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Single-nucleotide polymorphisms (SNPs), which are a big part of genetic variation in the human genome, and copy number variation (CNV), pose problems in single cell sequencing, as well as the limited amount of DNA extracted from a single cell. Due to scant amounts of DNA, accurate analysis of DNA poses problems even after amplification since ...
Analysis of single-cell sequencing presents many challenges, such as determining the best way to normalize the data. [8] Due to a new level of complications that arise from sequencing of both proteins and transcripts at a single-cell level, the developers of CITE-Seq and their collaborators are maintaining several tools to help with data analysis.
TCR-Seq (T-cell Receptor Sequencing) is a method used to identify and track specific T cells and their clones. [1] TCR-Seq utilizes the unique nature of a T-cell receptor (TCR) as a ready-made molecular barcode. [1] This technology can apply to both single cell sequencing technologies and high throughput screens [1]
SCell [124] integrated analysis of single-cell RNA-seq data. Seurat [125] [126] R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Sincell [127] an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq. SINCERA [128] A Pipeline for Single-Cell RNA-Seq Profiling 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 cell–cell interactions at the level of an individual cell, as opposed to more ...
Single-cell DNA template strand sequencing, or Strand-seq, is a technique for the selective sequencing of a daughter cell's parental template strands. [1] This technique offers a wide variety of applications, including the identification of sister chromatid exchanges in the parental cell prior to segregation, the assessment of non-random segregation of sister chromatids, the identification of ...
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]
Transcription can also be studied at the level of individual cells by single-cell transcriptomics. Single-cell RNA sequencing (scRNA-seq) is a recently developed technique that allows the analysis of the transcriptome of single cells, including bacteria. [25] With single-cell transcriptomics, subpopulations of cell types that constitute the ...