Fuzzy-based Spectral Alignment for Correcting DNA Sequence from Next Generation Sequencer

Kana Saputra S, Wisnu Ananta Kusuma, Agus Buono


Next generation sequencing technology is able to generate short read in large numbers and in a relatively short in single running programs. Graph based DNA sequence assembly used to handle these big data in assembly step. The graph based DNA sequence assembly is very sensitive to DNA sequencing error. This problem could be solved by performing an error correction step before the assembly process. This research proposed fuzzy inference system (FIS) model based spectral alignment method which can detect and correct DNA sequencing error. The spectral alignment technique was implemented as a pre-processing step before the DNA sequence assembly process. The evaluation was conducted using Velvet assembler. The number of nodes yielded by the Velvet assembler become a measure of the success of error correction. The results shows that FIS model based spectral alignment created small number of nodes and therefore it successfully corrected the DNA reads.


DNA Sequencing Error, Fuzzy Inference System Model, Next Generation Sequencing, Spectral Alignment, Velvet.

Full Text:



Rogers K. New Thinking about Genetics. New York: Britannica Educational Publishing. 2011: 132.

Chong ML, Ku CS, Wu M, Soong R. Characterising Somatic Mutations in Cancer Genome by Means of Next-generation Sequencing. 2012. In: eLS. John Wiley & Sons, Ltd: Chichester.

Chevreux B. MIRA: An Automated Genome and EST Assembler. German Cancer Research Center Heidelberg, Department of Molecular Biophysics. 2005: 18.

Kelley DR, Michael CS, Steven LS. Quake: Quality-Aware Detection and Correction of Sequencing Errors. Genome Biology. 2010.

Miller JR, Koren S, Sutton G. Assembly Algorithms for Next-Generation Sequencing Data. Genomics. 2010; 95(6): 315–327.

Yang X, Chockalingam SP, Aluru S. A Survey of Error-Correction Methods for Next-Generation Sequencing. Journal of Briefing in Bioinformatics. 2012.

Pevzner PA, Tang H, Waterman MS. An Eulerian Path Approach to DNA Fragment Assembly. Proceedings of the National Academy of Sciences. 2001; 98(17): 9748–9753.

Caesar N, Kusuma WA, Wijaya SH. DNA Sequencing Error Correcting using Spectral Alignment. ICACSIS. 2013; 279-284.

Wijaya E, Frith MC, Suzuki Y, Horton P. RECOUNT: Expectation Maximazation Based Error Correction Tool for Next Generation Sequencing Data. Proceedings Trim. 2009; 17(6).

Othman Z, Subari K, Morad N. Application of Fuzzy Inference Systems and Genetic Algorithm in Integrated Process Planning and Scheduling. International Journal of The Computer, The Internet, adn Management. 2002; 10(2): 81 - 96.

Abdullah L, Rahman MNA. Employee Likelihood of Purchasing Health Insurance using Fuzzy Inference System. International Journal of Computer Science Issues. 2012; 9(2): 112-116.

Qidway U, Shamim MS, Raquib F, Enam A. Failed Back Surgery Syndrome (FBSS) Prediction using Fuzzy Inference System (FIS). IEEE International Conference on Signal Processing and Communications (ICSPC). 2007; 880-883.

“Hobbes Genome Sequence Mapping Home Page”, [Online]. Available:


Cock PJA, Fields CJ, Goto N, Heuer ML, Rice PM. The Sanger FASTQ File Format for Sequences with Quality Scores, and the Solexa/Illumina FASTQ Variants. Nucleic Acids Research. 2009; 38(6): 1767-1771.

Aksoy S, Robert MH. Feature Normalization and Likelihood-based Similarity Measures for Image Retrieval. Elsevier. 2011; 22: 563-582.

Mamdani EH, Assilian S. An Experiment In Linguistic Synthesis With A Fuzzy Logic Controller. International Journal of Human-Computer Studies. 1999; 51:135–147.

Levenshtein VI. Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady. 1966; 10(8):707.

Zerbino DR, Birney E. Velvet: Algorithms for De Novo Short Read Assembly using De Bruijn Graphs. Genome Research. 2008.

Musi IID. Pengembangan Fuzzy Inferensi Sistem Untuk Seleksi Metode Peningkatan Perolehan Minyak Tingkat Lanjut. Thesis. Institut Pertanian Bogor; 2009.

Tang K, Tokinaga S. Optimization of Fuzzy Inference System Rules by Using the Genetic Algorithm and Its Application to the Bond Rating. Journal of the Operations Research Society of Japan. 1999; 42(3): 302-315.

DOI: http://dx.doi.org/10.12928/telkomnika.v14i2.2395

Article Metrics

Abstract view : 110 times
PDF - 115 times


  • There are currently no refbacks.

Copyright (c) 2016 Universitas Ahmad Dahlan

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.