Comparative Genomics 19th International Conference, RECOMB-CG 2022, La Jolla, CA, USA, May 20-21, 2022, Proceedings
This book constitutes the refereed proceedings of the 19th Annual RECOMB Satellite Workshop on Comparative Genomics, RECOMB-CG which took place in La Jolla, USA, during May 20-21, 2022. The 18 full papers included in this book were carefully reviewed and selected from 28 submissions. The papers were...
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| Main Authors | , |
|---|---|
| Format | eBook Conference Proceeding |
| Language | English |
| Published |
Cham
Springer Nature
2022
Springer International Publishing AG Springer International Publishing |
| Edition | 1 |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
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Table of Contents:
- References -- Quantifying Hierarchical Conflicts in Homology Statements -- 1 Introduction -- 2 Methodological Foundations -- 2.1 Overlapping Homology Statements and the Block Graph -- 2.2 Homology Witnesses and Block Hierarchies -- 2.3 Relating Block Hierarchy to Stars in the Block Graph -- 3 Algorithms -- 3.1 NP-Hardness of MDDS -- 3.2 A Heuristic for MDDS -- 4 Quantifying Hierarchical Conficts -- 4.1 Discordance Ratio and Distinction from Jaccard Index -- 4.2 Mycobacterium Tuberculosis Clinical Isolates -- 4.3 Alignathon -- 5 Discussion and Conclusions -- A NP-Hardness of MDDS -- B Collections of Block that are not Clean -- C Segmental Duplications -- References -- On Partial Gene Transfer and Its Impact on Gene Tree Reconstruction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Simulated Datasets -- 2.2 Biological Datasets -- 2.3 Gene Tree Construction and Comparison -- 2.4 Using PhyML-Multi to Detect PGTs -- 3 Trippd: Tri-Partition Based PGT Detection -- 4 Results -- 4.1 Impact of PGT on Gene Tree Reconstruction Accuracy -- 4.2 PGT Detection Accuracy -- 4.3 Application to Biological Datasets -- 5 Discussion and Conclusion -- References -- Genome Rearrangements -- Sorting by k-Cuts on Signed Permutations -- 1 Introduction -- 2 Basic Definitions -- 3 Breakpoints and Strips -- 3.1 SKCBR is NP-Hard for k 5 -- 4 An Approximation Algorithm for SKCBR -- 5 Cycle Graph and Complement Cycle Graph -- 6 Increasing the Number of Cycles in G() with 4-Cuts -- 7 A 1.5-Approximation Algorithm for SKCBR When k=4 -- 8 Conclusion -- References -- A New Approach for the Reversal Distance with Indels and Moves in Intergenic Regions -- 1 Introduction -- 2 Background -- 2.1 Weighted Breakpoint Graph -- 3 Results -- 3.1 Complexity Analysis -- 3.2 Lower Bounds -- 3.3 Reversal and Move Operations -- 3.4 Reversal, Move, and Indel Operations -- 4 Conclusion
- 3.1 Performance Comparison of Plasmid Binning Tools -- 3.2 Comparison of PlasBin and HyAsP -- 3.3 Computational Footprint -- 4 Discussion -- References -- Genomic Sequencing -- Benchmarking Penalized Regression Methods in Machine Learning for Single Cell RNA Sequencing Data -- 1 Introduction -- 2 Methods -- 2.1 Penalized Regression -- 2.2 Clustering -- 2.3 K-Fold Cross-validation -- 2.4 ROC AUC -- 3 Research Design and Data -- 3.1 Experimental Data -- 3.2 Research Design -- 4 Results -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Deciphering the Tissue-Specific Regulatory Role of Intronless Genes Across Cancers -- 1 Introduction -- 2 Results -- 2.1 Functional Assignment and Gene Expression of IGs in Normal Tissue -- 2.2 IGs Tend to Have a More Induced Gene Expression Pattern When Compared to MEGs -- 2.3 Upregulated IGs Across Cancer Types Encode for Highly Conserved HDAC Deacetylate Histones Involved in Negative Gene Regulation -- 2.4 IG Downregulation Is Conserved in Breast and Colon Cancers and Is Involved in Signaling and Cell-Specific Functions -- 2.5 Cancer-Specific Differentially Expressed IGs -- 2.6 Proteins Encoded by Cancer-Specific Deregulated IGs Interact with Distinct Groups of Proteins in PPI Networks -- 2.7 DE-IGs Participate in the Genetic ``rewiring'' of Cancer Cells -- 3 Discussion -- 4 Materials and Methods -- 4.1 Data Extraction and Curation for IG, and MEG Datasets -- 4.2 Gene Expression Profiles in Healthy Tissue Tissue -- 4.3 Bipartite Network and Quantification of Shared and Unique DE-IGs -- 4.4 Data Source and Differential Expression Analysis Across Cancer -- 4.5 Upregulation Significant Differences of IGs and MEGs Among Cancers -- 4.6 Functional Enrichment Analysis of Differentially Expressed IGs -- 4.7 DE-IGs PPI Network Construction and Protein Complex Identification -- 4.8 BRCA Network Deconvolution
- Intro -- Preface -- Organization -- Contents -- Evolution -- On the Comparison of Bacteriophage Populations -- 1 Introduction -- 1.1 Recombinations and Mosaicism in Phage Genomes -- 1.2 Recombination Between Phage Populations -- 2 Methods -- 2.1 Basic Definitions and Properties -- 2.2 Minimum Covers -- 2.3 Lower Bounds -- 3 Experiments -- 3.1 Dataset Construction -- 3.2 Comparing Factories -- 3.3 Shared Evolution -- 3.4 Population Structure -- 4 Discussion and Conclusion -- References -- Syntenic Dimensions of Genomic Evolution -- 1 Introduction -- 2 The Construction and Biological Significance of Synteny Blocks -- 3 Review of Sequence Divergence -- 4 Fractionation and Gap Size -- 5 Spatial Evolution -- 6 Data Summary -- 7 Correlational Analysis -- 8 Discussion -- References -- Phylogenetics -- Fast and Accurate Branch Support Calculation for Distance-Based Phylogenetic Placements -- 1 Introduction -- 2 Approach -- 2.1 Background on APPLES-2 -- 2.2 Distance-Based Support Estimation: Goals and Background -- 2.3 Non-parametric Bootstrapping -- 2.4 Parametric Bootstrapping (Binomial and Poisson Models) -- 3 Experimental Study -- 3.1 Dataset -- 3.2 Measurements -- 4 Results and Discussion -- 4.1 Simulated Single-Gene RNASim Dataset: Full-Length Sequences -- 4.2 Simulated Single-Gene RNASim Dataset: Fragmentary Sequences -- 4.3 Multi-gene Web of Life (WoL) Dataset -- 4.4 Runtimes -- 5 Discussions -- References -- The Sackin Index of Simplex Networks -- 1 Introduction -- 2 Basic Concepts and Notation -- 2.1 Tree-Child Networks -- 2.2 Node Depth, Network Height and Sackin Index -- 3 The Expected Sackin Index of Random Simplex Networks -- 3.1 Enumerating Simplex Networks -- 3.2 The Total Depths of the Nodes in the Top Tree Component -- 3.3 The Expected Total C-Depth of Random Simplex Networks -- 3.4 Bounds on the Sackin Index for a Random Simplex Network
- References -- .26em plus .1em minus .1emChromothripsis Rearrangements Are Informed by 3D-Genome Organization -- 1 Introduction -- 2 Materials and Methods -- 2.1 Hi-C Data -- 2.2 SVs Data -- 2.3 Chromothripsis Rearrangements Data -- 2.4 Breakpoints Pairwise Distances Analysis -- 2.5 Statistical Analysis -- 3 Results and Discussion -- 4 Conclusions -- References -- Metagenomics -- Using Computational Synthetic Biology Tools to Modulate Gene Expression Within a Microbiome -- 1 Introduction -- 2 Methods -- 2.1 Translation Efficiency Modeling -- 2.2 Transcription Optimization -- 2.3 Editing Restriction Site Presence -- 2.4 Data Curation for In-SilicoAnalysis -- 2.5 In-vitro Methods -- 3 Results -- 3.1 Editing Restriction Site Presence -- 3.2 Translation Efficiency Modeling -- 3.3 Transcription Optimization -- 3.4 In-vitro Results -- 4 Discussion -- 4.1 Future Plans -- 4.2 Applications -- References -- Metagenomics Binning of Long Reads Using Read-Overlap Graphs -- 1 Introduction -- 2 Methods -- 2.1 Step 1: Constructing Read-Overlap Graph -- 2.2 Step 2: Obtaining Read Features -- 2.3 Step 3: Performing Probabilistic Sampling -- 2.4 Step 4: Detecting Clusters for Sampled Reads -- 2.5 Step 5: Binning Remaining Reads by Inductive Learning -- 3 Experimental Setup -- 3.1 Simulated Datasets -- 3.2 Real Datasets -- 3.3 Baselines and Evaluation Criteria -- 4 Results and Discussion -- 4.1 Binning Results -- 4.2 Assembly Results -- 5 Implementation -- 6 Conclusion -- A Dataset Information -- B Interpretation of AMBER Per-bin F1-Score -- References -- A Mixed Integer Linear Programming Algorithm for Plasmid Binning -- 1 Introduction -- 2 Hybrid Approach for Plasmid Binning Using Mixed Integer Linear Programming -- 2.1 Input: Contigs and the Assembly Graph -- 2.2 PlasBin Workflow -- 2.3 MILP Formulation -- 3 Experimental Results
- References
- 4 Conclusion -- References -- Phylogenetic Placement Problem: A Hyperbolic Embedding Approach -- 1 Introduction -- 2 Background on Hyperbolic Spaces -- 3 Problem Definition -- 4 H-DEPP -- 5 Experimental Setup -- 5.1 Datasets -- 5.2 Evaluation -- 6 Results and Discussions -- 6.1 Comparison of H-DEPP Alternatives -- 6.2 Comparison to Euclidean Embedding -- 6.3 Tree Updates -- 7 Conclusions and Future Work -- References -- Phylogenetic Network Dissimilarity Measures that Take Branch Lengths into Account -- 1 Introduction -- 2 Methods -- 2.1 Rooted Network Branch Score (rNBS) -- 2.2 Average Path Distance (APD) -- 3 Results and Discussion -- 3.1 Dissimilarity Under Various Network Perturbations -- 3.2 Analyzing Posterior Samples Using the Dissimilarity Measures -- 3.3 Runtime Comparison -- 4 Conclusions and Future Work -- References -- Homology and Reconciliation -- The Complexity of Finding Common Partitions of Genomes with Predefined Block Sizes -- 1 Introduction -- 2 Preliminary Notions -- 3 The Exact F-Strip Recovery Problem with Fixed F -- 4 GSR-F in Polynomial Time for Fixed F and Alphabet -- 5 Fixed Alphabet with Unbounded F is NP-Hard -- 6 Conclusion -- References -- Reconciliation with Segmental Duplication, Transfer, Loss and Gain -- 1 Introduction -- 2 Preliminary Definitions -- 3 Evolutionary Histories for Syntenies -- 4 Most Parsimonious Super-Reconciliations -- 5 A Two-Steps Method -- 6 A Dynamic Programming Algorithm for DTL Super-Reconciliation -- 7 Application to CRISPR-Associated (Cas) Gene Syntenies -- 7.1 Cas Gene Syntenies -- 7.2 Dataset -- 8 Results -- 8.1 DTL Super-Reconciliation Settings -- 8.2 An Evolutionary Scenario -- 9 Conclusion -- A Additional Content for Sect. 4 (``Most Parsimonious Super-Reconciliations'') -- B Additional Content for Sect. 6 (``A Dynamic Programming Algorithm for DTL Super- Reconciliation'')