GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery.
Giacomo B MarinoMichael NgaiDaniel J B ClarkeReid H FleishmanEden Z DengZhuorui XieNasheath AhmedAvi Ma'ayanPublished in: Nucleic acids research (2023)
Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.
Keyphrases
- single cell
- rna seq
- gene expression
- electronic health record
- endothelial cells
- genome wide
- cell therapy
- big data
- poor prognosis
- copy number
- induced pluripotent stem cells
- induced apoptosis
- high throughput
- genome wide identification
- stem cells
- data analysis
- machine learning
- binding protein
- magnetic resonance
- dna methylation
- cell proliferation
- oxidative stress
- genome wide analysis
- quality improvement
- deep learning
- long non coding rna
- cell death
- endoplasmic reticulum stress
- contrast enhanced