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  2. List of RNA-Seq bioinformatics tools - Wikipedia

    en.wikipedia.org/wiki/List_of_RNA-Seq...

    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.

  3. List of single cell omics methods - Wikipedia

    en.wikipedia.org/wiki/List_of_single_cell_omics...

    Method Reference Sequencing Mode Early Estimate Late Estimate Tang method [2] Short Reads 2008 2009 CyTOF [3] Short Reads 2011 2012 STRT-seq / C1 [4] Short Reads 2011 2012 SMART-seq [5] Short Reads 2012 2013 CEL-seq [6] Short Reads 2012 2013 Quartz-Seq [7] Short Reads 2012 2013 PMA / SMA [8] Short Reads 2012 2013 scBS-seq [9] Short Reads 2013 ...

  4. Single-cell sequencing - Wikipedia

    en.wikipedia.org/wiki/Single-cell_sequencing

    In 2017, two approaches were introduced to simultaneously measure single-cell mRNA and protein expression through oligonucleotide-labeled antibodies known as REAP-seq, [59] and CITE-seq. [60] Collecting cellular contents following electrophysiological recording using patch-clamp has also allowed development of the Patch-Seq method, which is ...

  5. Single-cell transcriptomics - Wikipedia

    en.wikipedia.org/wiki/Single-cell_transcriptomics

    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 ...

  6. DESeq2 - Wikipedia

    en.wikipedia.org/wiki/DESeq2

    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.

  7. RNA-Seq - Wikipedia

    en.wikipedia.org/wiki/RNA-Seq

    scRNA-Seq is becoming widely used across biological disciplines including Development, Neurology, [44] Oncology, [45] [46] [47] Autoimmune disease, [48] and Infectious disease. [ 49 ] scRNA-Seq has provided considerable insight into the development of embryos and organisms, including the worm Caenorhabditis elegans , [ 50 ] and the regenerative ...

  8. MA plot - Wikipedia

    en.wikipedia.org/wiki/MA_plot

    By minimizing these systematic variations, true biological differences can be found. To determine whether normalization is needed, one can plot Cy5 (R) intensities against Cy3 (G) intensities and see whether the slope of the line is around 1. An improved method, which is basically a scaled, 45 degree rotation of the R vs. G plot is an MA-plot. [4]

  9. RNA spike-in - Wikipedia

    en.wikipedia.org/wiki/RNA_spike-in

    An RNA spike-in is an RNA transcript of known sequence and quantity used to calibrate measurements in RNA hybridization assays, such as DNA microarray experiments, RT-qPCR, and RNA-Seq. [1] A spike-in is designed to bind to a DNA molecule with a matching sequence, known as a control probe. [2] [3] [4] This process of specific binding is called ...