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Keanu: a novel visualization tool to explore biodiversity in metagenomes
Adam Thrash1, Mark Arick1, Robyn A Barbato2
1Institute for Genomics, Biocomputing & Biotechnology, Mississippi State University, Starkville, MS, USA.
BMC Bioinformatics
|March 16, 2019
Summary
Keanu is a new command-line tool that simplifies the visualization of metagenomics data. It helps researchers easily explore and understand the organisms present in complex biological samples.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Analyzing complex metagenomics data presents challenges in presenting large datasets comprehensibly.
- Visualizing organism presence in FASTQ sequencing data is crucial for metagenomics and contamination detection.
Purpose of the Study:
- To develop and apply Keanu, an interactive command-line tool for visualizing and exploring metagenomics sample content.
- To simplify the process of viewing and understanding complex biological data.
Main Methods:
- Developed Keanu, a command-line tool written in Python.
- Analyzed alignments against a taxonomy database.
- Presented data interactively via a web page using collapsible trees or bilevel partition graphs.
Main Results:
- Keanu visualizes the presence and abundance of organisms in metagenomics samples.
- The tool offers interactive exploration through collapsible trees (node size indicates abundance) and bilevel partition graphs (arc size indicates abundance).
- Demonstrated Keanu's application using shotgun metagenomics data from an Alaskan alpine site.
Conclusions:
- Keanu offers a straightforward method for researchers to explore and visualize species in complex environmental sequencing data.
- The tool is freely available on GitHub, promoting accessibility for the scientific community.
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