Tweet
I ran FatsQC on a set of 4 Illumina PE exomes and got really weird graphs for the kmer distribution. I've never seen this pattern before - does anyone know what is going on and what to do about it?
-
-
I am not entirely sure about what is going on since I don't have the full length Illumina adapter sequences handy, but my guess is that the plot shows an adapter or primer dimer of some sort. Seeing that GGAAG is part of the start of the Illumina adapter (GATCGGAAGAGC..., wihtout the A-tail at the start) and the spacing is always the same this might simply be full-length adapter dimers. Such sequences should not align anywhere, so you probably don't need to worry about them. This doesn't mean that you don't have to trim your sample at all, this is difficult to say without seeing the rest of the plots. -
removing over-represented sequences does not help
@fkrueger: I found that two TruSeq adaptors were listed among the over-represented sequences in the fastqc report so I removed them for one of the samples and re-ran fastqc. The graph changed somewhat but there are still non-random peaks (below). Any ideas?
Comment
-
I don't know how you removed the adapters, but the k-mers could still result from adapter dimers. Trim Galore for example only removes the entire adapter sequence AGATCGGAAGAGC but only if it starts with the A from the A-tailing process. In your case the sequence seems to start with GATC(GGAAG) and thus wouldn't be affected by adapter trimming.
The CACAC and ACACA is often a highly occurring sequence in MeDIP-Seq or the like. I wouldn't worry too much about the plot for the moment, and only come back to it if you find that you've got an unusually low mapping efficiency or similar.Comment
-
Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
...07-09-2026, 11:10 AM -
Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.
There is no single reason why many patients don’t respond to treatment as expected. Cancer is...07-08-2026, 05:17 AM -
Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
Comment