BlindCall: Ultra-fast base-calling of high-throughput sequencing data by blind deconvolution

Bioinformatics

Volumn 30, Issue 9, 2014, Pages 1214-1219

  Ye, Chengxi ^a,b   Hsiao, Chiaowen ^b   Bravo, Hector Corrada ^a,b  

^a Department of Computer Science ^*  (United States)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; COMPUTER PROGRAM; DNA SEQUENCE; HIGH THROUGHPUT SEQUENCING; HUMAN; METHODOLOGY; PROBABILITY; REPRODUCIBILITY; TIME;

ALGORITHMS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; PROBABILITY; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, DNA; SOFTWARE; TIME FACTORS;

EID: 84899577392     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu010     Document Type: Article

References (22)

1. Aird, D. et al. (2011) Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol., 12, R18.
2. Alkan, C. et al. (2011) Limitations of next-generation genome sequence assembly. Nat. Methods, 8, 61-65.
3. Belkin, M. and Niyogi, P. (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. Adv. Neural Inf. Process. Syst., 14, 585-591.
4. Bentley, D.R. et al. (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature, 456, 53-59.
5. Bravo, H.C. and Irizarry, R.A. (2010) Model-based quality assessment and basecalling for second-generation sequencing data. Biometrics, 66, 665-674.
6. Dohm, J.C. et al. (2008) Substantial biases in ultra-short read data sets from highthroughput DNA sequencing. Nucleic Acids Res., 36, e105.
7. Erlich, Y. et al. (2008) Alta-Cyclic: a self-optimizing base caller for next-generation sequencing. Nat. Methods, 5, 679-682.
8. Illumina (2013) HiSeq Systems Comparison. http://www.illumina.com/ systems/hiseq-2500-1500/performance-specifications.ilmn.
9. Kao, W.-C. and Song, Y.S. (2011) naiveBayesCall: an efficient model-based base-calling algorithm for high-throughput sequencing. J. Comput. Biol. A J. Comput. Mol. Cell Biol., 18, 365-377.
10. Kao, W.-C. et al. (2009) BayesCall: A model-based base-calling algorithm for highthroughput short-read sequencing. Genome Res., 19, 1884-1895.
11. Kircher, M. et al. (2009) Improved base calling for the Illumina Genome Analyzer using machine learning strategies. Genome Biol., 10, R83.
12. Langmead, B. and Salzberg, S.L. (2012) Fast gapped-read alignment with Bowtie 2. Nat. Methods, 9, 357-359.
13. Levin, A. et al. (2011) Understanding blind deconvolution algorithms. IEEE Trans. Pattern Anal. Mach. Intell., 33, 2354-2367.
14. Mallat, S.G. (2009) A Wavelet Tour of Signal Processing : the Sparse Way. Elsevier/Academic Press, Amsterdam/Boston.
15. Massingham, T. and Goldman, N. (2012) All Your Base: a fast and accurate probabilistic approach to base calling. Genome Biol., 13, R13.
16. Page, L. et al. (1999) The PageRank citation ranking: bringing order to the web. Stanford InfoLab. Technical Report. Stanford InfoLab.
17. Renaud, G. et al. (2013) freeIbis: an efficient basecaller with calibrated quality scores for Illumina sequencers. Bioinformatics, 29, 1208-1209.
18. Shi, J.B. and Malik, J. (2000) Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell, 22, 888-905.
19. Wang, Y. and Yin, W. (2010) Sparse signal reconstruction via iterative support detection. SIAM J. Imaging Sci., 3, 462-491.
20. Wang, Y.L. et al. (2008) A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imaging Sci., 1, 248-272.
21. Xu, L. et al. (2013) Unnatural L 0 sparse representation for natural image deblurring. IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), 1107-1114.
22. Ye, C. et al. (2012) Exploiting sparseness in de novo genome assembly. BMC Bioinform., 13 (Suppl 6), S1.