Dtyjlui

I want to ask about why the sequence duplication levels are high after I trimmed by using Trimmomatic? I am using the following Trimmomatic operations: HEADCROP = 19 TRAILING = 20 MINLEN = 66.

How can i solve this problem? Thank You.

To answer your direct question, there are a few reasons why there might be high levels of sequence duplication. From the FastQC help:

The underlying assumption of this module is of a diverse unenriched library. Any deviation from this assumption will naturally generate duplicates and can lead to warnings or errors from this module.

• As @DevonRyan mentioned, with certain sequencing protocols such as RNA-Seq, two sequence reads at exactly the same location aren't that uncommon. This isn't a problem with RNA-Seq data, or with Trimmomatic, or with FastQC. It's just that this kind of data violates the assumption, and therefore should be ignored in those circumstances.


• PCR duplicates are another possible cause. PCR duplicates can give the false impression of high coverage at a particular locus when in fact it's just a single observed read that has been duplicated many times (see here for more details). PCR duplicates can usually be detected and removed if your analysis involves mapping to a reference genome. But whether this is actually a problem you need to fix depends on what type of data you have and what types of analysis you want to do.


• Large numbers of adapter dimers or rRNA may be present in your sample.

But I think it's also important to address how quality control (QC) is run. It can be tempting to run and re-run QC tools like Trimmomatic until all errors go away, but to be blunt these tools cannot think for you. For example, it's possible to get rid of most adapters by aggressively cropping/trimming both ends of each read, but you'll likely throw away a lot of good data that way. You may want to look into Trimmomatic's ILLUMINACLIP operation. It's also may be tempting to crop/trim reads aggressively if there are compositional biases near the beginning or end of the read. In fact, random hexamer priming can cause the Per-base Sequence Content module to fail on almost any RNA-Seq sample. Again, from the FastQC help (emphasis mine):

Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis. It will however produce a warning or error in this module.

So in other words, it's best to determine what can cause each FastQC module to fail, investigate whether this is actually a problem for your data set (referring to documentation as needed), and make a deliberate QC plan that addresses the issues that need attention.

FastQC assumes that all samples are for whole genome sequencing and will flag them as failed if they differ too much from that assumption. This will, for example, cause essentially all RNA-seq, ChIP-seq, and ATAC-seq samples to fail in one module or another. This is not any cause for concern and is completely expected. Primarily concern yourself with whether all of your samples are similar in their metrics.