Abstract
The Next-Generation sequencing technologies produce large sets of short reads that may contain errors of different types. These errors represent a great obstacle to utilize data in sequencing projects; such as assemblers. Consequently, error correction is a vital process that aims to reduce the error rate. So, the correction of all errors types becomes very challenging. H-RACER is an error correcting tool for all types of errors (substitutions, insertions, and deletions) in a mixed set of reads. It mainly depends on RACER algorithm in detecting the error and correcting it. The major advantage presented by H-RACER is the correction of substitution errors as well as the insertions and deletions with the highest accuracy and the least time compared to other existing algorithms that specialize in correcting all types of errors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Butler, J., MacCallum, I., Kleber, M., et al.: ALLPATHS: de novo assembly of whole-genome shotgun microreads. Genome Res. 18, 810–820 (2008)
Chaisson, M., Pevzner, P., Tang, M.: Fragment assembly with short reads. Bioinformatics 20, 2067–2074 (2004)
Ilie, L., Fazayeli, F., Ilie, S.: HiTEC: accurate error correction in high-throughput sequencing data. Bioinformatics 27, 295–302 (2011)
Ilie, L., Molnar, M.: RACER: rapid and accurate correction of errors in reads. Bioinformatics 19, 2490–2493 (2013)
Kelley, D., Schatz, M., Salzberg, S.: Quake: quality-aware detection and correction of sequencing errors. Genome Biol. 11, R116 (2010)
Li, R., Zhu, H., Ruan, J., et al.: De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 20, 265–272 (2010)
Marinier, E., Brown, D., McConkey, B.: Pollux: platform independent error correction of single and mixed genomes. BMC Bioinform. 15, 435 (2015)
Saha, S., Rajasekaran, S.: EC: an efficient error correction algorithm for short reads. BMC Bioinform. 16(Suppl 17), S2 (2015)
Salmela, L.: Correction of sequencing errors in a mixed set of reads. Bioinformatics 26, 1284–1290 (2010)
Salmela, L., Schrder, J.: Correcting errors in short reads by multiple alignments. Bioinformatics 27, 1455–1461 (2011)
Shendure, J., Ji, H.: Next-generation dna sequencing. Nat. Biotechnol. 26, 1135–1145 (2008)
Shi, H., Schmidt, B., Liu, W., et al.: A parallel algorithm for error correction in high throughput short-read data on CUDA-enabled graphics hardware. J. Comput. Biol 17, 603–615 (2010)
Yang, X., Chockalingam, S., Aluru, S.: A survey of error-correction methods for next-generation sequencing. Brief. Bioinform. 14, 56–66 (2013)
Yang, X., Dorman, K., Aluru, S.: Reptile: representative tiling for short read error correction. Bioinformatics 26, 2526–2533 (2010)
Zerbino, D., Birney, E.: Velvet: algorithms for de novo short read assembly using De Bruijn graphs. Genome Res. 18, 821–829 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Gomaa, S., Belal, N.A., El-Sonbaty, Y. (2017). H-RACER: Hybrid RACER to Correct Substitution, Insertion, and Deletion Errors. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-56148-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56147-9
Online ISBN: 978-3-319-56148-6
eBook Packages: Computer ScienceComputer Science (R0)