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Assessment of single cell RNA-seq normalization methods

Assessment of single cell RNA-seq normalization methods

UCSD researchers have assessed the performance of seven normalization methods for single cell RNA-seq using data generated from dilution of RNA samples. Their analyses showed that methods considering spike-in ERCC RNA molecules significantly outperformed those not considering ERCCs. This work ...

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Integrating gene expression profiles across different platforms

Integrating gene expression profiles across different platforms

Determining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene expression levels. However, considerable discrepancy has been found between DEGs detected ...

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RNA-Seq analysis in SeqMonk with DESeq

RNA-Seq analysis in SeqMonk with DESeq

This video shows a walk-through of a full 2-condition, 3-replicate RNA-Seq experiment, from loading the data, through QC, quantitation and differential expression analysis using both DESeq2 and SeqMonk’s own Intensity Difference filter. http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/

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Optimization of miRNA-seq data preprocessing

Optimization of miRNA-seq data preprocessing

The past two decades of microRNA (miRNA) research has solidified the role of these small non-coding RNAs as key regulators of many biological processes and promising biomarkers for disease. The concurrent development in high-throughput profiling technology has further advanced our ...

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