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Bioinformatics
using the practice of information processing ( management, analysis and interpretaton of data) to study biological systems
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Computational Biology:
using biological data to develop algorithms and methods of examining biological systems
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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
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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…?
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Genome Components: Repetitive Elements
- • Functional
- • dispersed gene familes (e.g. actin, globin)
- • Tandem gene family arrays
- • rRNA genes (250 copies)
- • tRNA (
- • histone
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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
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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%)
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Pseudogenes
gene duplications
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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
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How do genomes determine who we are?
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• Phenotype -> observable traits
- • Genotype -> DNA
- • Life History -> Development (integrated total of your experiences)
- • Epigenetics -> interface between genotype and life history (“on-top-of genetics”
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Databases
- • Systematic genome sequencing
- • Protein expression palerns
- • Metabolic pathway
- • Protein interaction patterns and regulatory networks
- • Scientific literature
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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.
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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
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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
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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)
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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
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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
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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
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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
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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
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Comparative Genomics
- • Synteny – conservation of
- chromosomal gene order
- • e.g. chromosome painting
- • Homology – shared ancestry
- • Orthologs – “true” homologs
- • Paralogs – arose from gene
- duplication
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• 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
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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
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PaBern description
- • Inductive
- • Observation of pattern or departure from pattern in nature
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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
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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.
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Alternatives to falsification
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