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Trimmomatic Vs. 36 Rf Case Study

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Trimmomatic v.0.36 –REF was used to trim reads by removing leading bases with Phred33 quality scores < 5, trailing bases with Phred33 quality scores < 3, using a sliding window of 4 bases and removing the 5’ terminal base if the average Phred33 score of the 4 bases was < 15, and completely discard trimmed reads with less than 36 remaining bases. The high quality reads were subsequently mapped to the pig genome Sscrofa build 11.1 using the Star software v.2.5.2a and default parameters while simultaneously adding unique read groups to the files – REF. Gene prediction coordinates (release 11.1.90) were obtained from Ensembl (http://www.ensembl.org) Samtools v.1.3.1 –REF was used to merge bams from the same sample and to convert bam files to sam files before HTSeq -REF was run with the reverse stranded option to calculate the number of reads mapped to each gene. …show more content…

The two breeds were analyzed separately and the samples were divided into a high-low contrast based on their hyperactivity values. Filtering was done to keep only genes that achieved at least one count per million in at least half of the samples, and the data was normalized for differences in the abundance of read counts mapped to genes between samples using the TMM (trimmed mean of M-values) normalization method. Variance in gene expression was estimated using a tagwise dispersion model before differential expression was detected with a likelihood ratio test model. FDR was calculated using the Bejamini-Hochberg procedure –REF, and a FDR < 0.05 was considered

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