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A survey of best practices for RNA-seq data analysis

Overview of attention for article published in Genome Biology (Online Edition), January 2016
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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 (#21 of 2,662)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
1 news outlet
blogs
2 blogs
twitter
405 tweeters
facebook
10 Facebook pages
wikipedia
3 Wikipedia pages
googleplus
3 Google+ users
reddit
1 Redditor

Citations

dimensions_citation
349 Dimensions

Readers on

mendeley
4676 Mendeley
citeulike
31 CiteULike
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Title
A survey of best practices for RNA-seq data analysis
Published in
Genome Biology (Online Edition), January 2016
DOI 10.1186/s13059-016-0881-8
Pubmed ID
Authors

Ana Conesa, Pedro Madrigal, Sonia Tarazona, David Gomez-Cabrero, Alejandra Cervera, Andrew McPherson, Michał W Szcześniak, Daniel J Gaffney, Laura L Elo, Xuegong Zhang, Ali Mortazavi, Conesa, Ana, Madrigal, Pedro, Tarazona, Sonia, Gomez-Cabrero, David, Cervera, Alejandra, McPherson, Andrew, Szcześniak, Michał Wojciech, Gaffney, Daniel J, Elo, Laura L, Zhang, Xuegong, Mortazavi, Ali, Michał Wojciech Szcześniak, Daniel J. Gaffney, Laura L. Elo, Tarazona Campos, Sonia, Gómez Cabrero, David, Wojciech Szczesniak, Michal, Gaffney, Daniel J., Elo, Laura L., Sonia Tarazona Campos, David Gómez Cabrero, Michal Wojciech Szczesniak

Abstract

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

Twitter Demographics

The data shown below were collected from the profiles of 405 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4,676 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 83 2%
United Kingdom 31 <1%
Germany 24 <1%
Brazil 19 <1%
Spain 15 <1%
Italy 11 <1%
Mexico 9 <1%
Sweden 9 <1%
Belgium 8 <1%
Other 103 2%
Unknown 4364 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1482 32%
Researcher 958 20%
Student > Master 744 16%
Student > Bachelor 418 9%
Student > Doctoral Student 265 6%
Other 809 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 2226 48%
Biochemistry, Genetics and Molecular Biology 1296 28%
Unspecified 286 6%
Computer Science 214 5%
Medicine and Dentistry 184 4%
Other 470 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 260. 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 31 July 2018.
All research outputs
#36,384
of 11,615,012 outputs
Outputs from Genome Biology (Online Edition)
#21
of 2,662 outputs
Outputs of similar age
#1,852
of 343,246 outputs
Outputs of similar age from Genome Biology (Online Edition)
#2
of 70 outputs
Altmetric has tracked 11,615,012 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,662 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.2. This one has done particularly well, scoring higher than 99% 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 343,246 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 99% of its contemporaries.
We're also able to compare this research output to 70 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 97% of its contemporaries.