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I would like to use some long reads from new nanopore data to lengthen and bridge scaffolds in an existing genome release. Does anyone have a recommendation for an assembler that would work well or is built for this task and tolerate (or preferably fill in) the NNNNs in the existing scaffolds?
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Typically due to high error rate you have to correct your nanopore reads by more accurate short reads from e.g. illumina. There are several methods to do that. Then you can use something like SSPACE longread for scaffolding. -
This is necessary for finishing a genome, but not for the initial scaffolding.You can use something like Miniasm to do a high-error scaffold, which may work if you just throw your current scaffolds in with nanopore reads.Typically due to high error rate you have to correct your nanopore reads by more accurate short reads
Alternatively, depending on how big your genome is, you could do another de-novo assembly from scratch using Canu. People have been quite successful with Nanopore assemblies of bacterial and yeast genomes using Canu.Comment
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My plan was to use the long reads with high error rate to bridge scaffolds and then map Illumina on to them, swap bases to favor Illumina, and hopefully fill in some of the NsComment
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canu http://biorxiv.org/content/early/2016/08/24/071282
canu can assemble mammalian genome with cheap ws. https://arxiv.org/abs/1703.10231Comment
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...06-18-2026, 07:11 AM -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM
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