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Unfortunately, despite these steps, the alignment rate of each aligner was significantly lower than expected, so to offset this, the fastx toolkit was used to filter out low quality reads (Table 1).

Using Bowtie 2 (Langmead and Salzberg 2012), reads were aligned to the reference genome sequence UMD 3.1 with an average alignment rate of 97.6%, that covered 99.23% of the genome (supplementary table S1, Supplementary Material online).

Sequencing samples have an average depth of 9.75 million reads, with an average transcriptome alignment rate of 49% (Table S1).

The alignment across all 18 samples produced a mean Overall Read Alignment Rate of 96.4 ± 0.4 % and a mean Concordant Pair Alignment Rate of 88.0 ± 0.2 %, confirming that the read alignment was excellent and that data was of high quality.

In these tables, we report the very low alignment rate of Bowtie2, which is at most about 13%.

This approach limits consideration to known SNPs, but equalizes the alignment rate of reads containing known variants.

We focused on these subsets because of the low alignment rate on relatively short sequences, as reported in Figure 3.

The alignment rates of the reads generated from the 12 samples which mapped against the pseudo-reference range from 67.21 to 85.31 % while the alignment rates range from 66.11 to 84.19 % if the reads were mapped against the original MSU7 cDNA sequences.

The low alignment rates of Bowtie1 means that methods have available only a tiny fraction of the useful data, leading to less accurate results.

Section 4 provides a dataset analysis, which reports alignment rates of Bowtie2, BWA, and BLASR on corrected and uncorrected PacBio datasets.

By analyzing the alignment rate increase of OPTIMA compared to Gentig, a 1.5 to 2.9 times increase on average, we computed the corresponding cost reduction to be 33 66 %, with an average cost reduction of 54 % for relaxed filtering of data (r) and 49 % for stringent filtering (s).