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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Published on: June 28, 2018

User-friendly bioinformatics pipeline gDAT (graphical downstream analysis tool) for analysing rDNA sequences.

Martti Vasar¹, John Davison¹, Lena Neuenkamp²

¹Department of Botany, University of Tartu, Tartu, Estonia.

Molecular Ecology Resources

|February 2, 2021

Summary

A new graphical pipeline, gDAT, simplifies high-throughput sequencing data analysis for microbial community studies. This tool makes complex bioinformatics accessible for researchers, enabling faster and more reliable metabarcoding results.

Keywords:

arbuscular mycorrhizal fungi high-throughput sequencing pipeline sequencing data analysis software teaching tool

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Area of Science:

Bioinformatics
Computational Biology
Environmental Microbiology

Background:

High-throughput sequencing (HTS) enables parallel analysis of microbial communities (metabarcoding) in environmental samples.
Analyzing HTS data requires expertise in command-line tools for error identification and downstream processing.
Existing methods can be complex and inaccessible to researchers without specialized bioinformatics skills.

Purpose of the Study:

To introduce gDAT, a user-friendly graphical pipeline for HTS metabarcoding data analysis.
To provide an accessible tool that integrates common command-line bioinformatics tools.
To facilitate efficient and repeatable analysis of microbial community data.

Main Methods:

Development of a Python-based graphical user interface (GUI) pipeline named gDAT.
Integration of standard command-line tools for HTS data processing.
Support for various sequencing platforms (Sanger, 454, IonTorrent, Illumina, PacBio) and marker regions (SSU rRNA, ITS).
Implementation of both open and closed-reference operational taxonomic unit (OTU) picking.
Testing with diverse datasets including arbuscular mycorrhizal fungi, general eukaryotes, and fungal ITS regions.

Main Results:

gDAT offers a simplified workflow for HTS data analysis through its intuitive GUI.
The pipeline demonstrates compatibility across Windows, macOS, and Linux operating systems.
Performance and speed evaluations confirm the pipeline's efficiency for metabarcoding studies.
Repeatability and logging capabilities enhance data analysis reliability.

Conclusions:

gDAT provides an accessible and efficient solution for HTS metabarcoding data analysis.
The open-source nature of gDAT promotes community engagement and software improvement.
Researchers can leverage gDAT for rapid, reliable, and reproducible microbial community analysis.