Pairwise Alignment

Learn about how biological data is stored and transferred with different homology, scoring matrices and the global and local alignment algorithms.

  1. Introduction

    In this lesson, we'll go through what sequence / pairwise alignment is, how they are used in bioinformatics, look at PAM and BLOSUM matrices used to score alignments, and look at the techniques / algorithms used.

  2. Homology - a qualitative measure

    Learn how to qualitatively describe two sequences that have a common ancestor the two terms of homology - orthology vs. parology. Homologs, orthologs and paralogs arise in gene duplication and speciation.

  3. Identity and Similarity - a quantitative measure

    Learn how to quantitatively describe how well two sequences are aligned with the identity and similarity (positives) parameters which are part of interpreting BLAST results.

  4. Using BLAST

    A beginner\'s guide on how to use NCBI protein BLAST, a powerful program used for local alignment. Let\'s look at how to perform pairwise alignments and search databases for a specific query.

  5. Dayhoff Model and Accepted Point Mutations (PAMs)

    Learn about the Dayhoff model, which is used to score amino acid substitutions. Also find out about accepted point mutations (PAM) scoring matrices PAM1 and PAM250.

  6. Scoring methodology for gaps

    Learn how to score gaps to perform analysis in pairwise alignments.

  7. BLOcks SUbstitution Matrix (BLOSUM)

    Learn what the default scoring matrix for BLAST is - BLOSUM62. Find out how to construct one as a substitution matrix used to score pairwise alignments - BLOSUMs.


    Compare and find the difference between PAM and BLOSUM scoring and substitution matrices.

  9. Needleman and Wunsch - Global Alignment

    In this tutorial, you'll learn how to use the Needleman-Wunsch algorithm to create a matrix and find the optimal alignment between two sequences.

  10. Smith and Waterman - Local Alignment

    Learn how the algorithm behind local alignment works with the Smith and Waterman algorithm.