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iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data

Overview of attention for article published in BMC Genomics, August 2016
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

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9 tweeters

Citations

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2 Dimensions

Readers on

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17 Mendeley
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Title
iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2963-0
Pubmed ID
Authors

Chia-Chun Yang, Erik H. Andrews, Min-Hsuan Chen, Wan-Yu Wang, Jeremy J. W. Chen, Mark Gerstein, Chun-Chi Liu, Chao Cheng

Abstract

Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) or microarray hybridization (ChIP-chip) has been widely used to determine the genomic occupation of transcription factors (TFs). We have previously developed a probabilistic method, called TIP (Target Identification from Profiles), to identify TF target genes using ChIP-seq/ChIP-chip data. To achieve high specificity, TIP applies a conservative method to estimate significance of target genes, with the trade-off being a relatively low sensitivity of target gene identification compared to other methods. Additionally, TIP's output does not render binding-peak locations or intensity, information highly useful for visualization and general experimental biological use, while the variability of ChIP-seq/ChIP-chip file formats has made input into TIP more difficult than desired. To improve upon these facets, here we present are fined TIP with key extensions. First, it implements a Gaussian mixture model for p-value estimation, increasing target gene identification sensitivity and more accurately capturing the shape of TF binding profile distributions. Second, it enables the incorporation of TF binding-peak data by identifying their locations in significant target gene promoter regions and quantifies their strengths. Finally, for full ease of implementation we have incorporated it into a web server ( http://syslab3.nchu.edu.tw/iTAR/ ) that enables flexibility of input file format, can be used across multiple species and genome assembly versions, and is freely available for public use. The web server additionally performs GO enrichment analysis for the identified target genes to reveal the potential function of the corresponding TF. The iTAR web server provides a user-friendly interface and supports target gene identification in seven species, ranging from yeast to human. To facilitate investigating the quality of ChIP-seq/ChIP-chip data, the web server generates the chart of the characteristic binding profiles and the density plot of normalized regulatory scores. The iTAR web server is a useful tool in identifying TF target genes from ChIP-seq/ChIP-chip data and discovering biological insights.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 41%
Researcher 4 24%
Student > Postgraduate 2 12%
Professor 2 12%
Student > Master 1 6%
Other 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 47%
Biochemistry, Genetics and Molecular Biology 4 24%
Medicine and Dentistry 2 12%
Sports and Recreations 1 6%
Physics and Astronomy 1 6%
Other 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 15 September 2017.
All research outputs
#3,012,915
of 11,771,184 outputs
Outputs from BMC Genomics
#1,849
of 6,990 outputs
Outputs of similar age
#70,961
of 239,254 outputs
Outputs of similar age from BMC Genomics
#68
of 265 outputs
Altmetric has tracked 11,771,184 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 6,990 research outputs from this source. They receive a mean Attention Score of 4.2. This one has gotten more attention than average, scoring higher than 73% 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 239,254 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 70% of its contemporaries.
We're also able to compare this research output to 265 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.