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Old 04-30-2014, 03:41 PM   #1
roliwilhelm
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Location: Vancouver

Join Date: Jun 2012
Posts: 36
Default "Re-inflating Partioned" Metagenomic Data - KHMER

Hello,

My question has two parts. Starting with the most important question:

1) I've read that short-read assemblers designed for metagenomic data make use of read abundance in the assembly process. However, I like the idea of digital normalization (using khmer) as a tool for bringing out reads from genomes which are less abundant in the mix. Is it a wise idea to perform digital normalization and then use assemblers geared for metagenomic data, like MetaVelvet, IDBA_UD or RAY-meta?

2) I have already attempted to partition my metagenomic data using khmer to improve assembly, however found it is very very computationally intensive and slow. The authors of khmer seem to suggest that one viable method would be to perform digital normalization, then partitioning and then "re-inflated" your reads to pre-normalized abundances (see khmer documentation (HERE). I am keen to try that, but the script ("sweep-reads3.py") is no longer comes prepackaged with the new khmer release. I did find it on their git-hub account, HERE, but wonder why it is no longer packaged with the release. Before investing time and energy, I was wondering if anyone has thoughts on this?

Thanks in advance
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