Researchers are producing an unprecedented amount of genomic data to gain insights into the functioning of the genome and its impact on human health and disease. By 2025, approximately 40 exabytes of storage will be required to accommodate the global genome sequencing data. To put this into perspective, a mere 5 exabytes would be sufficient to store all the words ever spoken by humans. Such is the magnitude of biological data being generated! Here we discuss how biomedical data can be made findable and reusable in an efficient manner.
Biomedical data is inherently diverse as it stems from various experimental techniques, platforms, and omics technologies. The enormity of this data is overwhelming, further compounded by multiple data access policies. Insufficient annotation and curation also pose challenges, discouraging researchers from navigating this vast data repository. The sheer volume and diversity of available datasets make it time-consuming to locate specific ones. Hence, effective data management and retrieval systems are essential to optimize the utilization of this generated data. Let’s have a look at how these challenges impact finding relevant datasets on GEO.
Discovering the significance of a dataset can prove to be an uphill task, mainly when dealing with a public database such as GEO, which holds the potential for valuable information but is often inaccessible due to the many challenges associated with finding datasets on public databases. Let's check out a real-world example for the same.
Researchers working on novel therapies for neurodegenerative diseases will want to find datasets that study how a particular drug affects disease progression. However, finding such a dataset from a public database will be challenging due to the lack of proper clinical metadata associated with these datasets. Let's see the researchers' journey to find the relevance of a patient-derived dataset (Ex.GSE97709_GPL1730) for neurodegenerative disease on a public database such as GEO.
As we can see, finding datasets on a public database such as GEO is time-consuming. To find the relevance of any dataset, the user needs to read through the whole accession view page to understand the experimental design and confirm whether the datasets are clinically relevant. Even after being on the same page, all the information is scattered in sections the user must go through to shortlist it for his analysis. Many other details are saved in separate .tsv and .xlsx files, which must be downloaded locally to understand the make of the dataset. Let's improve data findability by adding a straightforward step to raw data pre-processing and see how it works wonders in finding data!
How Can Data Findability Be Improved?
Voila! Curation is the answer!
Curation is the extraction of knowledge from unstructured data into a structured, computable form. This process helps to find datasets easily by improving data organization, standardizing metadata, and enhancing data quality. Navigating and locating relevant data is easier once the datasets are organized into categories or topics. Standardized metadata ensures consistent annotation, enabling researchers to use specific search criteria and filters to refine their search. Curation also involves quality control, ensuring that datasets are reliable and scientifically sound. With organized data, standardized metadata, and improved quality, researchers can quickly identify and access datasets that align with their research needs, streamlining the process of finding relevant data. Let's look at how Polly helps solve the findability challenges.
How to Resolve the Challenges that You Face on GEO
Polly is an AI-enabled cloud platform that provides access to FAIR multi-omics data from various public sources, including GEO. The data on Polly is curated using standardized pipelines and ontologies, ensuring consistent and analysis-ready data. The data engineering and metadata harmonization process transforms messy data into structured data in the OmixAtlas (OA) data warehouse. OA contains millions of curated FAIR datasets from public and licensed sources, ready for machine learning and analysis. Polly's customizable interface lets users quickly find relevant data without multiple searches. OAs are tailored to specific research needs, like diseases or gene targets. Consistent processing allows reliable comparison and cohort creation across all data on Polly.
Searching the Same Dataset on Polly:
All the sample-level metadata from the source is now available on Polly, so users can get the whole picture by accessing the metadata table.
If you are scouring datasets to find relevant ones for downstream analysis, now is the time to reach out.
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