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Related Experiment Video

Updated: Dec 14, 2025

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Nine quick tips for efficient bioinformatics curriculum development and training.

Susan McClatchy¹, Kristin M Bass², Daniel M Gatti³

¹The Jackson Laboratory, Bar Harbor, Maine, United States of America.

Plos Computational Biology

|July 24, 2020

Summary

This summary is machine-generated.

Biomedical research increasingly relies on complex data from new technologies. This study presents an efficient curriculum development approach to train researchers in essential computational tools for data analysis.

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Area of Science:

Biomedical Sciences
Computational Biology
Bioinformatics Training

Background:

Biomedical research is generating large-scale, complex datasets due to technological advancements.
There is a growing demand for researchers skilled in computational tools to analyze this data.
Existing training methods may not be adequately addressing the evolving needs of data-intensive research.

Purpose of the Study:

To describe an efficient and effective method for creating educational materials.
To address the need for computational skills training in biomedical research environments.
To provide a scalable curriculum development approach for data-driven science.

Main Methods:

Developing modular curriculum components tailored to research needs.
Implementing a flexible deployment strategy for diverse research settings.
Focusing on practical application of computational tools relevant to current research.

Main Results:

Demonstrated an efficient process for curriculum material creation.
Showcased the effectiveness of the developed materials in a research context.
Validated the approach for adaptability across different research domains.

Conclusions:

The described curriculum development approach is efficient and effective for training researchers.
This method addresses the critical need for computational skills in modern biomedical research.
The approach facilitates the integration of essential data analysis tools into research environments.