RNA sequencing (RNA-seq) is the leading technology for genome-wide transcript quantification. However, publicly available RNA-seq data is currently provided mostly in raw form, a significant barrier for global and integrative retrospective analyses.
Gene functionality is closely connected to its expression specificity across tissues and cell types. RNA-Seq is a powerful quantitative tool to explore genome wide expression. The aim of this study is to provide a comprehensive RNA-Seq dataset across the same 13 tissues for mouse and rat, two ...
Study on long non-coding RNAs (lncRNAs) has been promoted by high-throughput RNA sequencing (RNA-Seq). However, it is still not trivial to identify lncRNAs from the RNA-Seq data and it remains a challenge to uncover their functions. We present a computat
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Integration of RNA-Seq data with results of QTL mapping from an advanced intercross reduced the number of positional candidates from 1099 genes residing throughout QTL regions to 49 candidate genes differentially expressed or with the coding polymorphisms (with likely functional consequences) between the...
Schübeler lab for providing unpublished RNA-seq data. The authors are grateful to T. Ye, and S. Le Gras for bioinformatics support; the IGBMC NGS platform for data generation; P. Eberling for generation of peptides and the IGBMC cell culture service. We thank D. Devys for critical ...
2.3. Single cell RNA-seq data analysis We preprocessed the scRNA-seq reads using the 10× GENOMICS Cell Ranger pipeline. In general, we aligned the reads to the mouse genome (mm10) with the setting “--r1-length=26 --r2-length=98.” We applied other default cell ranger parameters to ...
Single cell RNA-sequencing (scRNASeq) has advanced our understanding of lung biology, but utility is limited by the need for fresh samples, loss of cell types by death or inadequate dissociation, and transcriptional stress responses induced during tissue digestion. Single nucleus RNASeq (snRNASeq)...
3 Filtering of scRNAseq samples based on per sample count data. a, Histograms showing total counts, total features per counts as well as the percentage of mitochondrial counts for each of the 12 single-cell RNA-seq samples. b, Scatter plot showing total counts (x-axis) and total features ...
Differential gene expression analysis of RNA-seq data Reads were aligned to the mouse reference genome (mm10) with STAR v2.5.0a using default parameters18. Raw read counts were calculated with STAR using the GeneCounts option of the quantMode parameter since the libraries were unstranded. Librar...