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Related Experiment Video

Updated: Nov 27, 2025

Simple and Rapid Method to Obtain High-quality Tumor DNA from Clinical-pathological Specimens Using Touch Imprint Cytology

Simple and Rapid Method to Obtain High-quality Tumor DNA from Clinical-pathological Specimens Using Touch Imprint Cytology

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Tissue-associated microbial detection in cancer using human sequencing data.

Rebecca M Rodriguez^1,2,3, Vedbar S Khadka⁴, Mark Menor¹

¹Bioinformatics Core, Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii, Mānoa, Honolulu, HI, USA.

BMC Bioinformatics

|December 4, 2020

Summary

This summary is machine-generated.

Microbial infections contribute significantly to global cancer cases. This review explores bioinformatics tools using next-generation sequencing (NGS) data to analyze microbial composition for cancer treatment and prevention strategies.

Keywords:

Cancer microbiome Computational frameworks NGS

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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

Area of Science:

Oncology
Microbiology
Bioinformatics

Background:

Cancer remains a leading global cause of death, with microbial infections contributing up to 20% of the cancer burden.
The human microbiota's composition varies by organ and influences tumor progression, offering both detrimental and beneficial effects.
Next-generation sequencing (NGS) technologies enable pathogen detection in cancer through host-sequencing data analysis.

Purpose of the Study:

To review popular bioinformatics computational frameworks for analyzing microbial composition from NGS data in cancer.
To highlight how these frameworks can be adapted from viral to bacterial studies.
To emphasize the clinical relevance of deciphering microbial composition for cancer treatment and prevention.

Main Methods:

Review of existing popular bioinformatics computational frameworks.
Focus on frameworks utilizing next-generation sequencing (NGS) data as input.
Adaptation of viral analysis frameworks for bacterial studies.

Main Results:

Identification of various computational frameworks for microbial analysis in cancer.
Demonstration of NGS data's utility in deciphering microbial composition.
Potential for predicting functional compositional differences with clinical relevance.

Conclusions:

Bioinformatics frameworks analyzing NGS data are crucial for understanding the role of microbes in cancer.
These tools can predict functional microbial differences, aiding in the development of targeted cancer therapies.
The insights gained can inform novel treatment and prevention strategies for cancer, leveraging the human microbiome.