RNA functions, structure and Phylogenetics

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RNA functions, structure and Phylogenetics
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RNA functions

• Storage/transfer of genetic information
• Genomes
• many viruses have RNA genomes
• single-stranded (ssRNA)
• e.g., retroviruses (HIV)
• double-stranded (dsRNA)
• Transfer of genetic information
• mRNA "coding RNA" - encodes proteins

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RNA functions

• Structural
• e.g., rRNA, which is a major structural
  component of ribosomes
• BUT - its role is not just structural, also
• Catalytic
• RNA in the ribosome has peptidyltransferase
  activity
• Enzymatic activity responsible for peptide bond
  formation between amino acids in growing peptide
  chain
• Also, many small RNAs are enzymes "ribozymes
• Regulatory
• Recently discovered important new roles for RNAs
• In normal cells
• in "defense" - esp. in plants
• in normal development
• e.g., siRNAs, miRNA

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RNA types functions
L Samaraweera 2005
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Outline
RNA Structure

• RNA primary structure
• RNA secondary structure prediction
• RNA tertiary structure prediction

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Primary structure

• 5 to 3 list of covalently linked nucleotides,
  named by the attached base
• Commonly represented by a string S over the
  alphabet SA,C,G,U

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Secondary Structure
List of base pairs, denoted by ij for a pairing
between the i-th and j-th Nucleotides, ri and rj,
where iltj by convention. Helices are inferred
when two or more base pairs occur adjacent to one
another Single stranded bases within a stem are
called a bulge of bulge loop if the single
stranded bases are on only one side of the
stem. If single stranded bases interrupt both
sides of a stem, they are called an internal
(interior) loop.
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RNA secondary structure representation
..(((.(((......))).((((((....)))).))....))) AGCUAC
GGAGCGAUCUCCGAGCUUUCGAGAAAGCCUCUAUUAGC
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RNA structure prediction

• Two primary methods for ab initio RNA secondary
• structure prediction
• Co-variation analysis (comparative sequence
  analysis)
• . Takes into account conserved patterns of base
  pairs during
• evolution (more than 2 sequences)
• Minimum free-energy method
• . Determine structure of complementary regions
  that are
• energetically stable

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RNA folding Dynamic Programming
There are only four possible ways that a
secondary structure of nested base pair can be
constructed on a RNA strand from position i to j

• i is unpaired, added on to
• a structure for i1j
• S(i,j) S(i1,j)

• j is unpaired, added on to
• a structure for ij-1
• S(i,j) S(i,j-1)

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RNA folding Dynamic Programming

• i j paired, but not to each other
• the structure for ij adds together
• structures for 2 sub regions,
• ik and k1j
• S(i,j) max S(i,k)S(k1,j)

• i j paired, added on to
• a structure for i1j-1
• S(i,j) S(i1,j-1)e(ri,rj)

iltkltj
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RNA folding Dynamic Programming
Since there are only four cases, the optimal
score S(i,j) is just the maximum of the four
possibilities
To compute this efficiently, we need to make sure
that the scores for the smaller sub-regions have
already been calculated
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Other methods

• Base pair partition functions
• Calculate energy of all configurations
• Lowest energy is the prediction
• Statistical sampling
• Randomly generating structure with probability
  distribution energy function distribution
• This makes it more likely that lowest energy
  structure is found
• Sub-optimal sampling

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RNA tertiary structure (interactions)
In addition to secondary structural interactions
in RNA, there are also tertiary interactions,
including (A) pseudoknots, (B) kissing hairpins
and (C) hairpin-bulge contact.
Pseudoknot
Kissing hairpins
Hairpin-bulge
Do not obey parentheses rule
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Useful web sites on RNA

• Comparative RNA web site
• http//www.rna.icmb.utexas.edu/
• RNA world
• http//www.imb-jena.de/RNA.html
• RNA page by Michael Suker
• http//www.bioinfo.rpi.edu/zukerm/rna/
• RNA structure database
• http//www.rnabase.org/
• http//ndbserver.rutgers.edu/ (nucleic
  acid database)
• http//prion.bchs.uh.edu/bp_type/ (non
  canonical bases)
• RNA structure classification
• http//scor.berkeley.edu/
• RNA visualisation
• http//ndbserver.rutgers.edu/services/download/in
  dex.htmlrnaview
• http//rutchem.rutgers.edu/xiangjun/3DNA/

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Phylogenetics

• Phylogenetics is the branch of biology that deals
  with evolutionary relatedness
• Phylogenetics studying or estimating the
  evolutionary relationships among organisms
• Phylogenetics on sequence data is an attempt to
  reconstruct the evolutionary history of those
  sequences
• Relationships between individual sequences are
  not necessarily the same as those between the
  organisms they are found in
• The ultimate goal is to be able to use sequence
  data from many sequences to give information
  about phylogenetic history of organisms

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History

• Darwin (1872)?
• Included a tree diagram in On the Origin of
  Species
• Haeckel (1874)?
• Ontogeny recapitulates phylogeny
• Phenetics (Sneath, Sokal, Rohlf)?
• Common ancestry cannot be inferred so organisms
  should be grouped by overall similarity
• Distance-based methods

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Phylogenetic tree

• Node ancestral taxa
• Root common ancestor of all taxa on the tree
• Clade group of taxa and their common ancestor
• Branch length may be scaled to represent time,
  substitutions
• Nodes may be rotated without a change in meaning
• May include extant and extinct taxa

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Phylogenetic tree
Phylogenetic relationships usually depicted as
trees, with branches representing ancestors of
children the bottom of the tree (individual
organisms) are leaves. Individual branch points
are nodes.
C
A
D
time
B
A
B
C
D
A rooted tree
An unrooted tree
time?
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Characteristics of the tree

• We will only consider binary trees edges split
  only into two branches (daughter edges)
• rooted trees have an explicit ancestor the
  direction of time is explicit in these trees
• unrooted trees do not have an explicit ancestor
  the direction of time is undetermined in such
  trees

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Tree Construction

• Several methods
• Distance-based or Clustering methods
• Parsimony
• Likelihood
• Bayesian

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Types of phylogenetic analysis methods

• Phenetic trees are constructed based on
  observed characteristics, not on evolutionary
  history
• Cladistic trees are constructed based on fitting
  observed characteristics to some model of
  evolutionary history

Distance methods
Parsimony and Maximum Likelihood methods
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Distance matrix methods

• Create a matrix of the distance between each pair
  of organisms and create a tree that matches the
  distances as closely as possible
• Pairwise distance, Least squares, minimum
  evolution, UPGMA, neighbor-joining methods
• Distance scoring matrices for amino acid
  sequences

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Parsimony

• Parsimony methods are based on the idea that the
  most probable evolutionary pathway is the one
  that requires the smallest number of changes from
  some ancestral state
• For sequences, this implies treating each
  position separately and finding the minimal
  number of substitutions at each position
• Convergent evolution, parallel evolution,
  reversals gt homoplasy
• Susceptible to long-branch attraction (due to
  high probability of convergent evolution)?

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Maximum Likelihood

• Search among all possible trees for the tree with
  the highest probability or likelihood of
  producing our data given a particular model of
  evolution
• Maximum likelihood reconstructs a tree according
  to an explicit model of evolution.
• But, such models must be simple, because the
  method is computationally intensive

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Bayesian Analysis

• Similar to Likelihood, but it searches among all
  possible trees to find the tree with the highest
  likelihood or probability of occurring given our
  data

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Models of evolution

• Vary in the number and type of parameters to be
  optimized
• base frequencies
• substitution rates
• transition/transversion ratios
• Separate models of evolution in individual
  nucleotides, codons, or amino acids

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How many possible trees?!?

• Organisms Trees
• 1 1
• 2 1
• 3 3
• 4 15
• 5 105
• 6 945
• 7 10,395
• 8 135,135
• 9 2,027,025
• 10 34,459,425
• 15 213,458,046,676,875
• 30 4.9518E38
• 50 2.75292E76

Searching for the optimal tree
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Support for phylogenetic methods

• Bacteriophage T7 (Hillis et al. 1992) Picked
  correct tree topology out of 135,135
  possibilities using 5 different methods. Branch
  lengths varied.
• Lab mice (Atchely Fitch 1991) Almost
  perfectly identified the known genealogical
  relationships among 24 strains of mice.

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Assessing trees

• The bootstrap randomly sample all positions
  (columns in an alignment) with replacement --
  meaning some columns can be repeated -- but
  conserving the number of positions build a large
  dataset of these randomized samples

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The bootstrap sampling

• Then use your method (distance, parsimony,
  likelihood) to generate another tree
• Do this a thousand or so times
• Note that if the assumptions the method is based
  on hold, you should always get the same tree from
  the bootstrapped alignments as you did originally
• The frequency of some feature of your phylogeny
  in the bootstrapped set gives some measure of the
  confidence you can have for this feature

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Phylogeny programs

• PHYLIP- one of the earliest (1980), freely
  distributed, parsimony, maximum likelihood, and
  distance matrix methods
• PAUP- probably most widely used,
• parsimony, likelihood, and distance matrix
  methods, more features than PHYLIP
• MacClade, MEGA, PAML, TREE-PUZZLE, DAMBE, NONA,
  TNT, many others

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Orthologs vs. Paralogs

• When comparing gene sequences, it is important to
  distinguish between identical vs. merely similar
  genes in different organisms.
• Orthologs are homologous genes in different
  species with analogous functions.
• Paralogs are similar genes that are the result of
  a gene duplication.
• A phylogeny that includes both orthologs and
  paralogs is likely to be incorrect.
• Sometimes phylogenetic analysis is the best way
  to determine if a new gene is an ortholog or
  paralog to other known genes.