RNA sequencing data for APP, Shal/Kv4 and dTrpA1 mutants (Drosophila)

Published: 15 December 2021| Version 1 | DOI: 10.17632/yrmd2mm37h.1
Yong Ping


RNA-seq raw data.


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For each sample, total RNA was isolated from approximately 50 adult male Drosophila heads. RNA was extracted using UNlQ-10 Column Trizol Total RNA Isolation Kit (Sangon Biotech) and treated with DNaseI (BBI) to digest DNA. Oligo dT magnetic beads were used to enrich and purify the qualified RNA samples. Purified mRNA was fragmented and used as a template to synthesize the first strand of cDNA with N6 random primer. The second strand of cDNA was synthesized by adding dNTPs, RNaseH and DNA Polymerase I. Eluted and purified double-stranded cDNA was then repaired with phosphate at 5' end and stickiness 'A' at 3' end, then ligate and adaptor with stickiness 'T' at 3' end. Then the target size fragments are recovered by agarose gel electrophoresis and PCR amplification is performed to complete the entire library preparation. Use Qubit3.0 for preliminary quantification, dilute the library to 1ng/ul, and then use Agilent 2100 to detect the insert size of the library. After the insert size meet expectations, performs QPCR to accurately quantify the effective concentration of the library (>10nM). Sequencing was performed on the prepared library with Illumina platform and paired end (PE) strategy. Raw reads from Illumina platform were trimmed to obtain clean reads by removing read duplicates, low quality reads, contaminating sequences, etc. Based on Clean Reads, transcriptome sequencing information analysis process was mainly divided into three parts: (i) data quality control; (ii) data comparison analysis, and (iii) transcriptome in-depth analysis such as Multi-platform Gene Ontology (GO) annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. For statistical analysis, applying the fold-change together with the false discovery rate (FDR) as the cutoff criterion is now widely considered reliable since the combination criteria typically find more biologically meaningful sets of genes than purely in terms of fold-change or p-values. A previous study has shown that differentially expressed genes (DEGs) with a larger fold-change (effect size) leads to relatively more accurate and reproducible results under the same FDR threshold (Sweeney et al., 2017). In this work, a strict inclusion criterion was used, that is, DEGs with statistical significance were selected through volcano plot filtering with the criteria of |log2-fold change| ≥ 1 and FDR < 0.05. We have also applied RT-qPCR and immunoblot analysis to verify the critical DEGs.


Shanghai Jiao Tong University


RNA Sequencing