Casava 1.8.2 software suite

RNA samples were processed by the Yale Center for Genome Analyses according to standard Illumina protocols. Libraries were sequenced on the NovaSeq flow cell with 100 bp paired-end reads. Signal intensities were converted to individual base calls during the run using the system’s Real Time Analysis (RTA) software. Base calls were transferred from the machine’s dedicated personal computer to the Yale High Performance Computing cluster via a 1 Gigabit network mount for downstream analysis. Sample de-multiplexing and alignment to the human genome - was performed using Illumina’s CASAVA 1.8.2 software suite.
Untrimmed FASTA files were mapped with Salmon (version 0.8.2) using GENECODE version 38 (GRCh38.p12) reference transcriptome. DeSeq2 package was used to determine differential expression between samples. Data was processed on the Galaxy server (https://usegalaxy.org/).
Genes with expression fold changes greater than 2 and p values less than 0.05 were considered differentially expressed. Additional filtering was applied to remove genes with expression levels below 5 TPM in all samples. Gene ontologies were defined using Panther (www.pantherdb.org/) and NIH David (https://david.ncifcrf.gov/) packages. List of NR2F2 target genes identified by ChIP was obtained from (Churko et al., 2018 (link); Wu et al., 2013 (link)).

Signal intensities were converted to individual base calls during a run using the system’s Real Time Analysis (RTA) software. Base calls were transferred from the machine’s dedicated personal computer to the Yale High-Performance Computing cluster via a 1 Gigabit network mount for downstream analysis. Primary analysis - sample de-multiplexing and alignment to the human genome - was performed using Illumina’s CASAVA 1.8.2 software suite. The data returned to the user if the sample error rate was less than 1% and the distribution of reads per sample in a lane within a reasonable tolerance. Fastq files were aligned to the Human reference genome Hg38 using BWA-Meth (19 ). Analysis for methylation status was done using methylKit, and data visualization and downstream analysis were performed using custom R Script.

Signal intensities were converted to individual base calls during a run using Real Time Analysis (RTA) software (Illumina). Primary sample analysis de-multiplexing and alignment to the human genome was performed using CASAVA 1.8.2 software suite (Illumina, San Diego, CA). The raw RNA-sequencing reads were first assessed for quality using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). FASTX-toolkit [32 (link)] was then used to trim off adaptor sequences and low quality bases. Reads passing the quality control were aligned to the Ensembl annotation of mouse genome reference sequence GRCm38 by TopHat 2 [33 (link)] and Bowtie [34 (link)] alignment engines. Reads mapped to multiple locations were discarded from further analysis. Raw gene counts (gene expression levels) were established by HTSeq package [35 (link)] with the default union-counting mode, and normalized using the TMM method in edgeR package [36 ]. A negative binomial generalized linear model implemented in edgeR [37 (link)-40 (link)] was used to determine differentially-expressed genes with multiple experimental factors accounted for. Genes from Cat^-/- mice with fold-change (> 2 or < 0.5) and false discovery rate (FDR)-controlled (P < 0.05) were considered differentially expressed relative to WT mice.