Each R script is extensively annotated, below explains the overall goal of each script.
The scripts are in the order in which they would be used within the pipeline.

##### VCFmerge.R #####
# The goal of the script is to merge together multiple VCF files
# Outputting a single "super" SNP list used for filtering in RNAidentfie.R below.

##### RNAmappe.R #####
# The overall goal of this script is to identify wildtype SNPs and use this info to plot the frequency of the SNP in mutants
# This allows for the identification of regions of linkage to the mutation
# The general steps are:
  # STEP1 - ID SNPs in .vcf files - I've found that variant callers such as bcftools -v throw away many RNA-Seq SNPs
    # .vcf files MUST be v4.0 or v4.1 and only for indivdual chromsomes labeled wt_chr[1-25] and mut_chr[1-25]
    # a lynix for loop is used to input each chromosome sequentially into this script
    # --> output ALL SNPs and INDELs identified in widltype (wt) and mutant (mut) for user access
  # STEP2 - count allele frequency - this data is buried in the INFO column of the .vcf file and must be extracted
  # STEP3 - find high quality SNPs (markers) in wt useful for mapping
  # STEP4 - count the allele frequency of SNPs in mut at the marker positions
  # STEP5 - graph the data to identify regions of linkage in the mutant
    # --> output mutant SNP frequency at markers across ENTIRE genome
    # --> output mutant SNP frequency at markers on individual chromsomes
    
##### RNAidentifie.R #####
# The overall goal of this script is to extract ALL SNP/INDEL information from mutants within a defined region of the genome
# The region will most likely be that defined by RNAmappe.R as being linked
# Th mutant SNPs are then filtered against known widltype SNPs to identify potentially deleterious changes
# The general steps are:
  # STEP1 - Extract ALL the SNP information from the region of linkage/interest
    # this will be a "dirty" list that needs to be filtered, but also want to retain all possible info for user to evaluate
    # vcf formatted file corresponding to the chromsome or interest is imported, must have DP4=... or I16=... in INFO column
    # importing SNPs from chrX named mut_chrX_allALT.vcf and wt_chrX_allALT.vcf from RNAmappe.R
    # and extracting information between positions Y and Z
    # X, Y, and Z are defined either by user or will be automated within pipeline
  # STEP2 - Identify INDELs within the region and filter against known INDELs
    # --> output  mutant INDEL list
  # STEP3 - Filter mutant SNPs against known wildtype SNPs
    # single vcf formatted file of SNPs to filter against is imported, must have DP4=... or I16=... in INFO column
    # ideally this is a vcf created from VCFmerge.R - which concatenates many vcfs together and outputs a more confident single list
    # --> output filtered mutant SNP list in vcf format allowing for piping into Variant Effect Predictor
    # --> output UNfiltered mutant SNP with wildtype SNP appended on - allows user to evaluate directly

##### VEPsorte.R #####
# The goal of this script is to sort the VEP output file
# The sorting is prioritzed based on effect of the SNP to the gene

##### RNAeffecto.R #####
# The goal of this script is to extract and sort info on genes within linked regions from the cufflinks output files
# must be a subdirectory _settings as described in RNAidentifier above
# must be in the subdirectory _cuffdiff containing files from cuffdiff program
# files must be called cds_exp.diff, cds.diff, gene_exp.diff, isoform_exp.diff, promoters.diff, splicing.diff, tss_group_exp.diff