Hongyi Xin1,2, Mingfu Shao3, Carl Kingsford4. 1. Computer Science Department, Carnegie Mellon University, Pittsburgh, 15213 USA. 2. Present Address: UM-SJTU Joint Institute, Shanghai Jiaotong University, Shanghai, 200240 China. 3. Department of Computer Science and Engineering, Pennsylvania State University, State College, 16801 USA. 4. Computational Biology Department, Carnegie Mellon University, Pittsburgh, 15213 USA.
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, 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 significantly reduces seed frequencies 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.
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, 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 significantly reduces seed frequencies 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.
Authors: Alexander Dobin; Carrie A Davis; Felix Schlesinger; Jorg Drenkow; Chris Zaleski; Sonali Jha; Philippe Batut; Mark Chaisson; Thomas R Gingeras Journal: Bioinformatics Date: 2012-10-25 Impact factor: 6.937
Authors: Jason L Weirather; Mariateresa de Cesare; Yunhao Wang; Paolo Piazza; Vittorio Sebastiano; Xiu-Jie Wang; David Buck; Kin Fai Au Journal: F1000Res Date: 2017-02-03