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Updated: Jun 13, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

A how-to guide for code sharing in biology.

Richard J Abdill¹, Emma Talarico², Laura Grieneisen^2,3

¹Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, United States of America.

|September 10, 2024

Summary

This summary is machine-generated.

Computational biology is essential for modern biological research. This guide offers practical recommendations for biologists on sharing computational code, promoting open science principles and reproducibility.

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Area of Science:

Computational Biology
Bioinformatics
Open Science in Biology

Background:

Biology is increasingly reliant on computational analysis, with wet lab researchers adopting new techniques.
Accessibility of computational tools is rising due to decreasing costs and increasing datasets.
Guidance on implementing computational methods is more available than on reporting them, creating a knowledge gap.

Purpose of the Study:

To provide a comprehensive guide for biologists on computational code sharing best practices.
To address the challenges faced by researchers new to open science principles in computational biology.
To bridge the gap between computational method implementation and transparent reporting.

Main Methods:

Review of existing literature on computational biology and code sharing.
Summarization of common tips and best practices for reporting computational work.
Compilation of resources for training in computational biology and open science.

Main Results:

Identification of key challenges in computational code sharing for biologists.
Development of practical recommendations for sharing code effectively.
Curation of relevant resources for further learning and training.

Conclusions:

Biologists need clear guidance on computational code sharing to adopt open science principles.
Implementing best practices for code sharing enhances research transparency and reproducibility.
This guide serves as a starting point for biologists seeking to improve their computational workflows.