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Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

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Published on: December 22, 2014

Phylogenetic Heatmaps Highlight Composition Biases in Sequenced Reads.

Sulbha Choudhari¹, Andrey Grigoriev²

¹Department of Biology, Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA. sulbha@scarletmail.rutgers.edu.

|January 27, 2017

Summary

Advancements in sequencing technology enable metagenomics studies. However, new technologies introduce biases, primarily due to GC-content variation in sequence reads, which can be detected using phylogenetic heatmaps.

Keywords:

computational analysis genome sequencing metagenomics nucleotide composition sequencing bias

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Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Advancements in sequencing technology have revolutionized microbiology, enabling metagenomic studies of uncultured microbes and environments.
Metagenomic sequencing, while powerful, introduces biases affecting nucleotide distribution in sequence reads.

Purpose of the Study:

To illustrate and detect biases in metagenomic sequencing data.
To introduce and validate a novel visualization method for identifying sequence composition differences.

Main Methods:

Phylogenetic heatmaps (PGHMs) for visualizing sequence composition differences between sample groups.
Principal Component Analysis (PCA) to support findings and illustrate PGHM utility.
Analysis of read length and GC-content variation as sources of bias.

Main Results:

PGHMs effectively detect noise and biases in metagenomic data.
Biases were identified across different DNA extraction protocols, sequencing platforms, and experimental frameworks.
GC-content variation was identified as the primary source of bias in most cases.

Conclusions:

PGHMs are a valuable tool for identifying biases in metagenomic datasets.
Understanding and mitigating sequencing biases are crucial for accurate metagenomic analysis.
GC-content is a significant factor influencing metagenomic data quality.