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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.
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.
Free open source GNU GPLv2 or later EMBOSS: Suite of packages for sequencing, searching, etc. written in C: Linux, macOS, Unix, Windows [4] GPL and LGPL: Collaborative project Galaxy: Scientific workflow and data integration system Unix-like: Academic Free: Collaborative project GenePattern
List of proprietary bioinformatics software; List of open-source bioinformatics software; Alternatively, here is a categorization according to the respective bioinformatics subfield specialized on: Sequence analysis software. List of sequence alignment software; List of alignment visualization software; Alignment-free sequence analysis
Normalisation of RNA-seq data accounts for cell to cell variation in the efficiencies of the cDNA library formation and sequencing. One method relies on the use of extrinsic RNA spike-ins (RNA sequences of known sequence and quantity) that are added in equal quantities to each cell lysate and used to normalise read count by the number of reads ...
TopHat is an open-source bioinformatics tool for the throughput alignment of shotgun cDNA sequencing reads generated by transcriptomics technologies (e.g. RNA-Seq) using Bowtie first and then mapping to a reference genome to discover RNA splice sites de novo. [1] TopHat aligns RNA-Seq reads to mammalian-sized genomes. [2]
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 ...
Single-cell RNA sequencing (scRNA-Seq) provides the expression profiles of individual cells. Although it is not possible to obtain complete information on every RNA expressed by each cell, due to the small amount of material available, patterns of gene expression can be identified through gene clustering analyses. This can uncover the existence ...