diff --git a/workflows/GWAS_Research_Methodology.md b/workflows/GWAS_Research_Methodology.md
index a56886bb51e43156af7502ec1dd9c6514d3d0588..cbc573737385e9420f9ed7f17207988f7b6430d3 100644
--- a/workflows/GWAS_Research_Methodology.md
+++ b/workflows/GWAS_Research_Methodology.md
@@ -1,8 +1,8 @@
-# GWAS Analysis Workflow Documentation
+# GWAS Analysis Workflows Documentation
 
 ## Preprocessing
 
-The aforementioned analysis section's scripts and files are located in the directory [workflow/preprocessing_data](workflow/preprocessing_data).
+The aforementioned analysis section's scripts and files are located in the directory [workflows/preprocessing_data](workflows/preprocessing_data).
 
 1. **Conversion of Genetic Data**:
    The initial 'raw' genetic data, provided in VCF format, was converted to hapmap format using TASSEL software [doi:10.1093/bioinformatics/btm308]. The files were loaded directly into TASSEL and saved as diploid hapmap files. The respective raw data files used can be found in the following directories:
@@ -26,7 +26,7 @@ The cleaned and filtered genotype and phenotype files were saved in the `/mnt/da
 
 ## GWAS Analysis
 
-The analysis scripts and files mentioned above are available in the directory [workflow/gwas_gapit_pipeline](workflow/gwas_gapit_pipeline)
+The analysis scripts and files mentioned above are available in the directory [workflows/gwas_gapit_pipeline](workflows/gwas_gapit_pipeline)
 
 The Genome-Wide Association Studies (GWAS) were conducted using the GAPIT tool, evaluating several models including the General Linear Model (GLM), Mixed Linear Model (MLM), FarmCPU, MLMM, CMLM, SUPER, and BLINK. QQ plots were analyzed to assess the performance of the models, with FarmCPU and MLMM identified as the most effective for the dataset. Population structure models were selected based on BIC model selection criteria. Input files for the analysis included genotype and phenotype files located at `/mnt/data/joseph/TEOSINTE/analyses/GWAS/data/` or `studies/processed_genotype_phenotype_teosinte_data`, and the resulting GWAS output is stored in `runs` or `/mnt/data/joseph/TEOSINTE/analyses/GWAS/gapit_pipeline/results/.` Directories.
 
@@ -47,7 +47,7 @@ GAPIT parameters were fine-tuned to identify the most suitable principal compone
 
 ### GWAS Significant SNPs annotation and Summary tables
 
-The analysis scripts and files mentioned above are available in the directory [workflow/snp_gene_neighborhood_pipeline/scripts](workflow/snp_gene_neighborhood_pipeline/scripts)
+The analysis scripts and files mentioned above are available in the directory [workflows/snp_gene_neighborhood_pipeline/scripts](workflows/snp_gene_neighborhood_pipeline/scripts)
 
 Significant SNPs were annotated using the B73 reference genome. This process involved downloading the B73 reference sequence and its annotations, running Mercator and ProtScriber, and generating annotations for each SNP. The annotation pipeline utilizes `snp_physical_mapping_pl_v2.py` for mapping SNPs to nearby _+50kb window_ features and `generate_reference_protein_function_annotations_v2.py` for annotating the SNPs with functional information from Mercator and InterProScan. The annotated results for each trait are stored in /`mnt/data/joseph/TEOSINTE/analyses/GWAS/pipelines/snp_gene_neighborhood_pipeline/results/taxon/all_features/trait/`. Additionally, heatmaps were generated to summarize the results, with scripts available in the directory `/mnt/data/joseph/TEOSINTE/analyses/GWAS/pipelines/snp_gene_neighborhood_pipeline/src/mercator_results_summarizer/`. These scripts provide both raw data and Z-transformed data.
 
@@ -136,7 +136,7 @@ Rscript plot_heatmap.R diplo_perennis_bin_depth_2_vs_traits_df.csv diplo_perenni
 
 ## Teosinte GWAS SNPs and literature SNPs comparisons
 
-The analysis scripts and files mentioned above are available in the directory [workflow/snp_gene_neighborhood_pipeline/gwas_snps_vs_published_snps](workflow/snp_gene_neighborhood_pipeline/gwas_snps_vs_published_snps)
+The analysis scripts and files mentioned above are available in the directory [workflows/snp_gene_neighborhood_pipeline/gwas_snps_vs_published_snps](workflows/snp_gene_neighborhood_pipeline/gwas_snps_vs_published_snps)
 
 ### Description and retrival of the Literature data