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  2. Single-cell sequencing - Wikipedia

    en.wikipedia.org/wiki/Single-cell_sequencing

    Single-cell DNA genome sequencing involves isolating a single cell, amplifying the whole genome or region of interest, constructing sequencing libraries, and then applying next-generation DNA sequencing (for example Illumina, Ion Torrent). Single-cell DNA sequencing has been widely applied in mammalian systems to study normal physiology and ...

  3. Multiomics - Wikipedia

    en.wikipedia.org/wiki/Multiomics

    A branch of the field of multiomics is the analysis of multilevel single-cell data, called single-cell multiomics. [8] [9] This approach gives us an unprecedent resolution to look at multilevel transitions in health and disease at the single cell level. An advantage in relation to bulk analysis is to mitigate confounding factors derived from ...

  4. Single-cell analysis - Wikipedia

    en.wikipedia.org/wiki/Single-cell_analysis

    This single cell shows the process of the central dogma of molecular biology, which are all steps researchers are interested to quantify (DNA, RNA, and Protein).. In cell biology, single-cell analysis and subcellular analysis [1] refer to the study of genomics, transcriptomics, proteomics, metabolomics, and cellcell interactions at the level of an individual cell, as opposed to more ...

  5. Single-cell multi-omics integration - Wikipedia

    en.wikipedia.org/wiki/Single-cell_multi-omics...

    Single-cell omics technologies has extended beyond the transcriptome to profile diverse physical-chemical properties at single-cell resolution, including whole genomes/exomes, DNA methylation, chromatin accessibility, histone modifications, epitranscriptome (e.g., mRNAs, microRNAs, tRNAs, lncRNAs), proteome, phosphoproteome, metabolome, and more.

  6. Single-cell transcriptomics - Wikipedia

    en.wikipedia.org/wiki/Single-cell_transcriptomics

    Detecting differences in gene expression level between two populations is used both single-cell and bulk transcriptomic data. Specialised methods have been designed for single-cell data that considers single cell features such as technical dropouts and shape of the distribution e.g. Bimodal vs. unimodal. [23]

  7. RNA-Seq - Wikipedia

    en.wikipedia.org/wiki/RNA-Seq

    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 .

  8. Transcriptomics technologies - Wikipedia

    en.wikipedia.org/wiki/Transcriptomics_technologies

    A single-end sequence is usually quicker to produce, cheaper than paired-end sequencing and sufficient for quantification of gene expression levels. Paired-end sequencing produces more robust alignments/assemblies, which is beneficial for gene annotation and transcript isoform discovery. [ 10 ]

  9. Tcr-seq - Wikipedia

    en.wikipedia.org/wiki/Tcr-seq

    TCR sequencing can be performed in on pooled cell populations (“bulk sequencing”) or single cells (“single cell sequencing”). [4] Bulk sequencing is useful to explore entire TCR repertoires - all the TCRs within an individual or a sample - and to generate comparisons between repertoires of different individuals. [4] This method can ...