Deconvolution is a mathematical process of resolving an observed function into its constituent elements. In the field of biomedical research, deconvolution analysis is applied to obtain single cell-type or tissue specific signatures from a mixed signal and most of them ...
Read More »External calibration with whole-cell spike-ins delivers absolute mRNA fold changes
Gene expression measurements are typically performed on a fixed-weight aliquot of RNA, which assumes that the total number of transcripts per cell stays nearly constant across all conditions. In cases where this assumption does not hold (e.g., when comparing cell ...
Read More »Outlier detection for improved differential splicing quantification from RNA-Seq experiments with replicates
A key component in many RNA-Seq based studies is the production of multiple replicates for varying experimental conditions. Such replicates allow to capture underlying biological variability and control for experimental ones. However, during data production researchers often lack clear definitions ...
Read More »RNAtor – a mobile application for designing RNA-seq experiments
RNA sequencing (RNA-seq) is a powerful technology for identification of novel transcripts (coding, non-coding and splice variants), understanding of transcript structures and estimation of gene and/or allelic expression. There are specific challenges that biologists face in determining the number of ...
Read More »Power Analysis of Single Cell RNA Sequencing Experiments
High-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published ...
Read More »LPEseq – accurately test differential expression with a limited number of replicates
RNA-Sequencing (RNA-Seq) provides valuable information for characterizing the molecular nature of the cells, in particular, identification of differentially expressed transcripts on a genome-wide scale. Unfortunately, cost and limited specimen availability often lead to studies with small sample sizes, and hypothesis ...
Read More »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 ...
Read More »RNA-seq mixology – designing realistic control experiments to compare protocols and analysis methods
Carefully designed control experiments provide a gold standard for benchmarking new platforms, protocols and pipelines in genomics research. RNA profiling control studies frequently use the mixture design, which takes two distinct samples and combines them in known proportions to induce ...
Read More »Experimental Design Considerations for Microbial RNA-seq
RNA-seq is being used increasingly for gene expression studies and it is revolutionizing the fields of genomics and transcriptomics. However, the field of RNA-seq analysis is still evolving. Now, researchers at the University of Tennessee, Knoxville specifically designed a study ...
Read More »Empirical estimation of sequencing error rates using smoothing splines
Next-generation sequencing has been used by investigators to address a diverse range of biological problems through, for example, polymorphism and mutation discovery and microRNA profiling. However, compared to conventional sequencing, the error rates for next-generation sequencing are often higher, which ...
Read More »