Research in Computational Molecular Biology

Name: Research in Computational Molecular Biology | 22nd Annual International Conference, RECOMB 2018, Paris, France, April 21-24, 2018, Proceedings
Brand: Springer
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22nd Annual International Conference, RECOMB 2018, Paris, France, April 21-24, 2018, Proceedings

Benjamin J. Raphael(Herausgeber*in)

Springer (Verlag)

Erschienen am 17. April 2018

XIV, 298 Seiten

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978-3-319-89929-9 (ISBN)

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Inhalt

Intro
Preface
Organization
Contents
Long Reads Enable Accurate Estimates of Complexity of Metagenomes
1 Introduction
2 Results
3 Methods
4 Discussion
References
Chromatyping: Reconstructing Nucleosome Profiles from NOMe Sequencing Data
1 Introduction
2 Methods
2.1 ChromaClique Overview
2.2 Encoding the Reads
2.3 Definition of Edge Probabilities
2.4 Efficient Calculation of Edge Probabilities in ChromaClique
3 Data Simulation and Evaluation
3.1 Simulating Chromatypes
3.2 Simulating NGS Reads
3.3 Evaluation Metric for Chromatype Reconstructions
3.4 Evaluation of the Chromatype Predictions
4 Results
5 Discussion and Conclusion
References
GTED: Graph Traversal Edit Distance
1 Introduction
2 Problem Definition
3 Methods
3.1 Brute Force Computation of Graph Traversal Edit Distance
3.2 Graph Traversal Edit Distance as a Constrained Shortest Path Problem
3.3 Lower Bound via Linear Programming Relaxation
3.4 Algorithm for Graph Traversal Edit Distance
3.5 Total Unimodularity
3.6 Homology Theory of Alignment Graph
4 Experiments
4.1 Using GTED to Make a Kernel
4.2 Using GTED on Genomic Data
5 Conclusion
References
Statistical Inference of Peroxisome Dynamics
1 Introduction
2 Background
2.1 Organelle Dynamics via Fluorescence Microscopy
2.2 Modeling and Inference of Organelle Dynamics
2.3 Bayesian Rate Inference in Unreplicated Systems
2.4 Bayesian Rate Inference in Replicated Systems
3 Methods
3.1 Expressing the Biochemical Model of Peroxisome Dynamics as a Stochastic Differential Equation
3.2 Parallel Inference for Replicated Experiments with Homogeneous Rates
3.3 Inference of Cell to Cell Rate Variations
3.4 Implementation
3.5 Imaging and Counting Peroxisomes by Confocal Microscopy
4 Datasets
4.1 Experimental Datasets
4.2 Simulated Datasets
5 Results
5.1 Parallel Inference Shortened Computation Time
5.2 The Approach Accurately Inferred the Rates
5.3 Replicate Cells Improved Inference of the Rates
5.4 Inference from Replicate Cells Revealed Maintenance of Peroxisome Counts by Prominent Fission, Compared to Low de novo generation rates
5.5 Rates Varied Little Between Cells
6 Discussion
References
Loss-Function Learning for Digital Tissue Deconvolution
1 Introduction
2 Methods
2.1 Notations
2.2 Loss-Function Learning
3 Results
3.1 DTD of Melanomas
3.2 Loss-Function Learning Improves DTD Accuracy in the Case of Incomplete Reference Data
3.3 Loss-Function Learning Improves the Quantification of Small Cell Populations
3.4 Loss-Function Learning Improves the Distinction of Closely Related Cell Types
3.5 Loss-Function Learning is Beneficial Even for Small Training Sets, and the Performance Improves as the Training Dataset Grows
3.6 HPC-Empowered Loss-Function Learning Rediscovers Established Cell Markers and Complements Them by New Discriminatory Genes for Improved Performance
3.7 Loss-Function Learning Shows Similar Performance as CIBERSORT for the Dominating Cell Populations and Improves Accuracy for Small Populations and in the Distinction of Closely Related Cell Types
3.8 Loss-Function Learning Improves the Decomposition of Bulk Melanoma Profiles
4 Discussion
References
Inference of Population Structure from Ancient DNA
1 Introduction
2 Methods
2.1 Preliminaries
2.2 Posterior Inference
2.3 Variational Kalman Filtering
2.4 Stochastic Variational Inference
2.5 Simulated Data
2.6 Real Data
3 Results
3.1 Simulated Data
3.2 Real Data
3.3 Running Time
4 Discussion
References
Using Minimum Path Cover to Boost Dynamic Programming on DAGs: Co-linear Chaining Extended
1 Introduction
2 The MPC Algorithm
3 The Dynamic Programming Framework
4 The LCS Problem
5 Co-linear Chaining
5.1 Co-linear Chaining on Sequences Revisited
5.2 Co-linear Chaining on DAGs Using a Minimum Path Cover
6 Discussion and Experiments
References
Modeling Dependence in Evolutionary Inference for Proteins
1 Introduction
1.1 Motivation
2 A Site-Dependent Structural Diffusion Model
2.1 Challis-Schmidler Model
2.2 Dependence in a Multivariate Ornstein-Uhlenbeck Process
2.3 Computational Tractability in Phylogenetic Models
2.4 Constructing a Dependent Structural Diffusion Model
2.5 Dynamic Programming
2.6 Bayesian Inference for the Site-Dependent Model
3 Joint Sequence-Structure Model for Phylogenetic Inference
3.1 Amino Acid Sequence Model
3.2 Site-Dependent Random Effect Model
4 Results
4.1 Improved Estimation of Evolutionary Distances
4.2 Effect on Phylogeny of Ignoring Structural Dependence in Globin Structures
5 Discussion
A Appendix
A.1 Modified Dynamic Programming for a Pair HMM with Dependence
A.2 Dependent Binary Sequence Model
References
Constrained De Novo Sequencing of neo-Epitope Peptides Using Tandem Mass Spectrometry
1 Introduction
2 Method
3 Results
4 Discussion
References
Reverse de Bruijn: Utilizing Reverse Peptide Synthesis to Cover All Amino Acid k-mers
1 Introduction
2 Preliminaries
3 Results
3.1 A Lower Bound for the Length of an RdB Sequence
3.2 A de Bruijn Graph Representing Two Reverse RdB Sequences
3.3 Constructing a Near-Optimal RdB Sequence in Linear Time
3.4 Integer Linear Programming Formulation
4 Summary and Discussion
References
Circular Networks from Distorted Metrics
1 Introduction
2 Background
3 Main Results
References
A Nested 2-Level Cross-Validation Ensemble Learning Pipeline Suggests a Negative Pressure Against Crosstalk snoRNA-mRNA Interactions in Saccharomyces Cerevisae
1 Introduction
2 Approach
3 Methods
3.1 Dataset
3.2 Features Description
3.3 Ensemble Learning Pipeline
3.4 Main Experiments
3.5 Additional Experiments
4 Conclusion
References
Context-Specific Nested Effects Models
1 Introduction
2 Background: Nested Effects Models
2.1 Likelihood Computation
3 Methods: Context-Specific Nested Effects Models
3.1 The Likelihood of a k-CSNEM
3.2 Compact Visualization and Identifiability of a k-CSNEM
4 Results
4.1 Evaluation on Simulated Data
4.2 Application to NaCl Stress Response in S. cerevisiae
5 Discussion
References
Algorithmic Framework for Approximate Matching Under Bounded Edits with Applications to Sequence Analysis
1 Introduction
2 Our Algorithmic Framework
3 Details of the Construction of Uk
3.1 Correctness Proof (via Mathematical Induction)
3.2 Time and Space Complexity Analysis
3.3 Obtaining the Parts of Uk with Its Elements Sorted
4 Our Algorithm for Computing the Array L
5 Solving Approximate Sequence Matching Problems
5.1 Computing the k-edit Average Common Substring
5.2 Our Algorithm for the Mapping Problem
5.3 All-Pair Maximal k-edit Common Substrings
5.4 All-Pair Maximal k-edit Suffix/Prefix Overlaps
References
Accurate Reconstruction of Microbial Strains from Metagenomic Sequencing Using Representative Reference Genomes
1 Introduction
2 Database Indexing
2.1 Background
2.2 SPARSE Reference Database
3 Metagenomic Read Sampling
3.1 SPARSE Sampling Probability
3.2 Optimization Problem
3.3 ANI Cluster Abundances
3.4 Taxonomic Labels for ANI Clusters
4 Evaluation
4.1 Representative Database
4.2 Simulated Data
4.3 Ancient Metagenomes
5 Conclusion
References
Short Papers
Targeted Genotyping of Variable Number Tandem Repeats with AdVNTR
Reference
Positive-Unlabeled Convolutional Neural Networks for Particle Picking in Cryo-electron Micrographs
References
Designing RNA Secondary Structures Is Hard
References
Generalizable Visualization of Mega-Scale Single-Cell Data
1 Introduction
2 Methods
3 Results
References
Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis
References
Fixed-Parameter Tractable Sampling for RNA Design with Multiple Target Structures
References
Contribution of Structural Variation to Genome Structure: TAD Fusion Discovery and Ranking
References
Assembly of Long Error-Prone Reads Using Repeat Graphs
References
A Multi-species Functional Embedding Integrating Sequence and Network Structure
References
Deciphering Signaling Specificity with Deep Neural Networks
1 Introduction
2 Methods
3 Results
References
Integrative Inference of Subclonal Tumour Evolution from Single-Cell and Bulk Sequencing Data
Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index
References
Tensor Composition Analysis Detects Cell-Type Specific Associations in Epigenetic Studies
Assembly-Free and Alignment-Free Sample Identification Using Genome Skims
References
Efficient Algorithms to Discover Alterations with Complementary Functional Association in Cancer
References
Latent Variable Model for Aligning Barcoded Short-Reads Improves Downstream Analyses
References
ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules
SCI: Single-Cell Mutation Identification via Phylogenetic Inference
AptaBlocks: Accelerating the Design of RNA-Based Drug Delivery Systems
References
A Unifying Framework for Summary Statistic Imputation
References
Characterizing Protein-DNA Binding Event Subtypes in ChIP-Exo Data
Reference
Continuous-Trait Probabilistic Model for Comparing Multi-species Functional Genomic Data
Deep Learning Reveals Many More Inter-protein Residue-Residue Contacts than Direct Coupling Analysis
References
Author Index

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