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Hi Titus,<br>
<br>
With the great help of Eric, I went through the first 2 steps of
Metagenome Assembly.<br>
I'm moving to the step 3 : Partinionning.<br>
My questions are : <br>
Given the amount of the reads (75 nt) I have (between 2.5 and 4
million) per fasta file,<br>
What would be your best choice for the workflow ? : <br>
- load-graph, partition-graph, ... etc like described in the
handbook fore "Large data sets" which is not my case ?<br>
or something else ?<br>
- More important, given my difficulties with the previous steps,
what would you chose as parameters for the scripts ?: <br>
load-graph, partition-graph, ... ?<br>
<br>
Thanks again<br>
<br>
Alexis, khmer addict ;)<br>
<br>
<div class="moz-cite-prefix">Le 08/03/2013 15:50, C. Titus Brown a
écrit :<br>
</div>
<blockquote cite="mid:20130308145037.GP30267@idyll.org" type="cite">
<pre wrap="">On Fri, Mar 08, 2013 at 12:12:38PM +0100, Alexis Groppi wrote:
</pre>
<blockquote type="cite">
<pre wrap="">I'm starting to use your tools (khmer) for paleometagenomics analysis
(25000 years old DNA samples)
In the Handbook, for metagenome assembly, the step 2 consist in trimming
sequences at a min k-mer abundance with filter-abund.py (in the handbook
the script is named filter-below-abund , but I guess it's the same)
The counting hash table <input.kh> must be built before with
load-into-counting.py... but on the original fasta file or on the .keep
file resulting from the step 1 (normalize-bymedian.py) ?
</pre>
</blockquote>
<pre wrap="">
Hi Alexis,
it's not the same -- see 'sandbox/filter-below-abund.py'. This one
gets rid of repeats, while filter-abund will eliminate real info from
your data set (low-abundance components that show up in metag data).
Use --savehash to generate a hash table on the normalize-by-median step (step
#1), OR use load-into-counting on the .keep file. That is, you want to
run it on the results of digital normalization.
cheers,
--titus
</pre>
</blockquote>
<br>
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