Title |
Single-cell RNA sequencing technologies and bioinformatics pipelines
|
---|---|
Published in |
Experimental & Molecular Medicine, August 2018
|
DOI | 10.1038/s12276-018-0071-8 |
Pubmed ID | |
Authors |
Byungjin Hwang, Ji Hyun Lee, Duhee Bang |
Abstract |
Rapid progress in the development of next-generation sequencing (NGS) technologies in recent years has provided many valuable insights into complex biological systems, ranging from cancer genomics to diverse microbial communities. NGS-based technologies for genomics, transcriptomics, and epigenomics are now increasingly focused on the characterization of individual cells. These single-cell analyses will allow researchers to uncover new and potentially unexpected biological discoveries relative to traditional profiling methods that assess bulk populations. Single-cell RNA sequencing (scRNA-seq), for example, can reveal complex and rare cell populations, uncover regulatory relationships between genes, and track the trajectories of distinct cell lineages in development. In this review, we will focus on technical challenges in single-cell isolation and library preparation and on computational analysis pipelines available for analyzing scRNA-seq data. Further technical improvements at the level of molecular and cell biology and in available bioinformatics tools will greatly facilitate both the basic science and medical applications of these sequencing technologies. |
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Geographical breakdown
Country | Count | As % |
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Spain | 5 | 8% |
United Kingdom | 4 | 6% |
Germany | 3 | 5% |
China | 3 | 5% |
Italy | 2 | 3% |
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Switzerland | 1 | 2% |
Japan | 1 | 2% |
Other | 7 | 11% |
Unknown | 23 | 35% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 33 | 50% |
Members of the public | 32 | 48% |
Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 3840 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 738 | 19% |
Researcher | 515 | 13% |
Student > Master | 483 | 13% |
Student > Bachelor | 461 | 12% |
Student > Doctoral Student | 193 | 5% |
Other | 375 | 10% |
Unknown | 1075 | 28% |
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Medicine and Dentistry | 231 | 6% |
Neuroscience | 169 | 4% |
Immunology and Microbiology | 165 | 4% |
Other | 492 | 13% |
Unknown | 1173 | 31% |