Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

AbstractComparative genomic approaches have been used to identify sites where mutations are under purifying selection and of functional consequence by searching for sequences that are conserved across distantly related species. However, the performance of these approaches has not been rigorously evaluated under population genetic models. Further, short-lived functional elements may not leave a footprint of sequence conservation across many species. Here, we use simulations to study how one measure of conservation, the GERP score, relates to the strength of selection (Nes). We show that the GERP score is related to the strength of purifying selection. However, changes in selection coefficients or functional elements over time (i.e. functional turnover) can strongly affect the GERP distribution, leading to unexpected relationships between GERP and Nes. Further, we show that for functional elements that have a high turnover rate, the optimal tree size is not necessarily the largest possible tree, and more turnover reduces the optimal tree size. Finally, we use the distribution of GERP scores across the human genome to compare models with and without turnover of sites where mutations under purifying selection. We show that mutations in 4.51% of the noncoding human genome are under purifying selection and that most of this sequence has likely experienced changes in selection coefficients throughout mammalian evolution.

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THE RELATIONSHIP BETWEEN FINE SCALE DNA STRUCTURE, GC CONTENT, AND FUNCTIONAL ELEMENTS IN 1% OF THE HUMAN GENOME

Genome Informatics 2008 ◽

10.1142/9781848163003_0017 ◽

2008 ◽

Cited By ~ 2

Author(s):

STEPHEN C. J. PARKER ◽

ELLIOTT H. MARGULIES ◽

THOMAS D. TULLIUS

Keyword(s):

Human Genome ◽

Gc Content ◽

Dna Structure ◽

Fine Scale ◽

Functional Elements ◽

The Relationship

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ChIP-seq for the Identification of Functional Elements in the Human Genome

Methods in Molecular Biology - Promoter Associated RNA ◽

10.1007/978-1-4939-6716-2_1 ◽

2017 ◽

pp. 3-18 ◽

Cited By ~ 6

Author(s):

Georgi K. Marinov

Keyword(s):

Human Genome ◽

Functional Elements

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Identification of Novel Functional Elements in the Human Genome

Cold Spring Harbor Symposia on Quantitative Biology ◽

10.1101/sqb.2003.68.317 ◽

2003 ◽

Vol 68 (0) ◽

pp. 317-322

Author(s):

Z. LIAN ◽

G. EUSKIRCHEN ◽

J. RINN ◽

R. MARTONE ◽

P. BERTONE ◽

...

Keyword(s):

Human Genome ◽

Functional Elements

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Understanding sequence conservation with deep learning

10.1101/103929 ◽

2017 ◽

Cited By ~ 1

Author(s):

Yi Li ◽

Daniel Quang ◽

Xiaohui Xie

Keyword(s):

Deep Learning ◽

Human Genome ◽

Dna Sequences ◽

Mammalian Species ◽

Neural Net ◽

Functional Elements ◽

Sequence Element ◽

Functional Roles ◽

Convolution Kernels ◽

Transcriptional Start Sites

AbstractMotivationComparing the human genome to the genomes of closely related mammalian species has been a powerful tool for discovering functional elements in the human genome. Millions of conserved elements have been discovered. However, understanding the functional roles of these elements still remain a challenge, especially in noncoding regions. In particular, it is still unclear why these elements are evolutionarily conserved and what kind of functional elements are encoded within these sequences.ResultsWe present a deep learning framework, called DeepCons, to uncover potential functional elements within conserved sequences. DeepCons is a convolutional neural net (CNN) that receives a short segment of DNA sequence as input and outputs the probability of the sequence of being evolutionary conserved. DeepCons utilizes hundreds of convolution kernels to detect features within DNA sequences, and automatically learns these kernels after training the CNN model using 887,577 conserved elements and a similar number of nonconserved elements in the human genome. On a balanced test dataset, DeepCons can achieve an accuracy of 75% in determining whether a sequence element is conserved or not, and the area under the ROC curve of 0.83, based on information from the human genome alone. We further investigate the properties of the learned kernels. Some kernels are directly related to well-known regulatory motifs corresponding to transcription factors. Many kernels show positional biases relative to transcriptional start sites or transcription end sites. But most of discovered kernels do not correspond to any known functional element, suggesting that they might represent unknown categories of functional elements. We also utilize DeepCons to annotate how changes at each individual nucleotide might impact the conservation properties of the surrounding sequences.AvailabilityThe source code of DeepCons and all the learned convolution kernels in motif format is publicly available online athttps://github.com/uci-cbcl/[email protected]

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