Multiple Sequence Alignment

• The learning objectives involve applying previous knowledge, viewing and editing sequences in Bioedit, trimming using Gblocks, and performing multiple sequence alignment using ClustalX2 and web-based tools.
• Necessary tools include internet connection, Java, Bioedit, and ClustalX.

Multiple Sequence Alignment

• Similar genes are conserved across organisms and have the same or similar functions.
• Simultaneous alignment of similar genes reveals regions subject to mutation, regions of conservation, and mutations or rearrangements causing change in conformation or function.
• Aligning a new sequence with known sequences provides insight into structure and function.
• Multiple alignment can detect important features or motifs.

Why MSAs are Important

• Genes exist in families with homologous domains.
• MSAs offer significant potential for learning about structure and function compared to single alignments.
• Multiple alignments highlight areas of conservation and regions potentially used in the function more strongly.
• MSAs enable understanding gene evolution.

Approaches to Multiple Alignment

• Dynamic programming is initially used on all pairs of query sequences, and then the alignment space is filled in by considering possible matches or gaps at intermediate positions.
• Iterative alignment involves making an initial alignment of the sequences, which are then revised to give a more reasonable result.
• Statistical Modeling is an approach to multiple alignment.
• Progressive Alignment is an approach to multiple alignment.

Progressive Alignments

• This alignment begins with the alignment of the most alike sequences and builds upon the alignment using other sequences.
• Progressive alignments work by first aligning the most alike sequences using dynamic programming, and then progressively adding less related sequences to the initial alignment.
• The alignment involves aligning the most related sequences first.
• Add less related sequences to the initial alignment

Shortcoming of Progressive Approach

• Dependence upon initial pair-wise sequence alignments occurs
• It is okay if sequences are similar.
• Errors in alignment are propagated if not similar
• Suitable scoring matrices and gap penalties must be chosen to apply to the sequences as a set.

CLUSTALW and CLUSTALX

• Alignments are associated with a weight based on their distance from the root node.
• Gaps are added to an existing profile in progressive methods.
• CLUSTALW incorporates a statistical model to place gaps where they are most likely to occur.

Lab Activity Steps

• Download the unaligned matrix of sequences from the google classroom.
• Perform a multiple sequence alignment using ClustalX and server-based tools.
• Trim the sequence using Gblocks.
• View the sequences in Bioedit.
• Answer the questions in your lab activity sheet.