Computational Biology
- Page ID
- 40956
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Computational biology, which includes many aspects of bioinformatics, is the science of using biological data to develop algorithms or models in order to understand biological systems and relationships. Until recently, biologists did not have access to very large amounts of data. This data has now become commonplace, particularly in molecular biology and genomics. Researchers were able to develop analytical methods for interpreting biological information, but were unable to share them quickly among colleagues.
- Book: Computational Biology - Genomes, Networks, and Evolution (Kellis et al.)
- 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.
- Front Matter
- 1: Introduction to the Course
- 2: Sequence Alignment and Dynamic Programming
- 3: Rapid Sequence Alignment and Database Search
- 4: Comparative Genomics I- Genome Annotation
- 5: Genome Assembly and Whole-Genome Alignment
- 6: Bacterial Genomics--Molecular Evolution at the Level of Ecosystems
- 7: Hidden Markov Models I
- 8: Hidden Markov Models II-Posterior Decoding and Learning
- 9: Gene Identification- Gene Structure, Semi-Markov, CRFS
- 10: RNA Folding
- 11: RNA Modifications
- 12: Large Intergenic Non-Coding RNAs
- 13: Small RNA
- 14: MRNA Sequencing for Expression Analysis and Transcript Discovery
- 15: Gene Regulation I - Gene Expression Clustering
- 16: Gene Regulation II - Classification
- 17: Regulatory Motifs, Gibbs Sampling, and EM
- 18: Regulatory Genomics
- 19: Epigenomics/Chromatin States
- 20: Networks I- Inference, Structure, Spectral Methods
- 21: Regulatory Networks- Inference, Analysis, Application
- 22: Chromatin Interactions
- 23: Introduction to Steady State Metabolic Modeling
- 24: The Encode Project- Systematic Experimentation and Integrative Genomics
- 25: Synthetic Biology
- 26: Molecular Evolution and Phylogenetics
- 27: Phylogenomics II
- 28: Population History
- 29: Population Genetic Variation
- 30: Medical Genetics--The Past to the Present
- 31: Variation 2- Quantitative Trait Mapping, eQTLS, Molecular Trait Variation
- 32: Personal Genomes, Synthetic Genomes, Computing in C vs. Si
- 33: Personal Genomics
- 34: Cancer Genomics
- 35: Genome Editing
- Back Matter
- Book: A Primer for Computational Biology (O'Neil)
- A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work.
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Contributors and Attributions
Wikipedia: https://en.Wikipedia.org/wiki/Computational_biology