bioconductor-cageminer

name

bioconductor-cageminer

description

This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.

cageminer

Workflows

Standard Workflow

Integrate GWAS SNPs and coexpression networks to mine and score high-confidence candidate genes.

library(cageminer)
library(GenomicRanges)

# 1. Visualize SNP distribution across chromosomes
plot_snp_distribution(snp_pos)
plot_snp_circos(chr_length, gene_ranges, snp_pos)

# 2. Identify genes close to SNPs
candidates1 <- mine_step1(gene_ranges, snp_pos)

# 3. Infer a gene coexpression network
sft <- BioNERO::SFT_fit(pepper_se, net_type = "signed", cor_method = "pearson")
gcn <- BioNERO::exp2gcn(pepper_se, net_type = "signed", cor_method = "pearson", 
                        module_merging_threshold = 0.8, SFTpower = sft$power)

# 4. Filter candidates in coexpression modules enriched in guide genes
candidates2 <- mine_step2(pepper_se, gcn = gcn, guides = guides$Gene, candidates = candidates1$ID)

# 5. Identify candidates with altered expression in a condition of interest
candidates3 <- mine_step3(pepper_se, candidates = candidates2$candidates, sample_group = "PRR_stress")

# Alternatively, perform steps 2-5 automatically using mine_candidates:
# candidates <- mine_candidates(gene_ranges = gene_ranges, marker_ranges = snp_pos, 
#                               exp = pepper_se, gcn = gcn, guides = guides$Gene, 
#                               sample_group = "PRR_stress")

# 6. Score and prioritize candidates
hubs <- BioNERO::get_hubs_gcn(pepper_se, gcn)
scored <- score_genes(candidates3, hubs$Gene, tfs$Gene_ID)

Note: Inputs are GRanges of SNP and gene coordinates, a SummarizedExperiment of expression data, and a vector of guide genes; output is a ranked data frame of scored candidate genes.

When to Use

• To integrate GWAS-derived SNPs with transcriptomic coexpression networks to prioritize candidate genes.
• To identify genes physically close to SNPs using mine_step1.
• To find coexpression modules enriched in known guide genes using mine_step2.
• To score and rank candidate genes based on their correlation with a condition, hub status, and transcription factor annotation using score_genes.

When NOT to Use

• When you do not have a set of known "guide genes" associated with the phenotype of interest, as mine_step2 relies on guide gene enrichment.
• When you only have SNP data without matching transcriptomic data.

Data Requirements

• Gene coordinates and SNP positions stored as GRanges or GRangesList (for multiple traits) objects.
• Expression data stored as a SummarizedExperiment object.
• Guide genes and transcription factors provided as character vectors of gene IDs.

Key Parameters

• gene_ranges: A GRanges object containing gene coordinates.
• snp_pos / marker_ranges: A GRanges or GRangesList object containing SNP positions.
• expand_intervals (TRUE): Logical indicating whether to expand SNP coordinates upstream and downstream.
• net_type ("signed"): Network type for GCN inference in BioNERO::SFT_fit.
• cor_method ("pearson"): Correlation method for GCN inference.
• sample_group: The condition of interest in the sample metadata (e.g., "PRR_stress").

Best Practices

• Use plot_snp_distribution and plot_snp_circos to visually inspect SNP distributions across chromosomes before running the mining pipeline.
• Use simulate_windows to evaluate different sliding window sizes for selecting putative candidates in mine_step1.
• Ensure that the same gene IDs are used across the gene ranges, expression data, guide genes, and transcription factor lists.

Common Pitfalls

• No candidates found in Step 2: Ensure the guide genes are present in the expression dataset and that the coexpression network has well-defined modules.
• Mismatched gene IDs: Verify that gene IDs in gene_ranges, pepper_se, and guides use the exact same nomenclature.

Alternatives

• BioNERO for general gene coexpression network analysis.
• rtracklayer for importing genomic coordinates from GFF/GTF files.

Citations

• Almeida-Silva, F., & Venancio, T. M. (2022). cageminer: an R/Bioconductor package to prioritize candidate genes by integrating genome-wide association studies and gene coexpression networks. in silico Plants, 4(2), diac018.

References

• Homepage: bioconductor.org/packages/cageminer
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cageminer/inst/doc/cageminer.html