Tweet
What are the best methods/programs for attempting to close gaps between scaffolds after a de novo assembly has resulted in a large number of scaffolds (before resorting to further sequencing)?
-
-
First, I don't know why you've chosen to prefix all your posts with your username; not a desirable strategy.
Second, you've failed to describe any details about the genomes you are working on, the data you've acquired & how you've tried to assemble. The size & complexity of the genome are critical to understand as well what you have in hand and what scientific goals you really want to address. -
This series of posts was intended to be topics from a conference/discussion panel to continue discussions here...I should've prefaced it with this before all the posts went up.Comment
-
Okay, apologies for the previous comment.
There are a number of scaffolders out there, though I have used only SSPACE and without digging in much.
One thing that would benefit all of these programs is if there was a rigorous language for defining relationships that scaffolding might be based on. Ideally this would a XML; I know many folks aren't XML fans but the value of being able to validate inputs is powerful.
One distinction amongst scaffolders is their modularity. To give two examples, SSPACE is non-modular and must be run en masse. At the other extreme, SGA can apparently be used as a scaffolder & simply takes BAM files and a few parameters.Comment
-
I've found that using the following sequence can massively improve contiguity in transcriptome assemblies - the same is likely true for genomes:
1. GapFiller
2. cap3
3. cycle through 1 and 2 again until contiguity gains plateauComment
-
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