Current Opportunities

NIDAP Bioinformatics Developer Community

NIDAP, the NIH Integrated Data Analysis Platform, is a cloud-based and collaborative data aggregation and analysis platform. The NIDAP platform hosts user-friendly bioinformatics workflows (Bulk RNA-Seq, scRNA-Seq, Digital Spatial Profiling) and other component analysis and visualization tools that have been created and maintained by the NCI developer community based on open-source tools.

Spatial Transcriptomics Analysis Group

We invite analysts interested in developing Spatial Transcriptomics Workflows focusing on the Visium and Xenium platforms to join us for this meeting. Our discussion will center on the development, testing, and deployment of tools for these innovative platforms. Contributions are welcome at all levels, from discussing useful tools to the creation and implementation of cutting-edge tools in Spatial Transcriptomics.

NCI Long Read Technologies Analysis Community Group

This group was created for bioinformaticians to share, get information, and discuss methods (software, tools, and best practices) used in the analysis of data derived from long read technologies (ONT, PacBio, Bionano).

The AI Community Group

This group is designed to share knowledge and best practices in AI applications for bioinformatics, discuss cutting-edge AI techniques relevant to genomics, proteomics, pathology images, and bioinformatics fields, collaborate on AI-driven projects to solve complex biological problems and stay updated on the latest AI tools and frameworks applicable to bioinformatics.

Single-Cell Community Group

The Single-Cell Community Group provides a collaborative space for researchers and bioinformaticians to discuss best practices and innovations in single-cell cancer research. Key topics include optimizing sample collection and preservation to maintain cell viability, minimizing technical variability through standardized protocols, and using high-quality single-cell sequencing platforms like 10x Genomics and SMART-seq. The group also emphasizes the importance of stringent quality control during data processing, leveraging multi-omics integration (e.g., genomics, transcriptomics, and proteomics), and addressing tumor heterogeneity by stratifying cell populations.