Our goal is to allow the user to cut not only based on when the mean value of a window falls below a threshold, but also when the standard deviation or variance of the quality scores in a window exceeds some threshold.
The first step we propose to accomplish, is produce a table that the user can use as a guide to set his/hers threshold values:
| WindowPosition | MeanQuality | SDQuality |
|---|---|---|
| 1 | 35 | 2.57 |
| 2 | 37 | 2.89 |
| 3 | 38 | 3.00 |
| ... | ||
| 15 | 20 | 10.00 |
| 16 | 15 | 11.22 |
python parse_fastq.py [fastq file] [window size] [offset]
Output should be a summary file called fastq_summary_stats.csv which contains a comma separated table with window position, mean quality score and standard deviation