Biostats

Multiple Sequence Alignment

Multiple Sequence Alignment

combines both optimal (global/local) and heuristic alignment; cannot compare DNA to protein due to different scoring matrices

Profile Alignment

profile is created by taking a finished alignment and counting the frequency of every letter and gap and each location; progressively aligns all sequences pairwise, starting with the most similar

ClutalW

cluster alignment weighted; progressive alignment strategy; neighbor joining guide tree; lower accuracy; medium speed; use for small datasets with similar sequences

T-Coffee

tree-based consistency objective function for alignment evaluation; consistency-based alignment strategy; neighbor-joining and consistency weights guide tree; medium accuracy; lower speed; use for small datasets

MUSCLE

multiple sequence comparison by log-expectation; iterative progress and refinement alignment strategy; UPGMA guide tree; higher accuracy; higher speed; use for medium-large datasets

MAFFT

multiple alignment using fast fourier transform; progressive and iterative refinement alignment strategy; UPGMA/NJ guide tree; highest accuracy; highest speed; use for large datasets

Molecular Evolution

Mutation Types

Single Base Substitutions

AKA point mutations; a single base is replaced by another

Transition

same class of nucleotide; purine to purine or pyrimidine to pyrimidine

Transversion

different class of nucleotide; purine to pyrimidine or pyrimidine to purine

Synonymous

encodes for the same amino acid

Silent Mutation

the new nucleotide alters the codon but does not alter the amino acid for which it encodes

Nonsynonymous

encodes for a different amino acid

Missense Mutation

the new nucleotide alters the codon to produce an altered amino acid in the protein product (ex

Nonsense Mutation

the new nucleotide changes a codon that specified an amino acid to a stop codon; translation of the mRNA transcribed from this mutant gene will stop prematurely

Indels

the addition or subtraction of extra base pairs; creates a change in the reading frame

Frameshift

change in the reading frame

Genome Rearrangements

large scale chromosome structure changes; can alter phenotype by 1) destroying gene function, 2) change in expression via influence of different promoters and enhancers, or 3) creating hybrid genes

Deletion and Duplication

occurs on the same chromosome

Inversion (Reversal)

occurs on the same chromosome

Translocation

occurs between different chromosomes; usually between paternal and maternal

Homolog

a gene related to other genes by evolutionary descent from a common ancestral DNA sequence

Identity

((number of identical residues))/((number of residues and gaps in th? alignment)) x 100

Similarity

some amino acid substitutions have similar side chains, leading to a smaller effect in the final protein

((number of similar residues))/((number of residues and gaps in th? alignment) ) x 100

Point Accepted Mutation (PAM)

quantifies the rate at which amino acids change over evolutionary time; assumes constant rate of change for amino acids

Constant Rate

mutations occur at a relatively steady pace over time

Independence

each amino acid position mutates independently of its neighbor

Natural Selection

only count "accepted" mutations that don't break down the protein's function and are passed down

Matrices

PAM matrices are a series, as the number increases the evolutionary distance grows

PAM #

of mutations per 100 amino acids

PAM 1

very conserved; observable mutation; small-scale evolution

PAM 250

same amino acid mutation repeatedly; not observable but extrapolated; has error associated with it; large-scale evolution

Block Substitution Matrices (BLOSUM)

based on observed alignments; aligned sequences from functional domains (blocks) of proteins; look at domains (blocks) rather than looking at entire sequence

Blocks

represents highly conserved regions that have survived natural selection

Matrices

BLOSUM matrices represent the minimum percentage identity of the sequences used to build it

Lower #

distant relatives; BLOSUM45 used for very divergent sequences

Higher #

close relatives; BLOSUM80 used for very similar sequences

Similarity Score

not all amino acid matches produce the same similarity score; add all numbers for individual score, the higher the better

Ortholog

a gene present in different species that evolved from a common ancestral gene by speciation; retain the same/similar function in the course of evolution; speciation to give two separate species

Paralog

one gene of a set of genes that underwent a duplication event in a common ancestor; evolve new functions (can be related to the original function); gene duplication and divergence

Phylogenetic Trees

Phylogenetics

method of classification of organisms based upon their evolutionary history

Phylogenetic Tree

shows the evolutionary relationships among various species or other entities that likely have a common ancestor; multiple trees possible showing multiple plausible evolutionary scenarios

Gene-Specific Phylogenies

different genes may show different phylogenetic histories; can avoid this by using multiple genes and many single-gene analyses then concatenating them

Neutral Marker

genes under similar positive selection regimes in different taxa can result in convergent evolution; can make confusing phylogenetic analysis

Connected Graph

graph containing at least one path between any two nodes

Tree

type of connected graph in which there is exactly one path between every two nodes

Rooted Tree

shows evolutionary history of the taxa; single unique node which is the ancestor of all other nodes; directed tree which shows change over time; best done by using an outgroup

Outgroup

a species or molecule that is known to be more distantly related than everything else in the tree

Ingroup

taxa being analyzed to view relationships

Unrooted Tree

shows evolutionary relationships between the taxa; can't make any statement about the direction of evolution, only the closeness of relationships

Nodes

common ancestor; rotating a tree at a node does not change the relationships between the taxa, only the way those relationships are visualized; each node called an operational taxonomic unit

Branches

evolutionary lineages

Tips/Leaves

the most recent taxa in the analysis

Cladogram

branch lengths do not represent time; branching is determined by distinguishing characteristics which identify a particular clade

Phylogram

explicitly represents number of character changes through its branch lengths; indicates the amount of evolutionary time separating taxa

Distance-Based Methods

calculate the genetic distance between pairs of taxa and construct a tree based on these distances

Unweighted Pair Group Method with Arithmetic Mean (UPGMA)

determination of phylogenetic relationships are explicitly non-historical; simply based on similarity/dissimilarity; assumes an ultrametric tree in which the distances from the root to every branch tip are equal

Steps

(1) create tree by first selecting the most closely related sequences and insert a node to represent their common ancestor

(2) then replace the selected sequences by a set containing both and replace the distances from the pair to the others by the average distances

(3) repeat

Neighbor-Joining

clustering creates an additive unrooted tree using pairwise distances; all the taxa do not diverge from a most common ancestor; does not assume that all sequences have the same rate of substitution; fast and often used as a starting point in phylogenetic analyses

Steps

(1) determine the pairwise distances between all the sequences

(2) identify the two sequences closest to each other based on their distances

(3) combine these two sequences into a single node

(4) update the distances between this new node and the other sequences

(5) repeat until all sequences are joined into a single tree

Strengths

Weaknesses

Cladistic Methods

consider the various possible trees and choose the best possible tree; tree selection criteria varies depending on the approach; slower than neighbor joining, but usually more accurate

Maximum Parsimony

finds the tree that requires the fewest number of evolutionary changes to explain the observed data

Strengths

Weaknesses

Maximum Likelihood

finds the tree that has the highest probability of producing the observed data given a specific model of evolution

Strengths

Weaknesses

Newick Format

field standard for representing trees in computer-readable form; allows us to see the connections between nodes

Tree Accuracy and Validation

Methods for Testing Accuracy

Purpose of Bootstrapping

DNA Sequencing

First Generation

Sanger-Sequencing

AKA chain termination method; AKA dideoxynucleoside sequencing; method for determining the nucleotide sequence of DNA; DNA template from PCR required