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cqn [35] is a normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. EDASeq [36] is a Bioconductor package to perform GC-Content Normalization for RNA-Seq Data. GeneScissors A comprehensive approach to detecting and correcting spurious transcriptome inference due to RNAseq reads misalignment.
Small RNA sequencing (Small RNA-Seq) is a type of RNA sequencing based on the use of NGS technologies that allows to isolate and get information about noncoding RNA molecules in order to evaluate and discover new forms of small RNA and to predict their possible functions.
DESeq2 is a software package in the field of bioinformatics and computational biology for the statistical programming language R.It is primarily employed for the analysis of high-throughput RNA sequencing (RNA-seq) data to identify differentially expressed genes between different experimental conditions.
Current scRNA-seq protocols involve isolating single cells and their RNA, and then following the same steps as bulk RNA-seq: reverse transcription (RT), amplification, library generation and sequencing. Early methods separated individual cells into separate wells; more recent methods encapsulate individual cells in droplets in a microfluidic ...
Perturb-seq (also known as CRISP-seq and CROP-seq) refers to a high-throughput method of performing single cell RNA sequencing (scRNA-seq) on pooled genetic perturbation screens. [ 1 ] [ 2 ] [ 3 ] Perturb-seq combines multiplexed CRISPR mediated gene inactivations with single cell RNA sequencing to assess comprehensive gene expression ...
snRNA-seq uses isolated nuclei instead of the entire cells to profile gene expression. That is to say, scRNA-seq measures both cytoplasmic and nuclear transcripts, while snRNA-seq mainly measures nuclear transcripts (though some transcripts might be attached to the rough endoplasmic reticulum and partially preserved in nuclear preps). [7]
There is so far no standardized technique to generate single-cell data: all methods must include cell isolation from the population, lysate formation, amplification through reverse transcription and quantification of expression levels. Common techniques for measuring expression are quantitative PCR or RNA-seq. [6]
Applications include analysis of unique cDNAs to avoid PCR biases in iCLIP, [1] variant calling in ctDNA, gene expression in single-cell RNA-seq (scRNA-seq) [2] [3] and haplotyping via linked reads [clarification needed].