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  • SHB
    Junior Member
    • Sep 2011
    • 6

    #1

    de novo or not de novo

    Hello,

    We have sequenced an E coli strain using 454 and now we want to publish its genome.

    One basic, but apparently fundamental question I have is: should I prefer a de novo approach or a mapping approach to some ref. strain?

    I have enough data for de novo analysis. De novo assembling gave me about 90 contigs, some of them pretty long which are easy to work with, some medium sized and a few were small ones.

    Mapping of reads with a ref. E coli strain (K-12) tells that our data cover 94% of the E coli genome and 88% of reads were successfully mapped.


    However, when I tried to map the de novo contigs (and not the reads!), I got only 20% of the genome covered.

    So I wonder what to do. On one side, I am looking for novell genes on this particular strain. I have no idea (yet) what strain is the closest to it phylogenetically. So de novo approach seems the right thing to do.

    But then, I have trouble mapping the contigs in their predicted order.

    On the other hand, ref. mapping makes life much easier. The genome is almost entirely covered. I have 12% of reads unmapped which are apparently an insertion and a plasmid. The problem is, am I loosing data here?

    De novo x mapping is apparently giving my different contigs (as far as I understand it), which will give me different annotations. Or not? I am a microbiologist trying to understand some bioinformatics. The people I am working with on the analysis do not have a straight answer for me on this issue. I hope someone here has experience on bacteria genome analysis and is willing to give me some help on this question. What is the most acceptable thing to do?

    Thanks a lot!
  • swaraj
    Member
    • Feb 2012
    • 50

    #2
    I suggest a hybrid assembly. This pulication can be useful to you "http://www.biomedcentral.com/1471-2164/11/243". The softwares used by you can be different but the methods in principle can be applied to build the genome.

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