Literature DB >> 29943302

Emergence of Bias During the Synthesis and Amplification of cDNA for scRNA-seq.

Qiankun Luo1, Hui Zhang2.   

Abstract

The advent of single-cell omics technology has promoted our understanding of the genomic, epigenomic, and transcriptomic heterogeneity in individual cells. Compared to traditional sequencing studies using bulk cells, single-cell transcriptome technology is naturally more dynamic for in depth analysis of genomic variation resulting from cell division and is useful in unraveling the regulatory mechanisms of gene networks in many diseases. However, there are still some limitations of current single-cell RNA sequencing (scRNA-seq) protocols. Biases that arise during the RNA reverse transcription and cDNA pre-amplification steps are the most common problems and play pivotal roles in limiting the quantitative accuracy of scRNA-seq. In this review, we will describe how these biases emerge and impact scRNA-seq protocols. Moreover, we will introduce several current and convenient modified scRNA-seq methods that allow for bias to be decreased and estimated.

Keywords:  Amplification; Single-cell; Technical noise; Transcriptomic

Mesh:

Substances:

Year:  2018        PMID: 29943302     DOI: 10.1007/978-981-13-0502-3_12

Source DB:  PubMed          Journal:  Adv Exp Med Biol        ISSN: 0065-2598            Impact factor:   2.622


  3 in total

1.  Regulation of gene expression in the bovine blastocyst by colony-stimulating factor 2 is disrupted by CRISPR/Cas9-mediated deletion of CSF2RA.

Authors:  Yao Xiao; Kyungjun Uh; Veronica M Negrón-Pérez; Hannah Haines; Kiho Lee; Peter J Hansen
Journal:  Biol Reprod       Date:  2021-05-07       Impact factor: 4.285

2.  An improved method for specific-target preamplification PCR analysis of single blastocysts useful for embryo sexing and high-throughput gene expression analysis.

Authors:  Yao Xiao; Froylan Sosa; Lesley R de Armas; Li Pan; Peter J Hansen
Journal:  J Dairy Sci       Date:  2021-01-15       Impact factor: 4.034

3.  baredSC: Bayesian approach to retrieve expression distribution of single-cell data.

Authors:  Lucille Lopez-Delisle; Jean-Baptiste Delisle
Journal:  BMC Bioinformatics       Date:  2022-01-12       Impact factor: 3.169

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.