# Bionar

> BioNAR

- Skill: `biomate-ai/bionar` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/bionar`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bionar/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/bionar

---


# BioNAR

## Workflows

### Standard Workflow

Build, annotate, and analyze a protein-protein interaction (PPI) network to identify central proteins, functional communities, bridging proteins, and disease/annotation overlaps.

```r
library(BioNAR)

# 1. Build the network from a data frame
file <- system.file("extdata", "PPI_Presynaptic.csv", package = "BioNAR")
tbl <- read.csv(file, sep="\t")
gg <- buildNetwork(tbl)

# 2. Annotate vertices with gene names and diseases
gg <- annotateGeneNames(gg)
afile <- system.file("extdata", "flatfile_human_gene2HDO.csv", package = "BioNAR")
dis <- read.table(afile, sep="\t", skip=1, header=FALSE, strip.white=TRUE, quote="")
gg <- annotateTopOntoOVG(gg, dis)

# 3. Estimate vertex centrality measures
gg <- calcCentrality(gg)
mc <- getCentralityMatrix(gg)

# 4. Fit degree distribution to power law and estimate entropy rate
pFit <- fitDegree(as.vector(igraph::degree(graph=gg)), threads=1, Nsim=5, plot=FALSE)
ent <- getEntropyRate(gg)
SRprime <- getEntropy(gg, maxSr = NULL)

# 5. Perform community detection (clustering) and evaluate modularity
nm <- normModularity(gg, alg = 'louvain')
mem <- calcMembership(gg, alg = 'louvain')
gg <- calcClustering(gg, alg = 'louvain')

# 6. Recluster dense communities
remem <- calcReclusterMatrix(gg, mem, alg = 'louvain', 10)

# 7. Build consensus matrix and evaluate cluster robustness
conmat <- makeConsensusMatrix(gg, N = 5, alg = 'louvain', type = 2, mask = 10, reclust = FALSE)
clrob <- getRobustness(gg, alg = 'louvain', conmat)

# 8. Calculate and plot bridgeness
br <- getBridgeness(gg, alg = 'louvain', conmat)
gg <- calcBridgeness(gg, alg = 'louvain', conmat)
g <- plotBridgeness(gg, alg = 'louvain', VIPs = c('8495', '22999'), Xatt = 'SL')
```
*Input: A data frame of protein-protein interactions. Output: An annotated igraph object with calculated centralities, community memberships, and bridgeness metrics.*

## When to Use
- Analyzing protein-protein interaction (PPI) networks to identify key topological features and central proteins using `calcCentrality()`.
- Detecting functional communities or complexes using multiple clustering algorithms (e.g., Louvain, Walktrap) via `calcAllClustering()`.
- Identifying bridging proteins that connect different functional communities using `getBridgeness()`.
- Evaluating network scale-free properties and entropy rates using `fitDegree()` and `getEntropyRate()`.

## When NOT to Use
- For reconstructing networks directly from raw gene expression data (e.g., co-expression network construction); use `WGCNA` instead.
- For basic, non-biological general graph theory operations where standard `igraph` or `tidygraph` suffices without biological annotations.

## Data Requirements
- Input data frame with interaction pairs (columns representing interacting nodes, e.g., Entrez IDs).
- Annotation files mapping Entrez IDs to gene names, diseases (HDO), or GO terms.

## Key Parameters
- **alg**: Clustering algorithm to use (e.g., `"louvain"`, `"wt"`, `"fc"`, `"infomap"`, `"lec"`).
- **N**: Number of randomization rounds for consensus matrix generation (typically 500).
- **type**: Sampling scheme for consensus matrix (1 for sampling edges, 2 for sampling vertices).
- **mask**: Percentage of edges or vertices to mask during perturbation.
- **Xatt**: Centrality measure to plot against bridgeness (e.g., `"SL"` for semilocal centrality).

## Best Practices
- Ensure all nodes have non-empty `GeneName` attributes before running downstream annotation and analysis functions.
- Use `calcAllClustering()` to compare multiple community detection algorithms and evaluate their modularity using `clusteringSummary()`.
- Run at least 500 randomization rounds (`N = 500`) in `makeConsensusMatrix()` for real-world robustness analysis.

## Common Pitfalls
- Using too few randomization rounds for consensus matrix: Leads to inaccurate bridgeness and robustness estimates. Fix: Increase `N` to at least 500 in `makeConsensusMatrix()`.
- Missing gene name annotations: Causes downstream functions that rely on gene names to fail. Fix: Run `annotateGeneNames()` and verify with `any(is.na(V(gg)$GeneName))`.

## Alternatives
- `igraph`: For general network analysis and basic clustering without specialized biological workflows.
- `pRoloc`: For spatial proteomics and organelle assignment (not network-topology based).

## Citations
- Menche et al. 2015, Science (for disease-disease overlap).
- Nepusz et al. 2008 (for bridgeness).

## References
- Homepage: bioconductor.org/packages/BioNAR
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/BioNAR/inst/doc/BioNAR.html