The plots generated depends on read type and user configuration. It
may include fragment length distribution, mate length distribution,
-read start position distribution (RSPD), quality score vs percentage
-of sequecing error given the reference base, position vs percentage of
-sequencing error given the reference base.
+read start position distribution (RSPD), quality score vs observed
+quality given a reference base, position vs percentage of sequencing
+error given a reference base.
+fragment length distribution and mate length distribution: x-axis is fragment/mate length, y axis is the probability of generating a fragment/mate with the associated length
+
+RSPD: Read Start Position Distribution. x-axis is bin number, y-axis is the probability of each bin. RSPD can be used as an indicator of 3' bias
+
+Quality score vs. observed quality given a reference base: x-axis is Phred quality scores associated with data, y-axis is the "observed quality", Phred quality scores learned by RSEM from the data. Q = -10log_10(P), where Q is Phred quality score and P is the probability of sequencing error for a particular base
+
+Position vs. percentage sequencing error given a reference base: x-axis is position and y-axis is percentage sequencing error
+
## <a name="example"></a> Example
Suppose we download the mouse genome from UCSC Genome Browser. We will
delete[] tau_denoms;
- sprintf(command, "rm -f %s", tmpF);
+ sprintf(command, "rm -f %s", tmpF);
int status = system(command);
if (status != 0) {
fprintf(stderr, "Cannot delete %s!\n", tmpF);
vecG <- as.numeric(list[[3]])
vecT <- as.numeric(list[[4]])
if (sum(c(vecA, vecC, vecG, vecT)) < 1e-8) break
- peA <- c(peA, ifelse(sum(vec) < 1e-8, NA, 1.0 - vecA[1]))
- peC <- c(peC, ifelse(sum(vec) < 1e-8, NA, 1.0 - vecC[2]))
- peG <- c(peG, ifelse(sum(vec) < 1e-8, NA, 1.0 - vecG[3]))
- peT <- c(peT, ifelse(sum(vec) < 1e-8, NA, 1.0 - vecT[4]))
+ peA <- c(peA, ifelse(sum(vec) < 1e-8, NA, (1.0 - vecA[1]) * 100))
+ peC <- c(peC, ifelse(sum(vec) < 1e-8, NA, (1.0 - vecC[2]) * 100))
+ peG <- c(peG, ifelse(sum(vec) < 1e-8, NA, (1.0 - vecG[3]) * 100))
+ peT <- c(peT, ifelse(sum(vec) < 1e-8, NA, (1.0 - vecT[4]) * 100))
}
x <- 1 : length(peA)