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Web-based bioinformatics workflows for end-to-end RNA-seq data computation and analysis in agricultural animal species

Overview of attention for article published in BMC Genomics, September 2016
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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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

blogs
1 blog
twitter
8 X users

Citations

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64 Dimensions

Readers on

mendeley
81 Mendeley
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2 CiteULike
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Title
Web-based bioinformatics workflows for end-to-end RNA-seq data computation and analysis in agricultural animal species
Published in
BMC Genomics, September 2016
DOI 10.1186/s12864-016-3118-z
Pubmed ID
Authors

Weizhong Li, R. Alexander Richter, Yunsup Jung, Qiyun Zhu, Robert W. Li

Abstract

Remarkable advances in Next Generation Sequencing (NGS) technologies, bioinformatics algorithms and computational technologies have significantly accelerated genomic research. However, complicated NGS data analysis still remains as a major bottleneck. RNA-seq, as one of the major area in the NGS field, also confronts great challenges in data analysis. To address the challenges in RNA-seq data analysis, we developed a web portal that offers three integrated workflows that can perform end-to-end compute and analysis, including sequence quality control, read-mapping, transcriptome assembly, reconstruction and quantification, and differential analysis. The first workflow utilizes Tuxedo (Tophat, Cufflink, Cuffmerge and Cuffdiff suite of tools). The second workflow deploys Trinity for de novo assembly and uses RSEM for transcript quantification and EdgeR for differential analysis. The third combines STAR, RSEM, and EdgeR for data analysis. All these workflows support multiple samples and multiple groups of samples and perform differential analysis between groups in a single workflow job submission. The calculated results are available for download and post-analysis. The supported animal species include chicken, cow, duck, goat, pig, horse, rabbit, sheep, turkey, as well as several other model organisms including yeast, C. elegans, Drosophila, and human, with genomic sequences and annotations obtained from ENSEMBL. The RNA-seq portal is freely available from http://weizhongli-lab.org/RNA-seq . The web portal offers not only bioinformatics software, workflows, computation and reference data, but also an integrated environment for complex RNA-seq data analysis for agricultural animal species. In this project, our aim is not to develop new RNA-seq tools, but to build web workflows for using popular existing RNA-seq methods and make these tools more accessible to the communities.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Switzerland 1 1%
Brazil 1 1%
New Caledonia 1 1%
Benin 1 1%
United Kingdom 1 1%
Unknown 74 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 25%
Student > Ph. D. Student 14 17%
Student > Master 13 16%
Student > Doctoral Student 6 7%
Student > Bachelor 6 7%
Other 11 14%
Unknown 11 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 36%
Biochemistry, Genetics and Molecular Biology 22 27%
Computer Science 7 9%
Medicine and Dentistry 3 4%
Engineering 2 2%
Other 4 5%
Unknown 14 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 24 January 2017.
All research outputs
#2,940,697
of 22,889,074 outputs
Outputs from BMC Genomics
#1,084
of 10,669 outputs
Outputs of similar age
#52,276
of 322,819 outputs
Outputs of similar age from BMC Genomics
#24
of 282 outputs
Altmetric has tracked 22,889,074 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,669 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 89% 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 322,819 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 83% of its contemporaries.
We're also able to compare this research output to 282 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 91% of its contemporaries.