AMAS

AMAS performs manipulation and summarization of large-scale amino acid and nucleotide alignments for phylogenomic data preparation and alignment summary statistics.

Key Features:

• Alignment format conversion: Converts among popular alignment formats to enable interoperability.
• Concatenation: Concatenates multiple alignments into supermatrices for phylogenomic analyses.
• Partitioning and site extraction: Extracts specific sites and splits alignments based on predefined partitioning schemes.
• Dataset replication and taxon management: Creates replicate datasets and removes taxa from alignments.
• Comprehensive summary statistics: Calculates number of taxa, alignment length, total matrix cells, undetermined characters, percent missing data, AT and GC content for DNA alignments, counts and proportions of variable sites including parsimony-informative sites, and per-character counts for nucleotide or amino acid alphabets.
• Scalability and parallelization: Designed for very large alignments (hundreds of taxa and thousands of loci) and leverages parallel processing to improve performance, particularly for concatenation tasks.
• Implementation: Implemented in Python 3 using only Python core modules.

Scientific Applications:

• Phylogenomic dataset assembly: Preparing concatenated supermatrices and partitioned datasets from hundreds or thousands of loci for downstream phylogenetic inference.
• Locus- and gene-level analyses: Generating per-locus or subset gene alignments and replicate datasets for individual-locus analyses.
• Alignment summary for evolutionary analyses: Producing alignment statistics (missing data, base composition, variable sites, parsimony-informative sites, etc.) to inform phylogenetic inference and evolutionary interpretation.

Methodology:

Performs alignment format conversion, concatenation, site extraction, partition-based splitting, taxon removal, and replicate dataset creation; computes alignment statistics including number of taxa, alignment length, matrix cells, undetermined characters, percent missing data, AT and GC content for DNA, counts and proportions of variable and parsimony-informative sites, and per-character counts for nucleotide or amino acid alphabets; implements parallel processing and is implemented in Python 3 using only core modules.