AMAS: a fast tool for alignment manipulation and computing of summary statistics

By M. Borowiec. PeerJ, 2016

728 citations recorded by the source corpus.

The amount of data used in phylogenetics has grown explosively in the recent years and many phylogenies are inferred with hundreds or even thousands of loci and many taxa. These modern phylogenomic studies often entail separate analyses of each of the loci in addition to multiple analyses of subsets of genes or concatenated sequences. Computationally efficient tools for handling and computing properties of thousands of single-locus or large concatenated alignments are needed. Here I present AMAS (Alignment Manipulation And Summary), a tool that can be used either as a stand-alone command-line utility or as a Python package. AMAS works on amino acid and nucleotide alignments and combines capabilities of sequence manipulation with a function that calculates basic statistics. The manipulation functions include conversions among popular formats, concatenation, extracting sites and splitting according to a pre-defined partitioning scheme, creation of replicate data sets, and removal of taxa. The statistics calculated include the number of taxa, alignment length, total count of matrix cells, overall number of undetermined characters, percent of missing data, AT and GC contents (for DNA alignments), count and proportion of variable sites, count and proportion of parsimony informative sites, and counts of all characters relevant for a nucleotide or amino acid alphabet. AMAS is particularly suitable for very large alignments with hundreds of taxa and thousands of loci. It is computationally efficient, utilizes parallel processing, and performs better at concatenation than other popular tools. AMAS is a Python 3 program that relies solely on Python’s core modules and needs no additional dependencies. AMAS source code and manual can be downloaded from http://github.com/marekborowiec/AMAS/ under GNU General Public License.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/be33489550fb79aa3bad2a94782c74e64c4fa3f8

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In Genetics

1. Alan R. Lemmon
2. E. Lemmon
3. Mosè Manni
4. A. Ślipiński
5. E. Zdobnov
6. J. Skevington
7. J. Oswald
8. D. Mckenna

In Molecular Biology

1. Alan R. Lemmon
2. E. Lemmon
3. Mosè Manni
4. Mathieu Seppey
5. Matthew Berkeley
6. A. Ślipiński
7. E. Zdobnov
8. J. Skevington

In Plant Science

1. Alan R. Lemmon
2. E. Lemmon
3. A. Ślipiński
4. E. Zdobnov
5. Mosè Manni
6. D. Mckenna
7. P. Svacha
8. D. Windsor

Citations in the corpus

• Evolution of lacewings and allied orders using anchored phylogenomics (Neuroptera, Megaloptera, Raphidioptera) (2018, methodology): “Basic alignment statistics, including percentage of missing data, A/T, C/G content, alignment length and proportion of variable sites were obtained using AMAS (Borowiec, 2016).”
• BUSCO: Assessing Genomic Data Quality and Beyond (2021, methodology): “As described in Basic Protocol 1 and additionally: conda (and optionally mamba), Snakemake, AGAT, MAFFT, TrimAl, IQ-TREE2, AMAS.”
• Anchored hybrid enrichment provides new insights into the phylogeny and evolution of longhorned beetles (Cerambycidae) (2018, methodology): “Finally, aligned files were concatenated with a modified AMAS.sh (alignment manipulation and summary statistics) script executed in AMAS 0.97 (Borowiec, 2015).”