Expression and Quantification

Transposable elements (TEs) are mobile genetic elements in eukaryotic genomes. Recent research highlights the important role of TEs in the embryogenesis, neurodevelopment, and immune functions...

RNA-Sequencing is the current method of choice for studying bacterial transcriptomes. To date, many computational pipelines have been developed to predict differentially expressed genes from RNA-Seq...

Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs...

An important challenge in pre-processing data from droplet-based single-cell RNA sequencing protocols is distinguishing barcodes associated with real cells from those binding background reads. Existing...

Allele-specific expression (ASE) analysis, which quantifies the relative expression of two alleles in a diploid individual, is a powerful tool for identifying cis-regulated gene expression variations that underlie...

Calculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, a requirement that is at times financially or physiologically infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), ...

Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify...

One primary reason that makes single-cell RNA-seq analysis challenging is dropouts, where the data only captures a small fraction of the transcriptome of each cell. Almost all computational algorithms developed for single-cell RNA-seq adopted gene selection, dimension reduction or imputation ...

Single-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an ...

Emerging single-cell RNA-seq technologies has made it possible to capture and assess the gene expression of individual cells. Based on the similarity of gene expression profiles, many tools have been developed to generate an in silico ordering of cells in...