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Front-end node = FE, Back-end node = BE Fig. 10 Data quality evaluation (sequence 1).
Figures 9 and 10 present how metadata is created for Twitter-based data sets, and how the data quality evaluation process is started in the Back-end node: Fig. 9 Data quality evaluation (sequence 1).
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After quality evaluation of sequencing data (Additional file 1: Figure S1), a total of 28.35 Gb high quality data were collected for the B. juncea genome and used to compare with whole genome sequence of B. rapa.
Several algorithms, usually referred to as objective quality metrics, have been proposed in the literature for the in-service objective quality evaluation of video sequences.
We believe that such a publicly available database will allow easier comparison and performance evaluation of the existing and future objective metrics for quality evaluation of video sequences, contributing to the advance of the research in the field of objective quality assessment.
The results of the quality evaluation of the sequencing data were excellent, with bases being distributed evenly; thus, we continued our analyses.
After the run, image analysis, sequencing quality evaluation and data production summarization were performed with Illumina/Solexa Pipeline.
The alignment results proved that miRSeq is competent for sequencing quality evaluation.
Three DGE sequencing quality evaluation and alignment statistics were shown in Table 6.
Thus, the percentage of self-ligation read is a crucial index for sequencing quality evaluation.
miRSeq is a user-friendly standalone toolkit for sequencing quality evaluation and miRNA profiling from NGS data.
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