Bioinfopipe is an AWS Cloud-based web application that provides a complete solution for conducting data analysis in bioinformatics. The platform consists of four functional components: Data management, Tool management, Analysis management, and Documentation. These components are integrated together to achieve a one-stop solution.
Essentially, Bioinfopipe is a versatile platform capable of managing and running tools in any field. Customers can not only run tools managed by Bioinfopipe but also wrap and run their own in-house tools. The benefits of using Bioinfopipe can be explained in the following aspects.
In Bioinfopipe, all command-line tools are configured to have a form interface and documentation. Users can easily initiate an analysis job by selecting tools and setting parameters in a comprehensive analysis session GUI. Building an instant pipeline aided with a flowchart is almost intuitive for users. As a result, it helps improve productivity, especially when processing multiple input data files in batches.
Managing in-house tools locally can be challenging in terms of accessibility, reproducibility, and maintenance. With Bioinfopipe, the management of in-house tools/scripts is greatly facilitated. Once your in-house tools have been packaged and wrapped into Bioinfopipe, your team members can use them immediately and incorporate them into pipelines seamlessly.
Data is typically stored on disk drives in in-house servers, which can be expensive to maintain and may struggle to handle the rapid growth of data. Bioinfopipe leverages the AWS S3 storage service, allowing users to securely and efficiently store and access their data. Users can choose a specified storage class based on their usage frequency and cost requirements. For instance, old data can be archived into Glacier storage, which offers extremely low-cost storage with high durability.
It can be frustrating when your in-house server is aging with limited computing power or when you have to wait for available HPC slots. With Bioinfopipe, you can allocate the right amount of computing resources required by each tool and its data. You can configure resources as large as 96 vCPUs in CPU size and 512GB in memory size, eliminating any concerns about computing resource limitations.
The cost of cloud computing is based on usage, which means you pay for the amount of storage and computing resources used. Although the bare unit usage cost of cloud computing may be higher than in-house servers, it can actually result in significant cost savings in the long term due to usage-based pricing. This is not to mention the cost of maintenance and operation for in-house servers.
The goal of Bioinfopipe is to maximize usability, accessibility, productivity, and reproducibility in managing and running tools, while also reducing the cost of computation through AWS cloud computing.