Many existing pipelines for scRNA-seq data apply pre-processing steps such as
normalization or imputation to account for excessive zeros or “drop-outs." Here, we …

RNA-seq experiments generate reads derived not only from mature RNA transcripts but also
from pre-mRNA. Here we present a computational approach called exon-intron split analysis …

Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods,
there is still little agreement on how to best normalize such data. Starting from the basic …

Background Gene expression analysis by RNA sequencing is now widely used in a number
of applications surveying the whole transcriptomes of cells and tissues. The recent …

We have mapped and quantified mouse transcriptomes by deeply sequencing them and
recording how frequently each gene is represented in the sequence sample (RNA-Seq) …

Due to alternative splicing events in eukaryotic species, the identification of mRNA isoforms
(or splicing variants) is a difficult problem. Traditional experimental methods for this purpose …

Alternative splicing (AS) is a widespread process underlying the generation of transcriptomic
and proteomic diversity and is frequently misregulated in human disease. Accordingly, an …

High-throughput single-cell RNA-seq (scRNA-seq) is a powerful tool for studying gene
expression in single cells. Most current scRNA-seq bioinformatics tools focus on analysing …

Background RNA-seq is now widely used to quantitatively assess gene expression,
expression differences and isoform switching, and promises to deliver results for the entire …

Applying a log-transformation to normalized expression values is one of the most common
procedures in exploratory analyses of single-cell RNA sequencing (scRNA-seq) data …