Computational Biology
- Page ID
- 40956
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Computational biology, which overlaps significantly with bioinformatics, is a field dedicated to analyzing biological data through the development of computational algorithms, mathematical models, and statistical techniques to better understand biological processes, systems, and interactions. In earlier years, biologists had access to only limited amounts of experimental data, which restricted large-scale analysis and discovery. With advances in technology, especially in genomics and molecular biology, enormous volumes of biological data are now generated routinely. This rapid growth in data created new opportunities for scientific research, but it also introduced major challenges related to data analysis, storage, and interpretation. To address these challenges, researchers designed a variety of analytical tools and computational methods capable of extracting meaningful insights from complex biological information. However, despite these advancements, scientists often struggled to distribute, share, and collaborate on these methods efficiently within the research community.
- 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.
Thumbnail: Simple example of a molecular dynamics cage that shows the folding of a small amino acid chain, the tryptophan cage, in implicit solvent. (CC BY-SA 3.0 Unported; Robinmbetz via Wikipedia)