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Material Type: Notes; Class: Applied Bioinformatics; Subject: Biotechnology; University: University of California - Davis; Term: Unknown 2006;
Typology: Study notes
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Chapter 12 &Notredame 2002.pdf
Fig. from Boris Steipe U. ofToronto
for function
-^ Sensitive database searching
algorithms
MSA of
ZCCT
genes
I HvCO4I OsCI HvCO
I OsF I HvCO3I OsBI AtCO I AtCOL
I OsHd
I HvCO I HvCO I OsG
II AtCOL6II OsJ
IV HvCO9IV OsH
IV OsIZCCT2 HaZCCT2 Hb
ZCCT2 Tm ZCCT2 TdZCCT1 TmG
ZCCT1 TmDV92ZCCT1 Td
III AtCOL9III OsN
83
82 91
99
60 66 73
94
56 53
99
I^ HvCO I^ OsC^ IV^ OsI
III^
Os N
I^ HvCO7^ I
OsF I^ HvCO3 I OsB^ I^ AtCO I^ AtCOL
I^ OsHD
I^ HvCO I^ HvCO I^ OsG
II^ AtCOL
6 II^ OsJ
IV^ HvCO9 IV^ OsH
III^
AtCOL
9
HvZCCT-Ha
HvZCCT-Hb
TmZCCT TdZCCT2TmZCCT1-G
TmZCCT1-DV92TdZCCT
Photoperiodresponse
Cluster analysis of the CCT domains
Exon 1
Exon 2
CCT
ZF
+^
+^
++ +
+
+^
+^
+ +
+^
+^
+^
+
+
IV II III
CCT domains
EMSco-
ZCCT I
Fig. from Boris Steipe Univ. of Toronto
Seq.
Induced
Seq.
alignment
Seq.
Seq.
Distance
scheme:
Seq.
Seq.
#^
mismatches
(including
-)
After
the best MSA is obtained, non-alignable sequences and spaces facingspaces are removed and a score is calculated for the
induced MAS
using
any chosen scoring scheme (distance or similarity).
3
Seq.
Seq.
Seq.
:^ The SP of a MSA is the sum of the scores of all the
scores of the
induced
pairwise global alignments
3
of the sequences (instead of character-
to-character) without gap penalties is the strategy used by
. This
approach is efficient for
local
similarities, (genomic DNA, many protein families)
http://bibiserv.techfak.uni-bielefeld.de/dialign/
.
-^
: (e.g.
). The optimal
MSA is defined as the one that agrees the most with all the optimal pair-wisealignments. “Given a set of independent observations the most consistent areoften closer to “the truth”.
Seq.
Seq.
Clustal
Seq.
Distance
scheme
Seq.
Gap
open=
Seq.
#^
mismathes
(including
-)
Seq.
Gap
ext.=
From Boris Steipe Univ. of Toronto
Multidimensional dynamic programming
-^
Optimizes sum-of-pairs
-^
More accurate than progressive methods
-^
BUT… Time proportional to L
n
-^
ivide &
onquer
lgorithm)
Sits on top of MSA program
-^
Produces simultaneous MSA
-^
-^
-^
ptimal
ultiple
ligment)
Iterative implementation of DCA
-^
Speeds up DCA
-^
Decreases memory requirements
, MultAlign, AMPS)
-^
-^
GARFIELDTHEFASTCAT---GARFIELDTHELASTFATCAT
GARFIELD
THE
LAST
FAST
CAT
GARFIELD
THE
FAST
CAT
GARFIELD
THE
VERY
FAST
CAT
THE
FAT
CAT
GARFIELDTHEVERYFASTCATGARFIELDTHEFASTCAT----GARFIELDTHELASTFAT-CAT
DCA--------THEFA-----TCATGARFIELDTHEVERYFASTCATGARFIELDTHEFAS----TCATGARFIELDTHELASTFA-TCAT--------THEFAT-----CATGARFIELDTHEVERYFASTCATGARFIELDTHEFASTCAT----GARFIELDTHELASTFAT-CAT
alignment
ClustalW
Blosum
Gap
11-
Cheaper to open distal gapthan to align C and F
Uses a ‘Genetic Algorithm’
-^
Can use different objective functions (e.g. Coffee)
-^
Mutations randomly insertion or shift gaps
-^
Sequences can recombine
-^
Gibbs sampler
( Local
Finds un-gapped motifs
-^
Segments are removed or added to increasea P value
-^
http://bayesweb.wadsworth.org/gibbs/gibbs.html
s and
s have been rather
disappointing in
ab initio
alignments.
Better: Pre-compute MSA with otherprogram and then use this ones foroptimization
Evolution of a seq. alignment by recombination
Compatible ends
Hammer
( also SAM)
Build Hidden Markov Models based on seq.
-^
Align sequences to HMM
-^
