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RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
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MeStanG-Resource for High-Throughput Sequencing Standard Data Sets Generation for Bioinformatic Methods Evaluation

Daniel Ramos Lopez^1,2, Francisco J Flores^3,4, Andres S Espindola^1,2

¹Institute for Biosecurity and Microbial Forensics (IBMF), Oklahoma State University, Stillwater, OK 74078, USA.

|January 25, 2025

Summary

MeStanG generates artificial high-throughput sequencing (HTS) data for validating microbiome analysis pipelines. This tool ensures reliable microbiome diversity measurements and disease diagnostics by creating standardized mock metagenomic samples.

Keywords:

bioinformatics high-throughput sequencing metagenomics

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Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Metagenomics enables microbiome diversity analysis without prior enrichment.
High-throughput sequencing (HTS) applications range from species discovery to disease diagnostics.
Reliable validation requires standard samples and artificial controls for bioinformatics pipelines.

Purpose of the Study:

To introduce MeStanG, a novel resource for generating HTS Nanopore datasets.
To enable the evaluation of current and emerging bioinformatics pipelines for metagenomics.
To facilitate the creation of mock metagenomic samples with user-defined parameters.

Main Methods:

MeStanG simulates HTS Nanopore data with user-defined organism abundances and error profiles.
The pipeline was evaluated using mock samples with known read abundances.
Analysis involved read mapping, genome assembly, and taxonomic classification across three scenarios.

Main Results:

MeStanG accurately reproduced known organism abundances in simulated metagenomic samples.
The simulator demonstrated consistent performance in bacterial, plant pathogen, and viral dilution scenarios.
Evaluation confirmed the ability to generate datasets with exact read counts.

Conclusions:

MeStanG provides a robust method for generating artificial HTS datasets for pipeline validation.
Scientists can use MeStanG to develop mock metagenomic samples for assessing diagnostic performance.
The tool supports research and training by offering customizable error models and read generation.