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QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model

Overview of attention for article published in BMC Bioinformatics, August 2017
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
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Mentioned by

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4 tweeters

Citations

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15 Dimensions

Readers on

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14 Mendeley
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Title
QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model
Published in
BMC Bioinformatics, August 2017
DOI 10.1186/s12859-017-1808-4
Pubmed ID
Authors

Lian Liu, Shao-Wu Zhang, Yufei Huang, Jia Meng

Abstract

As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m(1)A-Seq, Par-CLIP, RIP-Seq, etc.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Student > Bachelor 2 14%
Student > Doctoral Student 1 7%
Other 1 7%
Researcher 1 7%
Other 1 7%
Unknown 4 29%
Readers by discipline Count As %
Agricultural and Biological Sciences 3 21%
Biochemistry, Genetics and Molecular Biology 3 21%
Computer Science 2 14%
Unspecified 1 7%
Engineering 1 7%
Other 0 0%
Unknown 4 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 04 September 2017.
All research outputs
#6,548,495
of 11,700,978 outputs
Outputs from BMC Bioinformatics
#2,487
of 4,294 outputs
Outputs of similar age
#120,525
of 263,745 outputs
Outputs of similar age from BMC Bioinformatics
#54
of 100 outputs
Altmetric has tracked 11,700,978 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,294 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 263,745 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.