Update README.md

Author: xihaoli

README.md

@@ -28,7 +28,7 @@ The following steps are for the widely used operating system (Ubuntu) on a virtu
 Note: The physical positions of variants in the GDS file (of each chromosome) should be sorted in ascending order.
 
 #### Step 2: Annotate the variants using the FAVOR database through xsv software
-##### Script: <a href="FAVORannotator_csv/Annotate.R">**Annotate.R**</a> 
+##### Script: <a href="FAVORannotator_csv/Annotate.R">**Annotate.R**</a>
 ##### Input: CSV files of the variants list to be annotated, the FAVOR database information <a href="FAVORannotator_csv/FAVORdatabase_chrsplit.csv">**FAVORdatabase_chrsplit.csv**</a>,
 the FAVOR database, and the directory xsv software. For more details, please see the R script.
 ##### Output: CSV files of the annotated variants list. 
@@ -37,7 +37,7 @@ the FAVOR database, and the directory xsv software. For more details, please see
 The annotations in this file is a subset of `Anno_chrXX.csv`. <br>
 
 #### Step 3: Generate the annotated GDS (aGDS) file
-##### Script: <a href="FAVORannotator_csv/gds2agds.R">**gds2agds.R**</a> 
+##### Script: <a href="FAVORannotator_csv/gds2agds.R">**gds2agds.R**</a>
 ##### Input: GDS files and the CSV files of annotated variants list (`Anno_chrXX.csv` or `Anno_chrXX_STAARpipeline.csv`). For more details, please see the R script.
 ##### Output: aGDS files including both the genotype and annotation information. 
 
@@ -109,66 +109,66 @@ The number of output files is the summation of the column "scang_num" for the ob
 
 ## Summarization and visualization of association analysis results using STAARpipelineSummary
 ### Step 0 (Optional): Select independent variants from a known variants list to be used in conditional analysis
-#### Script: <a href="STAARpipelineSummary_Known_Loci_Pruning.r">**STAARpipelineSummary_Known_Loci_Pruning.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Known_Loci_Pruning.r">**STAARpipelineSummary_Known_Loci_Pruning.r**</a>
 Perform LD pruning (stepwise selection) to select the subset of independent variants from a known variants list to be used in conditional analysis. 
 #### Input: aGDS files, a list of known variants (CHR, POS, REF and ALT) and the STAAR null model.
 <a href="STAARpipelineSummary_Known_Loci_Info.r">**STAARpipelineSummary_Known_Loci_Info.r**</a> extracts the information of CHR, POS, REF and ALT from #rs. For more details, please see the R script.
 #### Output: a Rdata file containing a list of independent variants to be used in conditional analysis.
 <a href="STAARpipelineSummary_Known_Loci_Pruning_Combination.r">**STAARpipelineSummary_Known_Loci_Pruning_Combination.r**</a> combines chromosome-wide results into genome-wide.
 
 ### Step 1: Summarize individual (single-variant) analysis results
-#### Script: <a href="STAARpipelineSummary_Individual_Analysis.r">**STAARpipelineSummary_Individual_Analysis.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Individual_Analysis.r">**STAARpipelineSummary_Individual_Analysis.r**</a>
 Summarize single-variant analysis results and perform conditional analysis of unconditionally significant variants by adjusting a list of known variants.
 #### Input: aGDS files, individual analysis results generated by STAARpipeline, STAAR null model and a list of known variants. For more details, please see the R script.
 #### Output: The summary includes the Manhattan plot, Q-Q plot, and conditional p-values of unconditionally significant variants.
 
 Note: <a href="STAARpipelineSummary_Known_Loci_Individual_Analysis_Pruning.r">**STAARpipelineSummary_Known_Loci_Individual_Analysis_Pruning.r**</a> and <a href="STAARpipelineSummary_Known_Loci_Individual_Analysis_Pruning_Combination.r">**STAARpipelineSummary_Known_Loci_Individual_Analysis_Pruning_Combination.r**</a> show an example to select independent variants from both the known variants in literature and significant single variants detected in individual analysis, which can be used for variant-set conditional analysis.
 
 ### Step 2.1: Summarize gene-centric coding analysis results
-#### Script: <a href="STAARpipelineSummary_Gene_Centric_Coding.r">**STAARpipelineSummary_Gene_Centric_Coding.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Gene_Centric_Coding.r">**STAARpipelineSummary_Gene_Centric_Coding.r**</a>
 Summarize gene-centric coding analysis results and perform conditional analysis of unconditionally significant coding masks by adjusting a list of known variants.
 #### Input: aGDS files, gene-centric coding analysis results generated by STAARpipeline, STAAR null model and a list of known variants. For more details, please see the R script.
 #### Output: The summary includes the Manhattan plot, Q-Q plot, and conditional p-values of unconditionally significant coding masks.
 
 ### Step 2.2: Summarize gene-centric noncoding analysis results
-#### Script: <a href="STAARpipelineSummary_Gene_Centric_Noncoding.r">**STAARpipelineSummary_Gene_Centric_Noncoding.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Gene_Centric_Noncoding.r">**STAARpipelineSummary_Gene_Centric_Noncoding.r**</a>
 Summarize gene-centric noncoding analysis results and perform conditional analysis of unconditionally significant noncoding masks by adjusting a list of known variants.
 #### Input: aGDS files, gene-centric noncoding analysis results generated by STAARpipeline, STAAR null model and a list of known variants. For more details, please see the R script.
 #### Output: The summary includes the Manhattan plot, Q-Q plot, and conditional p-values of unconditionally significant noncoding masks.
 
 ### Step 3: Summarize sliding window analysis results
-#### Script: <a href="STAARpipelineSummary_Sliding_Window.r">**STAARpipelineSummary_Sliding_Window.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Sliding_Window.r">**STAARpipelineSummary_Sliding_Window.r**</a>
 Summarize sliding window analysis results and perform conditional analysis of unconditionally significant genetic regions by adjusting a list of known variants.
 #### Input: aGDS files, sliding window analysis results generated by STAARpipeline, STAAR null model and a list of known variants. For details, see the R scripts. 
 #### Output: The summary includes the Manhattan plot, Q-Q plot, and conditional p-values of unconditionally significant sliding windows.
 
 ### Step 4: Summarize dynamic window analysis results
-#### Script: <a href="STAARpipelineSummary_Dynamic_Window.r">**STAARpipelineSummary_Dynamic_Window.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Dynamic_Window.r">**STAARpipelineSummary_Dynamic_Window.r**</a>
 Summarize dynamic window analysis results and perform conditional analysis of unconditionally significant genetic regions by adjusting a list of known variants.
 #### Input: aGDS files, dynamic window analysis results generated by STAARpipeline, STAAR null model and a list of known variants. For more details, please see the R script.
 #### Output: The summary includes the Manhattan plot, Q-Q plot, and conditional p-values of unconditionally significant dynamic windows.
 
 ### Step 5.1: Functionally annotate a list of variants
-#### Script: <a href="STAARpipelineSummary_Individual_Analysis_Annotation.r">**STAARpipelineSummary_Individual_Analysis_Annotation.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Individual_Analysis_Annotation.r">**STAARpipelineSummary_Individual_Analysis_Annotation.r**</a>
 Functionally annotate a list of variants.
 #### Input: aGDS files and a list of variants.
 The list of variants could be the individual analysis results generated by STAARpipelineSummary.
 #### Output: a Rdata file containing the input variants together with the corresponding functional annotations.
 
 ### Step 5.2: Functionally annotate rare variants in coding masks
-#### Script: <a href="STAARpipelineSummary_Gene_Centric_Coding_Annotation.r">**STAARpipelineSummary_Gene_Centric_Coding_Annotation.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Gene_Centric_Coding_Annotation.r">**STAARpipelineSummary_Gene_Centric_Coding_Annotation.r**</a>
 Functionally annotate rare variants of each of the input coding masks.
 #### Input: aGDS files and coding masks (chr, gene name and functional category).
 #### Output: For each input coding mask, the script outputs a Rdata file containing the rare variants and the corresponding functional annotations.
 
 ### Step 5.3: Functionally annotate rare variants in noncoding masks
-#### Script: <a href="STAARpipelineSummary_Gene_Centric_Noncoding_Annotation.r">**STAARpipelineSummary_Gene_Centric_Noncoding_Annotation.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Gene_Centric_Noncoding_Annotation.r">**STAARpipelineSummary_Gene_Centric_Noncoding_Annotation.r**</a>
 Functionally annotate rare variants of each of the input noncoding masks.
 #### Input: aGDS files and noncoding masks (chr, gene name and functional category).
 #### Output: For each input noncoding mask, the script outputs a Rdata file containing the rare variants and the corresponding functional annotations.
 
 ### Step 5.4: Functionally annotate rare variants in genetic regions
-#### Script: <a href="STAARpipelineSummary_Genetic_Region_Annotation.r">**STAARpipelineSummary_Genetic_Region_Annotation.r**</a> 
+#### Script: <a href="STAARpipelineSummary_Genetic_Region_Annotation.r">**STAARpipelineSummary_Genetic_Region_Annotation.r**</a>
 Functionally annotate rare variants of each of the input genetic regions.
 #### Input: aGDS files and noncoding masks (chr, start position and end position).
 #### Output: For each input genetic region, the script outputs a Rdata file containing the rare variants and the corresponding functional annotations.