Proteomics and transcriptomics are two major disciplines within the field of molecular biology, each offering unique insights into the complex machinery of cellular processes. Together, they form the backbone of systems biology, providing a comprehensive view of gene expression and protein dynamics that underlie biological complexity and function. Integrating data from these omics disciplines is essential for gaining a holistic understanding of cellular processes, disease mechanisms, and therapeutic targets in biomedical research.

What is Proteomics Data?

Proteomics focuses on the large-scale study of proteins, their identification, quantification, and characterization within biological systems. By analyzing the entirety of proteins present in a cell, tissue, or organism, proteomics provides invaluable information about protein structure, function, interactions, and modifications, shedding light on the underlying mechanisms of cellular physiology and pathology. 

What is Transcriptomics Data?

On the other hand, transcriptomics deals with the study of gene expression at the RNA level, particularly focusing on the transcriptome—the complete set of RNA transcripts produced by a cell or organism. Transcriptomics techniques, such as RNA sequencing (RNA-seq), enable researchers to quantify and analyze gene expression patterns, splice variants, alternative transcripts, and non-coding RNAs, offering insights into regulatory networks, developmental processes, and disease mechanisms. 

Challenges While Working with Proteomics and Transcriptomics Data

Proteomics and transcriptomics offer many benefits when leveraged in combination as they are complementary approaches to biological research questions. These complementary technologies can expand the field of potential biological interpretations for researchers and address a wider variety of questions. However, data harmonization is also an essential step in omics research as data obtained from these techniques are heterogeneous and complex. 

1. Handling Data Complexity and Volume

Transcriptomics data involve high-throughput data sources like bulk RNA-sequencing data. Proteomics data are frequently high-dimensional acquired from microarrays or mass spectroscopy techniques. These high-throughput experiments yield data that is complex, high-dimensional and especially voluminous. Datasets like this require streamlined statistical analyses. Effectively dealing with these issues requires good data infrastructure, expert input and computational resources. 

2. Controlling Data Heterogeneity and Scale

In combining transcriptomics and proteomics, data harmonization is necessary to make data structures compatible and fit for downstream analysis. Datasets come from different sources and are heterogeneous in type and structure. Public repositories like Clinical Proteomic Tumor Analysis Consortium (CPTAC) contain data from diverse diseases and tumors, including whole genome sequencing, whole exome sequencing and RNA sequences. These heterogeneous sources have different levels of metadata - at the data level and sample level. Harmonizing across such heterogeneity also makes analytical solutions much more scalable and consistent.

3. Strength of Biological Interpretation

As research is beginning to show, some diseases are caused by changes in regulation of multiple genes or the interaction of the environment on a specific tissue. The development of approaches that harmonize different branches of omics are making it possible to discover biomarkers in these cases. In rare diseases, when patient samples are more difficult to access, harmonizing different datasets from these precious samples provides valuable insights. 

Methods for Harmonizing Proteomics and Transcriptomics

In light of these challenges, implementing data harmonization to integrate omics datasets requires a strategic, systematic approach. Polly by Elucidata is a robust data harmonization platform that helps to mitigate these challenges. Polly serves as a one-stop solution for all complex data integration needs, offering a suite of bioinformatics analysis, visualization, data processing, machine learning, and data management tools. From integrating diverse datasets to standardizing data formats and streamlining downstream analysis, Polly empowers researchers to drive research progress and deliver real-world applications faster through it's suite of solutions.

Data Harmonization:

Polly harmonizes data from public and in-house data, using a configurable, granular, and transparent curation process. Polly's powerful harmonization engine processes measurements, links to ontology-backed metadata, and transforms datasets into a consistent data schema.

The data harmonization process completes metadata annotations with 99.99% accuracy and annotates them with 30+ metadata fields. All data is checked for quality and completeness with around 50 QA/QC checks. The process ensures uniformity across data formats, structures, and semantics making it suitable for downstream analysis.  

Pipeline Development:

Data processing pipelines can be customized to data and analysis requirements, chosen from a suite of 30+ scientifically validated pipelines, or further optimized to reduce costs and runtimes. Polly also offers to develop and deploy customized pipelines tailored to specific omics data type & analysis requirements. Our platform runs complex, multi-threaded pipelines at a fraction of the cost and runtime of typical high-throughput data pipelines. 

ML Solutions & Bioinformatics Analysis:

‘Polly Verified’ data is delivered ready for functional models or analysis pipelines using machine learning (ML-ready). ML models can be built, fine-tuned, trained and deployed as per specific needs on top harmonized data to accelerate research. Polly unlocks powerful bioinformatics use-cases through its harmonized datasets and its Data Concierge services. Our experts can help find relevant datasets from Polly’s expansive data corpus with detailed annotations to best match specific inclusion or exclusion criteria. We help target and predict biomarker search, compare signatures, cell type annotations, and more. We also have domain experts who can help with metadata-based exploration, differential expression, knowledge graphs, and interactive dashboards according to specific research needs. 

Visualization & Analysis:

Polly offers a host of data visualization tools, including web applications and custom dashboards. Native web apps can be integrated on Polly to analyze data arrays. Polly also provides data in extendable data models that can be streamed into applications of choice like Spotfire, Tableau, etc. Our experts help build or customize and deploy production-ready proprietary applications to run research-specific analyses on our secure cloud platform. 

Case Study - Polly Provides Harmonized Proteomics and Transcriptomics Dataset

1. A leading genomics-based drug discovery company wanted to accelerate the identification of putative targets across immunological diseases and cancer by complementing their research with publicly available data.
2. The highly curated data on Elucidata’s Public OmixAtlas played a pivotal role in helping the company identify 1 target for an immunology group in just 6 months, as compared to the usual time period of 2-3 years.
3. The partnership helped de-risk advancement to phase II leading to lower trial costs there by saving ~ $3M. It also worked in favor of saving time of R&D and bioinformatics personnel by freeing up ~2000 hours annually.

Read the full case-study here. 

Want to know how to integrate proteomics and transcriptomics data? Read the blog here!

Polly is a pioneer in accelerating research timelines and making data integration a priority. From eliminating redundant efforts to fostering collaborative analysis and facilitating breakthroughs in diagnostics and therapeutics, Polly's impact resonates across every stage of omics research. 

Connect with us or reach out to us at info@elucidata.io to learn more.

‍