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We have produced 2000 ESTs (1.2 mega bases) from a marsupial mouse and characterized the data for their use in phylogenetic analysis.
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It is important to analyze and characterize the data processing performance for a given cloud cluster and to evaluate the performance bottlenecks in a cloud cluster that contribute to higher or lower computing processing time.
Descriptive statistical analyses (mean, median, interquartile range) will be used to describe and characterize the data.
LF and GB generated and characterized the sequence data.
These codes were grouped into categories and subcategories which best characterized the data collected.
The resulting prediction for the 1- and two-dimensional classifier characterized the data for untrained sequences quite well.
AUC24/MIC and %T > MIC characterized the data well, whereas Cmax/MIC did not.
To optimize the application of the ideal reference genes, we reprocessed the data and characterized the reference genes as a two-way table (Table 6).
We validated our approach using simulated data and characterized the types and locations of errors.
For eight dominant and co-occurring grassland plant species, we reconstructed a phylogeny based on DNA data and characterized the AMF communities of their roots at each site.
We compared the assemblies from each single NGS data and characterized the systematic biases (coverage, GC content, k-mer diversity, substitution error) of each platform.
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