computational biology or bioinformatics

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  1. Bioinformatics
    using the practice of information processing ( management, analysis and interpretaton of data) to study biological systems
  2. Computational Biology:
    using biological data to develop algorithms and methods of examining biological systems
  3. systems biology
    • The study of the interactions between the components of biological
    • systems, and how these interactions give rise to the function and behavior
    • of that system


    • The objective is a model of the interactions in a system
    • • the experimental techniques are those that are system-wide and atempt to be as
    • complete as possible
    • • Complex models
    • • Broad scale
  4. Genomics: The Human Genome
    • • First published in 2000/2001
    • • Took 10 years to finish and cost ~$3
    • billion
    • • 3.2 gigabases
    • • 22 paired chromosome
    • • 2 heterogameGc chromosomes
    • • # of Genes: 100,000 or 30,000 or
    • 25,000 or 20,000
    • • ~2-3% of the genome
    • • Or maybe 80% of the genome
    • functional…?
  5. Genome Components: Repetitive Elements
    • • Functional
    • • dispersed gene familes (e.g. actin, globin)
    • • Tandem gene family arrays
    • • rRNA genes (250 copies)
    • • tRNA (
    • • histone
  6. Histones
    • highly alkaline proteins found in
    • eukaryotic cell nuclei that
    • package and order the DNA into
    • structural units called
    • nucleosomes
    • • 5 Types (H1/H5, H2A, H2B, H3
    • and H4)
    • • Functions:
    • • Compact DNA
    • • Chromatin Regulation
    • • Transcription Regulation
    • • DNA Damage
  7. Transposable elements – viral origin
    • • Retrotransposons - copy & paste
    • • SINEs (short-interspersed elements) – 200-300bp
    • long, e.g. Alu elements, 13%
    • • LINEs – long-interspersed elements, 1-5kb long,
    • 21%
    • • LTRs – long-terminal repeat retrotransposons (8%)
    • • DNA transposons – cut & paste(2%)
  8. Pseudogenes
    gene duplications
  9. Simple repeats
    • • Minisatellites – 14-500bp repeats, 1-5kb long
    • • Microsatellite – up to 13bp, 100s kb long
    • • Telomeres – 250-1000 repeat at chromosome
    • ends
    • • Centromeres - part of a chromosome that links
    • sister chromatids
  10. How do genomes determine who we are?
  11. • Phenotype -> observable traits
    • • Genotype -> DNA
    • • Life History -> Development (integrated total of your experiences)
    • • Epigenetics -> interface between genotype and life history (“on-top-of genetics”
  12. Databases
    • • Systematic genome sequencing
    • • Protein expression palerns
    • • Metabolic pathway
    • • Protein interaction patterns and regulatory networks
    • • Scientific literature
  13. Genomics and Ethics
    • Ethical, legal and social issues
    • • National Human Genome Research Institute's (NHGRI) Ethical, Legal and
    • Social Implications (ELSI)
    • • Psychosocial and ethical issues in genomics research.
    • • Psychosocial and ethical issues in genomic medicine.
    • • Legal and public policy issues.
    • • Broader societal issues.
  14. Genomics: Genome Science
    • • Study of the structure, content,
    • and evolution of genomes
    • • Genome Project Core Aims:
    • • Establish integrated web-based
    • database and research interface
    • • Assemble physical (bp distance)
    • and geneGc maps (linkage) of the
    • genome
    • • Generate and order genomic and
    • expressed gene sequences
    • • Identify and annotate complete
    • set of genomic elements
  15. Genetic and physical maps
    • Genetic map
    • • Gene linkage:
    • • The closer the two markers ->
    • more likely they are to be passed
    • on to the next generation together
    • (co-segregation)
    • • patterns of all markers used to
    • reconstruct their order
    • • Need parents and offspring
    • • Thomas Hunt Morgan and Arthur
    • Sturtevant
    • • Partial linkage
    • • Recombination Frequency
  16. Genetic and physical maps
    • • Physical map
    • • “Physical” distance: Number of
    • base pairs
    • • Genetic map depends on
    • recombination
    • • Observable? Random?
    • • Methods:
    • • Restriction Mapping
    • • FISH (fluorescent in situe
    • hybridization)
    • • STS (sequence tagged site)
  17. Generate and order gene sequences
    • • Reads – Sequence
    • • Contig – set of sequences order
    • into a continuous linear stretch
    • • Scaffold – set of ordered contigs
    • • Supercontig – like scaffold, but
    • still has some gaps
  18. Identify and annotate genomic elements
    • • Gene prediction
    • • Align coding data to genome (cDNA
    • and protein sequences)
    • • Predict genes based on rules
    • • Open-reading frames (ORFs)
    • • Transcription start and stop sites
    • • exon/intron boundaries
    • • Repeat prediction
    • • Annotation
    • • Link sequence to genetic data about
    • function, expression, phenotype, etc.
    • • Homology - shared ancestry;
    • sequence similarity
  19. Characterize DNA Sequence Diversity
    • • Restriction Sites
    • • Micro/Minisatellites
    • • SNPs
    • • Single nucleotide polymorphisms
    • • Largest amount of quantitative
    • genetic variation
    • • Phasing (Gametic phasing)
    • • Build Haplotypes (genes inherited
    • from a single parent)
    • • Linkage
  20. Atlases of Gene Expression
    • • Where are genes expressed and
    • under what conditions
    • • Function by association
    • • Transcription profiling
    • • Methods
    • • Northern blots, in situ
    • hybridization, quantitaGve PCR
    • • EST(expressed sequence tags)
    • sequencing and SAGE(serial
    • analysis of gene expression)
    • • Microarrays; RNA-seq
  21. Functional genomics
    • • Ascertain biochemical, cellular
    • and/or physiological properGes of
    • each and every gene product
    • (beyond homology)
    • • Near-saturation mutagenesis
    • • Screening of 100ks mutants
    • • High-throughput reverse geneGcs
    • • Knockouts
    • • Proteomics
    • • Protein expression
    • • Protein-protein interacGons
    • • Protein modificaGons
    • • Protein structure
  22. Comparative Genomics
    • • Synteny – conservation of
    • chromosomal gene order
    • • e.g. chromosome painting
    • • Homology – shared ancestry
    • • Orthologs – “true” homologs
    • • Paralogs – arose from gene
    • duplication
  23. • Inducitve Reasoning
    • • A sufficient number of confirmatory observations and no contradictory
    • observations allow us to conclude that a theory or law is true
    • • No amount of confirmatory observations can ever prove a theory
    • • "Absence of evidence is not evidence of absence” – Martin Rees
  24. Deductive Reasoning
    • • Process of deriving explanations or predictons from laws or theories
    • • Principle of falsificaion:
    • • Theories (hypotheses) are disproved because proof is logically impossible
    • • Hypothesis is falsifiable if there are logically possible observaHon that are inconsistent
    • with it
  25. PaBern description
    • • Inductive
    • • Observation of pattern or departure from pattern in nature
  26. Models (theory)
    • • Explanation of an observed pattern; a series of statements (or
    • formulae) that explain why the observaHons have occurred)
    • • Come from the interaction between insight, existing theory, belief,
    • and previous observation (inductive process!)
    • • 3 Types:
    • • Verbal Models: non-mathemaHcal explanaHon for how nature works
    • • Empiric Models: mathematical descriptions of relationships resulting from
    • process rather than process itself; often statistical and describe relationship
    • between response and predictors
    • • e.g. RelaHonship between metabolism and body mass
    • • Theoretic models: study processes
    • • e.g. spatial variation in snail abundances caused by variation in settlement of larvae
  27. Hypotheses and tests
    • • Predictions deduced from models; research or logical hypotheses
    • • If model is correct, we would predict a specific set of observations
    • • Use critical or formal tests to evaluate models by falsifying hypotheses.
    • • The proof of a theory is considered to be logically impossible
    • • How do you pick a hypothesis to test?
    • • Then process of falsificaHon test -> specify a null hypothesis (H0)that
    • includes all possibilities expect the prediction in the hypothesis -> disprove
    • the null hypothesis
    • • Experimentally test hypothesis -> if H0 rejected, logical hypothesis (or
    • alternative hypothesis (HA) and model are supported.
  28. Alternatives to falsification

Card Set Information

Author:
doncheto
ID:
316160
Filename:
computational biology or bioinformatics
Updated:
2016-02-23 08:27:15
Tags:
computational biology or bioinformatics
Folders:
bioinformatics
Description:
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