Encoding Data Using Biological Principles: The Multisample Variant Format for Phylogenomics and Population Genomics.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• The fields of phylogenomics and population genomics are experiencing rapid growth, leading to larger multi-genomic datasets and more complex analyses.
• Existing data formats may not be optimally designed for the efficient storage, filtering, and analysis of these increasingly large and complex datasets.

Purpose of the Study:

• To introduce a new data format, the Multisample Variant Format (MVF), specifically designed for storing multiple sequence alignments.
• To enhance the efficiency of phylogenomic and population genomic analyses through improved data handling and encoding.

Main Methods:

• Development of the Multisample Variant Format (MVF) with a distinctive encoding for aligned sites based on biological information content (e.g., invariant, low-coverage).
• Creation of the MVFtools package for data conversion, filtering, quality control, and visualization.
• Proposal of MVF variants for protein, codon alignments, quality scores, and mixed alignment types.

Main Results:

• MVF enables rapid filtering and quality control of genomic data through its biological pattern-based encoding.
• The MVFtools package facilitates fast data transformation, filtering, and visualization, even for large datasets.
• MVF serves as an effective format for data publication and sharing due to its simple structure and flexible header.

Conclusions:

• MVF and MVFtools offer a significant advancement in handling and analyzing large-scale genomic data for phylogenomics and population genomics.
• The proposed biological pattern-based encoding strategy can be broadly applied to improve sequence data formats.
• MVF provides a robust and efficient solution for modern genomic data challenges, promoting better data sharing and analysis.