MACER

MACER assembles taxonomically and marker-focused molecular sequence datasets from online repositories (including NCBI GenBank) as an R package to support dataset curation for genomics, phylogenetics, and evolutionary biology.


Key Features:

  • R package implementation: Implemented as an R package to script and reproduce dataset assembly workflows.
  • Data Assembly: Automated acquisition, accumulation, and organization of sequence data from online molecular repositories.
  • Dereplication: Identification and removal of redundant or duplicate sequence entries to maintain dataset integrity.
  • Cleaning: Processing of datasets to remove inconsistencies and prepare sequences for downstream analysis.
  • Customization for specific applications: Construction of taxonomically and marker-targeted datasets based on user-defined criteria.

Scientific Applications:

  • Genomics: Curation of sequence datasets for comparative and marker-based genomic analyses.
  • Phylogenetics: Assembly of marker-specific and taxonomically focused sequence sets for phylogenetic inference.
  • Evolutionary biology: Preparation of cleaned, non-redundant sequence datasets to support evolutionary analyses.
  • Comparative studies: Generation of consistent datasets across projects to enable comparative molecular studies.

Methodology:

Automated access to online molecular sequence repositories (e.g., NCBI GenBank); targeted searching and downloading of sequences based on user-defined criteria; dereplication to remove duplicate sequences; and dataset cleaning to remove inconsistencies and prepare sequences for analysis.

Topics

Details

License:
GPL-2.0
Cost:
Free of charge
Tool Type:
library
Operating Systems:
Linux, Mac, Windows
Programming Languages:
R
Added:
4/25/2022
Last Updated:
4/25/2022

Operations

Publications

Young R, Gill R, Gillis D, Hanner R. Molecular Acquisition, Cleaning and Evaluation in R (MACER) - A tool to assemble molecular marker datasets from BOLD and GenBank. Biodiversity Data Journal. 2021;9. doi:10.3897/bdj.9.e71378. PMID:34594153. PMCID:PMC8443542.

Documentation

Links