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The analytical approach typically uses several steps of data processing through mathematical equations or algebraic expressions and is often used to determine theoretical capacity of the segment/corridor.
The present study aimed to incorporate background knowledge on the evolution of molecular sequences in general and ribosomal RNA-genes in special into various steps of data processing.
After getting the read counts, data normalization is one of the most crucial steps of data processing, and this process must be carefully considered, as it is essential to ensure accurate inference of gene expression and subsequent analyses thereof.
The data normalization is one of the most crucial steps of data processing and this process must be carefully considered as it has a profound effect on the results of the analysis.
The steps of data processing and analysis are almost the same as described in Oinats et al. (2015).
We describe seven steps of data processing: collection of data from heterogeneous sources, data normalization, data brokering, data storage, data analysis, data visualization and decision support systems.
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It is worth noting that such a definition is a node-level provenance which encodes the nodes involved at each step of data processing.
Thus the first step of data processing involved conversion of reads to 'fasta' format.
To this end, eMZed provides a workspace and capability to inspect and visualize interim results at each step of data processing.
In brief, the first step of data processing involved conversion of reads to the "fasta" format, while all the reads with less than 20 base pairs length or with base quality values below 18 were discarded.
Use of the software via GUI requires a clear understanding of data processing steps in metabolomics.
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