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Sequencing quality was affirmed by the fastqc algorithm.
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Sequence reads were subjected to demultiplexing using the CASAVA 1.8 software (Illumina, San Diego, CA) followed by quality checking using the FastQC algorithm (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).ac.uk/projects/fastqc/
To generate high quality assemblies, we first assessed the quality of reads using the FastQC algorithm [ 36].
Sequence reads were obtained and their quality was analyzed using the quality control metrics provided by the FastQC pipeline (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).ac.uk/projects/fastqc/
First, the raw data for high-throughput sequencing were checked for quality by the FastQC tool.
Quality controls of the sequencing process produced by the FastQC program (http://www.bioinformatics.babraham.ac.uk) were also evaluated.
The quality score of RNA-seq reads was obtained by using the FastQC and the mean quality of each base pair in the samples was 28, indicating a good-quality call in the 50 bp reads [ 30].
To assess the quality of RNA-seq data, each base in the reads was assigned a quality score (Q) by a phred-like algorithm using the FastQC [ 38].
Mountcastle concluded that they are governed by the same algorithm.
Nowadays, that task is accomplished by the Google algorithm.
First, we evaluated the quality of short reads by searching them on the FastQC program.
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