Prediction of DNase I Hypersensitive Sites by Using Pseudo Nucleotide Compositions

DNase I hypersensitive sites (DHS) associated with a wide variety of regulatory DNA elements. Knowledge about the locations of DHS is helpful for deciphering the function of noncoding genomic regions. With the acceleration of genome sequences in the postgenomic age, it is highly desired to develop cost-effective computational methods to identify DHS. In the present work, a support vector machine based model was proposed to identify DHS by using the pseudo dinucleotide composition. In the jackknife test, the proposed model obtained an accuracy of 83%, which is competitive with that of the existing method. This result suggests that the proposed model may become a useful tool for DHS identifications.

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Identification of Regulatory DNA Elements Using Genome-wide Mapping of DNase I Hypersensitive Sites during Tomato Fruit Development

Molecular Plant ◽

10.1016/j.molp.2016.05.013 ◽

2016 ◽

Vol 9 (8) ◽

pp. 1168-1182 ◽

Cited By ~ 9

Author(s):

Zhengkun Qiu ◽

Ren Li ◽

Shuaibin Zhang ◽

Ketao Wang ◽

Meng Xu ◽

...

Keyword(s):

Fruit Development ◽

Tomato Fruit ◽

Dnase I ◽

Dnase I Hypersensitive Sites ◽

Genome Wide ◽

Hypersensitive Sites ◽

Dna Elements ◽

Regulatory Dna

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DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

10.1101/224527 ◽

2017 ◽

Cited By ~ 1

Author(s):

Balachandran Manavalan ◽

Tae Hwan Shin ◽

Gwang Lee

Keyword(s):

Support Vector Machine ◽

Random Forest ◽

Dna Sequences ◽

Feature Selection Method ◽

Regulatory Elements ◽

Dnase I ◽

Support Vector ◽

Large Set ◽

Dnase I Hypersensitive Sites ◽

Hypersensitive Sites

AbstractDNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at:http://www.thegleelab.org/DHSpred.html.

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Index and biological spectrum of accessible DNA elements in the human genome

10.1101/822510 ◽

2019 ◽

Cited By ~ 7

Author(s):

Wouter Meuleman ◽

Alexander Muratov ◽

Eric Rynes ◽

Jessica Halow ◽

Kristen Lee ◽

...

Keyword(s):

Coordinate System ◽

Human Genome ◽

High Precision ◽

Dnase I ◽

Global Perspective ◽

Phenotypic Trait ◽

Protein Coding ◽

Dnase I Hypersensitive Sites ◽

Hypersensitive Sites ◽

Regulatory Dna

AbstractDNase I hypersensitive sites (DHSs) are generic markers of regulatory DNA and harbor disease- and phenotypic trait-associated genetic variation. We established high-precision maps of DNase I hypersensitive sites from 733 human biosamples encompassing 439 cell and tissue types and states, and integrated these to precisely delineate and numerically index ~3.6 million DHSs encoded within the human genome, providing a common coordinate system for regulatory DNA. Here we show that the expansive scale of cell and tissue states sampled exposes an unprecedented degree of stereotyped actuation of large sets of elements, signaling the operation of distinct genome-scale regulatory programs. We show further that the complex actuation patterns of individual elements can be captured comprehensively by a simple regulatory vocabulary reflecting their dominant cellular manifestation. This vocabulary, in turn, enables comprehensive and quantitative regulatory annotation of both protein-coding genes and the vast array of well-defined but poorly-characterized non-coding RNA genes. Finally, we show that the combination of high-precision DHSs and regulatory vocabularies markedly concentrate disease- and trait-associated non-coding genetic signals both along the genome and across cellular compartments. Taken together, our results provide a common and extensible coordinate system and vocabulary for human regulatory DNA, and a new global perspective on the architecture of human gene regulation.

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pDHS-SVM: A prediction method for plant DNase I hypersensitive sites based on support vector machine

Journal of Theoretical Biology ◽

10.1016/j.jtbi.2017.05.030 ◽

2017 ◽

Vol 426 ◽

pp. 126-133 ◽

Cited By ~ 11

Author(s):

Shanxin Zhang ◽

Zhiping Zhou ◽

Xinmeng Chen ◽

Yong Hu ◽

Lindong Yang

Keyword(s):

Support Vector Machine ◽

Prediction Method ◽

Dnase I ◽

Support Vector ◽

Dnase I Hypersensitive Sites ◽

Hypersensitive Sites

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Atlas and developmental dynamics of mouse DNase I hypersensitive sites

10.1101/2020.06.26.172718 ◽

2020 ◽

Author(s):

Charles E. Breeze ◽

John Lazar ◽

Tim Mercer ◽

Jessica Halow ◽

Ida Washington ◽

...

Keyword(s):

Regulatory Elements ◽

Dnase I ◽

Mammalian Development ◽

Mouse Development ◽

Dnase I Hypersensitive Sites ◽

Hypersensitive Sites ◽

Nucleotide Resolution ◽

Developmental Dynamics ◽

Regulatory Dna ◽

Early Mammalian Development

AbstractEarly mammalian development is orchestrated by genome-encoded regulatory elements populated by a changing complement of regulatory factors, creating a dynamic chromatin landscape. To define the spatiotemporal organization of regulatory DNA landscapes during mouse development and maturation, we generated nucleotide-resolution DNA accessibility maps from 15 tissues sampled at 9 intervals spanning post-conception day 9.5 through early adult, and integrated these with 41 adult-stage DNase-seq profiles to create a global atlas of mouse regulatory DNA. Collectively, we delineated >1.8 million DNase I hypersensitive sites (DHSs), with the vast majority displaying temporal and tissue-selective patterning. Here we show that tissue regulatory DNA compartments show sharp embryonic-to-fetal transitions characterized by wholesale turnover of DHSs and progressive domination by a diminishing number of transcription factors. We show further that aligning mouse and human fetal development on a regulatory axis exposes disease-associated variation enriched in early intervals lacking human samples. Our results provide an expansive new resource for decoding mammalian developmental regulatory programs.

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