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A systematic sequencing-based approach for microbial contaminant detection and functional inference.

Sung-Joon Park1, Satoru Onizuka2,3, Masahide Seki4

  • 1Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, 108-8693, Japan.

BMC Biology
|September 15, 2019
PubMed
Summary

Microbial contamination in biological research can skew results. This study presents a computational method to detect and analyze contaminants in next-generation sequencing (NGS) data, revealing their impact on host cells.

Keywords:
ContaminationHost-microbe interactionMycoplasmaNext-generation sequencingNon-negative matrix factorization

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Area of Science:

  • Biological Research
  • Biomedical Research
  • Genomics

Background:

  • Microbial contamination is a significant challenge in biological and biomedical research.
  • Computational methods using next-generation sequencing (NGS) data can detect contaminants.
  • Existing methods struggle with intra- and interspecies sequence similarities in complex contamination scenarios.

Purpose of the Study:

  • To develop a computational approach for rigorous investigation of genomic origins of sequenced reads, including those mapped to multiple species.
  • To quantify microbial contamination in RNA-sequencing data.
  • To infer the functional impact of microbial contamination on host molecular landscapes.

Main Methods:

  • Analysis of large-scale synthetic and public next-generation sequencing (NGS) samples.
  • Development of a computational method to investigate genomic origins of sequenced reads.
  • Systematic inference of functional impact of contamination on host cells.

Main Results:

  • Estimated 1000-100,000 contaminating microbial reads per million host reads in RNA-seq data.
  • Identified Cutibacterium as a prevalent laboratory contaminant.
  • Demonstrated that host-contaminant interactions significantly alter host molecular landscapes, affecting pathways like inflammation and apoptosis.

Conclusions:

  • A computational method for profiling microbial contamination in NGS data is provided.
  • Laboratory contamination alters host cell molecular landscapes and can lead to phenotypic changes.
  • Accurate determination of contamination origins and functional impacts is crucial for research quality.