Build or Buy: Big Data Challenges in Omics Data Analysis

Comparing In-House Analysis Solutions vs. Buying External Solutions

Published on August 7th, 2023
 9 min read


Research into complex medical challenges such as cancer, autoimmune diseases, genetic and neurodegenerative diseases, and new therapeutic approaches such as regenerative medicine rely heavily on “omics” technologies to identify mechanisms, discover new proteins, and develop new drugs.

Scientists face the challenge of analyzing enormous amounts of data generated by these technologies. The complexity and sheer volume of datasets require advanced bioinformatics tools and strategies to gain insights.

In this article, we look at challenges that bioinformaticians in biotech and pharmaceutical companies face when analyzing large transcriptomics and proteomics datasets and deciding whether to develop an analysis solution in-house or buy a third-party solution.

Approaches to the Analysis of Transcriptomics and Proteomics Data

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Companies are usually faced with the choice of outsourcing the analysis to external partners or having it analyzed by their own bioinformatician or bioinformatics team.

In the outsourcing option, the data analysis is carried out entirely by an external service provider. The scientists simply submit their data and receive the results after some time. However, this alternative often posed difficulties as researchers sought greater flexibility in the static reports they were typically presented with.

On the other hand, there is the option of performing the data analysis in-house with scripts written by the scientists themselves. Bioinformaticians often reach a point where these scripts are no longer sufficient due to massive data growth and the challenge of scalability, so they usually have two options:

  1. Build their own platform that allows them to standardize and accelerate data analysis
  2. Buy ready-to-use bioinformatics software like the Omics Playground platform.

Omics Data Analysis Essentials: Must-Have Features in a Bioinformatics Analysis Platform

Bioinformatics tools and strategies for RNA-Seq analysis and proteomics data analysis enable researchers to extract meaningful insights from their high-throughput experiments. The workflows (or pipelines) consist of loosely connected computational tasks. 

The following steps are often involved and would thus have to be considered when building a bioinformatics platform:

1. Data Pre-processing

Quality control, alignment, quantification and normalization are performed to ensure accuracy and reliability of results.

2. Statistical Analysis

Statistical models and algorithms help identify differentially expressed genes or proteins and determine their significance.

3. Pathway and Functional Analysis

Insights into the biological processes and pathways affected by changes in gene expression or protein abundance can be obtained through pathway and functional analysis.

4. Network Analysis and Visualization

Network analysis tools are used to visualize and explore complex molecular interactions and facilitate system-level understanding of transcriptome and proteome data.

In addition, there are advanced analysis features such as the following:

5. Biomarker discovery

The development of various machine learning approaches can be leveraged to identify potential biomarkers for various clinical manifestations based on gene or protein expression levels.

6. Drug Discovery and Repurposing

The availability of extensive drug-related gene expression databases, such as the L1000 Drug Connectivity Map, has made it possible to understand the mechanisms of actions of novel compounds based on the expression profiles they induce in target cells and also to identify drug suitable for repurposing in the treatment of unrelated conditions than originally intended.

7. Comparative analysis with public dataset collections

Over the decades, large amounts of transcriptomic and proteomic datasets have been generated and collected in public databases such as the Gene Expression Omnibus (GEO) or the Proteomics Identification Database (PRIDE). They provide an invaluable resource for comparative analysis of newly generated experiments and help identify overarching themes that would otherwise be missed when focusing on individual experiments in isolation.

8. Integration of Multi-omics Data

Integrating transcriptomics and proteomics data with other omics data, such as genomics or metabolomics, can provide a deeper understanding of biological processes.

Build vs Buy: Advantages and Disadvantages

Disadvantages of Building Analysis Platforms

Data analysis for transcriptomics and proteomics require a variety of different tools, which can make in-house solutions labor intensive and error prone. Therefore, they come with challenges.

1. High technical expertise and infrastructure requirements

In-house analysis relies on a skilled team with expertise in bioinformatics and statistical analysis. Organizations must invest in infrastructure, computing resources, and software licenses. Acquiring and maintaining this expertise and infrastructure can be difficult and costly. In addition, this investment must be weighed against how much the team would use the in-house solution. Hiring a team of bioinformaticians and software engineers may not make sense for teams that perform only a few experiments per year.

2. Time and resource constraints

Hiring and training staff, ensuring data security, and managing hardware and software updates and maintenance can be challenging. An important factor to consider is bug testing, which in turn requires time and personnel. This also applies if one decides to outsource the development of the internal solution, as a specialist is then needed to maintain the system in-house.

3. Limited capacity and scalability

Internal analytics capacity is often limited in its ability to handle large volumes of data or high-throughput data. Scaling analytics infrastructure to handle growing data volumes can be difficult and costly.

4. Risk of technology obsolescence

Rapid advances in bioinformatics tools and techniques make it difficult for internal teams to keep up with the latest developments. Keeping up with evolving technologies and ensuring access to cutting-edge analytical methods requires ongoing training and investment – definitely a major disadvantage!

5. Risk of biased interpretations and continuity

Internal analyses can lead to biases due to researchers’ subjective decisions, assumptions, or lack of expertise in certain analytical techniques. In addition, researchers regularly leave teams as part of normal staff turnover, adding to the complexity as the successor must take over the project and catch up.

Advantages of Building In-House Analysis Platforms

In-house analysis of transcriptomics and proteomics data analysis can also have certain advantages if researchers are able to harness them.

1. Control and ownership

Performing the analysis in-house provides full control and ownership of the analysis process. This allows flexibility in experimental design and customization of analysis pipelines.

2. Independence

There is no dependence on external service providers.

3. Exploiting the sunk costs already invested if an in-house capability already exists

There are costs associated with building an in-house analytics infrastructure. If such capabilities are already in place, it may make sense to retain them.

Advantages of Buying an Omics Data Analysis Solution: Omics Playground

Specifically tailored for the analysis of RNA-Seq and proteomics data, BigOmics developed Omics Playground to enable scientists without deep bioinformatics knowledge to dive into advanced omics analysis with smart tools. The platform streamlines dataset integration, identifies key genes and proteins in disease biology, and contributes to a deeper understanding of disease mechanisms, including drug repurposing and unraveling new drug mechanisms.

From a bioinformatician’s perspective, analyzing large transcriptomics and proteomics datasets with Omics Playground offers numerous benefits:

1. Fewer* resources needed and ready to use

Outsourcing means that fewer resources need to be spent on the development, maintenance and deployment of in-house solutions. From the user’s point of view, the most important advantage is that they can use it immediately and don’t have to wait months or years for development.

* Metrics based on current BV studies conducted at the time of publication

2. Fewer repetitive and routine tasks

Outsourcing means that fewer resources need to be spent on the development, maintenance and deployment of in-house solutions. From the user’s point of view, the most important advantage is that they can use it immediately and don’t have to wait months or years for development.

3. Workflow standardization and reproducible results

Using an integrated analysis platform standardizes workflows, minimizes coding errors, and enables switching algorithms to optimize analyses. Moreover, it ensures robust and reproducible analysis thanks to the application of different statistical methods.

4. Increased transparency and collaboration

Omics Playground promotes transparency and helps create an environment of mutual learning and collaboration between biologists and bioinformaticians – not only in the same team but across institutes and organizations.

5. Tested, bug-free solution

Bug testing of internal solutions requires time and manpower, both for in-house developments and when outsourcing the development of the internal solution. One advantage of Omics Playground is that it is a tested solution backed by a team that ensures the software is bug-free.

6. More continuity in the event of staff changes

Employee turnover is much easier to absorb with Omics Playground because the same system is used and no knowledge is lost within the team. Academic labs, for example, often include the analysis solution in grant proposals to save resources. When the PhD student responsible for it leaves or the grant application expires, the institutions are then faced with the same challenges.

Want to try out Omics Playground for yourself and learn how it can help you as a bioinformatician solve your big data challenges and bottlenecks in transcriptome and proteome analysis?

Unlock the full potential of your RNA-seq and proteomics data.