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Research in Computational Molecular Biology

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Table of Contents

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    Book Overview
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    Chapter 1 Boosting Alignment Accuracy by Adaptive Local Realignment
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    Chapter 2 A Concurrent Subtractive Assembly Approach for Identification of Disease Associated Sub-metagenomes
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    Chapter 3 A Flow Procedure for the Linearization of Genome Sequence Graphs
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    Chapter 4 Dynamic Alignment-Free and Reference-Free Read Compression
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    Chapter 5 A Fast Approximate Algorithm for Mapping Long Reads to Large Reference Databases
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    Chapter 6 Determining the Consistency of Resolved Triplets and Fan Triplets
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    Chapter 7 Progressive Calibration and Averaging for Tandem Mass Spectrometry Statistical Confidence Estimation: Why Settle for a Single Decoy?
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    Chapter 8 Resolving Multicopy Duplications de novo Using Polyploid Phasing
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    Chapter 9 A Bayesian Active Learning Experimental Design for Inferring Signaling Networks
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    Chapter 10 $$BBK^*$$ (Branch and Bound over $$K^*$$ ): A Provable and Efficient Ensemble-Based Algorithm to Optimize Stability and Binding Affinity over Large Sequence Spaces
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    Chapter 11 Superbubbles, Ultrabubbles and Cacti
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    Chapter 12 EPR-Dictionaries: A Practical and Fast Data Structure for Constant Time Searches in Unidirectional and Bidirectional FM Indices
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    Chapter 13 A Bayesian Framework for Estimating Cell Type Composition from DNA Methylation Without the Need for Methylation Reference
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    Chapter 14 Towards Recovering Allele-Specific Cancer Genome Graphs
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    Chapter 15 Using Stochastic Approximation Techniques to Efficiently Construct Confidence Intervals for Heritability
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    Chapter 16 Improved Search of Large Transcriptomic Sequencing Databases Using Split Sequence Bloom Trees
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    Chapter 17 AllSome Sequence Bloom Trees
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    Chapter 18 Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-learning Predictive Model
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    Chapter 19 Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies
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    Chapter 20 The Copy-Number Tree Mixture Deconvolution Problem and Applications to Multi-sample Bulk Sequencing Tumor Data
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    Chapter 21 Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding
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    Chapter 22 aBayesQR: A Bayesian Method for Reconstruction of Viral Populations Characterized by Low Diversity
Attention for Chapter 21: Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding
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Chapter title
Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding
Chapter number 21
Book title
Research in Computational Molecular Biology
Published in
Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005-), January 2017
DOI 10.1007/978-3-319-56970-3_21
Pubmed ID
Book ISBNs
978-3-31-956969-7, 978-3-31-956970-3, 978-3-31-956969-7, 978-3-31-956970-3
Authors

Jingkang Zhao, Dongshunyi Li, Jungkyun Seo, Andrew S. Allen, Raluca Gordân

Abstract

Many recent studies have emphasized the importance of genetic variants and mutations in cancer and other complex human diseases. The overwhelming majority of these variants occur in non-coding portions of the genome, where they can have a functional impact by disrupting regulatory interactions between transcription factors (TFs) and DNA. Here, we present a method for assessing the impact of non-coding mutations on TF-DNA interactions, based on regression models of DNA-binding specificity trained on high-throughput in vitro data. We use ordinary least squares (OLS) to estimate the parameters of the binding model for each TF, and we show that our predictions of TF-binding changes due to DNA mutations correlate well with measured changes in gene expression. In addition, by leveraging distributional results associated with OLS estimation, for each predicted change in TF binding we also compute a normalized score (z-score) and a significance value (p-value) reflecting our confidence that the mutation affects TF binding. We use this approach to analyze a large set of pathogenic non-coding variants, and we show that these variants lead to significant differences in TF binding between alleles, compared to a control set of common variants. Thus, our results indicate that there is a strong regulatory component to the pathogenic non-coding variants identified thus far.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 24%
Student > Master 4 14%
Researcher 4 14%
Student > Doctoral Student 3 10%
Student > Bachelor 2 7%
Other 3 10%
Unknown 6 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 52%
Agricultural and Biological Sciences 5 17%
Social Sciences 1 3%
Engineering 1 3%
Unknown 7 24%