Tidy, Integrated Gene Annotation

A framework for intuitive, multi-source gene and protein annotation, with a focus on integrating functional genomics with disease and drug data for translational insights. Methods used include g:Profiler (Raudvere et al. (2019) ), biomaRt (Durinck et al. (2009) ), and the Open Targets Platform (Koscielny et al. (2017) ).

annotaR

The goal of annotaR is to provide a tidy, pipe-based framework for the integrated annotation of gene lists. It streamlines the process of fetching and combining functional annotations, disease associations, and known drug information from multiple bioinformatics databases.

Installation

You can install the development version of annotaR from GitHub with:

# install.packages("devtools")
devtools::install_github("Sulkysubject37/annotaR")

Example Workflow

annotaR uses a pipe-based (%>%) workflow to progressively add layers of information to your initial gene list.

1. Start with a list of genes.
2. Add functional annotations (e.g., Gene Ontology terms from g:Profiler).
3. Add disease and drug data (from OpenTargets).
4. Visualize the results.

Here is a quick example using a small list of cancer-related genes:

library(annotaR)
library(dplyr)

# 1. Define genes and initialize pipeline
genes_of_interest <- c("TP53", "EGFR", "BRCA1", "KRAS", "BRAF")
annotaR_obj <- annotaR(genes_of_interest)

# 2. Add annotations in a single pipeline
full_annotation <- annotaR_obj %>%
  add_go_terms(sources = c("GO:BP")) %>%
  add_disease_links() %>%
  add_drug_links()

# 3. Explore the results
# Filter for high-confidence disease links
full_annotation %>%
  filter(association_score > 0.8) %>%
  head()
  
# 4. Create a plot
plot_enrichment_dotplot(full_annotation, n_terms = 15)

This workflow generates a rich, tidy data frame containing integrated information, ready for downstream analysis and visualization.