fixing the term mininum quality to the more appropriate maximum base …

…error rate

src/base/pileup.rs

@@ -252,7 +252,7 @@ impl Filter for PileupLine {
                     return Err(Error::new(ErrorKind::Other, "Phred score out of bounds."));
                 } else {
                     let q = f64::powf(10.0, -(self.read_qualities[i][j] as f64 - 33.0) / 10.0);
-                    if q > filter_stats.min_quality {
+                    if q > filter_stats.max_bases_error_rate {
                         self.read_codes[i][j] = 78; // convert to N
                     }
                     if filter_stats.remove_ns & (self.read_codes[i][j] == 78) {
@@ -538,7 +538,7 @@ mod tests {
         let frequencies = *(pileup_line.to_frequencies().unwrap());
         let filter_stats = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 1,
             min_allele_frequency: 0.0,
             max_missingness_rate: 0.0,

src/base/structs_and_traits.rs

@@ -68,7 +68,7 @@ pub struct FileSyncPhen {
 #[derive(Debug, Clone)]
 pub struct FilterStats {
     pub remove_ns: bool,
-    pub min_quality: f64,
+    pub max_bases_error_rate: f64,
     pub min_coverage: u64,
     pub min_allele_frequency: f64,
     pub max_missingness_rate: f64,

src/base/sync.rs

@@ -1569,7 +1569,7 @@ mod tests {
         let frequencies = *(counts.to_frequencies().unwrap());
         let filter_stats = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 1,
             min_allele_frequency: 0.005,
             max_missingness_rate: 0.0,
@@ -1675,7 +1675,7 @@ mod tests {
         // let phen = file_phen.lparse().unwrap();
         // let filter_stats = FilterStats {
         //     remove_ns: true,
-        //     min_quality: 0.005,
+        //     max_bases_error_rate: 0.005,
         //     min_coverage: 0,
         //     min_allele_frequency: 0.0001,
         //     max_missingness_rate: 0.2,

src/base/vcf.rs

@@ -509,15 +509,15 @@ mod tests {
         );
         let filter_stats_1 = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 1,
             min_allele_frequency: 0.0,
             max_missingness_rate: 0.0,
             pool_sizes: vec![0.2; 10],
         };
         let filter_stats_2 = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 10,
             min_allele_frequency: 0.0,
             max_missingness_rate: 0.0,

src/gp/cv.rs

@@ -444,7 +444,7 @@ mod tests {
         let n_threads = 2;
         let filter_stats = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 1,
             min_allele_frequency: 0.005,
             max_missingness_rate: 0.0,

src/gwas/correlation_test.rs

@@ -148,7 +148,7 @@ mod tests {
             Array2::from_shape_vec((5, 2), vec![1, 9, 2, 8, 3, 7, 4, 6, 5, 5]).unwrap();
         let filter_stats = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 1,
             min_allele_frequency: 0.005,
             max_missingness_rate: 0.0,

src/gwas/gwalpha.rs

@@ -416,7 +416,7 @@ mod tests {
         .reversed_axes();
         let filter_stats = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 1,
             min_allele_frequency: 0.005,
             max_missingness_rate: 0.0,

src/gwas/ols.rs

@@ -548,7 +548,7 @@ mod tests {
         //         .unwrap();
         // let filter_stats = FilterStats {
         //     remove_ns: true,
-        //     min_quality: 0.005,
+        //     max_bases_error_rate: 0.005,
         //     min_coverage: 1,
         //     min_allele_frequency: 0.005,
         //     pool_sizes: vec![20.0, 20.0, 20.0, 20.0, 20.0],

src/imputation/adaptive_ld_knn_imputation.rs

@@ -497,7 +497,7 @@ mod tests {
         let file_sync_phen = *(file_sync, file_phen).lparse().unwrap();
         let filter_stats = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 1,
             min_allele_frequency: 0.005,
             max_missingness_rate: 0.0,

src/imputation/filtering_missing.rs

@@ -255,7 +255,7 @@ mod tests {
         ////////////////////////////////////////////////////////////////////////////////////////////////////////
         let filter_stats = FilterStats {
             remove_ns: false,
-            min_quality: 1.0,
+            max_bases_error_rate: 1.0,
             min_coverage: 0,
             min_allele_frequency: 0.000001,
             max_missingness_rate: 0.0,

src/imputation/mean_imputation.rs

@@ -182,7 +182,7 @@ mod tests {
         let file_sync_phen = *(file_sync, file_phen).lparse().unwrap();
         let filter_stats = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 1,
             min_allele_frequency: 0.005,
             max_missingness_rate: 0.0,

src/main.rs

@@ -37,9 +37,9 @@ struct Args {
     /// Output filename
     #[clap(short, long, default_value = "")]
     output: String,
-    /// Minimum base quality in terms of base calling error rate, i.e. lower values means higher quality
+    /// Maximum base sequencing error rate
     #[clap(long, default_value_t = 0.01)]
-    min_quality: f64,
+    max_bases_error_rate: f64,
     /// Minimum depth of coverage (loci with at least one pool below this threshold will be omitted)
     #[clap(long, default_value_t = 1)]
     min_coverage: u64,
@@ -186,7 +186,7 @@ fn main() {
     let phen = file_phen.lparse().unwrap();
     let filter_stats = base::FilterStats {
         remove_ns: !args.keep_ns,
-        min_quality: args.min_quality,
+        max_bases_error_rate: args.max_bases_error_rate,
         min_coverage: args.min_coverage,
         min_allele_frequency: args.min_allele_frequency,
         max_missingness_rate: args.max_missingness_rate,

src/popgen/gudmc.rs

@@ -484,7 +484,7 @@ mod tests {
         let file_sync_phen = *(file_sync, file_phen).lparse().unwrap();
         let filter_stats = FilterStats {
             remove_ns: false,
-            min_quality: 0.01,
+            max_bases_error_rate: 0.01,
             min_coverage: 1,
             min_allele_frequency: 0.001,
             max_missingness_rate: 0.0,

src/tables/chisq_test.rs

@@ -67,7 +67,7 @@ mod tests {
         };
         let filter_stats = FilterStats {
             remove_ns: true,
-            min_quality: 0.01,
+            max_bases_error_rate: 0.01,
             min_coverage: 1,
             min_allele_frequency: 0.005,
             max_missingness_rate: 0.0,

src/tables/fisher_exact_test.rs

@@ -148,7 +148,7 @@ mod tests {
             Array2::from_shape_vec((3, 2), vec![0, 3, 1, 5, 2, 6]).unwrap();
         let filter_stats = FilterStats {
             remove_ns: true,
-            min_quality: 0.005,
+            max_bases_error_rate: 0.005,
             min_coverage: 1,
             min_allele_frequency: 0.005,
             max_missingness_rate: 0.0,