Statistical Methods for Bioinformatics and Computational Biology

The purpose of this topic is to introduce statistical methodologies in important concepts in bioinformatics and computational biology. The course examines statistical methodologies used in two major areas: gene expression data analysis and high-throughput DNA sequence analysis. ANOVA, multivariate data analysis, Z-test, t-test, regression, Bayesian statistics, bootstrap, Monte-Carlo simulation, clustering algorithms, Markov Chain, Hidden Markov Chain, mixture model, and other statistical theory or methods will be introduced in this course. Students will learn the fundamental concepts and programming skills required to perform the most common bioinformatic analyses on data generated by current molecular biology research.
This interdisciplinary course may benefit students from various majors in different ways. The lectures will cover both genomics principles and simple R code writing to carry out statistical analyses. Students with no prior experience in biology or programming should not be intimidated, but rather motivated to learn. Students with a passion for bioinformatics, gene expression analysis, and high throughput sequence data analysis are especially encouraged to apply.

KEY FEATURE

Simple stochastic models or, more recently, machine learning techniques that can use (and needs) massive amounts of very heterogeneous data are able to analyse this type of data.

Computational neuroscience, computational embryology, whole-cell modelling, biosphere modelling, and many other broad fields fall under the umbrella of computational biology.

A wider variety of methods, including Dynamical Systems, Partial Differential Equations, Physical Chemistry, Dynamics on Networks, and more, are also part of Computational Biology.

Bioinformatics and computational biology have recently come into the spotlight due to the significance of the COVID-19 pandemic and climate change.

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    1. An overview of molecular biology. Contains the central dogma DNA/mRNA/proteins, post-translational protein modification and structure, signal transduction, and gene regulation. Probing methods for the genome, transcriptome, proteome, and metabolome.
    2. Data on DNA and protein sequences Methods for defining and determining sequence similarity, such as BLAST. Genes, regulatory regions, and other issues with gene sequence Protein structure and sequence Homonymy and evolution Linkage disequilibrium and sequence variation The structure of haplotype blocks is determined.
    3. Biological analysis methods PCR, gene expression arrays, mass spectrometry, and NMR spectroscopy are some of the techniques used. Methods of separation include 2D gel electrophoresis, gas and liquid chromatography. Analytical data properties Calibration, error models, “true values” correlations, and measurement error Transformation and normalisation, baseline correction, and other issues with pre-processing
    4. Determination of significance for a large number of variables in high-throughput biological data, such as gene expression arrays and proteomics by mass spectrometry. Problems with applying standard statistical methods variable by variable. Controlling error rates includes controlling the family-wise error rate and the false discovery rate. Taking into account variance heterogeneity and gene dependence.
    5. Biological research experimental design and analysis: group comparison, factorial, time course, dose-response, survival.
    6. Discrimination and categorising (supervised learning). Multivariate classification methods Variable selection, dimension reduction, and model selection methods Cross-validation is used to select parameters, models, and methods. Classification performance estimation Applications will include high-throughput biological assay data from gene expression arrays, proteomics via mass spectroscopy, and metabolomics via NMR spectroscopy.
    7. Grouping (unsupervised learning). Variable and subject clustering methods Evaluating clustering method performance. Other advanced topics include linkage, phylogeny, haplotype analysis, graphs as biological network models, and hidden Markov models for sequence analysis.

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