Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. Researchers developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information. High-quality visualization of biological networks often requires both manual curation for proper alignment and programming to map external data to the graphical components. Nezzle is a network visualization software written in Python, which provides programmable and interactive interfaces for facilitating both manual and automatic curation of the graphical components of networks to create high-quality figures.

GEO is a public functional genomics data repository supporting MIAME-compliant data submissions. ENCODE investigators employ a variety of assays and methods to identify functional elements. The discovery and annotation of gene elements is accomplished primarily by sequencing a diverse range of RNA sources, comparative genomics, integrative bioinformatic methods, and human curation. ArrayExpress Archive of Functional Genomics Data stores data from high-throughput functional genomics experiments. The EGA provides a service for the permanent archiving and distribution of personally identifiable genetic and phenotypic data resulting from biomedical research projects.