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Description

Cease the struggle with cloud infrastructure and unreliable informatics tools; begin uncovering biological insights immediately. The scientific exploration process is hindered by the disjointed nature of tools utilized by biology and bioinformatics teams. To address this issue, we developed a unified bioinformatics platform that bridges the gap between wet lab and dry lab operations in the cloud, enabling teams to expedite their research and development efforts. You can easily import raw data from your cloud, your service provider, or your team's instruments with minimal hassle. Create and implement tailored bioinformatics workflows in various programming languages without the frustration of complex infrastructure management. Effortlessly execute any workflow while maintaining a comprehensive log of every analysis performed. Our platform features ready-to-use interactive visualizations for NGS data that allow you to create point-and-click plots with ease. Additionally, Latch seamlessly integrates with your organization’s AWS S3, granting access to hundreds of terabytes of data within a user-friendly organic filesystem. You can define bioinformatics workflows and dynamically generate no-code interfaces using Python, with adjustable compute and storage options to suit your needs. This innovative approach not only streamlines the research process but also fosters collaboration among teams, ultimately leading to more impactful scientific discoveries.

Description

Loupe Browser stands out as a robust visualization tool, offering the user-friendly capabilities essential for delving into and interpreting 10x Genomics Chromium and Visium datasets. Additionally, the LoupeR package facilitates the transformation of Seurat objects into files compatible with Loupe Browser. The interactive features of the Loupe Browser interface are exemplified through its use of a lung squamous cell carcinoma dataset. Central to the user experience is the view panel, where individual points, each representing cell barcodes, are displayed across multiple projections. Each point typically corresponds to a single cell’s barcode, enabling focused analysis. The t-SNE plot generated by the cell ranger pipeline serves as the default projection, while alternative visualization options are also accessible. Users can effortlessly reposition the plot by dragging the mouse over the cells and can zoom in or out using the mouse wheel or trackpad. Moreover, as the mouse hovers over the plot, cluster labels become visible, which proves particularly beneficial when working with datasets that contain numerous precomputed clusters. This capability enhances the analytical experience, making it easier to identify and interpret complex data patterns.