Published on November 20th, 2023 by Murat Akhmedov
⏱ 9 min read
Omics experiments involve many complex technologies and tools for data generation, processing, and storage. For successful drug development, different platforms and tools must work together seamlessly.
This blog explores the experimental and data analysis process, providing examples of bioinformatics tools for each stage. We also discuss the importance of these tools’ interoperability in maximizing their capacity to provide end-to-end solutions for the challenges posed by complex omics data.
As you can see in the overview below, there are a variety of platforms and tools available for the individual stages from data collection to insights, where interoperability of such tools is critical for successful data analysis.
The biological data journey begins with careful consideration of experimental design, refinement of protocols, and systematic collection of sample information. In today’s dynamic landscape, electronic lab notebooks (ELNs) and laboratory information management systems (LIMS) play crucial roles in organizing and managing experimental data and are central to data-driven breakthroughs.
Let’s look at these systems in more detail.
ELNs are digital evolutions of traditional paper lab notebooks, serving as comprehensive platforms for researchers to record, manage, and share experimental data. They facilitate collaboration among team members, streamline documentation processes, and enable structured data organization, real-time collaboration, and detailed audit trails, thereby enhancing experiment reproducibility.
LIMS are robust software platforms designed to streamline and centralize lab data management. Particularly valuable in high-throughput environments, LIMS efficiently track and manage samples, integrate with instruments, and ensure regulatory compliance.
Several exemplary ELN and LIMS tools are offered by industry leaders, including Benchling, Dotmatics, CDD Vault, eLabNext, Starlims, Sapio Sciences, SciNext, Labguru, and various others. Overall, these tools represent a range of capabilities and meet the diverse needs of laboratories engaged in cutting-edge scientific research:
Omics data analysis typically involves three critical phases: primary, secondary, and tertiary analysis. We describe each phase below:
Data collection, storage, and management provide the foundation for subsequent analyses and insights. This stage goes beyond raw data generation and includes a comprehensive understanding of experimental conditions, sample complexity, and detailed instrumentation specifications.
Several renowned technologies are at the forefront of data collection:
This phase is dedicated to the intricacies of data preprocessing, and automating pipelines and workflows. This phase includes critical processes such as mapping, variant calling, quantification, annotation, data normalization, batch correction, etc. Together, these tasks form the backbone for refining raw data into structured and analyzable formats, paving the way for downstream analysis. This stage requires precision when dealing with complex data sets and emphasizes the need for streamlined and reproducible workflows.
As researchers grapple with the complexities of secondary analysis, they use advanced tools and algorithms to ensure meaningful insights are derived from the wealth of data generated in the primary phase. This phase, characterized by its intricate data manipulation and automation complexities, sets the stage for subsequent steps of interpretation and discovery, forming a critical bridge between raw data and actionable scientific knowledge.
Several leading tools represent excellence in this stage including DNAnexus, Dotmatics, Illumina Connected Analysis, Genedata, BC Platforms, CDD Vault, Lifebit, and numerous others.
Data storage and management tools offer several benefits, including improved data security, backup capabilities, maintenance, a reliable environment, flexibility, and ease of use.
In addition to the solutions presented so far, clinical research organizations (CROs) also have their place. These organizations often take over data generation, for example, sequencing, and can significantly speed up research. They usually partner with consulting firms to analyze the data. We have taken a closer look at CROs in a separate blog post.
Tertiary analysis marks the transition from preprocessed data to actionable knowledge. This stage deals with data visualization, interpretation, and comprehensive reporting. This phase includes various tasks ranging from statistical testing, differential expression, gene set enrichment and clustering, to pathway analysis and comparative analysis, each contributing a unique perspective to understanding the dataset. The aim is to penetrate the depth of the data, uncover hidden biological mechanisms, and enable well-founded predictions or diagnoses.
Beyond statistical analysis, this phase includes creating visually appealing figures and tables and synthesizing complex information into accessible representations. The process extends to producing final reports, summarizing key findings, and disseminating these findings to team members or employees.
As mentioned in the section above, the data discovery phase is composed of time-consuming tasks to grasp the underlying biological mechanisms. It also requires the collaboration of various teams, including those for data science (bioinformaticians, computational biologists), translational medicine and drug discovery (biologists), and management (PIs, project managers, and leadership).
Currently, the data analysis and discovery process involves sharing static reports and spreadsheets created by bioinformaticians and distributed to biologists and managers. Fully analyzing and interpreting the data can take weeks to months, often requiring iterative stakeholder discussions. This process is currently a bottleneck, and researchers need a better tool to speed up data discovery.
At BigOmics, we aim to solve this bottleneck by developing an interactive data analytics and discovery platform. Omics Playground is our flagship platform that enables centralized, cost-effective transcriptomics and proteomics data analytics, streamlines scaling, and increases productivity. Interactive visualizations give leaders a 360-degree view of their research and development.
Here’s how Omics Playground works.
RNA-Seq data is analyzed by peer-reviewed algorithms, allowing scientists to quickly identify the most promising therapeutic targets without the need for coding expertise. As all omics data is in one place, scientists spend less time repeating experiments. Newly added datasets are compared to previous results and more than 6,000 public datasets to provide the necessary context for scientific breakthroughs. In addition, more than 50,000 public gene sets and pathways can be accessed, as well as drug connectivity and drug sensitivity databases with more than 30,000 drug expression profiles.
Omics Playground helps managers and executives keep track of daily progress while providing a cohesive overview of all the different research projects. The platform improves data reproducibility, making predictions and setting informed timelines much easier. With the full history of their experiments to hand, executives can make quick, data-driven decisions.
The complete interoperability of individual tools is currently not fully established yet, with only individual partnerships between providers in place. This is an opportunity for all companies providing bioinformatics services and software (secondary analysis) as well as data acquisition companies (primary analysis).
For this reason, BigOmics recently started collaborating with primary and secondary analysis companies such as DNAnexus, the leading provider of cloud-based software for genomic and biomedical data access and accompanying analyses, to maximize the synergies.
Omics Playground, our collaborative analysis environment, is now available on the DNAnexus precision health data platform, providing customers with a fully integrated solution to understand large-scale proteomics and transcriptomics data better.
We work together to improve proteomics and transcriptomics data visualization and interpretation. With a fully collaborative environment and intuitive interface to solve common analysis problems, we accelerate the transition from scientific discovery to data-driven precision medicine.
Do you want to try out Omics Playground for yourself and learn how it can help you as a bioinformatician solve your significant data challenges and bottlenecks in transcriptome and proteome analysis? Get your free trial now!