Alignment Most alignment programs create an alignment that
- Slides: 31
Alignment Most alignment programs create an alignment that represents what happened during evolution at the DNA level. To carry over information from a well studied to a newly determined sequence, we need an alignment that represents the protein structures of today. ©CMBI 2001
The amino acids Most information that enters the alignment procedure comes from the physicochemical properties of the amino acids. Example: which is the better alignment (left or right)? CPISRTWASIFRCW CPISRT---LFRCW CPISRTWASIFRCW CPISRTL---FRCW ©CMBI 2001
A difficult alignment problem AYAYSY AGAPAPAPSP LGLPLP So, in an alignment of more than 2 sequences you can find more information than from just the 2 sequences you are interested in. How do we make these multisequence alignmnets? ©CMBI 2001
A difficult alignment problem solved AYAYSY AGAPAPAPSP LGLPLP ©CMBI 2001
Alignment order MIESAYTDSW QFEKSYVTDY -MIESAYTDSW QFEKSYVTDY- ©CMBI 2001
Alignment order MIESAYTDSW QFEKSYVTDY QWERTYASNF -MIESAYTDSW QFEKSYVTDYQWERTYASNF- ©CMBI 2001
Conclusion Align first the sequences that look very much like each other. So you ‘build up information’ while generating those alignments that most likely are correct. ©CMBI 2001
Alignment order In order to know which sequences look most like each other, you need to do all pairwise alignments first. This is exactly what CLUSTAL does. CLUSTAL builds a tree while doing the build-up of the multiple sequence alignment. ©CMBI 2001
MSA and trees Take, for example, the three sequences: 1 ASWTFGHK 2 GTWSFANR 3 ATWAFADR and you see immediately that 2 and 3 are close, while 1 is further away. So the tree will look roughly like: 3 2 1 ©CMBI 2001
Aligning sequences; start with distances D E Matrix of pair-wise distances between five sequences. 10 8 7 D and E are the closest pair. Take them, and collapse the matrix by one row/column. ©CMBI 2001
Aligning sequences D E A B ©CMBI 2001
Aligning sequences C D E A B ©CMBI 2001
Aligning sequences C D E A B ©CMBI 2001
Back to the alignment 1 ASWTFGHK 2 GTWSFANR 3 ATWAFADR Actually I cheated. 1 is closer to 3 than to 2 because of the A at position 1. How can we express this in the tree? For example: 3 2 1 2 I will call this 3 tree-flipping 1 ©CMBI 2001
Can we generalize tree-flipping? To generalize tree flipping, sequences must be placed ‘distancecorrect’ in 1 dimension: And then connect them, as we did before: 2 3 So, now most info sits in the horizontal dimension. Can we use the vertical dimension usefully? 1 ©CMBI 2001
The problem is actually bigger 1 ASWTFGHK 2 GTWSFANR 3 ATWAFADR d(i, j) is the distance between sequences i and j. d(1, 2)=6; d(1, 3)=5; d(2, 3)=3. So a perfect representation would be: 3 1 2 But what if a 4 th sequence is added with d(1, 4)=4, d(2, 4)=5, d(3, 4)=4? Where would that sequence sit? ©CMBI 2001
So, nice tree, but what did we actually do? 1)We determined a distance measure 2)We measured all pair-wise distances 3)We reduced the dimensionality of the space of the problem 4)We used an algorithm to visualize 5)In a way, we projected the hyperspace in which we can perfectly describe all pair-wise distances onto a 1 -dimensional line. 6)What does this sentence mean? ©CMBI 2001
Projection Gnomonic projection: Correct distances Fuller projection; Unfolded Dymaxion map Political projection Source: Wikepedia Mercator projection ©CMBI 2001
Back to sequences: ASASDFDFGHKMGHS ASASDFDFRRRLRIT ASLPDFLPGHSIGHS ASLPDFLPGHSIGIT ASLPDFLPRRRVRIT 1 2 5 3 6 3 The more dimensions we retain, the less information we loose. The three is now in 3 D… ©CMBI 2001
Projection to visualize clusters We want to reduce the dimensionality with minimal distortion of the pair-wise distances. One way is Eigenvector determination, or PCA. ©CMBI 2001
PCA to the rescue Now we have made the data one-dimensional, while the second, vertical, dimension is noise. If we did this correctly, we kept as much data as possible. ©CMBI 2001
Back to sequences: In we have N sequences, we can only draw their distance matrix in an N-1 dimensional space. By the time it is a tree, how many dimensions, and how much information have we lost? Perhaps we should cluster in a different way? ©CMBI 2001
Cluster on critical residues? QWERTYAKDFGRGH AWTRTYAKDFGRPM SWTRTNMKDTHRKC QWGRTNMKDTHRVW Gray = conserved Red = variable Green = correlated ©CMBI 2001
Conclusions from correlated residues ©CMBI 2001
Other algorithms Multi-sequence alignment can also be done with an iterative ‘profile’ alignment. A) Make an alignment of few, well-aligned sequences B) Align all sequences using this profile ©CMBI 2001
1. What is a profile? Normally, we use a PAM-like matrix to determine the score for each possible match in an alignment. This assumes that all matches between I <-> E are the same. But the aren’t. ©CMBI 2001
2. What is a profile? QWERTYIPASEF QWEKSFIPGSEY NWERTMVPVSEM QFEKTYLPSSEY NFIKTLMPATEF QYIRSLIPAGEM NYIQSLIPSTEL QFIRSLFPSSEI 1 2 3 At 1, E and I are both OK. At 2, I is OK, but E surely not. At 3, E is OK, but I surely not. ©CMBI 2001
3. What is a profile? The knowledge about which residue types are good at a certain position in the multiple sequence alignment can be expressed in a profile. A profile holds for each position 20 scores for the 20 residue types, and sometimes also two values for position specific gap open and gap elongation penalties. ©CMBI 2001
Conserved, variable, or in-between QWERTYASDFGRGH QWERTYASDTHRPM QWERTNMKDFGRKC QWERTNMKDTHRVW Gray = conserved Black = variable Green = correlated mutations ©CMBI 2001
Correlated mutations determine the tree shape 1 2 3 4 AGASDFDFGHKM AGASDFDFRRRL AGLPDFMNGHSI AGLPDFMNRRRV ©CMBI 2001
Correlation = Information 1, 2 and 5 bind calcium; 3 and 4 don’t. Which residues bind calcium? 1 2 3 4 5 123456789012345 ASDFNTDEKLRTTFI ASDFSTDEKLKTTFI LSFFTTDTRLATIYI LSHFLTNLRLATIYI ASDFTTDEKLALTFI Red has correct correlation, but wrong residue type. Brown has correct type, but wrong correlation. Green can be calcium-binders. ©CMBI 2001
- Cpmcd full form
- Block nhĩ thất độ 2 type 1
- Tìm vết của mặt phẳng
- Sau thất bại ở hồ điển triệt
- Thể thơ truyền thống
- Hãy nói thật ít để làm được nhiều
- Thơ thất ngôn tứ tuyệt đường luật
- Tôn thất thuyết là ai
- Ngoại tâm thu thất chùm đôi
- Chiến lược kinh doanh quốc tế của walmart
- Gây tê cơ vuông thắt lưng
- Apa itu mutiple queue dan one way list
- Dna substitution
- Gcg bioinformatics
- Sequence alignment
- Global vs local alignment
- Global alignment example
- Animation frame unit
- Which shoes create the most pressure
- In the name of allah the most
- Most general to most specific classification
- In the name of god the most gracious the most merciful
- In the name of allah most gracious most merciful in arabic
- In the name of allah the most beneficent the most merciful
- Arrangement of organisms
- Most general to most specific classification
- Most beneficent
- Beneficent pronunciation
- In the name of god, most gracious, most merciful prayer
- In the name of god most gracious most merciful
- Most general to most specific classification
- Guddi baji