Computational protocol: Therapeutic activity of modified U1 core spliceosomal particles

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Protocol publication

[…] The spinal cord RNA of wild-type (Smn1+/+, SMN2+/+, ExSpeU1−/−) and SM25 ExSpeU1 transgenic (Smn1+/+, SMN2+/+, ExSpeU1+/−) animals was purified with the TRIreagent (Invitrogen) for mRNA sequencing on HiSeq 2500 (Illumina Inc., San Diego, CA, USA). The quality of total RNA was assessed using Agilent RNA 6000 Nano Bioanalyzer microfluidic chips and a Nanodrop UV spectrophotometer. The template DNA molecules suitable for cluster generation were prepared from 2 μg of total RNA samples using the TruSeq RNA Sample Preparation Kit v2 (Illumina Inc) according to the manufacturer's instructions. The size distribution of the libraries was estimated by electrophoresis on Agilent High Sensitivity Bioanalyzer microfluidic chips. Libraries were quantified using the KAPA Library Quantification Kit (KK4824, Kapa Biosystems, Boston, MA, USA). The libraries were pooled at equimolar concentrations and diluted before loading onto the flow cell of the HiSeq 2500 (Illumina Inc.) for both clustering and sequencing. The libraries were extended and bridge-amplified to create single sequence clusters using the TruSeq Rapid PE Cluster Kit—HS (Illumina Inc.). Amplified clusters in the flow cell were then sequenced with 100-bp paired-end reads using the TruSeq Rapid SBS Kit—HS (Illumina Inc.). Real-time image analysis and base calling were performed on the the HiSeq 2500 instrument using the HiSeq Sequencing Control Software. CASAVA software version 1.8 was used for de-multiplexing and production of FASTQ sequence files. FASTQ raw sequence files were subsequently quality checked using FASTQC software version 0.11.3 http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc. [...] Paired-end RNAseq reads were mapped to the Mus Musculus reference genome to estimate gene and exon expression levels (University of California at Santa Cruz, UCSC, mm10) by using the ultrafast universal RNA-seq aligner STAR. Mapped reads for all transcript variants of a gene were combined into a single value to perform differential gene expression analysis. We used the Bioconductor packages GenomicFeatures version 1.18.7 (ref. ) in the framework of R software version 3.1.0 to download transcript annotations available at the UCSC Genome Browser and extract rounded gene or exon counts from the STAR Mapped reads. Rounded Gene counts were used as input to perform differential gene expression analysis using Bioconductor package DESeq2 version 1.4.5 (ref. ) to estimate the per-gene negative binomial distribution dispersion parameter. Rounded Exon counts were used as input to perform differential Exon expression analysis using Bioconductor packages DEXSeq version 1.12.2 (ref. ). To detect outlier data after normalization, we used the R packages arrayQualityMetrix and before testing differential gene expression we dropped all genes with normalized mean counts below 10 to improve testing power while maintaining type I error rates. The estimated P values for each gene or exon were adjusted using the Benjamini–Hochberg method. Features with adjusted P<0.05 and absolute logarithmic base 2 fold change >1 were considered as having a significant altered expression as previously reported. […]

Pipeline specifications

Software tools BaseSpace, FastQC, STAR, GenomicFeatures, DESeq2, DEXSeq
Application RNA-seq analysis
Organisms Mus musculus