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Single-cell RNA sequencing technologies and bioinformatics pipelines

Overview of attention for article published in Experimental & Molecular Medicine, August 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#17 of 1,544)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

news
5 news outlets
blogs
3 blogs
twitter
66 X users
patent
14 patents
facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

dimensions_citation
1227 Dimensions

Readers on

mendeley
3840 Mendeley
citeulike
1 CiteULike
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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 66 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3,840 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3840 100%

Demographic breakdown

Readers by professional status Count As %
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%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1152 30%
Agricultural and Biological Sciences 458 12%
Medicine and Dentistry 231 6%
Neuroscience 169 4%
Immunology and Microbiology 165 4%
Other 492 13%
Unknown 1173 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 108. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 February 2024.
All research outputs
#396,097
of 25,736,439 outputs
Outputs from Experimental & Molecular Medicine
#17
of 1,544 outputs
Outputs of similar age
#8,373
of 341,759 outputs
Outputs of similar age from Experimental & Molecular Medicine
#2
of 43 outputs
Altmetric has tracked 25,736,439 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,544 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 341,759 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.