1 | Introduction: Course Overview, Biology, Algorithms, Machine Learning |
Chapter 1: Introduction to the Course
- 1.1 Introduction and Goals
- 1.2 Final Project: Introduction to Research in Computational Biology
- 1.3 Additional Materials
- 1.4 Crash Course in Molecular Biology
- 1.5 Introduction to Algorithms and Probabilistic Inference
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2 | Alignment I: Dynamic Programming, Global and Local Alignment |
Chapter 2: Sequence Alignment and Dynamic Programming
- 2.1 Introduction
- 2.2 Aligning Sequences
- 2.3 Problem Formulations
- 2.4 Dynamic Programming
- 2.5 The Needleman-Wunsch Algorithm
- 2.6 Multiple Alignment
- 2.7 Current Research Directions
- 2.8 Further Reading
- 2.9 Tools and Techniques
- 2.10 What Have We Learned?
- 2.11 Appendix
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3 | Alignment II: Database Search, Rapid String Matching, BLAST, BLOSUM |
Chapter 3: Rapid Sequence Alignment and Database Search
- 3.1 Introduction
- 3.2 Global Alignment vs. Local Alignment vs. Semi-global Alignment
- 3.3 Linear-time Exact String Matching
- 3.4 The BLAST (Basic Local Alignment Search Tool) Algorithm
- 3.5 Pre-processing for Linear-time String Matching
- 3.6 Probabilistic Foundations of Sequence Alignment
- 3.7 Current Research Directions
- 3.8 Further Readings
- 3.9 Tools and Techniques
- 3.10 What Have We Learned?
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4 | Hidden Markov Models Part 1: Evaluation / Parsing, Viterbi, Forward Algorithms |
Chapter 7: Hidden Markov Models I
- 7.1 Introduction
- 7.2 Motivation
- 7.3 Markov Chains and HMMS: From Example to Formalizing
- 7.4 Apply HMM to Real World: From Casino to Biology
- 7.5 Algorithmic Settings for HMMs
- 7.6 An Interesting Question: Can We Incorporate Memory in Our Model?
- 7.7 Further Reading
- 7.8 Current Research Directions
- 7.9 Tools and Techniques
- 7.10 What Have We Learned?
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5 | Hidden Markov Models Part 2: Posterior Decoding, Learning, Baum-Welch |
Chapter 8: Hidden Markov Models II-Posterior Decoding and Learning
- 8.1 Review of Previous Lecture
- 8.2 Posterior Decoding
- 8.3 Encoding Memory in an HMM: Detection of CpG Islands
- 8.4 Learning
- 8.5 Using HMMs to Align Sequences with Affine Gap Penalties
- 8.6 Current Research Directions
- 8.7 Further Reading
- 8.8 Tools and Techniques
- 8.9 What Have We Learned?
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6 | Transcript Structure: GENSCAN, RNA-seq, Mapping, De Novo Assembly, Diff Expr |
Chapter 12: Large Intergenic Non-coding RNAs
- 12.3 Practical Topic: RNAseq
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7 | Expression Analysis: Clustering / Classification, K-Means, Hierarchical, Bayesian |
Chapter 15: Gene Regulation 1: Gene Expression Clustering
- 15.1 Introduction
- 15.2 Methods for Measuring Gene Expression
- 15.3 Clustering Algorithms
- 15.4 Current Research Directions
- 15.5 Further Reading
- 15.6 Resources
- 15.7 What Have We Learned?
Chapter 16: Gene Regulation 2: Classification
- 16.1 Introduction
- 16.2 Classification - Bayesian Techniques
- 16.3 Classification Support Vector Machines
- 16.4 Tumor Classification with SVMs
- 16.5 Semi-Supervised Learning
- 16.6 Current Research Directions
- 16.7 Further Reading
- 16.8 Resources
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8 | Networks I: Bayesian Inference, Deep Learning, Network Dynamics |
Chapter 20: Networks I Inference, Structure, Spectral Methods
- 20.1 Introduction
- 20.2 Network Centrality Measures
- 20.3 Linear Algebra Review
- 20.4 Sparse Principal Component Analysis
- 20.5 Network Communities and Modules
- 20.6 Network Diffusion Kernels
- 20.7 Neural Networks
- 20.8 Open Issues and Challenges
- 20.9 Current Research Directions
- 20.10 Further Reading
- 20.11 Tools and Techniques
- 20.12 What Have We Learned?
Chapter 21: Regulatory Networks: Inferences, Analysis, Application
- 21.1 Introduction
- 21.2 Structure Inference
- 21.3 Overview of the OGM Learning Task
- 21.4 Applications of Networks
- 21.5 Structural Properties of Networks
- 21.6 Network Clustering
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9 | Networks II: Network Learning, Structure, Spectral Methods |
10 | Regulatory Motifs: Discovery, Representation, PBMs, Gibbs Sampling, EM |
Chapter 17: Regulatory Motifs, Gibbs Sampling, and EM
- 17.1 Introduction to Regulatory Motifs and Gene Regulation
- 17.2 Expectation Maximization
- 17.3 Gibbs Sampling: Sample from Joint (M, Zjj) Distribution
- 17.4 De Novo Motif Discovery
- 17.5 Evolutionary Signatures for Instance Identification
- 17.6 Phylogenies, Branch Length Score, Confidence Score
- 17.7 Possibly Deprecated Stuff Below
- 17.8 Comparing Different Methods
- 17.9 OOPS, ZOOPS, TCM
- 17.10 Extension of the EM Approach
- 17.11 Motif Representation and Information Content
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11 | Epigenomics: ChIP-Seq, Read Mapping, Peak Calling, IDR, Chromatin States |
Chapter 19: Epigenomics / Chromatin States
- 19.1 Introduction
- 19.2 Epigenetic Information in Nucleosomes
- 19.3 Epigenomic Assays
- 19.4 Primary Data Processing of ChIP Data
- 19.5 Annotating the Genome Using Chromatin Signatures
- 19.6 Current Research Directions
- 19.7 Further Reading
- 19.8 Tools and Techniques
- 19.9 What Have We Learned?
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12 | RNA Modifications: RNA Editing, Translation Regulation, Splicing Regulation |
Chapter 11: RNA Modifications
- 11.1 Introduction
- 11.2 Post-transcriptional Regulation
- 11.3 Current Research Directions
- 11.4 Further Reading
- 11.5 Tools and Techniques
- 11.6 What Have We Learned?
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13 | Resolving Human Ancestry and Human History from Genetic Data |
Chapter 29: Population History
- 29.1 Introduction
- 29.2 Quick Survey of Human Genetic Variation
- 29.3 African European Gene Flow
- 29.4 Gene Flow on the Indian Subcontinent
- 29.5 Gene Flow Between Archaic Human Populations
- 29.6 European Ancestry and Migrations
- 29.7 Tools and Techniques
- 29.8 Further Directions
- 29.9 Further Reading
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14 | Disease Association Mapping, GWAS, Organismal Phenotypes |
Chapter 31: Medical Genetics-The Past to the Present
- 31.1 Introduction
- 31.2 Goals of Investigating the Genetic Basis of Disease
- 31.3 Mendelian Traits
- 31.4 Complex Traits
- 31.5 Genome-wide Association Studies
- 31.6 Current Research Directions
- 31.7 Further Reading
- 31.8 Tools and Techniques
- 31.9 What Have We Learned?
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15 | Quantitative Trait Mapping, Molecular Traits, eQTLs |
Chapter 32: Variation 2: Quantitative Trait Mapping, eQTLs, Molecular Trait Variation
- 32.1 Introduction
- 32.2 eQTL Basics
- 32.3 Structure of an eQTL Study
- 32.4 Current Research Directions
- 32.5 What Have We Learned?
- 32.6 Further Reading
- 32.7 Tools and Resources
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16 | Missing Heritability, Complex Traits, Interpret GWAS, Rank-based Enrichment |
Chapter 33: Missing Heritability
- 33.1 Introduction
- 33.2 Current Research Directions
- 33.3 Further Reading
- 33.4 Tools and Techniques
- 33.5 What Have We Learned?
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17 | Comparative Genomics and Evolutionary Signatures |
Chapter 4: Comparative Genomics I: Genome Annotation
- 4.1 Introduction
- 4.2 Conservation of Genomic Sequences
- 4.3 Excess Constraint
- 4.4 Diversity of Evolutionary Signatures: An Overview of Selection Patterns
- 4.5 Protein-coding Signatures
- 4.6 MicroRNA (miRNA) Gene Signatures
- 4.7 Regulatory Motifs
- 4.8 Current Research Directions
- 4.9 Further Reading
- 4.10 Tools and Techniques
- 4.11 Bibliography
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18 | Phylogenetics: Molecular Evolution, Tree Building, Phylogenetic Inference |
Chapter 27: Molecular Evolution and Phylogenetics
- 27.1 Introduction
- 27.2 Basics of Phylogeny
- 27.3 Distance Based Methods
- 27.4 Character Based Methods
- 27.5 Possible Theoretical and Practical Issues with Discussed Approach
- 27.6 Towards Final Project
- 27.7 What Have We Learned?
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19 | Phylogenomics: Gene / Species Trees, Reconciliation, Recombination Graphs |
Chapter 28: Phylogenomics II
- 28.1 Introduction
- 28.2 Inferring Orthologs / Paralogs, Gene Duplication and Loss
- 28.3 Reconstruction
- 28.4 Modeling Population and Allele Frequencies
- 28.5 SPIDIR
- 28.6 Ancestral Recombination Graphs
- 28.7 Conclusion
- 28.8 Current Research Directions
- 28.9 Further Reading
- 28.10 Tools and Techniques
- 28.11 What Have We Learned?
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20 | Personal Genomics, Disease Epigenomics: Systems Approaches to Disease |
Chapter 34: Personal Genomes, Synthetic Genomes, Computing in C vs. Si
- 34.1 Introduction
- 34.2 Reading and Writing Genomes
- 34.3 Personal Genomes
- 34.4 Current Research Directions
- 34.5 Further Reading
- 34.6 Tools and Techniques
- 34.7 What Have We Learned?
Chapter 36: Cancer Genomics
- 36.1 Introduction
- 36.2 Characterization
- 36.3 Interpretation
- 36.4 Current Research Directions
- 36.5 Further Reading
- 36.6 Tools and Techniques
- 36.7 What Have We Learned?
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21 | Three-Dimensional Chromatin Interactions: 3C, 5C, HiC, ChIA-Pet |
Chapter 30: Population Genetic Variation
- 30.1 Introduction
- 30.2 Population Selection Basics
- 30.3 Genetic Linkage
- 30.4 Natural Selection
- 30.5 Human Evolution
- 30.6 Current Research
- 30.7 Further Reading
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22 | Genome Engineering with CRISPR / Cas9 and Related Technologies | No readings for this lecture. |