Introduction to Bioinformatics: Overview

Syllabus

Some old slides (download)

What's bioinformatics?

Short: Bioinformatics is about connecting computational and biological ideas.
Long: Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline. There are three important sub-disciplines within bioinformatics: the development of new algorithms and statistics with which to assess relationships among members of large data sets; the analysis and interpretation of various types of data including nucleotide and amino acid sequences, protein domains, and protein structures; and the development and implementation of tools that enable efficient access and management of different types of information. (NCBI)

Related fields

Translational bioinformatics
Health informatics/Biomedical informatics
Complex systems
Systems biology
Biophysics
Mathematical biology, which tackles biological problems using methods that need not be numerical and need not be implemented in software or hardware

What do you need to know?

Biology/CS (Algorithm; Programming)/Math/Statistics/Physics/Chemistry

Biology primer

Prokaryotes vs Eukaryotes; model organisms (E.coli, Yeast, C. elegans, fruit fly)
Cells
Genome, gene, genotype, phynotype
DNA, protein, the Central Dogma, the Genetic Code
Evolution of genes

From biological problems to computational problems (algorithms)

Many biological problems can be formulated as well known computational problems
Examples
- How do we compare two proteins -- Dynamic programming algorithm (edit distance calculation)
- How do we assemble genomes -- Graph algorithms
Computational abstractions: biological sequences as strings; networks as graphs

Bioinformatics meets big data

Adapting bioinformatics curricula for big data [2015]
Challenges raised by big data
- Data unification: data wrangling, i.e. obtaining the necessary data in the appropriate format, as well as the normalization necessary to make them comparable across sources.
- Computational and storage limitations: the difficulties and costs associated with keeping data, moving data and analyzing data.
- Multiple hypothesis testing: statistically addressing the likelihood of finding spurious associations in large data sets.
- Bias and confounding in the data: refer to challenges related to which experiments have been performed or which processes are most frequently assayed.