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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.
Database normalization is the process of structuring a relational database accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model .
It is primarily employed for the analysis of high-throughput RNA sequencing (RNA-seq) data to identify differentially expressed genes between different experimental conditions. DESeq2 employs statistical methods to normalize and analyze RNA-seq data, making it a valuable tool for researchers studying gene expression patterns and regulation.
RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. Recent advances in RNA-Seq include single cell sequencing, bulk RNA sequencing, [6] 3' mRNA-sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing. [7]
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.
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 ...
In the field of relational database design, normalization is a systematic way of ensuring that a database structure is suitable for general-purpose querying and free of certain undesirable characteristics—insertion, update, and deletion anomalies that could lead to loss of data integrity.
Normally, in a traditional RNA-seq, microarray, or SAGE experiment RNA is extracted from a biological sample such as cultured cells, and the RNA is analyzed using the chosen method. The data obtained from such an experiment corresponds to abundance of RNA under the given experimental conditions at the time of harvest.