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The pacbio that I have are filtered through a pipeline with illumina reads. Most pacbio reads were "junked", but the rest were corrected to be HQ reads, so it should be much better in error rate and so on.
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At the moment you can simply align the pacbio reads to the contigs with a tool like MUMmer, and either make scaffolds yourself or feed pairing information to SSPACE or Bambus.
Originally posted by AdrianP View PostComment
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Hi all,
Need help. when doing pacbio assembly with SMRT 2.3.0 portal with 10 SMRT cells using HGAP got 245 contigs which is very high. I want to know how to reduce this number to 1 or 2.Comment
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With the the amount of information that you've provided so far, the best help I can give you is that, "You need to tweak some parameters."Originally posted by manjari.deshmukh View Post
If you would like help with an assembly, you would be better off posting a new thread(rather than continue a 2 year old stale thread) with much more information about what you've tried so far.
What is the organism?
What is the expected genome size?
Is it diploid or haploid?
Approximately how much coverage of the genome did 10 SMRTCells get you?
How was the library prepared?
What sequencing chemistry did you use?
Which protocols have you tried running so far?
With what parameters?Comment
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Hi,
Yes, i should haave posted this in a new thread.
Anyways, i am working on Bacteria whose genome size is approx 6MB. The coverage provided by 10 SMRT cells is 64X. I am using HGAP 3 with mainly default parameters. only changing Genome size and fiddling with subread length.
Thanks and regards,
ManjariComment
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What is the subread distribution (N50, mean). Why is the yield so low? Even at a conservative sequencing yield 10 SMRT cells should be ~800x for a 6Mb genome.
I suspect your assembly is limited by you library.Comment
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@rhall: There is a new thread with some additional information. http://seqanswers.com/forums/showthread.php?t=51427Comment
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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
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