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Increasing quality, throughput and speed of sample preparation for strand-specific messenger RNA sequencing

Overview of attention for article published in BMC Genomics, July 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

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1 blog
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Title
Increasing quality, throughput and speed of sample preparation for strand-specific messenger RNA sequencing
Published in
BMC Genomics, July 2017
DOI 10.1186/s12864-017-3900-6
Pubmed ID
Authors

Simon Haile, Richard D. Corbett, Tina MacLeod, Steve Bilobram, Duane Smailus, Philip Tsao, Heather Kirk, Helen McDonald, Pawan Pandoh, Miruna Bala, Martin Hirst, Diane Miller, Richard A. Moore, Andrew J. Mungall, Jacquie Schein, Robin J. Coope, Yussanne Ma, Yongjun Zhao, Rob A. Holt, Steven J. Jones, Marco A. Marra

Abstract

RNA-Sequencing (RNA-seq) is now commonly used to reveal quantitative spatiotemporal snapshots of the transcriptome, the structures of transcripts (splice variants and fusions) and landscapes of expressed mutations. However, standard approaches for library construction typically require relatively high amounts of input RNA, are labor intensive, and are time consuming. METHODS: Here, we report the outcome of a systematic effort to optimize and streamline steps in strand-specific RNA-seq library construction. RESULTS: This work has resulted in the identification of an optimized messenger RNA isolation protocol, a potent reverse transcriptase for cDNA synthesis, and an efficient chemistry and a simplified formulation of library construction reagents. We also present an optimization of bead-based purification and size selection designed to maximize the recovery of cDNA fragments. These developments have allowed us to assemble a rapid high throughput pipeline that produces high quality data from amounts of total RNA as low as 25 ng. While the focus of this study is on RNA-seq sample preparation, some of these developments are also relevant to other next-generation sequencing library types.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 25%
Student > Master 7 16%
Student > Doctoral Student 6 14%
Student > Bachelor 5 11%
Student > Ph. D. Student 3 7%
Other 6 14%
Unknown 6 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 25%
Agricultural and Biological Sciences 9 20%
Computer Science 7 16%
Engineering 4 9%
Pharmacology, Toxicology and Pharmaceutical Science 1 2%
Other 4 9%
Unknown 8 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 22 July 2017.
All research outputs
#2,208,919
of 23,655,983 outputs
Outputs from BMC Genomics
#628
of 10,778 outputs
Outputs of similar age
#43,146
of 314,245 outputs
Outputs of similar age from BMC Genomics
#19
of 228 outputs
Altmetric has tracked 23,655,983 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,778 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 94% 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 314,245 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 228 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 92% of its contemporaries.