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Thus, we ran Velvet and Oases using a range of kmers [ 21, 25, 29, 33, 37, 41], and then merged these assemblies to produce a non-redundant set of contigs.
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The common idea shared by these pipelines is to run an assembler at different k-mer lengths and to merge these assemblies into one.
Then the Cufflinks utility cuffmerge from Cufflinks was run to merge these assemblies into a comprehensive transcriptome.
To this end, we iteratively merged the assemblies two at a time, similar to the approach employed in [ 37] (Fig. 1 d).
The second step of the additive multiple-k method is to merge the assemblies using different k-mers, removing redundant contigs.
To generate a nonredundant set of contigs, we iteratively merged individual assemblies using the the methods similar to the [ 37] employed to merge the kmers.
However, how these merged reads influence assembly results remains unanswered.
In this study, PHRAP and Minimus merged assemblies often generated aberrant results (e.g., 1 2 Mb genome assemblies for 5 6 Mb Pseudomonas genomes) and contained a high number of singleton (non-assembled) sequences.
To cope with these difficulties, it is worthwhile to analyze multiple methods for merging assemblies, provided that the merged assemblies are compared and evaluated on reasonable grounds (see [ 11]).
Reads were preliminarily assembled using ABySS-P [ 96, 97] and SSAKE [ 98], and the results of these assemblies merged using PCAP [ 99] to create the final assembly.
It is reasonable that merging assemblies from multiple assemblers might yield a combined assembly with higher accuracy.
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