The Frustration of Manual Data Analysis – and How Automation Can Save You Hours

If you’ve ever found yourself stuck cleaning spreadsheets, adjusting thresholds, or redoing plots just because one sample didn’t behave, this is for you.

Manual data analysis in proteomics is not only tedious but also time-consuming and repetitive, and it is often the biggest hurdle between running experiments and interpreting biological insights.

The Problem

Tools like Skyline and DIA-NN have revolutionized DIA-based workflows, but once you export those quantification files, what comes next?

For many researchers, it’s a return to the copy-paste grind:

• Normalizing across replicates

• Filtering out low-confidence proteins

• Running statistical comparisons manually

• Creating plots from scratch

And worst of all, redoing everything if the parameters change.

It's mentally exhausting and a serious bottleneck to discovery.

The Solution: My Automated Proteomics Pipeline

To overcome this, I developed a modular and reproducible pipeline that automates downstream data analysis using exported quantification tables directly from Skyline and DIA-NN (note: it does not work with raw chromatograms).

What it does:

• Ingests tabular outputs from DIA-NN and Skyline

• Automatically filters and normalizes data

• Performs statistical analysis for differential expression

• Generates publication-ready, customizable plots

• Runs entirely in a well-structured, user-friendly Google Colab Notebooks.

This means no more switching between tools or repeating steps every time you change a threshold. Everything stays in one organized place, saving dozens of hours per project and reducing the chance for errors.

You can explore the full pipeline and its use case in my recent publication:

https://doi.org/10.1101/2025.03.24.645047

A Broader Vision: Automation for All Omics

While this pipeline was designed for proteomics, the same logic applies to metabolomics, transcriptomics, and other omics fields. These workflows also suffer from fragmented data processing and manual downstream wrangling.

Adapting similar automated pipelines could standardize analysis, enhance reproducibility, and empower researchers across disciplines to extract insights faster, with more confidence.

Cloud Platforms Exist—But Accessibility Matters

There are powerful cloud platforms like LatchBio, DNAnexus, Seven Bridges, and Terra that offer multi-omics support, scalable workflows, and real-time collaboration features. These tools provide robust environments for large-scale analysis and are widely used in industry and population-level studies.

However, many of these platforms are subscription-based or require institutional access, limiting their availability to smaller labs or early-career researchers.

That’s why building open, flexible, and scriptable tools is essential. Everyone should have access to automation, not just those with a cloud budget.

Final Thoughts

Automation isn't just a productivity boost—it’s a research enabler. Streamlining data handling and reducing friction in your workflow allows you to spend more time on what matters: interpretation, discovery, and innovation.