Bioinformatics and Computational Biology

• Bioinformatics applies computation and analysis techniques to biological data.
• It involves analyzing large datasets from automated systems like DNA sequencers to find new genes, mutations, and single nucleotide polymorphisms.

Nextstrain Example

• Nextstrain tracks pathogen evolution globally, providing up-to-date molecular data on agents like SARS-CoV-2, Influenza, Yersinia pestis, Mpox, and Ebola.

Computational Biology

• Computational biology develops predictive models based on experimental data, using bioinformatics techniques for analysis.
• Bioinformatics describes past events, while computational biology predicts future events.

"Omics" Fields

• Bioinformatics analysis supports biological fields known as "omics".
• These disciplines specialize in analyzing specific types of biological data.

Proteomics

• Proteomics studies the interactions, functions, composition, and structures of proteins and their cellular activities.
• It examines data like binding reactions, receptor activation, and phosphorylation.
• Proteomics is crucial in drug discovery and toxicology.

Transcriptomics

• Transcriptomics compares mRNA production in cells under different conditions, like cancer vs. non-cancerous cells or before and after fertilizer application.
• It can reveal previously unknown genetic pathways by correlating increased production from multiple genes.

Genomics

• Genomics examines the entirety of the genome, including structure, location, and comparison among organisms.
• Sequencing data is the primary data source.
• Decreasing sequencing costs have led to an exponential increase in available data and a need for faster, more accurate analysis techniques.

Metagenomics

• A genomics branch that analyzes multiple genomes in a sample.
• Often used when studying microbial communities in environmental or natural flora samples.
• Sequencing the 16s rRNA gene is a common technique.

Metabolomics

• Metabolomics analyzes metabolites in cells to create precise pictures of various disease states.
• Unlike genomics, which indicates potential, metabolomics shows what is actively working inside a cell.
• Analytical approaches are often shared with proteomics and lipidomics, such as HPLC and GC-MS.

Lipidomics

• Lipidomics studies pathways and networks of cellular lipids in biological systems.
• It's a subset of metabolomics focused on lipids and their role in cell function and disease states.
• Given the importance of lipids to biological structures, this field is expected to grow.

Epigenomics

• Epigenomics studies reversible modifications to the genome that affect gene expression without altering the genetic code.
• Examples include DNA methylation and histone modification.
• Research focuses on the effects of external modifiers from the environment, diet, or stress.

Language specific to "Omics"

• Genomics investigates the genome.
• Transcriptomics investigates the transcriptome.
• Metabolomics investigates the metabolome.
• Lipidomics investigates the lipidome.

Microarrays

• By the mid-90s, researchers used microarrays to investigate expression profile differences in tissues, evaluate bacterial cell lines, study genes involved in cell division, and investigate drug dosing series.

Microarray Process

• Templates for genes of interest are obtained and amplified by PCR.
• Aliquots (~5 nL) are printed on coated glass microscope slides after purification and quality control.
• Total RNA from test and reference samples is fluorescently labeled (Cy3 or Cy5) using reverse transcription.
• Fluorescent targets are pooled and hybridized to the clones on the array under stringent conditions.

Fluorescence Detection

• Fluorescence detection of the probe response is done using a specialized scanner.
• Monochrome images are imported into software, pseudo-colored, and merged.
• The software attaches information about the clones, including gene name, clone identifiers, intensity values, intensity ratios, normalization constants, and confidence intervals.

Microarray Data

• Data from a single hybridization experiment is viewed as a normalized ratio (Cy3/Cy5).
• Significant deviations from 1 indicate increased (>1) or decreased (<1) expression.

Bioinformatics uses computation to analyze biological data, like finding genes and mutations in large datasets. Nextstrain is an example, tracking pathogen evolution. Computational biology develops predictive models using bioinformatics analysis to forecast biological events.