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diff --git a/general/datasets/Jax_d2_mono1vpbmc_ret_rna_seq_0619/processing.rtf b/general/datasets/Jax_d2_mono1vpbmc_ret_rna_seq_0619/processing.rtf new file mode 100644 index 0000000..acc1087 --- /dev/null +++ b/general/datasets/Jax_d2_mono1vpbmc_ret_rna_seq_0619/processing.rtf @@ -0,0 +1,5 @@ +<h3 id="Sec8">RNA-sequencing and analysis</h3>
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+<p>Monocytes from single optic nerve heads or from peripheral blood (restrained cheek bleed) were FAC sorted into 100 μl buffer RLT + 1% βME and frozen at − 80 °C until further processing. Samples were defrosted on ice and homogenized by syringe in RLT Buffer (total volume 300 μl). Total RNA was isolated using RNeasy micro kits as according to manufacturer’s protocols (Qiagen) including the optional DNase treatment step, and quality was assessed using an Agilent 2100 Bioanalyzer. The concentration was determined using a Ribogreen Assay from Invitrogen. Amplified dscDNA libraries were created using a Nugen Ovation RNA-seq System V2 and a primer titration was performed to remove primer dimers from the sample to allow sample inputs as low as 50 pg RNA. The SPIA dscDNA was sheared to 300 bp in length using a Diogenode Disruptor. Quality control was performed using an Agilent 2100 Bioanalyzer and a DNA 1000 chip assay. Library size produced was analysed using qPCR using the Library Quantitation kit/Illumina GA /ABI Prism (Kapa Biosystems). Libraries were barcoded, pooled, and sequenced 6 samples per lane on a HiSeq 2000 sequencer (Illumina) giving a depth of 30–35 million reads per sample.</p>
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+<p>Following RNA-sequencing samples were subjected to quality control analysis by a custom quality control python script. Reads with 70% of their bases having a base quality score ≥ 30 were retained for further analysis. Read alignment was performed using TopHat v 2.0.7 [<a aria-label="Reference 34" data-test="citation-ref" data-track="click" data-track-action="reference anchor" data-track-label="link" href="https://molecularneurodegeneration.biomedcentral.com/articles/10.1186/s13024-018-0303-3#ref-CR34" id="ref-link-section-d88755e1228" title="Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:R36.">34</a>] and expression estimation was performed using HTSeq [<a aria-label="Reference 35" data-test="citation-ref" data-track="click" data-track-action="reference anchor" data-track-label="link" href="https://molecularneurodegeneration.biomedcentral.com/articles/10.1186/s13024-018-0303-3#ref-CR35" id="ref-link-section-d88755e1231" title="Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.">35</a>] with supplied annotations and default parameters against the DBA/2 J mouse genome (build-mm10). Bamtools v 1.0.2 [<a aria-label="Reference 36" data-test="citation-ref" data-track="click" data-track-action="reference anchor" data-track-label="link" href="https://molecularneurodegeneration.biomedcentral.com/articles/10.1186/s13024-018-0303-3#ref-CR36" id="ref-link-section-d88755e1234" title="Barnett DW, Garrison EK, Quinlan AR, Strömberg MP, Marth GT. BamTools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics. 2011;27:1691–2.">36</a>] were used to calculate the mapping statistics. Differential gene expression analysis between groups was performed using edgeR v 3.10.5 [<a aria-label="Reference 37" data-test="citation-ref" data-track="click" data-track-action="reference anchor" data-track-label="link" href="https://molecularneurodegeneration.biomedcentral.com/articles/10.1186/s13024-018-0303-3#ref-CR37" id="ref-link-section-d88755e1237" title="Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.">37</a>] following, batch correction using RUVSeq, the removal of outlier samples and lowly expressed genes by removing genes with less than five reads in more than two samples. Normalization was performed using the trimmed mean of M values (TMM). Unsupervised HC was performed in R (1-cor, Spearman’s <i>rho</i>). Following preliminary analysis, 1 sample was removed as an outlier. Adjustment for multiple testing was performed using false discovery rate (FDR). Genes were considered to be significantly differentially expression at a false discovery rate (FDR; <i>q</i>) of <i>q</i> < 0.05. Pathway analysis was performed in R, IPA (Ingenuity Pathway Analysis, Qiagen), and using publically available tools (see <a data-track="click" data-track-action="section anchor" data-track-label="link" href="https://molecularneurodegeneration.biomedcentral.com/articles/10.1186/s13024-018-0303-3#Sec15">Results</a>).</p>
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