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trumpet: transcriptome-guided quality assessment of m6A-seq data

Overview of attention for article published in BMC Bioinformatics, July 2018
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Title
trumpet: transcriptome-guided quality assessment of m6A-seq data
Published in
BMC Bioinformatics, July 2018
DOI 10.1186/s12859-018-2266-3
Pubmed ID
Authors

Teng Zhang, Shao-Wu Zhang, Lin Zhang, Jia Meng

Abstract

Methylated RNA immunoprecipitation sequencing (MeRIP-seq or m6A-seq) has been extensively used for profiling transcriptome-wide distribution of RNA N6-Methyl-Adnosine methylation. However, due to the intrinsic properties of RNA molecules and the intricate procedures of this technique, m6A-seq data often suffer from various flaws. A convenient and comprehensive tool is needed to assess the quality of m6A-seq data to ensure that they are suitable for subsequent analysis. From a technical perspective, m6A-seq can be considered as a combination of ChIP-seq and RNA-seq; hence, by effectively combing the data quality assessment metrics of the two techniques, we developed the trumpet R package for evaluation of m6A-seq data quality. The trumpet package takes the aligned BAM files from m6A-seq data together with the transcriptome information as the inputs to generate a quality assessment report in the HTML format. The trumpet R package makes a valuable tool for assessing the data quality of m6A-seq, and it is also applicable to other fragmented RNA immunoprecipitation sequencing techniques, including m1A-seq, CeU-Seq, Ψ-seq, etc.

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The data shown below were collected from the profiles of 3 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 38%
Researcher 5 16%
Student > Master 3 9%
Lecturer 2 6%
Student > Bachelor 2 6%
Other 3 9%
Unknown 5 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 31%
Biochemistry, Genetics and Molecular Biology 8 25%
Computer Science 6 19%
Nursing and Health Professions 1 3%
Earth and Planetary Sciences 1 3%
Other 1 3%
Unknown 5 16%
Attention Score in Context

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 August 2018.
All research outputs
#15,012,809
of 23,096,849 outputs
Outputs from BMC Bioinformatics
#5,082
of 7,328 outputs
Outputs of similar age
#197,540
of 327,048 outputs
Outputs of similar age from BMC Bioinformatics
#64
of 106 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,328 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% 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 327,048 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.