For samples from 10-week-old and 20-week-old mice, cells were also stained with 0.5μg of TotalSeq-C mouse hashing antibody (BioLegend) per genotype before cell sorting. Sorted CD45+ cells from each genotype were then pooled at equal cell numbers, washed, strained through a 40μm strainer (Flowmi), and resuspended in PBS containing 0.04% BSA at ~1500 cells/μL for superloading as previously described97 (link). Libraries were prepared using 10X Genomics 5’v2 chemistry with Feature Barcoding per manufacturer’s instructions and sequenced on an Illumina NovaSeq SP. Three mice were pooled together per genotype for each library.
Novaseq sp
The NovaSeq SP is a powerful next-generation sequencing (NGS) system produced by Illumina. It is designed to deliver high-throughput sequencing capabilities for a wide range of applications, including genomics research, clinical diagnostics, and personalized medicine. The NovaSeq SP system provides users with the ability to generate large volumes of high-quality sequencing data efficiently and cost-effectively.
Lab products found in correlation
42 protocols using novaseq sp
Single-cell RNA-seq of APOE genotypes
RNA-seq Analysis of Drosophila Histone Mutants
Drosophila Transcriptome Profiling Pipeline
Replicate 3 of Set2 1 and replicate 5 of ΔHisC; 12x H3K36K did not pass the quality control in RSeQC (Wang et al. 2012 (link)) and were therefore excluded in the downstream analysis. DESeq2 v1.18 (Love MI et al. 2014) was used for differential expression analysis using default parameters and normal log fold change shrinkage.
RNA-seq Analysis of Knockdown Transcriptomes
Transcriptomic Analysis of EpdSCs
ATAC-seq Library Preparation for Mouse AMs
ATAC-seq and RNA-seq Data Acquisition and Analysis
Transcriptomic Analysis of Hematopoietic Stem Cells
We filtered the count matrix using the filterByExpr() function from the R package edgeR. DESeq2 was used to perform differential expression analysis, and the false discovery rate (FDR) < 0.05 was considered statistically significant. Gene set enrichment analysis was done by sorting gene lists according to their increasing p value and running tmodCERNOtest() function from the tmod R package (Weiner third and Domaszewska).Panel plots with geneset enrichment were plotted with tmodPanelPlot() from tmod, other plots were plotted using ggplot2. Statistically significant results are listed in
ChIP-Seq Protocol for Ovary and OSCs
CRISPR Screen for Osteoclast Regulators
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