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The main advantage of RNA-Seq data in this kind of analysis over the microarray platforms is the capability to cover the entire transcriptome, therefore allowing the possibility to unravel more complete representations of the gene regulatory networks.
queryable-rna-seq-database Formally known as the Queryable RNA-Seq Database, this system is designed to simplify the process of RNA-seq analysis by providing the ability upload the result data from RNA-Seq analysis into a database, store it, and query it in many different ways.
The development of high-throughput RNA sequencing (RNA-seq) and microarrays has made gene expression analysis a routine. RNA analysis was previously limited to tracing individual transcripts by Northern blots or quantitative PCR. Higher throughput and speed allow researchers to frequently characterize the expression profiles of populations of ...
Data analysis usually requires a combination of bioinformatics software tools (see also List of RNA-Seq bioinformatics tools) that vary according to the experimental design and goals. The process can be broken down into four stages: quality control, alignment, quantification, and differential expression. [ 105 ]
The final step regards analysis of data and storage: after obtaining the sequencing reads, UMI and index sequences are automatically removed from the reads and their quality is analyzed by PHRED (software able to evaluate the quality of the sequencing process); reads can then be mapped or aligned to a reference genome in order to extract ...
It is the first step in sequence analysis to limit wrong conclusions due to poor quality data. The tools used at this stage depend on the sequencing platform. For instance, FastQC checks the quality of short reads (including RNA sequences), Nanoplot or PycoQC are used for long read sequences (e.g. Nanopore sequence reads), and MultiQC ...
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.
Single-cell RNA sequencing (scRNA-seq) provides the expression profiles of individual cells and is considered the gold standard for defining cell states and phenotypes as of 2020. [44] Although it is impossible to obtain complete information on every RNA expressed by each cell, due to the small amount of material available, gene expression ...
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