Design and creation of multiple sequence alignments Unit 15

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Design and creation of multiple sequence
alignmentsUnit 15

• BIOL221T Advanced Bioinformatics for
  Biotechnology

Irene Gabashvili, PhD
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IPA 6.0 license

• Need a list of e-mails to create accounts
• Will have a 6 weeks license (instead of 2 weeks)
• Problem Set 3 is Pathway Analysis, Lab of March
  19 will be on using IPA too

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Problem Set 2 Review

• Sensitivity and Specificity
• Parameters for Multiple Alignment (Databases,
  Search Terms, Scores)
• Transfac
• Dotplots

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Gene prediction flowchart
Fig 5.15 Baxevanis Ouellette 2005
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Evaluation of Splice Site Prediction
What do measures really mean?
Fig 5.11 Baxevanis Ouellette 2005
Note typo in BO
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ROC curves (plots of (1-Sn) vs Sp)

• A receiver operating characteristic (ROC), or
  simply ROC curve, is a graphical plot of the
  sensitivity vs. (1 - specificity) for a binary
  classifier system as its discrimination threshold
  is varied.
• The sensitivity and specificity of a diagnostic
  test depends on more than just the "quality" of
  the test--they also depend on the definition of
  what constitutes an abnormal test.

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Evaluation of Splice Site Prediction
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Careful different definitions for "Specificity"
Brendel definitions

• Specificity

cf. Guig?ó definitions Sn Sensitivity
TP/(TPFN) Sp Specificity TN/(TNFP) Sp- AC
Approximate Coefficient 0.5 x ((TP/(TPFN))
(TP/(TPFP)) (TN/(TNFP)) (TN/(TNFN))) - 1
Other measures? Predictive Values, Correlation
Coefficient
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Best measures for comparing different methods?

• ROC curves (Receiver Operating
  Characteristic?!!)
• http//www.anaesthetist.com/mnm/stats/roc/
• "The Magnificent ROC" - has fun applets
  quotes
• "There is no statistical test, however intuitive
  and simple, which will not be abused by medical
  researchers"
• Correlation Coefficient
• (Matthews correlation coefficient (MCC)
• MCC 1 for a perfect prediction
• 0 for a completely random assignment
• -1 for a "perfectly incorrect" prediction

Just FYI
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PromotersWhat signals are there? Simple
ones in prokaryotes
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Prokaryotic promoters

• RNA polymerase complex recognizes promoter
  sequences located very close to on 5 side
  (upstream) of initiation site
• RNA polymerase complex binds directly to these.
  with no requirement for transcription factors
• Prokaryotic promoter sequences are highly
  conserved
• -10 region
• -35 region

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Simpler view of complex promoters in eukaryotes
Fig 5.12 Baxevanis Ouellette 2005
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Eukaryotic genes are transcribed by 3 different
RNA polymerases
Recognize different types of promoters
enhancers
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Eukaryotic promoters enhancers

• Promoters located relatively close to
  initiation site
• (but can be located within gene,
  rather than upstream!)
• Enhancers also required for regulated
  transcription
• (these control expression in specific cell
  types, developmental stages, in response to
  environment)
• RNA polymerase complexes do not specifically
  recognize promoter sequences directly
• Transcription factors bind first and serve as
  landmarks for recognition by RNA polymerase
  complexes

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Eukaryotic transcription factors

• Transcription factors (TFs) are DNA binding
  proteins that also interact with RNA polymerase
  complex to activate or repress transcription
• TFs contain characteristic DNA binding motifs
• http//www.ncbi.nlm.nih.gov/books/bv.fcgi?r
  idgenomes.table.7039
• TFs recognize specific short DNA sequence motifs
  transcription factor binding sites
• Several databases for these, e.g. TRANSFAC
• http//www.generegulation.com/cgibin/pub/data
  bases/transfac

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Zinc finger-containing transcription factors

• Common in eukaryotic proteins
• Estimated 1 of mammalian genes encode
  zinc-finger proteins
• In C. elegans, there are 500!
• Can be used as highly specific DNA binding
  modules

• Potentially valuable tools for directed genome
  modification (esp. in plants) human gene
  therapy

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Promoter prediction Eukaryotes vs prokaryotes
Promoter prediction is easier in microbial
genomes Why? Highly conserved Simpler
gene structures More sequenced genomes!
(for comparative approaches) Methods?
Previously mostly HMM-based Now
similarity-based. comparative methods because
so many genomes available
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Predicting promoters Steps Strategies

• Closely related to gene prediction!
• Obtain genomic sequence
• Use sequence-similarity based comparison
• (BLAST, MSA) to find related genes
• But "regulatory" regions are much less
  well-conserved than coding regions
• Locate ORFs
• Identify TSS (if possible!)
• Use promoter prediction programs
• Analyze motifs, etc. in sequence (TRANSFAC)

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Predicting promoters Steps Strategies

• Identify TSS --if possible?
• One of biggest problems is determining exact
  TSS!
• Not very many full-length cDNAs!
• Good starting point? (human vertebrate genes)
• Use FirstEF
• found within UCSC Genome Browser
• or submit to FirstEF web server

Fig 5.10 Baxevanis Ouellette 2005
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Automated promoter prediction strategies

• Pattern-driven algorithms
• Sequence-driven algorithms
• Combined "evidence-based"
• BEST RESULTS? Combined, sequential

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Promoter Prediction Pattern-driven algorithms

• Success depends on availability of collections of
  annotated binding sites (TRANSFAC PROMO)
• Tend to produce huge numbers of FPs
• Why?
• Binding sites (BS) for specific TFs often
  variable
• Binding sites are short (typically 5-15 bp)
• Interactions between TFs ( other proteins)
  influence affinity specificity of TF binding
• One binding site often recognized by multiple BFs
  
• Biology is complex promoters often specific to
  organism/cell/stage/environmental condition

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Promoter Prediction Pattern-driven algorithms

• Solutions to problem of too many FP predictions?
• Take sequence context/biology into account
• Eukaryotes clusters of TFBSs are common
• Prokaryotes knowledge of ? factors helps
• Probability of "real" binding site increases if
  annotated transcription start site (TSS) nearby
• But What about enhancers? (no TSS nearby!)
• Only a small fraction of TSSs have been
  experimentally mapped
• Do the wet lab experiments!
• But Promoter-bashing is tedious

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Promoter Prediction Sequence-driven algorithms

• Assumption common functionality can be deduced
  from sequence conservation
• Alignments of co-regulated genes should highlight
  elements involved in regulation
• Careful How determine co-regulation?
• Orthologous genes from difference species
• Genes experimentally determined to be
• co-regulated (using microarrays??)
• Comparative promoter prediction
• "Phylogenetic footprinting" - more later.

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Promoter Prediction Sequence-driven algorithms

• Problems
• Need sets of co-regulated genes
• For comparative (phylogenetic) methods
• Must choose appropriate species
• Different genomes evolve at different rates
• Classical alignment methods have trouble with
• translocations, inversions in order of
  functional elements
• If background conservation of entire region is
  highly conserved, comparison is useless
• Not enough data (Prokaryotes gtgtgt Eukaryotes)
• Biology is complex many (most?) regulatory
  elements are not conserved across species!

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Examples of promoter prediction/characterization
software
Lab used MATCH, MatInspector TRANSFAC MEME
MAST BLAST, etc. Others? FIRST EF Dragon
Promoter Finder also see Dragon Genome
Explorer (has specialized promoter software for
GC-rich DNA, finding CpG islands, etc) JASPAR
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TRANSFAC matrix entry for TATA box

• Fields
• Accession ID
• Brief description
• TFs associated with this entry
• Weight matrix
• Number of sites used to build (How many here?)
• Other info

Fig 5.13 Baxevanis Ouellette 2005
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Global alignment of human mouse obese gene
promoters (200 bp upstream from TSS)
Fig 5.14 Baxevanis Ouellette 2005
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GenBank IDs and Accessions

• http//www.ncbi.nlm.nih.gov/RefSeq/key.htmlaccess
  ions (Accession Formats RefSeq)
• http//www.ncbi.nlm.nih.gov/Sitemap/samplerecord.h
  tml (GenBank Sample Record)

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Why we do multiple alignments?

• Help prediction of the secondary and tertiary
  structures of new sequences
• Preliminary step in molecular evolution analysis
  using Phylogenetic methods for constructing
  phylogenetic trees.

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An example of Multiple Alignment
VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGTSSNIGS--ITVNWY
QQLPG LRLSCSSSGFIFSS--YAMYWVRQAPG LSLTCTVSGTSFDD--
YYSTWVRQPPG PEVTCVVVDVSHEDPQVKFNWYVDG-- ATLVCLISDF
YPGA--VTVAWKADS-- AALGCLVKDYFPEP--VTVSWNSG--- VSLT
CLVKGFYPSD--IAVEWWSNG--
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Visualization example
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Other multiple alignment programs
ClustalW / ClustalX pileup multalign multal saga h
mmt
DIALIGN SBpima MLpima T-Coffee ...
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Other multiple alignment programs
ClustalW / ClustalX pileup multalign multal saga h
mmt
DIALIGN SBpima MLpima T-Coffee ...
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ClustalW- for multiple alignment

• ClustalW can create multiple alignments,
  manipulate existing alignments, do profile
  analysis and create phylogentic trees.
• Alignment can be done by 2 methods
• - slow/accurate
• - fast/approximate

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Running ClustalW
clustalw
CLUSTAL
W (1.7) Multiple Sequence Alignments

1. Sequence Input From Disc
2. Multiple Alignments 3. Profile /
Structure Alignments 4. Phylogenetic trees
S. Execute a system command H. HELP
X. EXIT (leave program) Your choice
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Running ClustalW
The input file for clustalW is a file containing
all sequences in one of the following
formats NBRF/PIR, EMBL/SwissProt, Pearson
(Fasta), GDE, Clustal, GCG/MSF, RSF.
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Using ClustalW
MULTIPLE ALIGNMENT MENU 1. Do
complete multiple alignment now (Slow/Accurate)
2. Produce guide tree file only 3. Do
alignment using old guide tree file 4.
Toggle Slow/Fast pairwise alignments SLOW
5. Pairwise alignment parameters 6.
Multiple alignment parameters 7. Reset gaps
between alignments? OFF 8. Toggle screen
display ON 9. Output format
options S. Execute a system command H.
HELP or press RETURN to go back to main
menu Your choice
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Output of ClustalW
CLUSTAL W (1.7) multiple sequence
alignment HSTNFR GGGAAGAG---TTCCCCAGGGACCTCTC
TCTAATCAGCCCTCTGGCCCAG------GCAG SYNTNFTRP
GGGAAGAG---TTCCCCAGGGACCTCTCTCTAATCAGCCCTCTGGCCCAG
------GCAG CFTNFA -----------------------------
--------------TGTCCAG------ACAG CATTNFAA
GGGAAGAG---CTCCCACATGGCCTGCAACTAATCAACCCTCTGCCCCAG
------ACAC RABTNFM AGGAGGAAGAGTCCCCAAACAACCTCCAT
CTAGTCAACCCTGTGGCCCAGATGGTCACCC RNTNFAA
AGGAGGAGAAGTTCCCAAATGGGCTCCCTCTCATCAGTTCCATGGCCCAG
ACCCTCACAC OATNFA1 GGGAAGAGCAGTCCCCAGCTGGCCCCTCC
TTCAACAGGCCTCTGGTTCAG------ACAC OATNFAR
GGGAAGAGCAGTCCCCAGCTGGCCCCTCCTTCAACAGGCCTCTGGTTCAG
------ACAC BSPTNFA GGGAAGAGCAGTCCCCAGGTGGCCCCTCC
ATCAACAGCCCTCTGGTTCAA------ACAC CEU14683
GGGAAGAGCAATCCCCAACTGGCCTCTCCATCAACAGCCCTCTGGTTCAG
------ACCC

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ClustalW options
Your choice 5 PAIRWISE ALIGNMENT
PARAMETERS Slow/Accurate
alignments 1. Gap Open Penalty
15.00 2. Gap Extension Penalty 6.66
3. Protein weight matrix BLOSUM30 4. DNA
weight matrix IUB Fast/Approximate
alignments 5. Gap penalty 5
6. K-tuple (word) size 2 7. No. of top
diagonals 4 8. Window size
4 9. Toggle Slow/Fast pairwise alignments
SLOW H. HELP Enter number (or RETURN to
exit)
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ClustalW options
Your choice 6 MULTIPLE ALIGNMENT
PARAMETERS 1. Gap Opening
Penalty 15.00 2. Gap Extension
Penalty 6.66 3. Delay divergent
sequences 40 4. DNA Transitions
Weight 0.50 5. Protein weight
matrix BLOSUM series 6. DNA
weight matrix IUB 7. Use
negative matrix OFF 8.
Protein Gap Parameters H. HELP Enter
number (or RETURN to exit)
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Blocks database and tools

• Blocks are multiply aligned ungapped segments
  corresponding to the most highly conserved
  regions of proteins.
• The Blocks web server tools are Block
  Searcher, Get Blocks and Block Maker. These are
  aids to detection and verification of protein
  sequence homology.
• They compare a protein or DNA sequence to a
  database of protein blocks, retrieve blocks, and
  create new blocks,respectively.

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The BLOCKS web server

• At URL http//blocks.fhcrc.org/
• The BLOCKS WWW server can be used to create
  blocks of a group of sequences, or to compare a
  protein sequence to a database of blocks.
• The Blocks Searcher tool should be used for
  multiple alignment of distantly related protein
  sequences.

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The Blocks Searcher tool

• For searching a database of blocks, the first
  position of the sequence is aligned with the
  first position of the first block, and a score
  for that amino acid is obtained from the profile
  column corresponding to that position. Scores are
  summed over the width of the alignment, and then
  the block is aligned with the next position.
• This procedure is carried out exhaustively for
  all positions of the sequence for all blocks in
  the database, and the best alignments between a
  sequence and entries in the BLOCKS database are
  noted. If a particular block scores highly, it is
  possible that the sequence is related to the
  group of sequences the block represents.

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The Blocks Searcher tool

• Typically, a group of proteins has more than one
  region in common and their relationship is
  represented as a series of blocks separated by
  unaligned regions. If a second block for a group
  also scores highly in the search, the evidence
  that the sequence is related to the group is
  strengthened, and is further strengthened if a
  third block also scores it highly, and so on.

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The BLOCKS Database

• The blocks for the BLOCKS database are made
  automatically by looking for the most highly
  conserved regions in groups of proteins
  represented in the PROSITE database. These blocks
  are then calibrated against the SWISS-PROT
  database to obtain a measure of the chance
  distribution of matches. It is these calibrated
  blocks that make up the BLOCKS database.

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The Block Maker Tool

• Block Maker finds conserved blocks in a group of
  two or more unaligned protein sequences, which
  are assumed to be related, using two different
  algorithms.
• Input file must contain at least 2 sequences.
• Input sequences must be in FastA format.
• Results are returned by e-mail.

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Progressive Approaches

• CLUSTALW
• Perform pairwise alignments
• Construct a tree, joining most similar sequences
  first (guide tree)
• Align sequences sequentially, using the
  phylogenetic tree
• PILEUP
• Similar to CLUSTALW
• Uses UPGMA to produce tree (chapter 6)

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Clustal method

• Higgins and Sharp 1988
• ref CLUSTAL a package for performing multiple
  sequence alignment on a microcomputer. Gene, 73,
  237244. Medline
• Progressive alignment method
• An approximation strategy (heuristic algorithm)
  yields a possible alignment, but not necessarily
  the best one

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First step
A B C D
Compute the pairwise alignments for all against
all (6 pairwise alignments) the similarities are
stored in a table
D C B A
A
11 B
1 3 C
10 2 2 D
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Second step
D C B A
A
11 B
1 3 C
10 2 2 D

• cluster the sequences to create a tree (guide
  tree)
• Represents the order in which pairs of sequences
  are to be aligned
• Highly similar sequences are neighbors in the
  tree
• Highly distant sequences are distant from each
  other in the tree

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Third step
Align most similar pairs
Align the alignments as if each of them was a
single sequence (with the use of a consensus
sequence or a profile)
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Clustal programs

• ClustalV
• ClustalW
• Thompson et al., 1994
• Uses sequence weighting, positions-specific gap
  penalties and weight matrix choice
• W stands for weight sequences
• clustalX - windows implementation

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ClustalW method rules (1) sequence weighting

• Each sequence is weighted according to how
  different it is from the other sequences.
• For the case where one specific subfamily is
  overrepresented in the data

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ClustalW method rules (2) weight matrix choice

• The substitution matrix used for each alignment
  step depends on the similarity of the sequences.

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ClustalW method rules (3) positions-specific gap
penalties

• Gaps found in initial alignments remain fixed
  through the process (ends gap)
• Hydrophobic residues have higher gap penalties
  than hydrophilic
• they are more likely to be in the hydrophobic
  core, where gaps should not occur.

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ClustalW method shortcomings

• (1) Sequences that are similar only in sub-
  regions
• ClustalW forces a global alignments, not local.
• (2) A sequence that contains a large
  insertion/deletion compared to the rest will
  extremely affect the alignment
• (again global not local).

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ClustalW method shortcomings

• (3) A sequence that contains a repetitive
• element (such as a domain), whereas all
  other sequences only contain one copy.

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Comments

• Pairwise alignment is an optimal algorithm
• Multiple alignment is not an optimal algorithm
  only a heuristic. Better alignments may exist!
• The algorithm yields a possible alignment, but
  not necessarily the best one.

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ClustalW in the web server

• Global multiple sequence alignment program for
  DNA or proteins
• Available from a number of sites
• EMBL-EBI

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Results
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Results
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Alignment with colors
identity
similarty
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CLUSTAL format

• CLUSTAL W(1.82) multiple sequence alignment
• YPK1 SQLSWKRLLMKGYIPPYKPAVSD-Q--NSMDTSN
  FDEEFTR--SEKPIDSVVDEYLSESV
• YPK2 KDISWKKLLLKGYIPPYKPIVKDTQ--SEIDTAN
  FDQEFTK---EKPIDSVVDEYLSASI
• KPCA_HUMAN RRIDWEKLENREIQPPFKPKVC------GKGAEN
  FDKFFTR---GQPVLTPPDQLVIANI
• KPCZ_HUMAN RSIDWDLLEKKQALPPFQPQIT---M-DDYGLDN
  FDTQFTS---EPVQLTPDDEDAIKRI
• KAPA KEVVWEKLLSRNIETPYEPPIQ----QGQGDTSQ
  FDKYPE----EDINYGVQGEDPYADL
• KAPC NEVIWEKLLARYIETPYEPPIQ----QGQGDTSQ
  FDRYPE-EVDEEFNYGIQGEDPYMDL
• KAPB SEVVWERLLAKDIETPYEPPIT----SGIGDTSL
  FDQYPE-DV-EQLDYGIQGDDPYAEY
• KS6_HUMAN RHINWEELLARKVEPPFKPLLQ-----SEEDVSQ
  FDSKFTR-V-QTPVDSP-DDSTLSES
• .
  
• YPK1 -----MQKQF
• YPK2 ----N-QKQF
• KPCA_HUMAN D--O--QSDF
• KPCZ_HUMAN D-----QSEF
• KAPA -D----FRDF

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ClustalW at EMBL - Jalview
Jalview is a multiple alignment editor
conservation
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Jalview

• color menu
• Taylor colors (each amino acid is colored
  differently)
• Zappo colors (amino acids are colored according
  to their physico-chemical properties)
• Hydrophobicity colors (colors amino aids
  according to a certain score scale that
  represents hydrophobicity)
• Coloring residues above a percentage identity
  threshold
• User defined color schemes

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Example - Zappo colors

• physico-chemical properties color-code

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Guide Tree
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ClustalX

• ClustalX provides a window-based user interface
  to the ClustalW program.
• It uses the developed by the NCBI as part of
  their NCBI SOFTWARE DEVELOPEMENT TOOLKIT.

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T-coffee

• Another MSA program
• Protein nucleotide MSA program
• Uses principles similar to ClustalW
• More accurate but longer running times
• Limits the number of sequences it can align
  (100)
• T-coffee at EMBnet

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(No Transcript)
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T-coffee results
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Phylip format

• 5 99
• Cabd_199509 PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMN
  LPGKWKPKIIGGI
• JCSB1_199401 PQITLWQRPIVTIKIGGQLKEALLDTGAD---LEEM-
  NPGRWKPKIIGGI
• JCSB2_199401 PQITLWQRPVT-IK-GG-QLEALLDTGADDTL-EEI-
  LPGRW-PKMIGGI
• JCSB4_199401 PQITLWQRPVT--K-GG-LKEALLDTGADDTE-----
  DPGRWKPKMIGGI
• JCSB5_199401 PQITLWQRPIVTIKVGGQLKEALLDTGADDTVL-EMN
  LPGRWKPKMIGGI
• GGFIKVRQYDQVPIEICGHKAIGTVLVGPTPSNIIGR
  NLLTQLGCTLNF
• GGFVKVRQYDQIPIDICGHKVIGTVL-GPTPANVIGR
  NLLTQIGCTLNF
• GGFVKVR-YDQVPIEICGH--IGTVLVGPTPANIIGR
  NLMTQLGCTLNF
• GGFLKVRQYDQIPVEICGHKAIGTVL-GPTPANIIGR
  NLLTQIG-TLNF
• GGFVKVRQYDQIPIEICGHKAIGTVLVGPTPANIVGR
  NLLTQIGCTLNF

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The Biology WorkBench

• http//workbench.sdsc.edu/
• http//www.ngbw.org/
• Nucleic Acid Sequence Tools, including BLAST,
  CLUSTALW, MFOLD, PRIMER3

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Muscle

• Protein nucleotide MSA program
• Improvements in both accuracy and speed
• exploiting a range of existing and new
  algorithmic techniques
• combination of progressive and iterative
  alignment strategies
• details of the method
• web server
• downloads Windows, Linux, Mac

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Muscle web server
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Editing MSA

• There are a variety of tools that can be used to
  modify a multiple alignment (SeaView, BioEdit,
  JalView)
• These programs can be very useful in formatting
  and annotating an alignment for publication.
• An editor can also be used to make modifications
  by hand to improve biologically significant
  regions in a multiple alignment created by one of
  the automated alignment programs.

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MSA approaches

• Progressive approach CLUSTALW (CLUSTALX),
  PileUp,
• T-COFFEE, MAFFT, MUSCLE
• Iterative approach Repeatedly realign subsets
  of sequences. MultAlin, DiAlig, MAFFT,
  MUSCLE,ProbCons
• Genetic algorithm
• SAGA
• Graph algorithm
• POA

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Conclusion

• There is no single method that always generates
  the best alignment
• It may thus be wise to use more than one method
• Alignment editors can be used to correct the
  alignments