PMC:6387395 / 33375-34412 JSONTXT

{"project":"2_test","denotations":[{"id":"30708960-25690850-51842996","span":{"begin":138,"end":140},"obj":"25690850"},{"id":"30708960-25748911-51842997","span":{"begin":335,"end":337},"obj":"25748911"},{"id":"30708960-20436464-51842998","span":{"begin":528,"end":530},"obj":"20436464"},{"id":"T30007","span":{"begin":138,"end":140},"obj":"25690850"},{"id":"T81355","span":{"begin":335,"end":337},"obj":"25748911"},{"id":"T79920","span":{"begin":528,"end":530},"obj":"20436464"}],"text":"4.4. Transcript Abundance Estimation and Differential Expression Analysis\nThe mapped reads of each sample were assembled using StringTie [65]. Then, the transcriptomes from all the samples were merged to reconstruct a comprehensive transcriptome using perl scripts. After the final transcriptome was generated, StringTie and Ballgown [66] were used to estimate the expression levels of the transcripts. StringTie was used to determine expression levels for mRNAs by calculating fragments per kilobase of exon per million reads [67].\nA differential expression analysis between the treatments (three biological replicates per treatment) was performed using the DESeq R package. DESeq provides statistical methods for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The differentially expressed mRNAs and genes were determined with log2 (fold change) \u003e 1 or log2 (fold change) ≤ 1 and with statistical significance (p-value \u003c 0.05) using the Ballgown R package."}