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Old 01-12-2017, 06:04 AM   #1
jov14
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Default Partitioning huge paired-end datasets with the khmer suite

The khmer suite has a workflow for partitioning reads based on graph-connectivity, which I want to test for a specific (huge) paired-end dataset.

However, from the documentation, I am not sure how exactly it treats read-pairs? It seems that arguments which specify how read-pairs should be treated only exist for the normalization-workflows?

e.g. load-graph.py (the first step of the partitioning workflow) accepts one or multiple input files. If I use multiple input files, will the script automatically assume paired-end reads in separate files (forward reads in the first, reverse reads in the second file)? Will it automatically assume interleaved reads if i only supply one input file?

In which form will the partitioned reads be output? Automatically Interleaved? Or possibly first all forward reads and then all reverse reads (requiring another processing step before assembly of paired reads)?
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khmer suite, metagenomics, read, read partitioning

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