Effect of Gap Penalty on Global Alignment Accuracy

Overview

Objectives/Goals The Needleman-Wunsch global alignment algorithm uses a scoring system consisting of a match, mismatch, and gap penalty to compare two large sequences of data. This research aims to examine effects of the gap penalty on sequence alignments when the match score is 1 and the mismatch score is 0. Methods/Materials Sequence-modifying, global alignment, and alignment scoring programs were written in Java and used to compare alignments. The sequence-modifying program controlled how similar sequences were by altering 50, 200, 350, 500, and 600 characters in an original 626-character long sequence. These altered sequences were then globally aligned to the original DNA sequence using 0, -1, -2, -3, and -5 as gap penalties. Finally the scoring program judged the accuracy of the alignment. Results At a gap penalty of 0, there were gaps throughout the alignments and alignment scores were low, but at a gap penalty of -5, the algorithm avoided gaps for all alignments. The ideal gap penalty was -2. Conclusions/Discussion At very small gap penalties, scores are low because the algorithm doesn't penalize for using many gaps. However, the algorithm quickly stops aligning as penalties grow, so the most effective penalties are small values still large enough to discourage unnecessary gaps.

Competition history

• CSEF 2011 Mathematics & Software · Entry S1421

Resources

• CSEF project summary ↗

Related projects

Closest projects by meaning, across every fair and year in the corpus.

Browse more like this

• All Computational Sciences projects
• CSEF Computational Sciences projects
• Every CSEF 2011 project