Tweet
Thank you!! 
-
-
Dear RAD/Assembly community,
I was hoping I could pick your brains again regarding my assembly.
I normalized coverage and together with using velvetoptimiser I am quite happy with the results I am getting! For instance, I assembled the reverse reads only, which gave me ~33,000 contigs (this is roughly what I am expecting based on preliminary data) with an N50 of 400. Kmer coverage distribution looks good, too.
However, I now wanted to feed these in as 'long reads' alongside my raw data in velvet to resolve repeats better and connect the forward and reverse reads properly. Weirdly, my N50 goes down to about 380? I have visualized the assembly in tablet, and also mapped back some of my raw data (using BWA) back to check whether forward and reverse reads were actually assembled correctly, and for the few contigs I surveyed by eye this seems to be the case.
I am not sure whether I need to be worried about the decreasing N50, this really seems to be a bit counter-intuitive, as my contigs surely should increase in size by quite a bit when assembling forward and reverse as opposed to just the reverse?
I was also wondering whether there is a good way of quality filtering my contigs from my final assembly. For instance, I only want to keep contigs for future analysis to which both forward and reverse reads map to.
Many thanks in advance for any pointers!!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