The de novo assembly of the whole dataset was time-consuming due to memory issues, so we used Diginorm (Digital normalization with khmer, [ 80]) to normalize the raw data digitally.
The software is developed to provide both a set of directly usable tools (e.g. normalize-by-median for digital normalization) as well as an experimental framework for developers looking to design new algorithms and methods.
Digital normalization is implemented in the script normalize-by-median.py.
To reduce the coverage of highly expressed genes and improve the ability to assemble unigenes and transcript isoforms originating from lowly expressed genes, k-mers (k = 25) from Illumina and simulated PE reads were normalized to 30X coverage using digital normalization.
To normalize and assemble RNA-seq reads for de novo assembly, digital normalization was used for 283.6 million reads.
The massive data redundancy is handled using the ( k )-mers median, and subsequently, digital normalization is employed as the data reduction technique.
As before, digital normalization was applied to 50× using Jellyfish.
Digital normalization (diginorm) removes highly redundant sequences while retaining read complexity and low abundance transcripts [ 13].
