Computational Biology - Genomes, Networks, and Evolution

by Manolis Kellis et al.

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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.

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Publisher
Massachusetts Institute of Technology
Subjects
Biology, Computational Biology
Licence
CC BY-NC-SA 4.0, as LibreTexts states it. Non-commercial use only.
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

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  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. 1: Introduction to the Course
  7. 1.1: Introduction and Goals
  8. 1.2: Final Project - Introduction to Research In Computational Biology
  9. 1.3: Additional materials
  10. 1.4: Crash Course in Molecular Biology
  11. 1.5: Introduction to algorithms and probabilistic inference
  12. 2: Sequence Alignment and Dynamic Programming
  13. 2.1: Introduction
  14. 2.2: Aligning Sequences
  15. 2.3: Problem Formulations
  16. 2.4: Dynamic Programming
  17. 2.5: The Needleman-Wunsch Algorithm
  18. 2.6: Multiple alignment
  19. 2.7: Tools and Techniques
  20. 2.8: Appendix
  21. 2.9: Bibliography
  22. 3: Rapid Sequence Alignment and Database Search
  23. 3.1: What Have We Learned?
  24. 3.2: Introduction
  25. 3.3: Global alignment vs. Local alignment vs. Semi-global alignment
  26. 3.4: Linear-time exact string matching
  27. 3.5: The BLAST algorithm (Basic Local Alignment Search Tool)
  28. 3.6: Pre-processing for linear-time string matching
  29. 3.7: Probabilistic Foundations of Sequence Alignment
  30. 4: Comparative Genomics I- Genome Annotation
  31. 4.1: Introduction
  32. 4.2: Conservation of genomic sequences
  33. 4.3: Excess Constraint
  34. 4.4: Diversity of evolutionary signatures- An Overview of Selection Patterns
  35. 4.5: Protein-Coding Signatures
  36. 4.6: microRNA (miRNA) Gene Signatures
  37. 4.7: Regulatory Motifs
  38. 4.8: Further Reading
  39. 4.9: Tools and Techniques
  40. Bibliography
  41. 5: Genome Assembly and Whole-Genome Alignment
  42. 5.1: Introduction
  43. 5.2: Genome Assembly I- Overlap-Layout-Consensus Approach
  44. 5.3: Genome Assembly II- String graph methods
  45. 5.4: Whole-Genome Alignment
  46. 5.5: Gene-based region alignment
  47. 5.6: Mechanisms of Genome Evolution
  48. 5.7: Whole Genome Duplication
  49. 5.8: Additional Resources and Bibliography
  50. Bibliography
  51. 6: Bacterial Genomics--Molecular Evolution at the Level of Ecosystems
  52. 6.1: Introduction
  53. 6.2: Study 1- Evolution of life on earth
  54. 6.3: Study 2- Pediatric IBD study with Athos Boudvaros
  55. 6.4: Study 3- Human Gut Ecology (HuGE) project
  56. 6.5: Study 4- Microbiome as the connection between diet and phenotype
  57. 6.6: Study 5- Horizontal Gene Transfer (HGT) between bacterial groups and its effect on antibiotic resistance
  58. 6.7: Study 6- Identifying virulence factors in Meningitis
  59. 6.8: Q/A
  60. 6.9: Current Research Directions
  61. 6.10 Further Reading
  62. 6.12 What have we learned?
  63. Bibliography
  64. 7: Hidden Markov Models I
  65. 7.1: Introduction
  66. 7.2: Motivation
  67. 7.3: Markov Chains and HMMS - From Example to Formalizing
  68. 7.4: Apply HMM to Real World- From Casino to Biology
  69. 7.5: Algorithmic Settings for HMMs
  70. 7.6: An Interesting Question- Can We Incorporate Memory in Our Model?
  71. 7.7: Further Reading, What Have We Learned?
  72. 8: Hidden Markov Models II-Posterior Decoding and Learning
  73. 8.1: Review of previous lecture
  74. 8.2: Posterior Decoding
  75. 8.3: Encoding Memory in a HMM- Detection of CpG islands
  76. 8.4: Learning
  77. 8.5: Using HMMs to align sequences with affine gap penalties
  78. 8.6: Current Research Directions, What Have We Learned?, Bibliography
  79. 8.9 What Have We Learned?
  80. Bibliography
  81. 9: Gene Identification- Gene Structure, Semi-Markov, CRFS
  82. 9.1: Introduction
  83. 9.2: Overview of Chapter Contents
  84. 9.3: Eukaryotic Genes- An Introduction
  85. 9.4: Assumptions for Computational Gene Identification
  86. 9.5: Hidden Markov Chains
  87. 9.6: Conditional Random Fields
  88. 9.7: Other Methods
  89. 9.8: Conclusion, Bibliography
  90. Bibliography
  91. 10: RNA Folding
  92. 10.1: Motivation and Purpose
  93. 10.2: Chemistry of RNA
  94. 10.3: Origin and Functions of RNA
  95. 10.4: RNA Structure
  96. 10.5: RNA Folding Problem and Approaches
  97. 10.6: Evolution of RNA
  98. 10.7: Probabilistic Approach to the RNA Folding Problem
  99. 10.8: Advanced topics, Summary and key points, Further Reading, Bibliography
  100. 11: RNA Modifications
  101. 11.1: Introduction
  102. 11.2: Post-Transcriptional Regulation
  103. 11.3: What Have We Learned?
  104. 12: Large Intergenic Non-Coding RNAs
  105. 12.1: Bibliography
  106. 12.2: Introduction
  107. 12.3: Noncoding RNAs from Plants to Mammals
  108. 12.4: Practical topic- RNAseq
  109. 12.5: Long non-coding RNAs in Epigenetic Regulation
  110. 12.6: Integergenic Non-coding RNAs- missing lincs in Stem/Cancer cells?
  111. 12.7: Technologies- in the wet lab, how can we find these?
  112. 13: Small RNA
  113. 13.1: Introduction
  114. 13.2: RNA Interference
  115. 13.3: Bibliography
  116. 14: MRNA Sequencing for Expression Analysis and Transcript Discovery
  117. 14.1: Introduction
  118. 14.2: Expression Microarrays
  119. 14.3: The Biology of mRNA Sequencing
  120. 14.4: Read Mapping - Spaced Seed Alignment
  121. 14.5: Reconstruction
  122. 14.6: Quantification
  123. 15: Gene Regulation I - Gene Expression Clustering
  124. 15.1: Introduction
  125. 15.2: Methods for Measuring Gene Expression
  126. 15.3: Clustering Algorithms
  127. 15.4: Current Research Directions
  128. 15.5: Further Reading
  129. 15.6: Resources
  130. 15.7: What Have We Learned, Bibliography
  131. 16: Gene Regulation II - Classification
  132. 16.1: Introduction
  133. 16.2: Classification--Bayesian Techniques
  134. 16.3: Classification Support Vector Machines
  135. 16.4: Tumor Classification with SVMs
  136. 16.5: Semi-Supervised Learning
  137. 16.6: Further Reading, Resources, Bibliography
  138. 17: Regulatory Motifs, Gibbs Sampling, and EM
  139. 17.1: Motif Representation and Information Content
  140. 17.2: Introduction to regulatory motifs and gene regulation
  141. 17.3: Expectation maximization
  142. 17.4: Gibbs Sampling- Sample from joint (M,Zij) distribution
  143. 17.5: De novo motif discovery
  144. 17.6: Possibly deprecated stuff below-
  145. 17.7: Comparing different Methods
  146. 17.8: OOPS,ZOOPS,TCM
  147. 17.9: Extension of the EM Approach
  148. 18: Regulatory Genomics
  149. 18.1: Introduction to Regulatory Genomics
  150. 18.2: De Novo Motif Discovery
  151. 18.3: Predicting Regular Targets
  152. 18.4: MicroRNA Genes and Targets
  153. 19: Epigenomics/Chromatin States
  154. 19.1: Introduction
  155. 19.2: Epigenetic Information in Nucleosomes
  156. 19.3: Epigenomic Assays
  157. 19.4: Primary data processing of ChIP data
  158. 19.5: Annotating the Genome Using Chromatin Signatures
  159. 19.6: Current Research Directions
  160. 19.7: Further Reading, Tools and Techniques
  161. 19.8: What Have We Learned?, Bibliography
  162. 20: Networks I- Inference, Structure, Spectral Methods
  163. 20.1: Introduction
  164. 20.2: Network Centrality Measures
  165. 20.3: Linear Algebra Review
  166. 20.4: Sparse Principal Component Analysis
  167. 20.5: Network Communities and Modules
  168. 20.6: Network Diffusion Kernals
  169. 20.7: Neural Networks
  170. 20.8: Open Issues and Challenges
  171. 20.9: Further Reading, What Have We Learned?, Bibliography
  172. 20.10: What Have We Learned?
  173. Bibliography
  174. 21: Regulatory Networks- Inference, Analysis, Application
  175. 21.1: Regulatory Networks- Inference, Analysis, Application
  176. 21.2: Structure Inference
  177. 21.3: Overview of the PGM Learning Task
  178. 21.4: Application of Networks
  179. 21.5: Structural Properties of Networks
  180. 21.6: Network clustering, Bibliography
  181. Bibliography
  182. 22: Chromatin Interactions
  183. 22.1: Introduction
  184. 22.2: Relevant terminology
  185. 22.3: Molecular Methods for Studying Nuclear Genome Organization
  186. 22.4: Mapping Genome-Nuclear Lamina Interactions (LADs)
  187. 22.5: Computational Methods for Studying Nuclear Genome Organization
  188. 22.6: Architecture of Genome Organization
  189. 22.7: Mechanistic Understanding of Genome Architecture
  190. 22.8: Current Research Directions
  191. 23: Introduction to Steady State Metabolic Modeling
  192. 23.1: Introduction
  193. 23.2: Model Building
  194. 23.3: Metabolic Flux Analysis
  195. 23.4: Applications
  196. 23.5: Further Reading, Tools and Techniques, Bibliography
  197. 23.6: Tools and Techiniques
  198. Bibliography
  199. 24: The Encode Project- Systematic Experimentation and Integrative Genomics
  200. 24.1: Introduction
  201. 24.2: Experimental Techniques
  202. 24.3: Computational Techniques
  203. 24.4: Current Research Directions
  204. 24.5: Further Reading, Tools and Techniques, Bibliography
  205. 24.6: Tools and Techniques
  206. Bibliography
  207. Section 7: What Have We Learned?
  208. 25: Synthetic Biology
  209. 25.1: Introduction to Synthetic Biology
  210. 25.2: Current Research Directions
  211. 25.3: Tools and Techniques
  212. 25.4: What Have We Learned?, Bibliography
  213. Bibliography
  214. 26: Molecular Evolution and Phylogenetics
  215. 26.1: Introduction
  216. 26.2: Basics of Phylogeny
  217. 26.3: Distance Based Methods
  218. 26.4: Character-Based Methods
  219. 26.5: Possible Theoretical and Practical Issues with Discussed Approach
  220. 26.6: Towards final project
  221. 26.7: What Have We Learned?
  222. Bibliography
  223. 27: Phylogenomics II
  224. 27.1: Introduction
  225. 27.2: SPIDR
  226. 27.3: Ancestral Recombination Graphs
  227. 27.4: Conclusion
  228. 27.5: Inferring Orthologs/Paralogs, Gene Duplication and Loss
  229. 27.6: Reconstruction
  230. 27.7: Modeling Population and Allele Frequencies
  231. 27.10 What Have We Learned?
  232. 27.9 Further Reading
  233. Bibliography
  234. 28: Population History
  235. 28.1: Introduction
  236. 28.2: Quick Survey of Human Genetic Variation
  237. 28.3: African and European Gene Flow
  238. 28.4: Gene Flow on the Indian Subcontinent
  239. 28.5: Gene Flow Between Archaic Human Populations
  240. 28.6: Tools and Techniques
  241. 28.7: Research Directions, Further Reading, Bibliography
  242. 28.8: European Ancestry and Migrations
  243. 29: Population Genetic Variation
  244. 29.1: Introduction
  245. 29.2: Population Selection Basics
  246. 29.3: Genetic Linkage
  247. 29.4: Natural Selection
  248. 29.5: Human Evolution
  249. 29.6: Current Research
  250. 29.7: Further Reading
  251. 30: Medical Genetics--The Past to the Present
  252. 30.1: Bibliography
  253. 30.2: Introduction
  254. 30.3: Goals of investigating the genetic basis of disease
  255. 30.4: Mendelian Traits
  256. 30.5: Complex Traits
  257. 30.6: Genome-wide Association Studies
  258. 30.7: Current Research Directions
  259. 30.8: Tools and Techniques
  260. 30.9: What Have We Learned?
  261. 31: Variation 2- Quantitative Trait Mapping, eQTLS, Molecular Trait Variation
  262. 31.1: Introduction
  263. 31.2: eQTL Basics
  264. 31.3: Structure of an eQTL Study
  265. 31.4: Current Research Directions
  266. 31.5: What Have We Learned?
  267. 31.6: Further Reading
  268. 31.7: Tools and Resources
  269. 31.8: Bibliography
  270. 32: Personal Genomes, Synthetic Genomes, Computing in C vs. Si
  271. 32.1: Introduction
  272. 32.2: Reading and Writing Genomes
  273. 32.3: Personal Genomes
  274. 32.4: Further Reading
  275. 32.5: Bibliography
  276. 33: Personal Genomics
  277. 33.1: Introduction
  278. 33.2: Epidemiology- An Overview
  279. 33.3: Genetic Epidemiology
  280. 33.4: Molecular Epidemiology
  281. 33.5: Causality Modeling and Testing
  282. 33.6: What Have We Learned?
  283. 34: Cancer Genomics
  284. Section 1: Introduction
  285. Section 2: Characterization
  286. Section 3: Interpretation
  287. Section 5: Further Reading
  288. Section 6: What Have We Learned?
  289. 35: Genome Editing
  290. 1: Introduction
  291. 2: Current Research Directions
  292. 3: What Have We Learned?
  293. Back Matter
  294. Index
  295. Index
  296. Glossary
  297. Detailed Licensing