bioinformatics final exam broad quizlet

EXPASY

Portal linking many bioinformatics tools, including UniProt and Prosite.

UniProt

Main site for full protein information: sequence, function, domains, variants.

Swiss-Prot

Manually reviewed, high-quality UniProt protein entries.

Prosite

Database of protein domains, families, and functional sites.

PDB

Protein structure database with 3D models from X-ray, NMR, cryo-EM, and AlphaFold uploads.

PDB-101

Educational site explaining how to interpret protein structures.

BLAST

Fast local alignment tool using heuristic seed-and-extend search.

Heuristic search

Fast method using rules and short matches instead of full comparisons.

E-value

Expected number of random matches; lower = more significant.

BLASTN

Compares nucleotide query to nucleotide database.

BLASTP

Compares protein query to protein database.

BLASTX

Translates nucleotide sequence and searches protein databases.

TBLASTN

Searches a protein query against translated nucleotide databases.

Ortholog

Genes in different species from a common ancestor; usually same function.

Paralog

Duplicated genes within the same species; may gain new functions.

Conserved domain

Shared functional region of a protein preserved across evolution.

Local alignment

Matches only the most similar region between sequences.

Compare splice variants (BLASTN)

Run BLASTN, check coding region coordinates, compare exon differences.

Find human ortholog (BLASTP)

BLAST yeast protein vs human; pick lowest E-value + longest alignment.

Identify paralogs

BLASTP in human database; multiple strong matches = paralogs.

Find conserved domains

Use conserved domain section of BLAST result to see matched regions.

Evaluate BLAST hits

Check E-value, identity, coverage, length of alignment, domain overlap.

Find protein length

Open UniProt entry → see aa count under “Sequence.”

Find transmembrane domains

Check “Topology” or “Domains” section.

Find protein family

See “Family and Domains” section.

Find interactions (BioGRID)

Scroll to “Interactions” → follow BioGRID link.

Check similar proteins in humans

Use “Homology” section.

How to solve 6.2–6.6

Use the correct database or BLAST tool, record lengths/E-values/domains, compare sequences.

Systems biology

uses large data sets to understand behavior of collective components

Holistic approach

Looks at entire system behavior and emergent properties rather than isolated components.

Interdisciplinary

Combines biology, math, computing, engineering, and data science.

Modeling

Uses computational and mathematical models to simulate pathways, reactions, and system behavior.

Data integration

Combines genomics, transcriptomics, proteomics, and metabolomics to analyze whole systems.

Applications

Used for medicine, pathway analysis, target discovery, metabolic engineering, synthetic biology.

Complex system

Biological system made of interacting components like genes, proteins, and metabolites with feedback loops and boundaries.

Inputs and outputs

Inputs include nutrients or signals; outputs include growth, metabolism, hormone release.

Feedback control

Positive and negative loops that maintain system balance.

Boundary

The physical limit of a system (such as a cell membrane) separating inside from outside.

Chaos

Highly sensitive system behavior where small changes cause major differences over time.

Emergent property

A property that appears only when parts work together (e.g., muscle contraction from multiple tissues).

Attractor

A stable long-term state a biological system tends to return to.

Tipping point

The threshold where a system shifts into a new state or behavior.

Therapeutic window

Range between helpful dose and harmful dose of a drug; tipping points mark its edges.

Genomics

Study of all DNA and gene sequences in an organism.

Transcriptomics

Study of gene expression using mRNA levels.

Proteomics

Study of proteins present in a cell, including amounts and functions.

Metabolomics

Study of small molecules and metabolic reactions in cells.

Omics integration

Combining all "omes" to understand how changes at one level affect the entire system.

Examples of system layering

Changes in genes affect transcripts, proteins, and metabolic outputs.

GLP-1

Glucagon-like peptide involved in glucose regulation and insulin signaling.

Modeling GLP-1 agonists

Simulates how GLP-1 drugs affect metabolism, insulin release, pathways, and dosing.

Benefits of GLP-1 modeling

Predicts glucose changes, tests doses, finds tipping points, reduces lab experiments, improves diabetes treatment.

System components

Genes, proteins, metabolites, and pathways that interact to produce behavior.

System modularity

Systems are built from smaller interacting modules (e.g., pathways, tissue types).

Chaos vs predictability

Systems can be predictable short-term but chaotic long-term.

Pioneers of systems science

Alan Turing, Benoit Mandelbrot, Leroy Hood.

Systems biology software

Includes Cytoscape for networks, COPASI for model simulations, Python/Jupyter for data, and KBase for large-scale analysis.

circulating tumor DNA (ctDNA)

Small DNA fragments shed by tumors into the bloodstream; used for noninvasive cancer detection.

How to analyze ctDNA

Compare mutations, methylation, and fragment size to normal DNA; use databases like COSMIC and TCGA.

ctDNA differences

Somatic mutations, shorter fragment sizes, abnormal methylation, copy number changes, rearrangements.

Why some cancers chosen for ctDNA

Common cancers with well-studied, targetable mutations and high levels of ctDNA shedding.

Evolution

Genetic change in a population over time; descent with modification.

Main mechanisms of evolution

Mutation, natural selection, genetic drift, gene flow.

Gradualism

Slow, steady evolutionary change.

Punctuated equilibrium

Rapid bursts of change with long periods of stasis.

Allopatric speciation

New species form in isolated populations.

Sympatric speciation

New species arise in the same geographic area.

Parsimony

In phylogenetics, the simplest tree requiring the fewest mutations is preferred.

Cladistics

Groups species by shared derived traits (synapomorphies).

Outgroup

Determines what traits are ancestral in a tree.

Molecular clock

Uses mutation rates to estimate divergence times.

Limit of molecular clocks

Mutation rates vary across genes and lineages, reducing accuracy.

Distance method

for trees

Informative site

Shared derived base supporting a specific grouping.

Noninformative site

Same in all taxa; gives no relationship information.

Metagenomics

Sequencing all DNA directly from an environment to study whole communities.

CGP

Comprehensive genomic profiling; broad NGS test detecting many mutation types.

Advantage of CGP

Detects SNVs, indels, CNVs, fusions, splice variants using small tissue and data can be reanalyzed.

SNV

Single base substitution.

Indel

Small insertion or deletion.

CNV

Gene amplification or deletion.

Fusion

Rearranged gene joining two gene parts.

Splice variant

Altered RNA isoform caused by changes in splicing.

TMB

Number of coding mutations; predicts immunotherapy response.

MSI

Instability in short DNA repeats; important biomarker for IO therapy.

ctDNA

Tumor DNA fragments in blood; used when tissue biopsy isn’t available.

Advantages of matching therapy

Improves response rates, survival, and side-effect profiles.

Most targeted NSCLC gene

EGFR has the most targeted drugs.

Hotspot panel limit

May miss ~81% of mutations and often fails to detect splice variants.

NCCN and ESMO

Groups that set cancer testing guidelines.

Proteins conserved because

Changes would disrupt essential or critical biological function.

Using DNA for phylogeny

DNA differences reveal species relationships and divergence timing.

How to build a tree

Align sequences, find informative sites, apply parsimony or distance methods.

Why emergent properties occur

Interactions among components create new behaviors not seen in individual parts.