Hi David,
From what I understand, this is something done before de novo assembly.
The abstracts says "Digital normalization substantially reduces the size
of shotgun data sets and decreases the memory and time requirements for
{\em de novo} sequence assembly, all without significantly impacting
content of the generated contigs."
The best test is to try it I guess.
From the paper, it says that it keeps reads with an estimated coverage
less than C. There are also figures about removal of erroneous k-mers too.
Also from C. Titus Brown's group is a paper that uses a Bloom filter as
the data storage backend for the de Bruijn graph [1].
In Ray, the de Bruijn graph is built, then features in the graph are
computed such as seeds. I think Ray is compatible with the work you pointed.
Ray utilizes a Bloom filter to weed out most of the k-mers occurring
once (it has been in Ray for more than a year).
In Ray, there is also something to ignore k-mers with too much coverage
for cases where you sequenced a host DNA and its virus DNA too and you
only want the host DNA.
-use-maximum-seed-coverage
Ignores any seed with a coverage depth above this threshold.
The default is 4294967295.
[1 http://www.pnas.org/content/early/2012/07/25/1121464109.abstract
David Eccles (gringer) a écrit :
> Hello,
>
> Does anyone know if Ray would be compatible with diginorm?
>
> http://arxiv.org/abs/1203.4802
>
> I ask because I'm not sure if the normalisation process outlined in the
> paper would mess with the determination of graph loops by Ray.
>
> Thanks,
>
> - David Eccles
>
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