DNA sequencing

Best bioinformatics software for single-cell RNA sequencing

RNA-sequencing is often performed on well-identified groups of cells thought to be homogeneous. However, quantification of molecular changes is made by estimating the mean value from millions of cells and averaging the signal of individual cells, thus ignoring cell-to-cell heterogeneity. Single-cell RNA-sequencing (scRNAseq) enables to unravel the heterogeneity of cell genotype, phenotype, and function within a given subpopulation.   ScRNA-seq now has a wide variety of applications, and numerous tools were developed to analyze this new kind of sequencing data. To help you perform your experiments in the best conditions, we asked OMICtools members to choose their favorite scRNA-seq analysis tools.   Main applications for scRNA-sequencing   Single-cell RNA sequencing finds its main applications in immunology, cancerology, and the study …

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Improving DNA amplification for single-cell genomics

Deep sequencing of genomes (Whole Genome Sequencing, WGS) is important not only to improve our knowledge in life sciences and evolutionary biology but also to make clinical progresses. The analysis of the genome and its variations at the cell level have major applications: analysis of mutation rates in somatic cells, including copy-number variations (CNVs)  and single-nucleotide variations (SNVs), evolution of cancer, recombination in germ cells, preimplantation genetic analysis for embryos or analysis of microbial populations (mini-metagenomics).   The single-cell DNA sequencing challenge Because of the low amount of DNA in a cell, single-cell whole genome sequencing requires whole genome amplification.  The 3 methods currently used are degenerate oligonucleotide-primed polymerase chain reaction (DOP-PCR), multiple displacement amplification (MDA), and multiple annealing and looping-based …

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