ADACT
ADACT computes alignment-free dissimilarities among nucleotide and protein sequences using Minimal Absent Words (MAW) and Relative Absent Words (RAW) to enable sequence comparison and phylogenetic analysis.
Key Features:
- Alignment-free analysis: Uses Minimal Absent Words (MAW) and Relative Absent Words (RAW) as core methods for sequence dissimilarity computation without alignment.
- Supported sequence types: Analyzes nucleotide and protein sequences and accepts multiple genome sequences as input.
- Configurable parameters: Allows specification of K-mer size, absent word type (MAW or RAW), dissimilarity index, and reverse complement setting (RC-setting).
- Distance outputs: Produces pairwise distance matrices quantifying dissimilarities between sequences.
- Species relationship outputs: Generates a sorted list of species relationships based on computed distances and infers phylogenetic trees using Neighbor Joining and UPGMA methods.
Scientific Applications:
- Evolutionary biology: Supports comparative analyses of species relationships and divergence using alignment-free dissimilarity measures.
- Genomics and comparative genomics: Facilitates large-scale sequence comparison and relationship inference across genomes and proteomes.
- Phylogenetic reconstruction: Provides distance-based inputs for Neighbor Joining and UPGMA tree construction to represent evolutionary relationships.
Methodology:
Computes Minimal Absent Words (MAW) and Relative Absent Words (RAW) of specified K-mer length, applies a dissimilarity index (with optional reverse-complement consideration), and derives pairwise distances between sequences without performing sequence alignments.
Topics
Details
- Tool Type:
- api, web application
- Programming Languages:
- PHP, JavaScript
- Added:
- 1/18/2021
- Last Updated:
- 1/21/2021
Operations
Publications
Akon M, Akon M, Kabir M, Rahman MS, Rahman MS. ADACT: a tool for analysing (dis)similarity among nucleotide and protein sequences using minimal and relative absent words. Bioinformatics. 2020;37(10):1468-1470. doi:10.1093/bioinformatics/btaa853. PMID:33016997.