Bioinformatics and Functional Genomics
a.y. 2022 - 2023

Basics of Linux and the BASH

Elements of Programming in C/C++, R, Python

Computational Tools and Systems Management, the Operating System

Computational Tools and Systems Management, the Operating System

Tracking code Git

Reproducible software environments with Conda, Containers (Docker, Apptainer/Singularity)

Introduction to algorithms and basic data up to complex data structures: definitions, algorithms, simple data structures

Lists, arrays, simple operations, examples, implementation

Complex data structures, computation efficiency and relation between computation efficiency and data structures

Stacks and queues (definition, examples, implementation)

Trees, visit algorithms of a tree and tree operations. Binary trees, search trees, balanced binary trees, B-trees, AVL trees

Sets, dictionaries and hash tables, their operations and implementation concepts

Graphs, visit algorithms for graphs, operations and implementation concepts

Algorithms in deep

Divide and conquer. Recursion. Greedy techniques (knapsack problem, radix sorting problem, Huffman codes). Binary search. Sorting algorithms. Sequence pattern analysis and matching. Algorithm optimization. Complexity analysis of algorithms

Sequence Alignments

• Smith–Waterman

• Basic Local Alignment SearchTool (BLAST)

• Burrows-Wheeler Aligner (BWA)

• Bowtie and Burrows–Wheeler transform

• Bowtie2 and FM-index

• Quasi alignments (Salmon)

• Pseudo alignment (Kallisto)

Data Structures for alignments

BAM and CRAM

Genome index and gene annotations

GTF, BED, BIGWIG, VCF

Graphs

concepts, algorithms and implementation

Programming languages

paradigms; iterative, recursion, procedural, objects, functional. R and Python; Other such as Perl, Javscript, Ruby, ... where/when needed.

Relational databases

• Introduction

• the SQL query language

• Advance SQL

• Database Design: Entity Relationship Diagrams, Transactions, Indexes, Relational Algebra, Join

NoSQL the altenatives to RDBMS

• when to use NoSQL and how they work

• Key-Value Store concepts

• Key-Value db, Redis a data structures server

• Document Store, Elastic

Graph Databases the alternatives to NoSQL and RDBMS

• advantages over RDBMS and NoSQL

• Graph db, Neo4j

• Graph db, Semantic Databases Triple stores

Data Integration and Visualization

Introduction to NGS technologies

• Sequencing technologies, from the beginning to the most advanced solutions

• Introduction to sequencing technologies from a data analysts view

• Applications of sequencing technologies

• NGS Terminolgy

NGS data analyses

• DNA-seq

• RNA-seq

• ChIP-seq

• ATAC-seq

• Single Cell RNAseq

• Introduction to Downstream Analysis

• Databases for NGS analysis

• Accessing public sequencing repositories

Single Cell Spatial Transcriptomics

Managing workflow at scale with Nextflow

Statistical modeling for DE analysis

parametric vs non-parametric methods, binomial distribution, overdispersion, negative binomial model, multiple comparisons the false discovery rate (Bonferroni and Benjamini-Hochberg)

Performing various types of enrichment analyses

main types of enrichment analysis (list-based, ranked-based, list-based with relationship), statistics to test for enrichment (Fisher exact, hypergeometric, Kolmogorov-Smirnov, permutation)

Enrichment tools

DAVID, GSEA, GOstat, topGO, BINGO

Applying clustering algorithms

hierarchical clustering, distance metrics, k-means

From genetic alterations to the perturbation of molecular phenotypes

From mutation to mutational signatures or Mutational Signatures

Liquid Biopsy and genetics in liquid biopsy analysis

Examples of clinical trials or Clinical Trials

Introduction to statistics

Populations and sets of data

Descriptive statistics

Univariate data (location and variability of the data, empirical distributions, histogram, boxplot; paired data: table, scatterplot, sample correlation coefficient)

Probability theory

Bayes theorem, random variables and their relative distributions (discrete: Bernoulli, Poisson; continuous: Weibull, Gaussian), laws obtained by means of gaussian distribution (t-distribution, chi-square distribution, Fisher distribution), weak law of large numbers, central limit theorem

Statistical inference

Point estimation (estimators: bias, mean squared error, efficiency) and interval estimation (confidence intervals for the mean of normal distributions with known and unkown variance, Confidence intervals for the variance of normal distributions confidence interval for the mean of Bernoulli distributions)

Hypothesis testing

Terminology, tests of hypothesis for the mean of normal distributions with known and unknown variance, Test of hypothesis for the variance of normal distribution, for the mean of Bernoulli distributions and for two means and two variances

Regression methods

linear regression models, multiple regression models

Multivariate Analysis

PCA, Classification (kNN) and Discriminant Analysis (LDA), Multiple Regression Analysis (PLS)

Theory of Graphs

key concepts and terminology ( graph, node,edge, path, tree, adjacency matrix)

Networks

• models and properties (shortest-longest path, diameter, degree or connectivity, random vs scale-free networks,power law degree distributions, clustering coefficient)

• network structure (hubs, bottlenecks, information flow)

Biological Networks

relational network, correlation network, regulatory network. WGCNA an example of correlation network

Machine Learning theory and approaches for dimensionality reduction

• basic concepts (vectors and matrices, eigenvalues and eigenvectors) and preprocessing (scaling and mean-centering)

• supervised learning (support vector machine, model and feature selection, k-fold cross validation, overfitting)

• unsupervised learning (clustering, PCA, ICA)

Database search and FDR

Protein quantification strategies and analysis (lecture and practical)

Statistical analysis of proteomics data (lecture and practical)

Bioinformatics of proteomics data (lecture and practical)