Computational Biology

Readings

Readings are from the course textbook, which has been transcribed and compiled by students in this course over many years. The instructions for student “scribes,” and the templates they used, are linked below.

• Scribing Guide (PDF)
• Scribe Templates (ZIP) (This zip file contains: 1 .sh file and 2 .txt files.)

The entire course textbook is available, courtesy of the professor and the students.

Kellis, Manolis, ed. Computational Biology: Genomes, Networks, Evolution. MIT course 6.047 / 6.878. 2016 (PDF - 43.5MB).

LEC #	TOPICS	READINGS
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
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
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?
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?
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?
6	Transcript Structure: GENSCAN, RNA-seq, Mapping, De Novo Assembly, Diff Expr	Chapter 12: Large Intergenic Non-coding RNAs 12.3 Practical Topic: RNAseq
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
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
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
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?
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?
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
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?
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
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?
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
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?
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?
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?
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
22	Genome Engineering with CRISPR / Cas9 and Related Technologies	No readings for this lecture.