Research Article K-Mer Spectrum-Based Error Correction Algorithm for Next-Generation Sequencing Data Hussah N. AlEisa , 1 Safwat Hamad , 2 and Ahmed Elhadad 3 1 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia 2 Department of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt 3 Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt Correspondence should be addressed to Hussah N. AlEisa; haleisa@pnu.edu.sa Received 18 May 2022; Accepted 13 June 2022; Published 14 July 2022 Academic Editor: Wei Xiang Copyright © 2022 Hussah N. AlEisa et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the mid-1970s, the first-generation sequencing technique (Sanger) was created. It used Advanced BioSystems sequencing devices and Beckman’s GeXP genetic testing technology. e second-generation sequencing (2GS) technique arrived just several years after the first human genome was published in 2003. 2GS devices are very quicker than Sanger sequencing equipment, with considerably cheaper manufacturing costs and far higher throughput in the form of short reads. e third-generation sequencing (3GS) method, initially introduced in 2005, offers further reduced manufacturing costs and higher throughput. Even though sequencing technique has result generations, it is error-prone due to a large number of reads. e study of this massive amount of data will aid in the decoding of life secrets, the detection of infections, the development of improved crops, and the improvement of life quality, among other things. is is a challenging task, which is complicated not just by a large number of reads and by the occurrence of sequencing mistakes. As a result, error correction is a crucial duty in data processing; it entails identifying and correcting read errors. Various k-spectrum-based error correction algorithms’ performance can be influenced by a variety of characteristics like coverage depth, read length, and genome size, as demonstrated in this work. As a result, time and effort must be put into selecting acceptable approaches for error correction of certain NGS data. 1. Introduction Nature methods named next-generation high-throughput DNA sequencing techniques as the method of the year in 2007. ese methods are creating interesting new potential in biology [1]. e road to garnering the approval of the revolutionary technology, on the other hand, was not simple. Until recently, the Sanger enzymatic dideoxy method, first explained in 1977, and the Maxam and Gilbert chemical degradation technique, first mentioned in the same year, were the methodologies used for sequence analysis. e Maxam and Gilbert chemical degradation technique was used in sequential cases that could not be solved easily with the Sanger method [2]. e potential to decipher genomes and conduct ground-breaking biomedical sciences has been made possible by the rapid synthesis and accessibility of enormous amounts of DNA sequencing obtained by next- generation sequencing (NGS) technology at a lower cost than traditional Sanger sequencing [3]. ere has been a significant trend apart from using automated Sanger se- quencing for genome analysis in the last four years. Previous to this departure, the automated Sanger sequencing had taken over the market for half a century, resulting in a slew of significant achievements, such as the production of the only completed human genome sequence. Despite numerous technological advances during this period, the drawbacks of automated Sanger sequencing demonstrated the need for new and superior methods for sequencing huge numbers of human genomes [4]. Sanger sequencing has seen less documented advancements as Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 8077664, 8 pages https://doi.org/10.1155/2022/8077664