In comparison to assembly-based methods, taxonomic binning with the read-based machine learning approach yielded final assemblies with much improved genome completeness.
The largest number of unique protein hits was obtained by Velvet assembly in comparison to the assemblies generated by CLC and ABySS.
In comparison to other assemblies, the optimal assembly will include the largest proportion of the unique bases present in the sum of all assemblies.
To study the extent of errors in metagenomic assemblies in comparison to single genome assembly, we performed a set of experiments on simulated datasets.
Amongst the simLC and simHC datasets, the performance of simLC was closer to the isolate assemblies, whereas, the simMC metagenomic assembly was far poorer in comparison to its isolated assembly.
A k-mer of 47 resulted in an optimal assembly in comparison to other k-mer assemblies based on different assembly quality parameters like N50 length, average contig length, total length of the contigs, total number of contigs, longest contig length and number of Ns.
In addition, the multiple k-mers based assembly by CLCbio, as it was used for the transcriptome de novo assembly, performed very well in comparison to other assemblers, (namely MIRA, Newbler, SOAPdenovo, Trinity and Velvet-Oases) [ 23].
The results reveal that a MMS configuration can lead to better utilizations of the exemplary manufacturing system in comparison to a sequential assembly line configuration.
We also provide data on the sedimentation kinetics of spider silk in the presence of different NaCl concentrations revealing very slow protein aggregation in comparison to the fast assembly triggered by phosphate ions published previously [1].
The Illumina only TAs, and especially the TA composed solely of 454 reads, performed poorly in comparison to the hybrid assemblies.
