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Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features

Overview of attention for article published in BMC Bioinformatics, January 2016
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  • Above-average Attention Score compared to outputs of the same age (57th percentile)

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6 tweeters

Citations

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27 Dimensions

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54 Mendeley
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Title
Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-015-0846-z
Pubmed ID
Authors

Sunil Kumar, Philipp Bucher

Abstract

Understanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often not very effective in predicting in vivo site occupancy and in any case could not explain cell-type specific binding events. Recent reports show that chromatin accessibility, nucleosome occupancy and specific histone post-translational modifications greatly influence TF site occupancy in vivo. In this work, we use machine-learning methods to build predictive models and assess the relative importance of different sequence-intrinsic and chromatin features in the TF-to-target-site recruitment process. Our study primarily relies on recent data published by the ENCODE consortium. Five dissimilar TFs assayed in multiple cell-types were selected as examples: CTCF, JunD, REST, GABP and USF2. We used two types of candidate target sites: (a) predicted sites obtained by scanning the whole genome with a position weight matrix, and (b) cell-type specific peak lists provided by ENCODE. Quantitative in vivo occupancy levels in different cell-types were based on ChIP-seq data for the corresponding TFs. In parallel, we computed a number of associated sequence-intrinsic and experimental features (histone modification, DNase I hypersensitivity, etc.) for each site. Machine learning algorithms were then used in a binary classification and regression framework to predict site occupancy and binding strength, for the purpose of assessing the relative importance of different contextual features. We observed striking differences in the feature importance rankings between the five factors tested. PWM-scores were amongst the most important features only for CTCF and REST but of little value for JunD and USF2. Chromatin accessibility and active histone marks are potent predictors for all factors except REST. Structural DNA parameters, repressive and gene body associated histone marks are generally of little or no predictive value. We define a general and extensible computational framework for analyzing the importance of various DNA-intrinsic and chromatin-associated features in determining cell-type specific TF binding to target sites. The application of our methodology to ENCODE data has led to new insights on transcription regulatory processes and may serve as example for future studies encompassing even larger datasets.

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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
France 1 2%
Unknown 52 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 24%
Student > Master 9 17%
Student > Bachelor 6 11%
Student > Doctoral Student 5 9%
Researcher 5 9%
Other 7 13%
Unknown 9 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 37%
Biochemistry, Genetics and Molecular Biology 12 22%
Computer Science 6 11%
Engineering 2 4%
Nursing and Health Professions 1 2%
Other 4 7%
Unknown 9 17%

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 29 January 2016.
All research outputs
#9,207,515
of 16,639,069 outputs
Outputs from BMC Bioinformatics
#3,278
of 5,985 outputs
Outputs of similar age
#140,030
of 343,730 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 7 outputs
Altmetric has tracked 16,639,069 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 5,985 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 42nd percentile – i.e., 42% 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 343,730 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 57% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.