RNA seq isogenic TBK hiPSC lines
Each sample consisted of 4 million cells, with RNA extracted from four independent passages. The RNA was harvested using the RNeasy Plus Mini Kit (Qiagen) and the concentration for each hiPSC sample was standardized to 500 ng/L.Messenger RNA was purified from total RNA using poly-T oligo-attached magnetic beads. After fragmentation, the first strand cDNA was synthesized using random hexamer primers, followed by the second strand cDNA synthesis using either dUTP for directional library or dTTP for non-directional library.For the non-directional library, it was ready after end repair, A-tailing, adapter ligation, size selection, amplification, and purification. For the directional library, it was ready after end repair, A-tailing, adapter ligation, size selection, USER enzyme digestion, amplification, and purification.The library was checked with Qubit and real-time PCR for quantificationand bioanalyzer for size distribution detection. Quantified libraries will bepooled and sequenced on Illumina platforms, according to effective libraryconcentration and data amount.Raw data (raw reads) of fastq format were firstly processed throughin-house perl scripts. In this step, clean data (clean reads) were obtainedbyremoving reads containing adapter, reads containing ploy-N and lowqualityreads from raw data. At the same time, Q20, Q30 and GC content thecleandata were calculated. All the downstream analyses were based on thecleandata with high quality.Reference genome and gene model annotation files were downloadedfrom genome website directly. Index of the reference genome was built usingHisat2 v2.0.5 and paired-end clean 1 reads were aligned to the referencegenome using Hisat2 v2.0.5. We selected Hisat2 [2] as the mapping tool for that Hisat2 can generate a database of splice junctions based on the genemodel annotation file and thus a better mapping result than other non-splice mappingtools.featureCounts [3] v1.5.0-p3 was used to count the reads numbers mappedto each gene. And then FPKM of each gene was calculated based onthelength of the gene and reads count mapped to this gene. FPKM, expectednumber of Fragments Per Kilobase of transcript sequence per Millions basepairs sequenced, considers the effect of sequencing depth and gene lengthfor the reads count at the same time, and is currently the most commonly usedmethod for estimating gene expression levels.(For DESeq2 [4] with biological replicates) Differential expression [5] analysisof two conditions/groups (two biological replicates per condition) wasperformed using the DESeq2Rpackage (1.20.0). DESeq2 provide statistical routines for determining differential expression in digital gene expressiondatausing a model based on the negative binomial distribution. The resultingP-values were adjusted using the Benjamini and Hochberg's approachfor controlling the false discovery rate . Genes with an adjusted P-value<=0.05found by DESeq2 were assigned as differentially expressed. (For edgeR[6] without biological replicates) Prior to differential gene expressionanalysis, for each sequenced library, the read counts were adjusted by edgeRprogram package through one scaling normalized factor. Differential expression analysis of two conditions was performed using the edgeRRpackage (3.22.5). The P values were adjusted using the Benjamini &Hochberg method. Corrected P-value of 0.05 and absolute foldchangeof 2were set as the threshold for significantly differential expression.