↓ Skip to main content

HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data

Overview of attention for article published in BMC Genomics, April 2015
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
43 Mendeley
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
HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data
Published in
BMC Genomics, April 2015
DOI 10.1186/1471-2164-16-s4-s2
Pubmed ID
Authors

Xiaodong Cui, Jia Meng, Manjeet K Rao, Yidong Chen, Yufei Huang

Abstract

Methylated RNA Immunoprecipatation combined with RNA sequencing (MeRIP-seq) is revolutionizing the de novo study of RNA epigenomics at a higher resolution. However, this new technology poses unique bioinformatics problems that call for novel and sophisticated statistical computational solutions, aiming at identifying and characterizing transcriptome-wide methyltranscriptome. We developed HEP, a Hidden Markov Model (HMM)-based Exome Peak-finding algorithm for predicting transcriptome methylation sites using MeRIP-seq data. In contrast to exomePeak, our previously developed MeRIP-seq peak calling algorithm, HEPeak models the correlation between continuous bins in an m6A peak region and it is a model-based approach, which admits rigorous statistical inference. HEPeak was evaluated on a simulated MeRIP-seq dataset and achieved higher sensitivity and specificity than exomePeak. HEPeak was also applied to real MeRIP-seq datasets from human HEK293T cell line and mouse midbrain cells and was shown to be able to recapitulate known m6A distribution in transcripts and identify novel m6A sites in long non-coding RNAs. In this paper, a novel HMM-based peak calling algorithm, HEPeak, was developed for peak calling for MeRIP-seq data. HEPeak is written in R and is publicly available.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 42 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 35%
Researcher 7 16%
Student > Master 5 12%
Student > Bachelor 3 7%
Student > Doctoral Student 3 7%
Other 6 14%
Unknown 4 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 37%
Agricultural and Biological Sciences 7 16%
Computer Science 5 12%
Medicine and Dentistry 3 7%
Immunology and Microbiology 2 5%
Other 6 14%
Unknown 4 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 January 2016.
All research outputs
#10,857,461
of 19,211,930 outputs
Outputs from BMC Genomics
#4,287
of 9,730 outputs
Outputs of similar age
#104,681
of 238,886 outputs
Outputs of similar age from BMC Genomics
#1
of 1 outputs
Altmetric has tracked 19,211,930 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,730 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 54% 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 238,886 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 55% 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