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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 8: Resolving Multicopy Duplications de novo Using Polyploid Phasing
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Chapter title
Resolving Multicopy Duplications de novo Using Polyploid Phasing
Chapter number 8
Book title
Research in Computational Molecular Biology
Published in
Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005-), May 2017
DOI 10.1007/978-3-319-56970-3_8
Pubmed ID
Book ISBNs
978-3-31-956969-7, 978-3-31-956970-3
Authors

Mark J. Chaisson, Sudipto Mukherjee, Sreeram Kannan, Evan E. Eichler

Abstract

While the rise of single-molecule sequencing systems has enabled an unprecedented rise in the ability to assemble complex regions of the genome, long segmental duplications in the genome still remain a challenging frontier in assembly. Segmental duplications are at the same time both gene rich and prone to large structural rearrangements, making the resolution of their sequences important in medical and evolutionary studies. Duplicated sequences that are collapsed in mammalian de novo assemblies are rarely identical; after a sequence is duplicated, it begins to acquire paralog specific variants. In this paper, we study the problem of resolving the variations in multicopy long-segmental duplications by developing and utilizing algorithms for polyploid phasing. We develop two algorithms: the first one is targeted at maximizing the likelihood of observing the reads given the underlying haplotypes using discrete matrix completion. The second algorithm is based on correlation clustering and exploits an assumption, which is often satisfied in these duplications, that each paralog has a sizable number of paralog-specific variants. We develop a detailed simulation methodology, and demonstrate the superior performance of the proposed algorithms on an array of simulated datasets. We measure the likelihood score as well as reconstruction accuracy, i.e., what fraction of the reads are clustered correctly. In both the performance metrics, we find that our algorithms dominate existing algorithms on more than 93% of the datasets. While the discrete matrix completion performs better on likelihood score, the correlation clustering algorithm performs better on reconstruction accuracy due to the stronger regularization inherent in the algorithm. We also show that our correlation-clustering algorithm can reconstruct on an average 7.0 haplotypes in 10-copy duplication data-sets whereas existing algorithms reconstruct less than 1 copy on average.

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The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 6%
Unknown 15 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 31%
Student > Bachelor 4 25%
Student > Ph. D. Student 3 19%
Professor > Associate Professor 1 6%
Unknown 3 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 31%
Agricultural and Biological Sciences 4 25%
Computer Science 2 13%
Veterinary Science and Veterinary Medicine 1 6%
Unknown 4 25%