MAVL and StickWRLD: visually exploring relationships in nucleic acid sequence alignments.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Sequence analysis tools often provide simple positional identities or statistical summaries.
• Existing methods may not capture complex inter-positional dependencies within sequence families.
• Understanding sequence variations beyond simple consensus is crucial for biological insights.

Purpose of the Study:

• Introduce MAVL (Multiple Alignment Variation Linker) and StickWRLD for analyzing nucleic acid sequence variations.
• Detect and visualize positive and negative inter-positional correlations in aligned sequences.
• Provide a novel representation of sequence data that highlights dependencies.

Main Methods:

• MAVL analyzes all positional pairs within pre-aligned nucleic acid sequences.
• It identifies pairs with frequencies deviating from predictions based on positional frequency matrices.
• Results are visualized using StickWRLD, generating VRML files.

Main Results:

• MAVL successfully detects significant inter-positional correlations (both positive and negative).
• StickWRLD provides a visual representation of these dependencies, going beyond standard sequence consensus.
• The web application and tutorial are available for user access and learning.

Conclusions:

• MAVL and StickWRLD offer a powerful new approach to sequence analysis.
• These tools enhance the understanding of sequence behavior by revealing inter-positional dependencies.
• The methods provide valuable insights into the structure and function of nucleic acid sequences.