Sequence Analysis
Hemant Kelkar
Center for Bioinformatics
University of North Carolina
Chapel Hill, NC 27599
Scope of Series
Talk I
• Overview and BLAST
Talk II
• Protein analysis/Sequence Alignment
Talk III
• Evolution
• Genomics and challenges
Bioinformatics
• Mathematical, Statistical and
computational methods that are used
for solving biological problems
• Glue that holds the “omics” data
together
Help …
• Is “my sequence” in the databases?
• Is it similar to any sequence in the DB?
• Does it have any know motifs/domains
that can help in identification?
• Is there a structural homolog?
• Are there any polymorphisms?
• Genetic Map location?
Bioinformatics TOOLS!
Bioinformatics Tools
• Genetic Code
 Similarity search e.g. BLAST, FASTA
• Protein Structure
http://restools.sdsc.edu/biotools/biotools9.html
• Protein Evolution
 e.g. CLUSTALW, T-COFFEE, Phylip
Primary Sequence Databases
• GenBank
(http://www.ncbi.nlm.nih.gov/Genbank/index.html)
• PIR (http://pir.georgetown.edu/)
• Swiss-Prot (http://us.expasy.org/sprot/)
Sequence information as is generated
in the laboratory
Derived Sequence Databases
Databases based on functional or
phylogenetic analysis
• PFAM (http://www.sanger.ac.uk/Software/Pfam/) :
Protein families based on HMM models
• InterPRO (http://www.ebi.ac.uk/interpro/) :
Protein families and domains based on functional
sites
• TransFac (http://www.gene-regulation.com/)
transcription factor db
• Cytochrome P450 database
(http://drnelson.utmem.edu/CytochromeP450.html)
Derived Sequence Databases
Databases based on taxonomy
• Flybase (http://www.flybase.org/) : Fly Genome
• Wormbase (http://www.wormbase.org/) : C. elegans
• Genome Browser (http://genome.ucsc.edu/) :
Human and Mouse
• MGI (http://www.informatics.jax.org/) : Mouse
• Microbial Genome Resource :
(http://www.tigr.org/tigrscripts/CMR2/CMRHomePage.spl)
Sequence Alignments
• Provide a measure of relation
between the nucleotide or protein
sequence
• This allows us to decipher:
 Structural relationships
 Functional relationships
 Evolutionary relationships
Sequence Similarity Searches
• Information conserved evolutionarily
• DNA sequences NOT coding for
proteins/rRNAs diverge rapidly
• When possible use protein sequences
for similarity searches
• Non-homologous protein
identification is much less reliable
• What is measured and what is
inferred?
Similarity
• Is always based on an observable
• Usually expressed as % identity
• Quantifies the divergence of two
sequences
• substitutions/insertions/deletions
• Residues crucial for structure
and/or function
Homology
• Homology always implies that the
molecules share a common ancestor
• Absolute answer
• Molecules ARE or ARE NOT
homologous
• No degrees
How to Find Similar Sequences
• Global Sequence Alignments
• Sequence comparison along entire length
• Homolog of similar length
• Local Sequence Alignments
• Similar regions in two sequences
• Regions outside the local alignment
excluded
• Sequences of different length/similarity
Dotplot
Scoring Matrices
• Empirical weighting schemes
• Considers important biology
• Side chain chemistry/structure/function
• Functional/Structural Conservation
• Ile/Val – small and hydrophobic
• Ser/Thr – both polar
• Size/Charge/Hydrophibicity
Nucleotide Matrix
A
C
G
T
A
5
-4
-4
-4
C
-4
5
-4
-4
G
-4
-4
5
-4
T
-4
-4
-4
5
PAM Scoring Matrices
• Margaret Dayhoff (1978)
• Point accepted mutations (PAM)
• Patterns of substitutions in highly related
proteins (>85% identical), based on multiple
sequence alignments
• New side chains must function similarly
• 1 PAM  1 AA change per 100 AA
• 1 PAM ~ 1 % Divergence
BLOSUM Matrices
• Henikoff and Henikoff (1992)
• Blocks Substitution Matrices
• Differences in conserved ungapped
regions
• Directly calculated no extrapolations
• Sensitive to structural/functional subs
• Generally perform better for local
similarity searches
Scoring Matrix – BLOSUM62
BLOSUM n
• Calculated from sequences sharing no
more than n% identity
• Sequences with more than n% identity
are clustered and weighted to 1
• Reducing the value of “n” yields more
divergent/distantly-related sequences
• BLOSUM62 used as default by many
of the online search sites
Matrices and more
PAM Matrices (Altschul, 1991)
PAM 40 Short alignments
>70%
PAM120
>50%
PAM250 Longer weaker local areas
>30%
BLOSUM Matrices (Henikoff, 1993)
BLOSUM 90
Short alignments
BLOSUM 80
>60%
>50%
BLOSUM 62
Commonly used
>35%
BLOSUM 30
Longer, weaker local alignments
Gaps
• Compensate for insertion and deletions
• Improvement alignments
• Must be kept to a reasonably small
number
• 1 per 20 residues is logical
• Need a different scoring scheme
Gap Penalties
• Penalty for gap introduction
• Penalty for Gap extension
Deductions for Gap = G + Ln
where
G = gap-opening penalty
Nuc
Prot
5
11
L = Gap-extension penalty 2
n = Length of gap
1
BLAST
• Basic Local Alignment Search Tool
• Seeks high-scoring segment pair (HSP)
• Sequences that can be aligned w/o
gaps
• have a maximal aggregate score
• score be above score threshold S
• Many HSP reported for ungapped blast
BLAST Algorithms
Program
Query
Target
BLASTN
BLASTP
BLASTX
Nucloetide
Protein
Nucleotide
(6-Frame)
Nucleotide
Protein
Protein
TBLASTN
TBLASTX
Protein
Nucleotide (6FR)
Nucloetide(6FR) Nucloetide(6FR)
Neighborhood Words
Query Word (W = 3)
Query:
SISPGQRVGLLGRTGSGKSTLLSAFLRMLN-IKGDIE
Neighborhood
Score
Threshold
(T = 8)
STL
13
SAL
8
SNL
8
SVL
8
SBL
7
SCL
7
SDL
7
Etc.
= 4 + 5 + 4
High-Scoring Segment Pairs
STL
13
SAL
8
SNL
8
SVL
8
SBL
7
SCL
7
SDL
7
Etc.
Query:
Sbjct:
SISPGQRVGLLGRTGSGKSTLLSAFLRMLN-IKGDIE
++ G + ++G G+GKS+LLSA L L+ ++G +
TVPQGCLLAVVGPVGAGKSSLLSALLGELSKVEGFVS
Extension
Query:
Cumulative Score
Sbjct:
SISPGQRVGLLGRTGSGKSTLLSAFLRMLN-IKGDIE
++ G + ++G G+GKS+LLSA L L+ ++G +
TVPQGCLLAVVGPVGAGKSSLLSALLGELSKVEGFVS
X
S
Significance
Decay
• Mismatches
• Gap penalties
T
Extension
Karlin Altschul Equation
E =
-λs
kmNe
m
Number of letters in query
N
Number of letters in db
mN Size of search space
λs
Normalized score
k
minor constant
http://www.ncbi.nlm.nih.gov