↓ Skip to main content

A survey of best practices for RNA-seq data analysis

Overview of attention for article published in Genome Biology (Online Edition), January 2016
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#29 of 3,773)
  • High Attention Score compared to outputs of the same age (99th percentile)

Mentioned by

news
1 news outlet
blogs
2 blogs
twitter
430 tweeters
patent
2 patents
facebook
11 Facebook pages
wikipedia
6 Wikipedia pages
googleplus
3 Google+ users
reddit
1 Redditor
video
1 video uploader

Citations

dimensions_citation
1255 Dimensions

Readers on

mendeley
9080 Mendeley
citeulike
31 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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 Wojciech Szcześniak, Daniel J. Gaffney, Laura L. Elo, Xuegong Zhang, Ali Mortazavi

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 430 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 9,080 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 76 <1%
United Kingdom 26 <1%
Germany 21 <1%
Brazil 18 <1%
Spain 14 <1%
Sweden 9 <1%
Mexico 9 <1%
Italy 8 <1%
France 8 <1%
Other 96 1%
Unknown 8795 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2521 28%
Researcher 1639 18%
Student > Master 1379 15%
Student > Bachelor 878 10%
Student > Doctoral Student 536 6%
Other 1116 12%
Unknown 1011 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 3152 35%
Biochemistry, Genetics and Molecular Biology 2855 31%
Medicine and Dentistry 358 4%
Computer Science 340 4%
Immunology and Microbiology 247 3%
Other 856 9%
Unknown 1272 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 269. 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 August 2021.
All research outputs
#78,821
of 18,866,097 outputs
Outputs from Genome Biology (Online Edition)
#29
of 3,773 outputs
Outputs of similar age
#1,813
of 357,058 outputs
Outputs of similar age from Genome Biology (Online Edition)
#1
of 1 outputs
Altmetric has tracked 18,866,097 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 3,773 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. 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 357,058 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them