Omics Studio

𝐄𝐯𝐞𝐫𝐲 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐧 𝐎𝐦𝐢𝐜𝐬 𝐒𝐭𝐮𝐝𝐢𝐨 𝐬𝐭𝐚𝐫𝐭𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐩𝐥𝐚𝐜𝐞 - 𝐲𝐨𝐮𝐫 𝐒𝐭𝐮𝐝𝐲 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 💾
Before running a single analysis, Study Preferences lets you define the defaults that apply across your entire study. Set them once, and they carry through every subsequent step - keeping your work consistent and reproducible from the start.

There are four areas to configure.

𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐢𝐞𝐫 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 determine how expression and metabolite values are mapped throughout the platform. For proteomics, you can choose between UniProt ID, Ensembl ID, Gene Name, or Protein Name. Switching to Gene Name makes data significantly more readable across downstream analyses without affecting the underlying mapping.

𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 set a Valid Value threshold - the minimum percentage of non-missing values required for a feature to be included. You can also pre-select specific samples to restrict analyses to a defined subset of your cohort.

𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 define default thresholds for differential analyses - p-value, adjusted p-value, and log2 fold-change cutoffs for both up- and down-regulated features. You also select which statistical contrasts to use as default reference comparisons.

𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 pre-populate the annotation database for ORA and GSEA. Options include Reactome pathways and Gene Ontology categories, filtered by organism and data type. You can always override these in individual analyses.

All settings are saved with the study and can be adjusted at any point.

See the full configuration walkthrough in the video below 👇 or try it out yourself for 14-days at omicsstudio.com

👉 𝐇𝐢𝐠𝐡-𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐨𝐦𝐢𝐜𝐬 𝐝𝐚𝐭𝐚 𝐫𝐚𝐫𝐞𝐥𝐲 𝐫𝐞𝐯𝐞𝐚𝐥𝐬 𝐢𝐭𝐬 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐨𝐧 𝐢𝐭𝐬 𝐨𝐰𝐧.
PCA in Omics Studio is built as an early exploratory step - a way to validate sample groupings, detect outliers, and assess data quality before any downstream analysis begins.

The workflow is guided. Select your expression dataset, assign a grouping variable to colour-code samples in the plot, and run the analysis. The result is a scatter plot where each point represents a sample - proximity indicates similarity, and clean clustering around experimental groups is a strong signal that your data is behaving as expected.

The axes are flexible. While PC1 and PC2 capture the largest sources of variance, you can switch to lower-ranked components to explore additional patterns or surface outliers that wouldn't otherwise be visible.

The Loading Plot gives you a complementary view - showing how strongly each individual feature contributes to the principal components. Selecting features from the loading plot adds them directly to My Lists for targeted downstream analysis.

Results can be downloaded as CSV files or exported as images for reports.

PCA doesn't tell you what the biology is. It tells you whether your data is ready to answer that question.

See how it works in the video below 👇 or sign up for a 14-day trial at omicsstudio.com

👉 𝐁𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚, 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐭𝐫𝐮𝐬𝐭 𝐢𝐭.
Two tools in Omics Studio are built specifically for that - Total Identification Overview and Summed Abundance Calculation. Both sit within the Expression View and are designed as quality control steps before any downstream analysis begins.

𝐓𝐨𝐭𝐚𝐥 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰 gives you immediate clarity on data depth and completeness. How many proteins or features were confidently identified across your conditions? Are all samples performing equally? The UpSet plot visualizes how distinct protein sets intersect across conditions - making coverage and overlap immediately readable.

𝐒𝐮𝐦𝐦𝐞𝐝 𝐀𝐛𝐮𝐧𝐝𝐚𝐧𝐜𝐞 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐨𝐧 aggregates abundance values across all features for each sample, giving you a macro-level view of total expression before you zoom into individual differences. The bar chart lets you compare total abundance across conditions at a glance. The box plot shows the distribution across groups - tight, uniform spread is a strong indicator of technical consistency and dataset reliability.

Large differences in summed abundance can flag batch effects or normalization issues worth investigating before running PCA or statistical tests. Consistent values, on the other hand, build confidence that your data is ready for deeper analysis.

Both tools are fast, guided, and require no code.

See them in action in the video below 👇 or learn more at omicsstudio.com

𝗠𝗼𝘀𝘁 𝗯𝗶𝗼𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝘀 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝗺𝗮𝗸𝗲 𝘆𝗼𝘂 𝘄𝗮𝗶𝘁. 𝗪𝗲'𝗿𝗲 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 🚀
Omics Studio is built natively on Microsoft Azure. Today, analyses run through a structured queue system - so heavy jobs don't crash the interface while they compute. Stable, reliable, and built to handle large-scale data without breaking down.

But we're going further.

We're expanding the infrastructure to support fully parallel cloud computing. Every analysis runs on its own isolated compute instance - completely independent from everything else happening on the platform.

A gene set enrichment analysis can run in the background while a faster expression analysis finishes in the foreground. Each result lives in its own view, ready when it is. No queues stalling active research.

This kind of parallel infrastructure is rare in the bioinformatics space - and it's what a modern data exploration platform should look like.

👉 𝗧𝗿𝘆 𝗼𝘂𝘁 𝗢𝗺𝗶𝗰𝘀 𝗦𝘁𝘂𝗱𝗶𝗼 𝗳𝗼𝗿 𝟭𝟰-𝗱𝗮𝘆𝘀 - no commitment - at omicsstudio.com

𝐆𝐒𝐄𝐀 𝐚𝐧𝐝 𝐎𝐑𝐀 𝐚𝐧𝐬𝐰𝐞𝐫 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬. 𝐎𝐦𝐢𝐜𝐬 𝐒𝐭𝐮𝐝𝐢𝐨 𝐫𝐮𝐧𝐬 𝐛𝐨𝐭𝐡.
Over-Representation Analysis identifies enriched biological pathways in a discrete list of significant genes, proteins, or metabolites. Gene Set Enrichment Analysis goes further - it evaluates whether predefined gene sets show coordinated enrichment across an entire ranked dataset, capturing biological signal that threshold-based methods can miss.

Both analyses follow a guided workflow in Omics Studio. Set your thresholds and comparisons, choose your annotation databases - Reactome, Gene Ontology, and others depending on your organism and data type - and select your visualizations. Results are processed in the background and ready to explore across three views: a table, a bar chart, and a dot plot.

When a pathway stands out, clicking it reveals the contributing genes directly in the Focus panel. Save them as a named list in My Lists and take them into Database Search for deeper biological context.

See it in action in the video below 👇 or 𝐭𝐫𝐲 𝐢𝐭 𝐨𝐮𝐭 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟 𝐟𝐨𝐫 14-𝐝𝐚𝐲𝐬 at omicsstudio.com

𝗧𝗵𝗲𝗿𝗲 𝗶𝘀 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗳𝗿𝘂𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗮𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝘁𝗵𝗮𝘁 𝗳𝗿𝗲𝗲𝘇𝗲𝘀 🥶
Anyone who has worked with large omics datasets knows the feeling. You click on a data point. The program slows to a crawl. You wait. You click again. It freezes.

This is one of the most common failure points in bioinformatics tools - and it happens because keeping a complex, data-heavy interface fully responsive is genuinely hard to solve. Most platforms don't.

It was one of the reasons we built our own plotting tools from scratch.

We spent months optimizing specifically for this - so that every interaction in Omics Studio, whether clicking, zooming, or panning across large datasets, stays fluid. No lag. No freezing. No interruptions to your workflow.

Responsiveness isn't a nice-to-have. For a platform built around data exploration, it's the foundation everything else depends on.

👉 Try it out yourself with our 14-day trial at omicsstudio.com

𝗬𝗼𝘂𝗿 𝗹𝗶𝘀𝘁 𝗼𝗳 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗲𝗱 𝗽𝗿𝗼𝘁𝗲𝗶𝗻𝘀 𝗶𝘀 𝗼𝗻𝗹𝘆 𝗮𝘀 𝘂𝘀𝗲𝗳𝘂𝗹 𝗮𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝘁 📚
Identifying up- or down-regulated proteins is a starting point - not a conclusion. The next step is understanding what those proteins actually do, where they're expressed, how they interact, and whether they're clinically relevant.

That's what Database Search in Omics Studio is built for.

From one or more lists of interest, you can pull cross-referenced data directly from UniProt, Reactome, the Human Protein Atlas, and ClinicalTrials.gov - without leaving the platform or manually querying each database separately.

Filter by what matters to your research. Molecular identity. Structure and features. Function and interaction. Expression and localization. The data populates instantly across dedicated tabs, and switching between saved lists updates everything in real time.

Context turns a list of identifiers into biological insight. That's the point.

👉 Try it out yourself with our 14-day trial at omicsstudio.com

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗢𝗺𝗶𝗰𝘀 𝗦𝘁𝘂𝗱𝗶𝗼 𝘄𝗮𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗯𝗶𝗼𝗹𝗼𝗴𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗜𝘁 𝘄𝗮𝘀 𝗮𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.
When we set out to build a platform that actually works for omics data at scale, we ran into three technical challenges that shaped every architectural decision we've made since.

𝟭. 𝗡𝗼 𝗦𝗵𝗶𝗻𝘆 𝗮𝗽𝗽𝘀. Most tools in this space are built on R Shiny - fast to prototype, but limited when it comes to performance and scalability. We chose to build on ASP.NET from day one. The result is a native web platform that feels responsive and gives us full control over how we scale and extend Omics Studio going forward.

𝟮. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝘂𝗿 𝗼𝘄𝗻 𝗽𝗹𝗼𝘁𝘁𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 - 𝗮𝗻𝗱 𝗽𝗮𝘆𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗲. We've talked before about why we built our own plotting library. What we didn't mention is how long it took to get right. Calculating correct plot margins. Registering a click on a single data point reliably. Problems that sound trivial in isolation took a lot of continuous work to resolve into a solution that holds together under real-world conditions.

𝟯. 𝗖𝗹𝗼𝘂𝗱 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝘄𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 𝗿𝗲𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗶𝗻 𝘀𝗶𝘅 𝗺𝗼𝗻𝘁𝗵𝘀. Deploying a data-heavy bioinformatics platform securely and sustainably requires getting the foundation right from the start. We invested in that upfront - so we're not facing a painful infrastructure overhaul the moment we start scaling.

None of this is the glamorous side of building a bioinformatics platform. But it's the work that makes everything else possible.

𝐖𝐡𝐲 𝐰𝐞 𝐛𝐮𝐢𝐥𝐭 𝐨𝐮𝐫 𝐨𝐰𝐧 𝐩𝐥𝐨𝐭𝐭𝐢𝐧𝐠 𝐭𝐨𝐨𝐥𝐬 𝐟𝐫𝐨𝐦 𝐬𝐜𝐫𝐚𝐭𝐜𝐡 🚧
When we started building Omics Studio, one of the first decisions we had to make was how to handle data visualization. We looked at the existing libraries - and none of them did what we needed.

Some could render the right plot types. Others had decent interactivity. But we couldn't find anything that delivered both - with the level of control required to build a tool that actually works for omics data.

So we built our own.

That decision gave us full control over every interaction - zooming, panning, precise data point selection, and specialized plot types like upset plots - without compromise. It's what allows us to keep building features that fit the biology, not the other way around.

💡 𝐖𝐡𝐚𝐭'𝐬 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐠𝐨𝐢𝐧𝐠 𝐨𝐧 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚𝐬𝐞𝐭 - 𝐛𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐬𝐭𝐚𝐫𝐭 𝐲𝐨𝐮𝐫 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬?
Before drawing any conclusions from your omics data, it helps to get a clear picture of what you're working with. That's exactly what Relative Expression Analysis in Omics Studio is designed for.

In this video, we walk through the interactive heatmap - one of the first places to go when you want a quick read on expression distribution across your samples. You can switch between individual data points and group means, apply Z-score scaling on either axis, flip the view, and hide or show identifiers depending on what you need to see.

When something catches your eye, just click the protein row to add it to your current selection - and save it as a custom list for downstream analysis.

Just load your data and start exploring.
👉 Learn more at omicsstudio.com