[khmer] Counting hash Table step 2 Metagenome assembly

Alexis Groppi alexis.groppi at u-bordeaux2.fr
Fri Mar 15 09:41:54 PDT 2013


Hi Titus,

With the great help of Eric, I went through the first 2 steps of 
Metagenome Assembly.
I'm moving to the step 3 : Partinionning.
My questions are :
Given the amount of the reads (75 nt) I have (between 2.5 and 4 million) 
per fasta file,
What would be your best choice for the workflow ? :
- load-graph, partition-graph, ... etc like described in the handbook 
fore "Large data sets" which is not my case ?
or something else ?
- More important, given my difficulties with the previous steps, what 
would you chose as parameters for the scripts  ?:
load-graph, partition-graph, ... ?

Thanks again

Alexis, khmer addict ;)

Le 08/03/2013 15:50, C. Titus Brown a écrit :
> On Fri, Mar 08, 2013 at 12:12:38PM +0100, Alexis Groppi wrote:
>> 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) ?
> 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

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