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BIOINFORMATICS DR. VÍCTOR TREVIÑO [email protected] Multiple Sequence Alignments and Phylogeny [email protected] SEQUENCE SIMILARITY Within a protein sequence, some regions will be more conserved than others. As more conserved, more important. for function for 3D structure for localization for modification for interaction for regulation/control for transcriptional regulation (in DNA) REASONS TO PERFORM SEQUENCE SIMILARITY ANALYSIS AND SEARCHES [email protected] SEQUENCE ALIGNMENT Procedure for comparing two (pair-wise alignment) or more (multiple sequence alignment) sequences by searching for similar patterns that are in the same order in the sequences Overall similitude Identical residues (nt or aa) are placed in the same column Non-identical residues can be placed in the same column or indicated as gaps Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press Wikipedia, http://www-personal.umich.edu/~lpt/fgf/fgfrcomp.htm [email protected] MULTIPLE SEQUENCE ANALYSIS – ADDITIONAL USES Interesting regions Promoter regions Consensus sequence for probe design [email protected] Multiple Sequence Alignment - MSA Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] MULTIPLE SEQUENCE ALIGNMENT - MSA Dynamical programming is designed for two sequences It would take quite a long time for three or more (see MSA program) Sequence B Sequence A [email protected] RELATION MSA & EVOLUIONARY TREE RECONSTRUCTION [email protected] MULTIPLE SEQUENCE ALIGNMENT – METHODS Extenstions of sequence pair alignment MSA Progressive Methods CLUSTALW Iterative Methods Hidden Markov Models (HMM) [email protected] MULTIPLE SEQUENCE ALIGNMENT - MSA Algorithm 1. 2. 3. 4. 5. 6. 7. 8. Calculate all pair-wise alignment scores (alignment costs). Use the scores (costs) to predict a tree. Calculate pair weights based on the tree. Produce a heuristic msa based on the tree. Calculate the maximum for each sequence pair. Determine the spatial positions that must be calculated to obtain the optimal alignment. Perform the optimal alignment. Report the epsilon found compared to the maximum epsilon. epsilon for a given sequence pair is the difference between the score of the alignment of that pair in the msa and the score of the optimal pair-wise alignment. The bigger the value of , the more divergent the msa from the pair-wise alignment and the smaller the contribution of tht alignment to the msa. For example, if an extra copy of one of the sequences is added to the alignment project, then for sequence pairs that do not include that sequence will increase, indicating a lesser role because the contributions of that pair have been out-voted by the alike sequences. [email protected] PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT Dynamical programming is designed for two sequences It would take quite a long time for three or more (see MSA program) Therefore… Pair-wise all sequences Determine "distances between each one" Align the two most similar then get the alignment Get the next more similar and perform the same steps until all sequences has been included E.G. 1. 2. 3. 4. 5. 1. 2. 3. 4. (S3+S4)=c1, (S1+S2)=c2 (c1+c2)=c3 (c3+S5)=final S1 S2 S3 S4 S5 [email protected] PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT CLUSTALW CLUSTALW METHOD (then normalized to largest = 1) Alignment Score for column Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT CLUSTALW 3 1 2 Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT PROBLEMS Dependency on the most similar sequences Nested problems when most similar sequences are actually different So, for closely related sequence, CLUSTALW is the best Choice of suitable scoring matrices Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] ITERATIVE MULTIPLE SEQUENCE ALIGNMENT Try to correct for the dependency on the most similar sequences in progressive methods Repeatedly realigning subgroups, then aligning these on the global alignment Based in tree ordering, separation of sequences, or random grupo selection Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] ITERATIVE MULTIPLE SEQUENCE ALIGNMENT Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] HMM MULTIPLE SEQUENCE ALIGNMENT D1 Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] MULTIPLE SEQUENCE ALIGNMENT - PROGRAMS Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] MULTIPLE SEQUENCE ALIGNMENT - OVERVIEW [email protected] PHYLOGENY ANALYSIS AND PREDICTION FROM DNA/PROTEIN SEQUENCES Determination of how the family might have been derived during evolution Sequences is depicted as branches on a tree Very similar sequences are located as neighbours in a branch The goal is to discover all the branching relationships and the branch lengths Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] PHYLOGENY ANALYSIS AND PREDICTION FROM DNA/PROTEIN SEQUENCES Phylogenetic relationships among the genes can help to predict which ones might have an equivalent function. Phylogenetic analysis may also be used to follow the changes occurring in a rapidly changing species, such as a virus Important for discovering function, 3D structure, localization, modification, interaction, regulation/control, transcriptional regulation Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] PHYLOGENY ANALYSIS AND PREDICTION FROM DNA/PROTEIN SEQUENCES Related to SEQUENCE ALIGNMENT Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] SEQUENCE SIMILARITY – EVOLUTIONARY RELATIONSHIP Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] GENOME COMPLEXITY Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] GENOME COMPLEXITY Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] EVOLUTIONARY TREE An evolutionary tree is a two-dimensional graph showing evolutionary relationships among organisms The separate sequences are referred to as taxa (singular taxon), defined as phylogenetically distinct units on the tree The tree is composed of outer branches (or leaves) representing the taxa and nodes and branches representing relationships among the taxa Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] EVOLUTIONARY TREE A and B are derived from a common ancestor each node in the tree represents a splitting of the evolutionary path of the gene into two different species that are isolated reproductively Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] EVOLUTIONARY TREE Beyond spliting, any further evolutionary changes in each new branch are independent of those in the other new branch The length of each branch to the next node represents the number of sequence changes that occurred prior to the next level of separation Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] EVOLUTIONARY TREE Uniform mutation rate Molecular Clock Hypothesis, suitable for closely related species Special cases could use non-uniform rates The root is defined by including a taxon that we are reasonably sure branched off earlier than the other Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] EVOLUTIONARY TREE The sum of all the branch lengths in a tree is referred to as the tree length. The tree is also a bifurcating or binary tree, in that only two branches emanate from each node. Trees can have more than one branch emanating from a node if the events separating taxa are so close that they cannot be resolved, or to simplify the tree. The unrooted tree also shows the evolutionary relationships among sequences A–D, but it does not reveal the location of the oldest ancestry. Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] EVOLUTIONARY TREE The number of possible rooted trees increases very rapidly with the number of sequences or taxa Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] METHODS TO BUILD EVOLUTIONARY TREES To find the evolutionary tree or trees that best account for the observed variation in a group of sequences Maximum Parsimony Distance Maximum Likelihood [email protected] METHOD SELECTION Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] CONSIDERATIONS Not Large number of gaps Phylogenetic methods analyze conserved regions that are represented in all the sequences (Local Alignments) [email protected] MAXIMUM PARSIMONY (OR MINIMUM EVOLUTION) Predicts the evolutionary tree by minimizing the number of steps required to generate the observed sequence changes Requires a multiple sequence alignment Method revise each informative position and each possible tree same residue in at least two sequences but not all Used for sequences that are quite similar and for small number of sequences [email protected] MAXIMUM PARSIMONY (OR MINIMUM EVOLUTION) Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press Non informative [email protected] DISTANCE METHODS Employs the number of changes between each pair Sequence pairs that have the smallest number of sequence changes are "neighbours" sharing a node in the tree Very related to Multiple sequence alignment method (CLUSTALW) which produced DISTANCE MATRICES then analysed by distance methods Remember Distance vs Similarity (and gaps) Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press [email protected] DISTANCE METHODS Bioinformatics – Sequence and Genome Analysis – Mount – CSH Lab Press "Idealized" [email protected] DISTANCE ALGORITHMS Fitch and Margoliash Method Neighbor-joining Method Unweighted Pair Group Method with Arithmetic Mean (UPGMA) [email protected] DISTANCE ALGORITHM Choosing a outgroup (Grupo Fuera) improves prediction because methods are informed about the "order" of the outgroup [email protected] MAXIMUM LIKELIHOOD Uses probability of the number of sequence changes Analysis is performed for each informative residue (like in maximum parsimony) All possible trees are considered (so, for small number of sequences) Consider variations in mutation rates, so it can be used for most distant sequences Main disadvantage: Computation Time [email protected] MAXIMUM LIKELIHOOD Needs a model that provides estimates of substitution rates for each residue pair [email protected] RELIABILITY OF PHYLOGENETIC PREDICTIONS Bootstrap method randomly resampling residues within columns (robustness test) Good evidence if more than 70% predictions are conserved then Collapse branches and confirm tree length Compare distinct methods and parameters [email protected] "CLASSIC" PROGRAMS PHYLIP http://evolution.genetics.washington.edu/phylip.html PAUP http://paup.csit.fsu.edu/downl.html Phylemon http://phylemon.bioinfo.cipf.es/cgi-bin/tools.cgi PHYLEMON WEB SERVICE [email protected] PROGRAMS – WEB SERVICES http://bioinformatics.ca/links_directory/index.php?search=phylogeny&submit=Search+Directory [email protected] PROGRAMS – WEB SERVICES http://bioinformatics.ca/links_directory/index.php?search=phylogeny&submit=Search+Directory [email protected] BOOK [email protected] [email protected] EXERCISE/HOMEWORK Select a gene Get the sequence in at least 7 species Select a site (Phylemon) Perform the multiple sequence alignment (ClustalW) Perform Phylogeny to obtain a tree At least 2 tree methods At least 3 parameter(s) changes Take DNA/Protein Report results and discussion 12 MSA+Trees [email protected] PAPERS TO REVISE Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis – Loytynoja, Goldman, Science 2008 Insertions and deletions treated as different events [email protected] PAPERS PENDING FOR THIS SESSION