COSINE: non-seeding method for mapping long noisy sequences

被引:3
|
作者
Afshar, Pegah Tootoonchi [1 ]
Wong, Wing Hung [2 ,3 ]
机构
[1] Stanford Univ, Sch Engn, Dept Elect Engn, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Stat, Stanford, CA 94305 USA
[3] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA
基金
美国国家卫生研究院;
关键词
FAST FOURIER-TRANSFORM; GENERATION;
D O I
10.1093/nar/gkx511
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Third generation sequencing (TGS) are highly promising technologies but the long and noisy reads from TGS are difficult to align using existing algorithms. Here, we present COSINE, a conceptually new method designed specifically for aligning long reads contaminated by a high level of errors. COSINE computes the context similarity of two stretches of nucleobases given the similarity over distributions of their short k-mers (k = 3-4) along the sequences. The results on simulated and real data show that COSINE achieves high sensitivity and specificity under a wide range of read accuracies. When the error rate is high, COSINE can offer substantial advantages over existing alignment methods.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences
    Li, Heng
    BIOINFORMATICS, 2016, 32 (14) : 2103 - 2110
  • [2] A New Method for Segmentation of Noisy, Low-Contrast Image Sequences
    Chuang, Hsiao-Chiang
    Comer, Mary L.
    2010 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, 2010, : 2868 - 2871
  • [3] A New Method for Inferring Hidden Markov Models from Noisy Time Sequences
    Kelly, David
    Dillingham, Mark
    Hudson, Andrew
    Wiesner, Karoline
    PLOS ONE, 2012, 7 (01):
  • [4] METHOD FOR IDENTIFICATION OF NOISY NON-LINEAR PLANTS
    KUTIN, GI
    AUTOMATION AND REMOTE CONTROL, 1981, 42 (08) : 1032 - 1039
  • [5] HOS-based method of global motion estimation for noisy image sequences
    Ibn-elhaj, E
    Aboutajdine, D
    Pateux, S
    Morin, L
    ELECTRONICS LETTERS, 1999, 35 (16) : 1320 - 1322
  • [6] invMap: a sensitive mapping tool for long noisy reads with inversion structural variants
    Wei, Ze-Gang
    Bu, Peng-Yu
    Zhang, Xiao-Dan
    Liu, Fei
    Qian, Yu
    Wu, Fang-Xiang
    BIOINFORMATICS, 2023, 39 (12)
  • [7] Image Scrambling Method Based On Chaotic Sequences and Mapping
    Wang Yanling
    PROCEEDINGS OF THE FIRST INTERNATIONAL WORKSHOP ON EDUCATION TECHNOLOGY AND COMPUTER SCIENCE, VOL III, 2009, : 453 - 457
  • [8] FAST CONVERGENT METHOD FOR OPTICAL-FLOW ESTIMATION IN NOISY IMAGE SEQUENCES
    KIM, JD
    KIM, SD
    KIM, JK
    ELECTRONICS LETTERS, 1989, 25 (01) : 74 - 75
  • [9] A Construction Method of Phase Sequences for the Selected Mapping Scheme
    Xie, Yinghai
    Li, Xianhuai
    Zhao, Haibo
    IEEE ACCESS, 2022, 10 : 133479 - 133486
  • [10] Learning sequential classifiers from long and noisy discrete-event sequences efficiently
    Dafe, Gesse
    Veloso, Adriano
    Zaki, Mohammed
    Meira, Wagner, Jr.
    DATA MINING AND KNOWLEDGE DISCOVERY, 2015, 29 (06) : 1685 - 1708