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Hi,<br>
<br>
Thanks for these helpful advices.<br>
The first step (digital normalisation) ran perfectly.<br>
I'm now trying to compute the abundance histogram.<br>
I launched : <br>
abundance-dist-single.py -N 4 -x 48e9 --threads 12
$PBS_O_WORKDIR/LCBL_1/LCBL_1.fasta.normalized
$PBS_O_WORKDIR/LCBL_1/LCBL_1.fasta.normalized.histogram<br>
<br>
But after 24 hours, the script is still running but without any
results.<br>
Is that normal ?<br>
<br>
Thanks again<br>
<br>
Alexis<br>
<br>
<div class="moz-cite-prefix">Le 04/04/2014 17:01, C. Titus Brown a
écrit :<br>
</div>
<blockquote cite="mid:49182BF6-4D02-4772-8C5E-BE4F51F62F0D@msu.edu"
type="cite">
<pre wrap="">
On Apr 4, 2014, at 10:49 AM, Alexis Groppi <a class="moz-txt-link-rfc2396E" href="mailto:alexis.groppi@u-bordeaux.fr"><alexis.groppi@u-bordeaux.fr></a> wrote:
</pre>
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<pre wrap="">Hi,
We want to analyse 454 metagenomics data (570 000 reads of ~700 nt per sample).
My questions are :
1/ Given that khmer is rather short-read/Illumina oriented, are we mistaken to try and apply it to our long 454 reads?
2/ Is there an actual benefit in feeding .fastq files to khmer (in our case separate .qual files at the moment, but that can be changed), or does it really only consider the sequence data? ie. are the fasta files sufficient ?
How do you define what data needs pre-normalization or what data can go straight to artifact removal? In the Iowa corn example, you do not start by a normalize/filter pass, how come?
3/ Thus, should the pipeline for our data be like :
DIGINORM (normalize-by-median -- filter-abund -- normalize-by-median) -- ARTIFACT REMOVAL (load-graph -- partition-graph, ...etc)
or
is the step DIGINORM useless in our case ?
Thanks for your help
And thanks for your great job on khmer 1.0 !
</pre>
</blockquote>
<pre wrap="">
Hi Alexis,
thanks!
Good questions…
I would suggest doing only a single pass of digital normalization, since the impact of both errors and low coverage will be different with longer reads. So, something like normalizing to a coverage of 5, in a single pass. Do not do any trimming (filter-abund) as this will potentially discard a lot of your sequences; trimming cuts off the ends of sequences, and is best applied to high-coverage short reads. Your main diagnostic tool here will be a k-mer abundance histogram after normalization: do you see that a bunch of real coverage has been lowered to 1 or 2, or are you primarily seeing normalization of coverage to an average of 5 with no increase in the number of sequences with a coverage of 1 or 2?
Partitioning should work just fine, but I would not do artifact removal (filter-stoptags, etc.). If you get a big blob where everything hangs together, there are some things you can do with stoptags: briefly, run the knot finding stuff, but then feed the resulting stoptags into partition-graph with the -S parameter. This will prevent partitioning across highly connected k-mers.
HTH! Please feel free to ask more ;)
cheers,
--titus
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</pre>
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</pre>
</blockquote>
<br>
<div class="moz-signature">-- <br>
<a href="http://www.u-bordeaux.fr" title="Université de Bordeaux"><img
style="border: 0;"
src="cid:part1.04030602.07010101@u-bordeaux.fr" alt="CBiB -
Université de Bordeaux"></a>
<p style="font-family: Arial, sans-serif; font-size: 13px; color:
#009DE0;line-height:17px"><span style="font-weight: bold"><a
href="mailto:alexis.groppi@u-bordeaux2.fr"
style="color:#009DE0; text-decoration:none;">Dr Alexis <span
style="text-transform:uppercase;">Groppi</span></a></span><br>
Directeur adjoint du CBiB - Chargé de mission du CGFB<br>
146, rue Léo Saignat - Case 68 - 33076 Bordeaux Cedex<br>
T. +33 5 57 57 12 18<br>
P. +33 6 35 95 04 87<br>
<a href="http://www.cbib.u-bordeaux2.fr" style="color:#009DE0;
text-decoration:none;">www.cbib.u-bordeaux2.fr</a><br>
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