Deriving actionable insights from biologic data has never been more important, or more challenging, for developing the next generation of therapeutics and diagnostics. Million-fold reductions in the cost of sequencing, massive improvements in automated image acquisition, and a slew of other transformational technologies have made it so that data storage, analysis, and interpretation, rather than data generation, now rate-limits drug hunters.
Across therapeutic areas, this data dilemma stems from a few key obstacles. Setting up the raw infrastructure needed to store and compute on these datasets requires biotech teams to navigate a maze of cloud computing plumbing and provisioning, a skill set most of us in biotech never developed. Once the infrastructure is in place, a mess of finicky (and often mutually incompatible) bioinformatics tooling needs to be loaded. Finally, a bioinformatician knowledgeable in working both the infrastructure and bioinformatics tools can finally start writing the thousands of lines of code across multiple coding languages necessary to turn data into results (and deal with the weeks of back-logged requests from their biology colleagues). These challenges are faced across the industry, from the principal scientist at a 5-person biotech startup to the data science and biology teams at major pharmaceutical organizations.
Perhaps you’re a Senior or Principal Scientist at a biotech company generating some amount of valuable data, yet you’re currently dealing with an extensive backlog that sits idly. The problem lies when it comes to asking a specific question of the data, without having the infrastructure or analysis toolkit to actually ask it. Not only is your data clogging your workflow, but you are unable to make the critical business decisions needed to propel your organization forward through scientific discovery.
Or maybe, you’re a CSO in a strategic management position, dealing with what we like to call, ‘the hair-on-fire problem’. You find yourself burning through significant revenue, while waiting on a specific result to happen in the dataset. The trouble comes when you know the exact question that you want to ask of the data, but without the language needed to do so, you can feel as if your hands are tied. Sound familiar? At Watershed Informatics, we’re well acquantainted with both of these role-based scenarios and are confident that our solutions will help you uncover the insights in your data and overcome the rate-limiting step to those valuable insights. There is a better way.
Two different approaches have emerged to solve the above. ‘No-code’ solutions, like DNANexus, Illumina’s BaseSpace, and Galaxy, let users drag-and-drop widgets into place to complete their analyses. No-code is fantastic for researchers unfamiliar with coding; they can instantly perform some of the analyses they’re interested in without having to spend months dealing with the above problems. This usability does have a cost, however; making any custom modifications requires building out a new widget, making these no-code solutions highly inflexible, especially in a multi-omics setting. High-code products (NextFlow, Terra) have the flexibility that no-code products lack for these custom analyses, but the amount of coding skill necessary to use them makes them inaccessible to most biomedical researchers without advanced bioinformatics training.
Here at Watershed Informatics, we’ve built our Cloud Data Lab to have the flexibility of high code solutions with the usability of no-code platforms. Thanks to a combination of our browser-based interface, patent-pending ‘no-code-conversion’ technology, and an easy-to-use low-code API, the Cloud Data Lab platform solves all of the above for both biologists and bioinformaticians, enabling them to translate data to insights to open up new avenues of scientific discovery. Read on to discover how Data Lab and our team of expert bioinformaticians will help you usher in the next biological revolution.
As an end-to-end bio-IT managed service provider, Data Lab enables you to design and run critical bioinformatics analyses such as single-cell, bulk, and spatial transcriptomics; bulk and single-cell ATACseq; whole genome sequencing with less cost and with less complexity than any other solution.
A few key benefits of Data Lab set it apart from other analysis tools.
At Watershed Informatics, we’re transforming how scientists learn from biologic big data. Our Cloud Data Lab enables you, biologists and bioinformaticians at life sciences organizations of any size, to perform mission-critical bioinformatics analyses (such as bulk, single-cell, and spatial transcriptomics; ATAC-Seq; proteomics, etc.) at an order of magnitude less cost and complexity than existing solutions.
We provide reliable, secure, scalable, and accessible storage, computation, and analysis tools specifically optimized for catalyzing the life sciences development pipeline. Our approach enables biologists to analyze their own -omics data and bioinformaticians to rapidly prototype and deploy bespoke analytic workflows across an organization. Our clients, including Revitope Oncology, SQZ Biotech, and Rarebase rave about our product, and we'd love the opportunity to enable your organization's analyses as well.
The time is now to feel empowered to plan, run, and refine your own data analyses and more rapidly derive actionable findings from your next-generation sequencing data with the Watershed Cloud Data Lab platform. Book a FREE Live Demo now to see for yourself how our Data Lab, which is trusted by biologists across the industry, can improve your current data workflow in minutes and open up new and exciting scientific discoveries.