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Watershed Informatics

Watershed Informatics Data Storage

$0.13 per gigabytes of fastq files

Bulk RNAseq analysis from Watershed Informatics.

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Description

Whether elucidating the molecular pathophysiology of a disease, identifying novel therapeutic targets, or segmenting a patient population to those most likely to benefit from a given therapy, multi-omics approaches and their subsequent integration hold tremendous promise for upgrading our therapeutic and diagnostic arsenal.

Unfortunately, most of the biologists who generate these complex datasets lack the computational expertise to manage and analyze them using existing tools, which require expert knowledge in esoteric computing languages and environments. This drives a reliance on highly over-subscribed bioinformatics experts to execute even basic analyses. Additionally, poor standardization across tools and the use of esoteric computing languages and environments make data processing and analysis difficult, even for bioinformaticians. It can take weeks to months for researchers to receive the results of even standardized workflows.

Given the rapid proliferation of NGS data and the aforementioned analytic bottlenecks, demand has far outpaced the supply of bioinformaticians, who now spend a significant fraction of their time simply shepherding data through common workflows. Inspired by the democratization approach, Watershed Informatics is developing the Watershed Cloud Data Lab (Data Lab), an easy-to-use sequencing data analysis tool for both biologists and bioinformaticians.

Product Details

Data Lab removes the bottleneck in scientific discovery by enabling biologists to process, store, and analyze their own NGS data and easily collaborate with computational colleagues. Our key innovation lies in balancing accessibility and robust data analytics, offering biologists a user-friendly, flexible tool unlike any other currently on the market. Data Lab’s GUI/Python hybrid offers a simple-yet-flexible set of tools that empower scientists to design and run their own data exploration with the ease of a “Dry Laboratory.”

This product combines a custom Python-based application programming interface; a high-performance, scalable, cloud-based compute cluster for high-speed computing and data storage; and a custom cloud notebook that enables drag-and-drop interactive analysis and visualization of NGS data. Data Lab’s cloud notebook offers an easily readable and interpretable alternative to the thousands of lines of code, written in multiple programming languages, encountered in existing tools.

Together, our simplified data management, cloud notebook, and scalable compute cluster eliminate the challenge of setting up a computational environment, reducing the time it takes to start a new data workflow and obtain results. From there, even those with little-to-no coding experience can fully leverage the suite of analysis tools available through Data Lab without concern for version compatibility between tools or sufficient computational resources. Watershed generates a one-to-one mapping between the specific computational manipulations a biologist wishes to perform and the lines of code required. This approach has two key advantages: first, it massively reduces the amount of required coding know-how by condensing hundreds of lines of Linux, Perl and/or R/Python into a single function, thereby enabling even novice coders to robustly and reproducibly execute an -omics workflow. Second, by having these functions serve as the atomic units of a templated workflow, Data Lab provides flexibility that is lacking in pure GUI or consultative-based approaches. By shortening time between iterations and enabling efficient and effective experimentation, Data Lab accelerates scientific discovery and expedites the development of targeted therapies and diagnostics for countless intractable illnesses.

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