The Evolution of Biodata Analysis and Visualization

From Script to Reports to Interactive Dashboards

Published on October 31st, 2023
 9 min read


The enormous amounts of data generated by “omics” technologies, from transcriptomics to proteomics, holds the key to unlocking the mysteries of life itself. Over the years, the methods employed to analyze and visualize this biodata have evolved significantly, mirroring the rapid advancement of technology and the growing complexity of biological research. 

In this article, we’ll take you on a journey through the evolution of biodata analysis and visualization. We explore each transformational phase in the data visualization chain and discuss the key limitations that have led to innovations that have shaped the way we interpret and communicate biological data, ultimately driving progress in omics data analysis.

The Evolution of Biodata Visualization

In the evolving field of bioinformatics, the way we analyze and visualize biodata has undergone a remarkable transformation. From the early days of labor intensive scripting and manual data interpretation to the modern era of interactive dashboards, the evolution of biodata analysis and visualization has revolutionized our ability to extract meaningful insights from the vast and complex omics data.

This journey not only reflects the advancements in computational methods but also underscores the increasing importance of user-friendly tools and interactive approaches in the world of biological research.

Based on our understanding, we see the transformation of biodata analysis and visualization in four phases:

Evolution of biodata analysis and visualization: From scripts to dashboards.

Next, we will explain each phase in more detail and also discuss the limitations that drove the transformation further :

1. The Early Days: Tables / Spreadsheets

In the early days of biodata visualization, tables and spreadsheet based tools  like Excel were widely used due to their simplicity. This of course had many limitations. Tables are inflexible, they cannot handle large data sets, and visualization is severely limited.

2. Static Reports

The next evolution stage of biodata visualization was characterized by static reports through scripts that are created using Perl, Bash, R, and Python scripts.

While these static reports are an improvement over spreadsheets, they still have significant limitations: Long reports composed of over 10-20 pages are difficult for biologists to keep track of. And creating static reports is difficult for biologists due to programming requirements. It is also time consuming to make or request any change on the report as it needs to be re-run by the bioinformatics team. On top, the biggest disadvantage is it is not interactive.

3. Transition to Interactive Reports

With Jupyter Notebook and R-markdown, it then became possible to generate interactive reports – a significant improvement in biodata visualization capabilities. 

As a web-based interactive computing platform, Jupyter Notebook is combining live code, equations, narrative text, and visualizations. R markdown is a simple and easy-to-use plain text language used to combine R code, results from data analysis including plots and tables, and written commentary into a single formatted and reproducible document.

However, the biggest limitations and challenges of these tools remained access to the latest datasets and analysis; Deployment, software updates, lack of centralized data management. Additionally, supporting the large user base with such tools posed a challenge.

4. The Rise of Interactive Dashboards

Finally, with Dash and R/Shiny, it became possible to create interactive dashboards. Shiny is an open-source R package that simplifies web application development using R, enabling interactive data visualization without HTML, CSS, or JavaScript skills. Dash apps extend this concept to Python, creating user-friendly dashboards. In data analytics, Shiny streamlines result presentation, making it accessible. Shiny acts as a bridge between R and the web, facilitating interactive dashboards in the browser.

Advantages of Interactive Dashboards

Omics technologies require sophisticated software solutions, seamless platform and tool integration, laboratory information management systems, data storage and management tools, and data visualization for enhanced insights.

Interactive dashboards for omics technologies have multiple advantages:

1. Automate Processes

Automating manual tasks  helps researchers to be more efficient. Since bioinformaticians often have to be available as service providers for numerous biologists, they need suitable software to be able to answer the large number of queries in adequate time. The automation of routine analyses is the first step to make this possible.

2. Empower Self-Service

Biologists gain access to data at their convenience, allowing them to address their own questions and data discovery  without relying on specialized teams. This promotes agility and responsiveness.

The more biologists are enabled to analyze their data sets themselves, the less the bioinformaticians will be burdened with routine analysis and can turn to higher-value tasks.

3. Facilitate Visual Analysis

Visual data analysis can be greatly simplified by intuitive dashboards.. This makes complex analysis much easier to understand for a broader audience and speeds up decision-making.

4. Promote Collaboration

Powerful analysis and visualization platforms for omics data also lead to a new kind of collaboration between colleagues and often even enable an efficient discussion across different teams in the first place. 

BigOmics' Mission to Revolutionize Traditional Analysis Methods

Our co-founders experienced firsthand the challenges of supporting a large group of biologists analyzing and visualizing omics data. They created a static report for each experiment and shared it with the biologists. As the experimental datasets became larger with more groups, the subsequent analysis became more complex with multiple comparisons and the reports became longer, exceeding 20-40 pages. This was confusing and difficult for biologists to fully understand the results and ask additional questions.

As bioinformaticians, often they were interrupted by follow-up requests from biologists asking to repeat the analysis with different parameters or to find out the expression level of a particular gene, redraw the heat map or volcano diagrams, etc. Then they had to stop the current analysis and return to the script of previous projects to remember all the details and run it again.

Additionally, the increasing number of experiments and data analysis requests was overwhelming. Overall, it was a challenge for two bioinformaticians to support more than 100 biologists with data analysis and visualization requests.

Afterwards they decided to develop a platform – Omics Playground – that would change the way biologists and bioinformaticians work together. It is a collaborative analysis platform that allows bioinformaticians and biologists to share data and results interactively. The platform aims to accelerate tertiary analyses, reduce the time bioinformaticians spend on routine tasks, and allow biologists to interact with the data from their experiments and collaborate more efficiently. It standardizes the omics data analysis process and delivers robust and reproducible results.

To get an idea of how to perform interactive visualizations of your RNA-Seq and proteomics data with Omics Playground and obtain results you can trust, visit our website:

We have also created some videos for you that show you the key features of Omics Playground: searching for similar experiments, comparing datasets, drug connectivity maps, and biomarker selection. For real-world examples, see our case studies:

Want to try Omics Playground for yourself and learn how our interactive dashboards save you time and allow you to serve a large number of biologists with their transcriptome and proteome analysis requests? Get your free trial now!