RNA functions, structure and
Phylogenetics
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
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
RNA types & functions
Types of RNAs
Primary Function(s)
mRNA - messenger
translation (protein synthesis)
regulatory
rRNA - ribosomal
translation (protein synthesis)
<catalytic>
t-RNA - transfer
translation (protein synthesis)
hnRNA - heterogeneous
nuclear
precursors & intermediates of mature
mRNAs & other RNAs
scRNA - small cytoplasmic
signal recognition particle (SRP)
tRNA processing
<catalytic>
snRNA - small nuclear
snoRNA - small nucleolar
mRNA processing, poly A addition
<catalytic>
rRNA processing/maturation/methylation
regulatory RNAs (siRNA,
miRNA, etc.)
regulation of transcription and translation,
other??
L Samaraweera 2005
Outline
RNA Structure
• RNA primary structure
• RNA secondary structure & prediction
• RNA tertiary structure & prediction
Primary structure
•5’ to 3’ list of covalently linked nucleotides, named
by the attached base
•Commonly represented by a string S over the
alphabet Σ={A,C,G,U}
Secondary Structure
List of base pairs, denoted by i•j for a pairing between the i-th and j-th Nucleotides, ri
and rj, where i<j 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.
RNA secondary structure representation
..(((.(((......))).((((((....)))).))....)))
AGCUACGGAGCGAUCUCCGAGCUUUCGAGAAAGCCUCUAUUAGC
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
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:
1. i is unpaired, added on to
a structure for i+1…j
S(i,j) = S(i+1,j)
2. j is unpaired, added on to
a structure for i…j-1
S(i,j) = S(i,j-1)
RNA folding: Dynamic Programming
3. i j paired, added on to
a structure for i+1…j-1
S(i,j) = S(i+1,j-1)+e(ri,rj)
4. i j paired, but not to each other;
the structure for i…j adds together
structures for 2 sub regions,
i…k and k+1…j
S(i,j) = max {S(i,k)+S(k+1,j)}
i<k<j
RNA folding: Dynamic Programming
Since there are only four cases, the optimal score S(i,j) is just the
maximum of the four possibilities:
S (i  1, j )


S (i, j  1)

S (i, j )  max 
S (i  1, j  1)  e( ri , rj )

S (i, k )  S (k  1, j )
max
i

k

j

ri unpaired
rj unpaired
i, j base pair
i, j paired , but not to each other
To compute this efficiently, we need to make sure that the scores for
the smaller sub-regions have already been calculated
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
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”
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/index.html#rnaview
http://rutchem.rutgers.edu/~xiangjun/3DNA/
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
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
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
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
A
B
C
A rooted tree
D
time
D
B
An unrooted tree
time?
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
Tree Construction
Several methods:
• Distance-based or Clustering methods
• Parsimony
• Likelihood
• Bayesian
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
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
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 ==> homoplasy
• Susceptible to long-branch attraction (due to high
probability of convergent evolution)
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
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
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
How many possible trees?!?
Organisms
1
2
3
4
5
6
7
8
9
10
15
30
50
Trees
1
1
3
15
105
945
10,395
135,135
2,027,025
34,459,425
213,458,046,676,875
4.9518E38
2.75292E76
Searching for the optimal tree…
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.
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
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
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
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.