Nguồn ngoài

Computational Biology - Genomes, Networks, and Evolution

This text covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

Computational Biology Biology

Tác giả: Manolis Kellis et al.
Nhà xuất bản: Massachusetts Institute of Technology
Nguồn: LibreTexts
Giấy phép: CC BY-NC-SA
Ngôn ngữ: EN
Danh mục: Công nghệ thông tin

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Mục lục· 287 mục

01: Introduction to the Course
1.01: Introduction and Goals
1.02: Final Project - Introduction to Research In Computational Biology
1.03: Additional materials
1.04: Crash Course in Molecular Biology
1.05: Introduction to algorithms and probabilistic inference
02: Sequence Alignment and Dynamic Programming
2.01: Introduction
2.02: Aligning Sequences
2.03: Problem Formulations
2.04: Dynamic Programming
2.05: The Needleman-Wunsch Algorithm
2.06: Multiple alignment
2.07: Tools and Techniques
2.08: Appendix
2.09: Bibliography
03: Rapid Sequence Alignment and Database Search
3.01: What Have We Learned
3.02: Introduction
3.03: Global alignment vs. Local alignment vs. Semi-global alignment
3.04: Linear-time exact string matching
3.05: The BLAST algorithm (Basic Local Alignment Search Tool)
3.06: Pre-processing for linear-time string matching
3.07: Probabilistic Foundations of Sequence Alignment
04: Comparative Genomics I- Genome Annotation
4.01: Introduction
4.02: Conservation of genomic sequences
4.03: Excess Constraint
4.04: Diversity of evolutionary signatures- An Overview of Selection Patterns
4.05: Protein-Coding Signatures
4.06: microRNA (miRNA) Gene Signatures
4.07: Regulatory Motifs
4.08: Further Reading Tools and Techniques Bibliography
4.9: Tools and Techniques
Bibliography
05: Genome Assembly and Whole-Genome Alignment
5.01: Introduction
5.02: Genome Assembly I- Overlap-Layout-Consensus Approach
5.03: Genome Assembly II- String graph methods
5.04: Whole-Genome Alignment
5.05: Gene-based region alignment
5.06: Mechanisms of Genome Evolution
5.07: Whole Genome Duplication
5.08: Additional Resources and Bibliography
Bibliography
06: Bacterial Genomics--Molecular Evolution at the Level of Ecosystems
6.01: Introduction
6.02: Study 1- Evolution of life on earth
6.03: Study 2- Pediatric IBD study with Athos Boudvaros
6.04: Study 3- Human Gut Ecology (HuGE) project
6.05: Study 4- Microbiome as the connection between diet and phenotype
6.06: Study 5- Horizontal Gene Transfer (HGT) between bacterial groups and its effect on antibiotic resistance
6.07: Study 6- Identifying virulence factors in Meningitis
6.08: Q A
6.09: Current Research Directions Further Reading What have we learned Bibliography
6.10 Further Reading
6.12 What have we learned
Bibliography
07: Hidden Markov Models I
7.01: Introduction
7.02: Motivation
7.03: Markov Chains and HMMS- From Example to Formalizing
7.04: Apply HMM to Real World- From Casino to Biology
7.05: Algorithmic Settings for HMMs
7.06: An Interesting Question- Can We Incorporate Memory in Our Model
7.07: Further Reading What Have We Learned
08: Hidden Markov Models II-Posterior Decoding and Learning
8.01: Review of previous lecture
8.02: Posterior Decoding
8.03: Encoding Memory in a HMM- Detection of CpG islands
8.04: Learning
8.05: Using HMMs to align sequences with affine gap penalties
8.06: Current Research Directions What Have We Learned Bibliography
8.9 What Have We Learned
Bibliography
09: Gene Identification- Gene Structure Semi-Markov CRFS
9.01: Introduction
9.02: Overview of Chapter Contents
9.03: Eukaryotic Genes- An Introduction
9.04: Assumptions for Computational Gene Identification
9.05: Hidden Markov Chains
9.06: Conditional Random Fields
9.07: Other Methods
9.08: Conclusion Bibliography
Bibliography
10: RNA Folding
10.01: Motivation and Purpose
10.02: Chemistry of RNA
10.03: Origin and Functions of RNA
10.04: RNA Structure
10.05: RNA Folding Problem and Approaches
10.06: Evolution of RNA
10.07: Probabilistic Approach to the RNA Folding Problem
10.08: Advanced topics Summary and key points Further Reading Bibliography
11: RNA Modifications
11.01: Introduction
11.02: Post-Transcriptional Regulation
11.03: What Have We Learned
12: Large Intergenic Non-Coding RNAs
12.01: Bibliography
12.02: Introduction
12.03: Noncoding RNAs from Plants to Mammals
12.04: Practical topic- RNAseq
12.05: Long non-coding RNAs in Epigenetic Regulation
12.06: Integergenic Non-coding RNAs- missing lincs in Stem Cancer cells
12.07: Technologies- in the wet lab how can we find these
13: Small RNA
13.01: Introduction
13.02: RNA Interference
13.03: Bibliography
14: MRNA Sequencing for Expression Analysis and Transcript Discovery
14.01: Introduction
14.02: Expression Microarrays
14.03: The Biology of mRNA Sequencing
14.04: Read Mapping - Spaced Seed Alignment
14.05: Reconstruction
14.06: Quantification
15: Gene Regulation I - Gene Expression Clustering
15.01: Introduction
15.02: Methods for Measuring Gene Expression
15.03: Clustering Algorithms
15.04: Current Research Directions
15.05: Further Reading
15.06: Resources
15.07: What Have We Learned Bibliography
16: Gene Regulation II - Classification
16.01: Introduction
16.02: Classification--Bayesian Techniques
16.03: Classification Support Vector Machines
16.04: Tumor Classification with SVMs
16.05: Semi-Supervised Learning
16.06: Further Reading Resources Bibliography
17: Regulatory Motifs Gibbs Sampling and EM
17.01: Motif Representation and Information Content
17.02: Introduction to regulatory motifs and gene regulation
17.03: Expectation maximization
17.04: Gibbs Sampling- Sample from joint (MZij) distribution
17.05: De novo motif discovery
17.06: Possibly deprecated stuff below-
17.07: Comparing different Methods
17.08: OOPSZOOPSTCM
17.09: Extension of the EM Approach
18: Regulatory Genomics
18.01: Introduction to Regulatory Genomics
18.02: De Novo Motif Discovery
18.03: Predicting Regular Targets
18.04: MicroRNA Genes and Targets
19: Epigenomics Chromatin States
19.01: Introduction
19.02: Epigenetic Information in Nucleosomes
19.03: Epigenomic Assays
19.04: Primary data processing of ChIP data
19.05: Annotating the Genome Using Chromatin Signatures
19.06: Current Research Directions
19.07: Further Reading Tools and Techniques
19.08: What Have We Learned Bibliography
20: Networks I- Inference Structure Spectral Methods
20.01: Introduction
20.02: Network Centrality Measures
20.03: Linear Algebra Review
20.04: Sparse Principal Component Analysis
20.05: Network Communities and Modules
20.06: Network Diffusion Kernals
20.07: Neural Networks
20.08: Open Issues and Challenges
20.09: Further Reading What Have We Learned Bibliography
20.10: What Have We Learned
Bibliography
21: Regulatory Networks- Inference Analysis Application
21.01: Regulatory Networks- Inference Analysis Application
21.02: Structure Inference
21.03: Overview of the PGM Learning Task
21.04: Application of Networks
21.05: Structural Properties of Networks
21.06: Network clustering Bibliography
Bibliography
22: Chromatin Interactions
22.01: Introduction
22.02: Relevant terminology
22.03: Molecular Methods for Studying Nuclear Genome Organization
22.04: Mapping Genome-Nuclear Lamina Interactions (LADs)
22.05: Computational Methods for Studying Nuclear Genome Organization
22.06: Architecture of Genome Organization
22.07: Mechanistic Understanding of Genome Architecture
22.08: Current Research Directions
23: Introduction to Steady State Metabolic Modeling
23.01: Introduction
23.02: Model Building
23.03: Metabolic Flux Analysis
23.04: Applications
23.05: Further Reading Tools and Techniques Bibliography
23.6: Tools and Techiniques
Bibliography
24: The Encode Project- Systematic Experimentation and Integrative Genomics
24.01: Introduction
24.02: Experimental Techniques
24.03: Computational Techniques
24.04: Current Research Directions
24.05: Further Reading Tools and Techniques Bibliography
24.6: Tools and Techniques
Bibliography
Section 7: What Have We Learned
25: Synthetic Biology
25.01: Introduction to Synthetic Biology
25.02: Current Research Directions
25.03: Tools and Techniques
25.04: What Have We Learned Bibliography
Bibliography
26: Molecular Evolution and Phylogenetics
26.01: Introduction
26.02: Basics of Phylogeny
26.03: Distance Based Methods
26.04: Character-Based Methods
26.05: Possible Theoretical and Practical Issues with Discussed Approach
26.06: Towards final project What Have We Learned Bibliography
26.7: What Have We Learned
Bibliography
27: Phylogenomics II
27.01: Introduction
27.02: SPIDR
27.03: Ancestral Recombination Graphs
27.04: Conclusion Further Reading What Have We Learned
27.05: Inferring Orthologs Paralogs Gene Duplication and Loss
27.06: Reconstruction
27.07: Modeling Population and Allele Frequencies
27.9 Further Reading
27.10 What Have We Learned
Bibliography
28: Population History
28.01: Introduction
28.02: Quick Survey of Human Genetic Variation
28.03: African and European Gene Flow
28.04: Gene Flow on the Indian Subcontinent
28.05: Gene Flow Between Archaic Human Populations
28.06: Tools and Techniques
28.07: Research Directions Further Reading Bibliography
28.08: European Ancestry and Migrations
29: Population Genetic Variation
29.01: Introduction
29.02: Population Selection Basics
29.03: Genetic Linkage
29.04: Natural Selection
29.05: Human Evolution
29.06: Current Research Further Reading
29.7: Further Reading
30: Medical Genetics--The Past to the Present
30.01: Bibliography
30.02: Introduction
30.03: Goals of investigating the genetic basis of disease
30.04: Mendelian Traits
30.05: Complex Traits
30.06: Genome-wide Association Studies
30.07: Current Research Directions
30.08: Tools and Techniques
30.09: What Have We Learned
31: Variation 2- Quantitative Trait Mapping eQTLS Molecular Trait Variation
31.01: Introduction
31.02: eQTL Basics
31.03: Structure of an eQTL Study
31.04: Current Research Directions
31.05: What Have We Learned
31.06: Further Reading
31.07: Tools and Resources
31.08: Bibliography
32: Personal Genomes Synthetic Genomes Computing in C vs. Si
32.01: Introduction
32.02: Reading and Writing Genomes
32.03: Personal Genomes
32.04: Further Reading
32.05: Bibliography
33: Personal Genomics
33.01: Introduction
33.02: Epidemiology- An Overview
33.03: Genetic Epidemiology
33.04: Molecular Epidemiology
33.05: Causality Modeling and Testing
33.6: What Have We Learned
34: Cancer Genomics
Section 1: Introduction
Section 2: Characterization
Section 3: Interpretation
Section 5: Further Reading
Section 6: What Have We Learned
35: Genome Editing
1: Introduction
2: Current Research Directions
3: What Have We Learned