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Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii

Overview of attention for article published in BMC Genomics, February 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)

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1 Facebook page

Citations

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Title
Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii
Published in
BMC Genomics, February 2016
DOI 10.1186/s12864-016-2442-7
Pubmed ID
Authors

Seth Raithel, Loretta Johnson, Matthew Galliart, Sue Brown, Jennifer Shelton, Nicolae Herndon, Nora M. Bello

Abstract

Differential expression (DE) analysis of RNA-seq data still poses inferential challenges, such as handling of transcripts characterized by low expression levels. In this study, we use a plasmode-based approach to assess the relative performance of alternative inferential strategies on RNA-seq transcripts, with special emphasis on transcripts characterized by a small number of read counts, so-called low-count transcripts, as motivated by an ecological application in prairie grasses. Big bluestem (Andropogon gerardii) is a wide-ranging dominant prairie grass of ecological and agricultural importance to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. Hallii) grows exclusively on sand dunes. Relative to big bluestem, sand bluestem exhibits qualitative phenotypic divergence consistent with enhanced drought tolerance, plausibly associated with transcripts of low expression levels. Our dataset consists of RNA-seq read counts for 25,582 transcripts (60 % of which are classified as low-count) collected from leaf tissue of individual plants of big bluestem (n = 4) and sand bluestem (n = 4). Focused on low-count transcripts, we compare alternative ad-hoc data filtering techniques commonly used in RNA-seq pipelines and assess the inferential performance of recently developed statistical methods for DE analysis, namely DESeq2 and edgeR robust. These methods attempt to overcome the inherently noisy behavior of low-count transcripts by either shrinkage or differential weighting of observations, respectively. Both DE methods seemed to properly control family-wise type 1 error on low-count transcripts, whereas edgeR robust showed greater power and DESeq2 showed greater precision and accuracy. However, specification of the degree of freedom parameter under edgeR robust had a non-trivial impact on inference and should be handled carefully. When properly specified, both DE methods showed overall promising inferential performance on low-count transcripts, suggesting that ad-hoc data filtering steps at arbitrary expression thresholds may be unnecessary. A note of caution is in order regarding the approximate nature of DE tests under both methods. Practical recommendations for DE inference are provided when low-count RNA-seq transcripts are of interest, as is the case in the comparison of subspecies of bluestem grasses. Insights from this study may also be relevant to other applications focused on transcripts of low expression levels.

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Geographical breakdown

Country Count As %
Japan 1 2%
United States 1 2%
Czechia 1 2%
Slovenia 1 2%
Unknown 46 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 24%
Student > Ph. D. Student 10 20%
Student > Bachelor 7 14%
Student > Master 4 8%
Student > Doctoral Student 3 6%
Other 9 18%
Unknown 5 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 42%
Biochemistry, Genetics and Molecular Biology 11 22%
Computer Science 2 4%
Environmental Science 2 4%
Engineering 2 4%
Other 6 12%
Unknown 6 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 08 March 2016.
All research outputs
#2,230,162
of 16,638,522 outputs
Outputs from BMC Genomics
#970
of 9,107 outputs
Outputs of similar age
#45,695
of 268,352 outputs
Outputs of similar age from BMC Genomics
#1
of 1 outputs
Altmetric has tracked 16,638,522 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,107 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 89% of its peers.
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