Bioinformatics Course Outline

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Bioinformatics Course Outline
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Terms
Homology Sequences that are related by
divergence from a common ancestor. Identity Seq
uences that are invariant. Similarity Sequences
that are related.
C A T C A T
C A G C A T
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Homologues Orthology vs Paralogy
Reproduced from NCBI education website
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Why DO Homologies ?

• A powerful tool to? compare newly discovered
  sequences
• with known genes.
• Both functional, structural and evolutionary
  information
• can be inferred.
• Regions of similarity in unrelated proteins may
  be detected.
• Re-construct long DNA sequences from short
  overlapping
• fragments.
• Explore frequently appearing patterns of
  nucleotides
• (homologous sequences, structure similarity, a
  common
• ancestor and similar function).

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The Limits of Sequence Similarity
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How Do We Measure Sequence Alignment ?
T C A T G C A T T G
or
T C A T G C A T T G
T C A T G C A T T G
?
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Most Common Mutation Types
insertion (in) AGCGGC deletion (del)
ACG_C ACGGC substitution (sub) CCGGC
(INDEL insertions deletions)
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Similarity Score of Alignment

• Each pair of characters in the alignment gets
  a value,
• depending on its identity.
• The similarity score of the alignment is the
  sum of
• pair values.
• Example for pair values (relevant to DNA)
• Identical characters (match) 1
• Different characters (mismatch) -1
• Indel (gap) -1

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Example for Similarity Scores
Score -1 0 -4
S AC_TG
T A_GT_
S ACTG
T AGT_
S
ACTG
T _AGT

S ACTG T AGT
S, T is the best of these three,
but is it the best of ALL alignments ???
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Algorithms Heuristics

• There are a number of exact algorithms and
  heuristics
• for finding alignment(s)
• Exact algorithm guarantees to find the best.
• Heuristics usually find the best or almost the
  best.
• Bottom line Heuristics are typically much faster
  but do not guarantee to find best homologues
  (time vs. quality trade-off).

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Pair-wise Alignment Programs

• Exact Algorithms
• Based on dynamic programming, a known
• algorithmic tool (not exhaustive search !).
• Most sensitive, but computational expensive
  and slow
• Heuristics, based on SW algorithm
• 1. FASTA (1985) (http//www2.ebi.ac.uk/
  fasta3/)
• 2. BLAST (1990) (http//www.ncbi.nlm.n
  ih.gov/BLAST/)

• Needlman-Wunch (1970).
• Smith-Waterman (SW 1981).

http//rna.informatics.indiana.edu/wtclark/sw.html
http//bioweb.pasteur.fr/seqanal/interfaces/wate
r.html
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Example for Gap Penalties
(Improved Pricing of InDels)
Motivation Aligning cDNAs to Genomic DNA
Example
Genomic DNA
In this case, if we penalize every single gap by
-1, the similarity score will be very low, and
the parent DNA will not be picked up.
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Types of Gap Penalties

• (insertions or deletions, indels)
• Insertions and deletions are rare in
  evolution.
• Once they are created, they are easy to
  extend.
• Examples
• BLAST Cost to open a gap 10 (high penalty).
• Cost to extend a gap 0.5 (low
  penalty).

FASTA
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Sensitivity of Algorithm The ability to
recognize distantly related sequences. Selectivity
of Algorithm The ability to discard false
positive matches between un-related sequences.
How does word size influence sensitivity
selectivity ? Large word size - fast, less
sensitive, more selective distant
relatives do not have many runs of matches,
un-related sequences stand no chance to be
selected. Small word size - slow, more
sensitive, less selective.
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Effect of Word Size
Large word size - fast, less sensitive, more
selective distant relatives do not have many
runs of matches, un-related sequences stand no
chance to be selected. Small word size - slow,
more sensitive, less selective. Example If
word size 3, we will find all words containing
TCG in this sequence (very sensitive compared to
large word size, but less selective and will find
all TCGs).
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FASTA Visualization
Identify all hot spots longer than Ktup.
Ignore all short hot spots. The longest hot spot
is called init1.
Merge diagonal runs. Optimize using SW in a
narrow band. Best result is called
Extend hot spots to longer diagonal runs.
Longest diagonal run is initn.
opt.
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Different Variants of Blast FastA
http//www-bioeng.ucsd.edu/research/research_group
s/compbio/workshop/
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Lets Run FastA (FAST-All) Against EMBL
Nucleotide Sequence Database
http//www.ebi.ac.uk/fasta33/
http//www2.ebi.ac.uk/fasta3/help.html Example
and interpretation of results http//www.ebi.ac.u
k/2can/tutorials/nucleotide/fasta2.html
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BLAST Tips http//www.ncbi.nlm.nih.gov/Education/
BLASTinfo/Blast_setup.html
http//www.ncbi.nlm.nih.gov/BLAST/ http//www.ncbi
.nlm.nih.gov/BLAST/about/ Step by step BLAST
http//www.ornl.gov/sci/techresources/Human_Genome
/posters/chromosome/blast.shtml
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FASTA vs BLAST

• BLAST is faster than FASTA.
• Similar search strategy.
• Sensitivity-
• Protein searches BLAST and FASTA are
  comparable.
• Nucleotide searches FASTA is more sensitive.
• S-W is the most sensitive, but time consuming.

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Blast A Family of Programs

• BlastN - nt versus nt database.
• BlastP - protein versus protein database.
• BlastX - translated nt versus protein database.
• tBlastN - protein versus translated nt database.
• tBlastX - translated nt versus translated nt
  database.

Query DNA Protein Database DNA Protein
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(No Transcript)
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http//www.ncbi.nlm.nih.gov/BLAST/bl2seq/wblast2.c
gi
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DNA or Protein

• DNA query can be translated and searched against
  protein databases.
• Translate all reading frames (3 3).
• Find long ORF (open reading frames).

• Protein query can be back-translated and searched
  
• against DNA databases.
• A protein sequence can be back translated to many
  
• possible DNA sequences, based on the codon
  table.
• During translation (DNA to protein) we loose
  information.

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Blink (BLAST Link)
BLink (BLAST Link) is a tool that displays the
pre-computed results of BLAST searches that have
been completed for every protein sequence in the
Entrez Proteins data domain.
BLink help http//www.cs.utk.edu/rcollins/bioin
f/tutorial/tutorial3.html
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Scoring Systems for Protein Alignments

• Identity Count the number of identical matches,
  divide by length of aligned region (in ).
• Similarity A less well defined measure of how
  close 2 sequences are.
• Chemical similarities among amino acids

http//www.imb-jena.de/IMAGE_AA.html
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Protein Scoring Matrices

• Family of matrices listing the likelihood of
  changes from one sequence to another during
  evolution.
• The two most popular matrices are the PAM and the
  BLOSUM matrices.

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PAM Matrix - Point Accepted Mutations
PAM matrices are based on related sequences.

• In these related proteins, the
• function was not significantly changed.

The changes are accepted by natural selection
(mutations survived during evolution).
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PAM Scoring Matrices
PAM units measure evolutionary distance.
PAM 1 matrix - Substitution scores arising from
sequences where one percent of amino acid
pairs are different. Note PAM 1 is a small
change -gt the sequences will be almost identical.
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PAM Family of Matrices (Dayhoff, 78)
(log odds)
Note Numbers along diagonals are not all equal.
Values gt 0 in the logs odd PAM matrix indicate
likely mutations, values 0 are neutral and
values lt 0 indicate unlikely mutations.
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THE BLOSUM Family of Matrices
Blocks Substitution Matrices- (BLOSUM
matrices based on a much larger dataset then PAM).

• Blocks are short conserved patterns of 3-60 aa
  long.
• Proteins can be divided into families by common
  blocks.
• Different BLOSUM matrices emerge by looking at
  sequences with different identity
  percentage.Example BLOSUM62 is derived from an
  alignment of sequences that share at least 62
  identity.

Block A B C D
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THE BLOSUM Family of Matrices
Blocks Substitution Matrices
(log odds)
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PAM vs. BLOSUM Matrices
Widely used

• Tips for protein similarity search
• Start with BLOSUM 62 or PAM 120, default gap
  penalties.
• If no significant results found, use BLOSUM 45
  or PAM 250
• and lower gap penalties, to find more
  divergent results.
• Examine results above E-value 0.05 for
  divergent sequences.
• Use PSI-BLAST to discover weak but biologically
  significant
• sequence similarities.

http//www.ncbi.nlm.nih.gov/Education/BLASTinfo/Sc
oring2.html
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From the BlastP Page Go To Taxonomy Report
Organism Report
Common name
Score
Blast (family) name
E-value
Scientific name

• TaxBLAST hits are sorted according to species
  containing the target sequence.
• All hits of the same organism are listed
  together.
• Within each species, TaxBLAST hits are sorted
  by score and E-value.
• See also Lineage report.

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PSI-BLAST - Position Specific Iterated BLAST

• A fast heuristic method for searching a profile,
  by using iterations. The profile is used as the
  query in the next iteration.
• Advantages of PSI-BLAST
• Identify week homologies (more distant
  relatives of
• a protein, not found directly in FASTA or
  BLAST).
• An important tool for predicting biochemical
  function.

Information http//www.ncbi.nlm.nih.gov/Educatio
n/BLASTinfo/psi1.html
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Multiple Sequence Alignment Motivation

• Helps identify common structures and functions
• Build gene families.
• Shared homologous regions.
• Conserved regions (consensus).
• Serves as a basis for constructing phylogeny
• (evolutionary) trees from homologous sequences.

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Multiple Sequence Alignment
http//www.ebi.ac.uk/Tools/clustalw/
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Input Format for MSA (ClustalW)
gtworm
Readseq -- biosequence conversion
tool http//iubio.bio.indiana.edu/cgi-bin/readseq
.cgi
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BOXSHADE Visualization of Multiple Sequence
Alignment
http//bioweb.pasteur.fr/seqanal/interfaces/boxsha
de-simple.html
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T-COFFEE Visualization of Multiple Alignment
http//www.ch.embnet.org/pages/services.html
http//www.ch.embnet.org/software/TCoffee.html
Results
More accurate program than ClustalW for sequences
with less than 30 identity, but it slower...
http//www.ch.embnet.org/software/ClustalW.html
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Other Important Options Sequence Utilities
ReadSeq - converts nucleic acid/protein sequences
to FASTA format. RepeatMasker - identify and mask
repeats in DNA sequences. WebCutter -
restriction maps using enzymes w/ sites gt 6
bases. 6 Frame Translation - translates a
nucleic acid sequence in 6 frames. Reverse
Complement - reverse complements a nucleic acid
sequence. Reverse Sequence - reverses sequence
order (BCM).
http//searchlauncher.bcm.tmc.edu/seq-util/seq-uti
l.html
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Phylogeny Reconstruction
Goal Given a set of taxa (a group of related
biological species), build a tree which best
represents the course of evolution for this set
over time.
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Trees Rooted or Un-rooted
Most reconstruction methods produce un-rooted
trees. To root a tree we need external
information (e.g. out-group). Roots provide
direction to a tree and set ancestral states.
Un-rooted
Rooted
gorilla
chimpanzee
human
orangutan
chimpanzee
human
gorilla
orangutan
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Tree Properties
Nodes External nodes (leaves) represent extant
(existing) species. Internal nodes represent
ancestral species (usually extinct). Branches
Length represent number of mutations. A longer
branch means more mutations, usually implying
longer evolutionary time. Typical time scale is
mya (millions years ago).
Root (the common ancestor of all taxa)
Internal nodes
Branch (length)
Another representation (A,B)(C(D(E,F)))
Time scale
chimpanzee
human
gorilla
orangutan
gibbon
siamang
Leaves
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Number of Trees
The problem of optimal tree identification
becomes computationally hard if the algorithm
has to test every tree. In this case,
heuristics must be used.
rooted un-rooted Nodes
trees trees
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PHYLIP Developed by J. Felsenstein, UOW
Phylogeny Inference Package
http//evolution.genetics.washington.edu/phylip.ht
ml
PHYLIP (the PHYLogeny Inference Package) is a
package of programs for inferring phylogenies
(evolutionary trees), available freely through
the internet.
http//bioweb.pasteur.fr/seqanal/phylogeny/phylip-
uk.html
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Graphical Display of Resulting Trees in Phylip
DRAWGRAM - Plots rooted phylogenies,
cladograms, and phenograms.
gened.emc.maricopa.edu/.../BIOBK/BioBookDiversclas
s.html
DRAWTREE - Similar to DRAWGRAM but plots
un-rooted trees.
http//genomebiology.com/2001/2/6/research/0018
RETREE - The user can re-root, flip branches,
change names of species, change or remove branch
lengths.
http//bioweb.pasteur.fr
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Phylodendron     Phylogenetic tree printer
http//iubio.bio.indiana.edu/treeapp/treeprint-for
m.html (use example data).
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Special Utilities
Splign is a utility for computing
cDNA-to-Genomic, or spliced sequence alignments)
global alignment algorithm).
http//www.ncbi.nlm.nih.gov/sutils/splign/splign.c
gi?textpageonlinelevelform
Specialized BLAST Choose a type of specialized
search (or database name in parentheses.) Search
trace archives Find conserved domains in your
sequence (cds) Find sequences with similar
conserved domain architecture (cdart) Search
sequences that have gene expression profiles
(GEO) Search immunoglobulins (IgBLAST) Search
for SNPs (snp) Screen sequence for vector
contamination (vecscreen) Align two sequences
using BLAST (bl2seq) http//www.ncbi.nlm.nih.gov/
BLAST/