Assessing the Statistical Significance of Pairwise Gapped Global Sequence Alignment of DNA Nucleotides Using Monte Carlo Techniques Rajashree Chaurasia and Udayan Ghose Abstract Generally, global pairwise alignments are used to infer homology or other evolutionary relationships between any two sequences. The significance of such sequence alignments is vital to determine whether an alignment algorithm is gener- ating the said alignment as evidence of homology or by random chance. Gauging the statistical significance of a sequence alignment obtained through the applica- tion of a global pairwise alignment algorithm is a difficult task, and research in this direction has only provided us with nebulous solutions. Moreover, the case of nucleotide alignments with gaps has been scarcely explored. Very little literature exists on the statistical significance of gapped global alignments employing affine gap penalties. This manuscript aims to provide insights into how the statistical signif- icance of gapped global pairwise alignments that may be inferred using Monte Carlo techniques. Keywords Global pairwise alignment · Scoring matrix · Statistical significance · Gapped alignments · Affine gap penalty · Monte Carlo method · Extreme value distribution 1 Background Global pairwise alignments are generally used to measure the evolutionary related- ness or homology of two sequences over their entire lengths. The most popular pair- wise global alignment algorithm is the Needleman–Wunsch algorithm [19]. However, the standard Needleman–Wunsch works well for minimal length sequences (of the R. Chaurasia (B ) Guru Nanak Dev Institute of Technology, Directorate of Training and Technical Education, Government of NCT of Delhi, Delhi, India e-mail: rajashree.14416490019@ipu.ac.in; rajashree.chaurasia@gmail.com R. Chaurasia · U. Ghose University School of Information, Communication & Technology, Guru Gobind Singh Indraprastha University, New Delhi, India e-mail: udayan@ipu.ac.in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Smys et al. (eds.), Computational Vision and Bio-Inspired Computing, Advances in Intelligent Systems and Computing 1318, https://doi.org/10.1007/978-981-33-6862-0_5 57