Related Experiment Video
Updated: Dec 4, 2025
06:02
A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
Published on: October 28, 2025
188
MetaLAFFA: a flexible, end-to-end, distributed computing-compatible metagenomic functional annotation pipeline
Alexander Eng1, Adrian J Verster1,2, Elhanan Borenstein3,4,5
1Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA.
BMC Bioinformatics
|October 22, 2020
Summary
MetaLAFFA is a new functional annotation pipeline for shotgun metagenomic data. It offers distributed computing compatibility and flexible customization for analyzing microbial community functions.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Microbial communities are crucial research subjects across disciplines.
- Shotgun metagenomic sequencing provides insights into microbial community genomic content.
- Functional annotation of metagenomic data reveals aggregate microbial functional capacities.
Purpose of the Study:
- To introduce MetaLAFFA, a novel functional annotation pipeline for shotgun metagenomic data.
- To address limitations in existing pipelines, such as customization and distributed computing integration.
Main Methods:
- MetaLAFFA accepts unfiltered shotgun metagenomic data as input.
- It is implemented as a Snakemake pipeline for distributed computing integration.
- Features a Python module-based configuration for flexible customization.
Main Results:
- MetaLAFFA generates functional profiles from raw metagenomic data.
- The pipeline integrates seamlessly with distributed computing clusters.
- Provides summary statistics for pre-processing and annotation quality assessment.
Conclusions:
- MetaLAFFA is an end-to-end pipeline for metagenomic functional annotation.
- It offers distributed computing compatibility and customizable options.
- Source code is available via GitHub and installation through Conda.
