Pair-end / Unpaired Reads

Trimming Ilumina Rat Reads

Working with Illumina reads requires assessing the quality of raw reads as a first step. Then through a series of bioinformatic tools of multiple processing steps, we enhance the read quality. I used Trimmomatic to trim adapters and fastqc to visualize the results. unlike Ecoli reads the Rat reads were larger and required processing time on the assigned GPU that exceeded the allocated time budget, this made it harder to create a single slurm-batch and a pipeline to perform all steps. I’m sharing the results of trimmomatic for pair-end reads with the 1P (shown in lower row) and 2P as well as 1U (shown in upper row) and 2U, from which we can see how the selected paired result shows consistently higher quality of read in the lower right corner, while the unpaired read on the top left corner shows varying read quality.

(click on the chart to enlarge)