DOCSLIB.ORG

Comparison of Heuristic Approaches to the Generalized Tree Alignment Problem

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Comparison of Heuristic Approaches to the Generalized Tree Alignment Problem Cladistics Cladistics (2015) 1–9 10.1111/cla.12142 Comparison of heuristic approaches to the generalized tree alignment problem Eric Forda,b,* and Ward C. Wheelerb aDepartment of Mathematics & Computer Science, Lehman College, CUNY, Bronx, NY 10468, USA; bDivision of Invertebrate Zoology, American Museum of Natural History, New York, NY 10024, USA Accepted 10 September 2015 Abstract Two commonly used heuristic approaches to the generalized tree alignment problem are compared in the context of phyloge- netic analysis of DNA sequence data. These approaches, multiple sequence alignment + phylogenetic tree reconstruction (MSA+TR) and direct optimization (DO), are alternative heuristic procedures used to approach the nested NP-Hard optimiza- tions presented by the phylogenetic analysis of unaligned sequences under maximum parsimony. Multiple MSA+TR implementa- tions and DO were compared in terms of optimality score (phylogenetic tree cost) over multiple empirical and simulated datasets with differing levels of heuristic intensity. In all cases examined, DO outperformed MSA+TR with average improvement in parsimony score of 14.78% (5.64–52.59%). © The Willi Hennig Society 2015. A central goal of biological systematics is mapping data (e.g. Lindgren and Daly, 2007; Lehtonen, 2008; the relationships among organisms and groups of Liu et al., 2009; Giribet and Edgecombe, 2013). In addi- organisms—both extant and extinct—based on the tion, the high degree of complexity in the settings of the reconstruction of phylogenetic trees using comparative software tools used for alignment and search only con- character data. The generalized tree alignment problem fuses the matter, as default settings are often used, and (GTAP; Sankoff, 1975) is defined as the search for these defaults do not necessarily correspond between phylogenetic tree(s)—and associated vertex (hypotheti- aligner and search engine. Here, we compare DO with cal ancestor) sequences—with lowest cost for those two-step solutions directly. We also test whether the data under maximum parsimony. results of searches where alignment and search setting There has been an ongoing debate in the literature correspond are better (i.e. more optimal) than those in regarding multiple sequence alignment (Katoh et al., which they do not. We find that DO results in the dis- 2002; Edgar, 2004; Wheeler, 2007), with several aligners covery of shorter trees, by an average factor of 15%. In available. In addition, much effort has been expended to addition, using the two-step approach we found signifi- improving search on aligned sequences (Goloboff et al., cantly (approximately 4%) shorter trees when using set- 2003). At the same time, other paradigmata for tings on alignment that match the settings of subsequent approaching the GTAP are also available, chief among tree search (as opposed to the default settings of multi- those being direct optimization (DO) (Wheeler, 1996, ple sequence alignment (MSA) implementations). 2003; Varon and Wheeler, 2012, 2013). It has been the experience of many investigators that DO gives signifi- cantly better results than the two-step process of align- Software tools ment followed by search for both real and simulated We ran comparisons using several pieces of align- *Corresponding author: ment software. What follows is a brief description of E-mail address: [email protected] each package. © The Willi Hennig Society 2015 2 E. Ford & W. C. Wheeler / Cladistics 0 (2015) 1–9 CLUSTAL OMEGA (Sievers et al., 2011) uses a Materials guide tree to align sequences. It is similar to CLUS- TAL W, described below, but speeds the process of Biological data creating the guide tree by re-encoding each sequence as an n-dimensional vector, which can be We ran alignment and search on six biological data viewed as its similarity to n reference sequences. sets and six synthetic data sets. The biological data This allows for more rapid clustering using hidden sets were chosen for their sizes. Each of the software Markov models. packages allows for the input of either genetic or pro- CLUSTAL W 2.0.12 (Larkin et al., 2007) pre-dates tein data. Here, we restricted our comparisons to CLUSTAL OMEGA and uses neighbour-joining (Saitou and nucleotide sequence data. Nei, 1987). In addition, CLUSTAL W weights the • 62 mantodean small subunit sequences (Svenson sequences, giving more-divergent sequences more and Whiting, 2004); weight, in an attempt to get improved results in pairwise • 208 metazoan small subunit sequences (Giribet comparisons. and Wheeler, 1999, 2001); MAFFT v7.029b (Katoh et al., 2002) performs pro- • 585 archaean small subunit sequence data from gressive alignments, also using a guide tree, and the European Ribosomal RNA Database uses fast Fourier transform to speed up identifica- (Wheeler, 2007); tion of homologous regions in sequences. Depend- • 1040 metazoan mitochondrial small subunit data ing on the number of taxa, MAFFT uses either from the European Ribosomal RNA Database progressive or iterative alignment algorithms. Both (Wheeler, 2007); MAFFT and MUSCLE, described below, use UPGMA • 1553 fungal small ribosomal subunit sequences (unweighted pair group method with arithmetic extracted from GenBank (Benson et al., 2013); mean, a hierarchical clustering method using a simi- • 1766 metazoan small subunit sequences extracted larity matrix), rather than neighbour-joining, as in from GenBank (Benson et al., 2013). CLUSTAL W. MUSCLE v3.8.31 (Edgar, 2004) also uses a guide Synthetic data tree to direct alignment. To build the initial tree, MUSCLE uses the approximate kmer pairwise dis- The synthetic data were created, using DAWG, by tances. A kmer is a subsequence sample of length k, varying the number of taxa and the distribution of and the kmer distance used by MUSCLE is the frac- edge (branch) lengths. Each of the synthetic data sets tion of kmers in common in a compressed alphabet. was rooted, and included an outgroup. All had initial This initial tree build is followed by progressive sequence lengths of 1800 bp (comparable to metazoan alignments using the Kimura distance (Kimura and small subunit data). We chose taxon counts to match Ohta, 1972). (approximately) the sizes of the smaller biological data In contrast to the above packages, POY (5.0 and sets, thus creating sets of 51, 201 and 601 taxa, includ- antecedents; Wheeler et al., 2015, 2013) uses DO. This ing a root taxon. For each of these taxon sets, we used approach optimizes median sequences on trees created a Python script to create a random tree, then assigned via heuristic search. The tree search itself is performed edge lengths to the tree using two distributions, an using heuristics similar to those implemented in TNT exponential distribution with mean of 0.1, and a uni- (Goloboff et al., 2003), but because the median opti- form distribution with values between 0.0 and 0.5. We mization and scoring are done as each tree is con- therefore generated a total of six synthetic data sets structed, a POY search is computationally more using DAWG. complex than a TNT search. In short, TNT is dealing with one NP-Hard optimization (tree search), whereas POY is dealing with two (tree search and tree align- Methods ment). After the alignment stage, we used TNT to search For each of the 12 data sets, a two-step process was for the shortest trees. In an attempt to compare followed. First, the sequences were aligned by each of DO with two-step searches more thoroughly, we the aligners (including POY implied alignment). The also ran TNT on the implied alignment results of resulting alignments were then fed into TNT for tree POY searches. search (specifics below). Thus, for each data set eight Finally, to create synthetic data with length varia- total alignments were generated (two each for MAFFT, tion in a more controlled manner (i.e. gaps, and thus POY/TNT and CLUSTAL OMEGA, one each for MUSCLE simulate unaligned sequences), we used DAWG 1.2-re- and CLUSTAL W), and for each of the eight alignments, lease (Cartwright, 2005). two TNT searches were run. Runs using POY alone (DO E. Ford & W. C. Wheeler / Cladistics 0 (2015) 1–9 3 without MSA) were performed on each unaligned data For the alignment stage, we first ran MAFFT with set (labelled “POY” in the tables and figures). This default settings, then reran it with gap opening cost of yielded a total of 192 runs through TNT, but 216 0 and extension cost 1 (–op 0 –ep 1 –lop 0 – searches, including POY solo searches. lep 2). MAFFT uses different alignment settings On several of the runs, POY found multiple trees. depending upon the size of the input data. To recreate Rather than performing multiple TNT searches on each those settings while changing the defaults for gap of the implied alignments, in these cases we used the costs, three different profiles were created: L-INS-i, first tree and implied alignment output. As POY outputs with commands –localpair –maxiterate these trees in the order that they were encountered 1000, FFT-NS-i (–retree 2 –maxiterate (with a randomized component), this choice was arbi- 2) and FFT-NS-2 (–retree 2 –maxiterate trary. DAWG takes as input a rooted tree with edge 0). For each data set, MAFFT was first run with default lengths and a sequence length, then evolves sequences settings. Its terminal output was then read to deter- following the tree. We generated random trees using a mine which of the three profiles was used, and that Python script, with edge lengths as noted above. Each profile was used when running MAFFT again with the of the six trees used to create the synthetic data was custom gap costs. The specifics of MAFFT options are fed into DAWG. We used a GTR model with defaults detailed in Katoh et al. (2002). The individual data settings for the frequency and parameters settings.
Load more

Recommended publications
  • Errors in Multiple Sequence Alignment and Phylogenetic Reconstruction
    Multiple Sequence Alignment Errors and Phylogenetic Reconstruction THESIS SUBMITTED FOR THE DEGREE “DOCTOR OF PHILOSOPHY” BY Giddy Landan SUBMITTED TO THE SENATE OF TEL-AVIV UNIVERSITY August 2005 This work was carried out under the supervision of Professor Dan Graur Acknowledgments I would like to thank Dan for more than a decade of guidance in the fields of molecular evolution and esoteric arts. This study would not have come to fruition without the help, encouragement and moral support of Tal Dagan and Ron Ophir. To them, my deepest gratitude. Time flies like an arrow Fruit flies like a banana - Groucho Marx Table of Contents Abstract ..........................................................................................................................1 Chapter 1: Introduction................................................................................................5 Sequence evolution...................................................................................................6 Alignment Reconstruction........................................................................................7 Errors in reconstructed MSAs ................................................................................10 Motivation and aims...............................................................................................13 Chapter 2: Methods.....................................................................................................17 Symbols and Acronyms..........................................................................................17
    [Show full text]
  • The Tree Alignment Problem Andres´ Varon´ and Ward C Wheeler*
    Varon´ and Wheeler BMC Bioinformatics 2012, 13:293 http://www.biomedcentral.com/1471-2105/13/293 RESEARCH ARTICLE Open Access The tree alignment problem Andres´ Varon´ and Ward C Wheeler* Abstract Background: The inference of homologies among DNA sequences, that is, positions in multiple genomes that share a common evolutionary origin, is a crucial, yet difficult task facing biologists. Its computational counterpart is known as the multiple sequence alignment problem. There are various criteria and methods available to perform multiple sequence alignments, and among these, the minimization of the overall cost of the alignment on a phylogenetic tree is known in combinatorial optimization as the Tree Alignment Problem. This problem typically occurs as a subproblem of the Generalized Tree Alignment Problem, which looks for the tree with the lowest alignment cost among all possible trees. This is equivalent to the Maximum Parsimony problem when the input sequences are not aligned, that is, when phylogeny and alignments are simultaneously inferred. Results: For large data sets, a popular heuristic is Direct Optimization (DO). DO provides a good tradeoff between speed, scalability, and competitive scores, and is implemented in the computer program POY. All other (competitive) algorithms have greater time complexities compared to DO. Here, we introduce and present experiments a new algorithm Affine-DO to accommodate the indel (alignment gap) models commonly used in phylogenetic analysis of molecular sequence data. Affine-DO has the same time complexity as DO, but is correctly suited for the affine gap edit distance. We demonstrate its performance with more than 330,000 experimental tests. These experiments show that the solutions of Affine-DO are close to the lower bound inferred from a linear programming solution.
    [Show full text]
  • Sequence Alignment in Molecular Biology
    JOURNAL OF COMPUTATIONAL BIOLOGY Volume 5, Number 2,1998 Mary Ann Liebert, Inc. Pp. 173-196 Sequence Alignment in Molecular Biology ALBERTO APOSTÓLICO1 and RAFFAELE GIANCARLO2 ABSTRACT Molecular biology is becoming a computationally intense realm of contemporary science and faces some of the current grand scientific challenges. In its context, tools that identify, store, compare and analyze effectively large and growing numbers of bio-sequences are found of increasingly crucial importance. Biosequences are routinely compared or aligned, in a variety of ways, to infer common ancestry, to detect functional equivalence, or simply while search- ing for similar entries in a database. A considerable body of knowledge has accumulated on sequence alignment during the past few decades. Without pretending to be exhaustive, this paper attempts a survey of some criteria of wide use in sequence alignment and comparison problems, and of the corresponding solutions. The paper is based on presentations and lit- erature at the on held at in November given Workshop Sequence Alignment Princeton, N.J., 1994, as part of the DIMACS Special Year on Mathematical Support for Molecular Biology. Key words: computational molecular biology, design and analysis of algorithms, sequence align- ment, sequence data banks, edit distances, dynamic programming, philogeny, evolutionary tree. 1. INTRODUCTION taxonomy is based ON THE assumption that conspicuous morphological and functional Classicalsimilarities in species denote close common ancestry. Likewise, modern molecular taxonomy pursues phylogeny and classification of living species based on the conformation and structure of their respective ge- netic codes. It is assumed that DNA code presides over the reproduction, development and susteinance of living organisms, part of it (RNA) being employed directly in various biological functions, part serving as a template or blueprint for proteins.
    [Show full text]
  • Algorithmic Complexity in Computational Biology: Basics, Challenges and Limitations
    Algorithmic complexity in computational biology: basics, challenges and limitations Davide Cirillo#,1,*, Miguel Ponce-de-Leon#,1, Alfonso Valencia1,2 1 Barcelona Supercomputing Center (BSC), C/ Jordi Girona 29, 08034, Barcelona, Spain 2 ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain. # Contributed equally * corresponding author: [email protected] (Tel: +34 934137971) Key points: ● Computational biologists and bioinformaticians are challenged with a range of complex algorithmic problems. ● The significance and implications of the complexity of the algorithms commonly used in computational biology is not always well understood by users and developers. ● A better understanding of complexity in computational biology algorithms can help in the implementation of efficient solutions, as well as in the design of the concomitant high-performance computing and heuristic requirements. Keywords: Computational biology, Theory of computation, Complexity, High-performance computing, Heuristics Description of the author(s): Davide Cirillo is a postdoctoral researcher at the Computational biology Group within the Life Sciences Department at Barcelona Supercomputing Center (BSC). Davide Cirillo received the MSc degree in Pharmaceutical Biotechnology from University of Rome ‘La Sapienza’, Italy, in 2011, and the PhD degree in biomedicine from Universitat Pompeu Fabra (UPF) and Center for Genomic Regulation (CRG) in Barcelona, Spain, in 2016. His current research interests include Machine Learning, Artificial Intelligence, and Precision medicine. Miguel Ponce de León is a postdoctoral researcher at the Computational biology Group within the Life Sciences Department at Barcelona Supercomputing Center (BSC). Miguel Ponce de León received the MSc degree in bioinformatics from University UdelaR of Uruguay, in 2011, and the PhD degree in Biochemistry and biomedicine from University Complutense de, Madrid (UCM), Spain, in 2017.
    [Show full text]
  • A New Approach for Tree Alignment Based on Local Re-Optimization
    A New Approach For Tree Alignment Based on Local Re-Optimization Feng Yue and Jijun Tang Department of Computer Science and Engineering University of South Carolina Columbia, SC 29063, USA yuef, jtang ¡ @engr.sc.edu Abstract be corrected later, even if those alignments conflict with sequences added later[8]. T-Coffee is another popular Multiple sequence alignment is the most fundamental sequence alignment tool and can be viewed as a variant task in bioinformatics and computational biology. In this of the progressive method. It has been reported to get the paper, we present a new algorithm to conduct multiple se- highest scores on the BAliBASE benchmark database [9]. quences alignment based on phylogenetic trees. It is widely The significant improvement is achieved by pre-processing accepted that a good phylogenetic tree can help produce a data set of all pair-wise alignments and thus allowing for high quality alignment, but the direct dynamic program- much better use of information in early stages. ming solution grows exponentially [13]. To overcome this An alternative way to compare multiple sequences is tree problem, we first devise a procedure that can produce opti- alignment, which is motivated by the fact that in most cases mal alignment for three sequences and infer their common the sequences are not independent of each other but rather ancestor. We then extend the above procedure to compute related by a revolutionary tree [18]. The tree alignment the alignment of a given tree with more sequences by itera- problem was developed principally by Sankoff, who also tively relabeling the internal nodes.
    [Show full text]
  • Tree Alignment
    Tree Alignment Tandy Warnow February 13, 2017 Tree alignment Tandy Warnow Today's material: I Review of last class I Optimization problems for multiple sequence alignment I Tree Alignment and Generalized Tree Alignment Section 9.5 in the textbook covers this material. Pairwise alignments and evolutionary histories Recall: 0 I If we know how a sequence s evolves into another sequence s , we know the true alignment between the two sequences. I We can use dynamic programming to find a minimum cost transformation of s into s0, where each single letter indel costs C and each substitution costs C 0. This algorithm is called Needleman-Wunsch. I We can extend the Needleman-Wunsch algorithm to handle more general cost functions: where the cost for a gap of length L isn't just CL, and where the cost of a substitution depends on the pair of letters. Multiple sequence alignment (MSA) Now that we have a way of defining the \cost" of a pairwise alignment, we can extend the definition to a set of three or more sequences in at least two ways: I Sum-of-pairs (SOP) score (find MSA to minimize the sum of costs of all induced pairwise scores) I Treelength (find tree and sequences at all nodes of the tree to minimize the sum of costs on the edges of the tree) Both problems are NP-hard. Treelength and Tree Alignments I Input: We are given a tree T with sequences at the leaves, and a cost C for indels and cost C 0 for substitutions. I Output: Sequences at each node to minimize the total treelength (summing over all the edges) of the tree.
    [Show full text]
  • Multiple Sequence Alignment
    Chapter 7 Multiple Sequence Alignment In multiple sequence alignment, the goal is to align k sequences, so that residues in each column share a property of interest, typically descent from a common ancestor or a shared structural or functional role. Applications of multiple sequence alignment include identifying functionally important mutations, predicting RNA secondary structure, and constructing phylogenetic trees. 0 0 Given sequences s1,...,sk of lengths n1,...,nk, α = fs1,...,skg is an alignment of s1,...,sk if and only if 0 0 ∗ • sa 2 (Σ ) , for 1 ≤ a ≤ k 0 • jsaj = l, for 1 ≤ a ≤ k, where l ≥ max(n1; : : : ; nk) 0 • sa is the sequence obtained by removing gaps from sa • No column contains all gaps 7.0.1 Scoring a multiple alignment As with pairwise alignment, multiple sequence alignments (MSAs) are typically scored by assigning a score to each column and summing over the columns. The most common approach to scoring individual columns in a multiple alignment is to calculate a score for each pair of symbols in the column, and then sum over the pair scores. This is called sum-of-pairs or SP-scoring. For global multiple sequence alignment, SP-scoring can be used with either a distance metric or a similarity scoring function. The sum-of-pairs similarity score of an alignment of k sequences is 0 0 l k 0 0 Ssp(s1; : : : ; sk) = Σi=1Σa=1Σb>a p(sa[i]; sb[i]); (7.1) Computational Molecular Biology. Copyright c 2019 D. Durand. All rights reserved. 133 Chapter 7 Multiple Sequence Alignment where l is the length of the alignment.
    [Show full text]
  • An Efficient Heuristic for the Tree Alignment
    City University of New York (CUNY) CUNY Academic Works Computer Science Technical Reports CUNY Academic Works 2008 TR-2008015: An Efficient Heuristic for ther T ee Alignment Problem Andrés Varón Ward Wheeler Amotz Bar-Noy How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_cs_tr/320 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] An Efficient Heuristic for the Tree Alignment Problem Andr´esVar´on1,2, Ward Wheeler1, and Amotz Bar-Noy2 1 Division of Invertebrate Zoology, American Museum of Natural History, Central Park West at 79th Street, New York, US. 2 Computer Science Department, The Graduate School and University Center, The City University of New York, 365 Fifth Avenue, New York, US. Abstract. Phylogeny and alignment estimation are two important and closely related biological prob- lems. One approach to the alignment question is known in combinatorial optimization as the Tree Alignment Problem (TAP). A number of heuristic solutions exist, with the most competitive algo- rithms using iterative methods to find solutions. However, these methods are either non-scalable, the bound is too loose, or the time and memory complexity are difficult to predict. For the simplest dis- tance functions, a widely used quick heuristic to find a solution to an instance of the problem is Direct Optimization (DO). Unfortunately, this algorithm has not been formally described, and it is currently unable to find correct solutions under an edition distance function called affine indel cost, of great im- portance for biologists analyzing DNA sequences.
    [Show full text]
  • Lecture 5: Multiple Sequence Alignment
    Introduction to Bioinformatics for Computer Scientists Lecture 5 Advertisement I ● Next round of excellence initiative ● Algorithm Engineering for Scientific Computing ● Joint Master theses with – Ralf Reussner – Carsten Sinz – Dorothea Wagner – Anne Koziolek – Peter Sanders Advertisement II ● This summer ● Not only the Bioinformatics seminar ● But also, Algorithmic Methods in Humanities with chair of Dorothea Wagner → two places available → a lot of bioinformatics methods can be applied to language analysis Missing Slides ● Taxonomy slides not discussed in lecture 2 → in next lecture: introduction to phylogenetics BLAST Index – Solution 1 The scanning phase raised a classic algorithmic problem, i.e. search a long sequence for all occurrences of certain short sequences. We investigated 2 approaches. Simplified, the first works as follows. suppose that w = 4 and map each word to an integer between 1 and 204, so a word can be used as an index into an array of size 204 = 160,000. Let the ith entry of such an array point to the list of all occurrences in the query sequence of the ith word. Thus, as we scan the database, each database word leads us immediately to the corresponding hits. Typically, only a few thousand of the 204 possible words will be in this table, and it is easy to modify the approach to use far fewer than 204 pointers. BLAST Index – Solution 2 The second approach we explored for the scanning phase was the use of a deterministic finite automaton or finite state machine (Mealy, 1955; Hopcroft & Ullman, 1979). An important feature of our construction was to signal acceptance on transitions (Mealy paradigm) as opposed to on states (Moore paradigm).
    [Show full text]
  • From Sequence Alignment to Phylogenetic Tree
    From sequence alignment to phylogenetic tree Jia-Ming Chang Department of Computer Science National Chengchi University, Taiwan 1973 https://biologie-lernprogramme.de/daten/programme/js/homologer/daten/lit/Dobzhansky.pdf 若不採用演化論，生物學的一切都說不通 Evolution • Charles Darwin’s 1859 book (On the Origin of Species By Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life) introduced the theory of evolution. • At the molecular level, evolution is a process of mutation with selection. • Molecular evolution is the study of changes in genes and proteins throughout different branches of the tree of life. • Phylogeny is the inference of evolutionary relationships. 1960s: globin phylogeny • tree of 13 orthologs by Margaret Dayhoff and colleagues • Arrow 1: node corresponding to last common ancestor of a group of vertebrate globins. • Arrow 2: ancestor of insect and vertebrate globins B&FG 3e Fig. 7.1 Page 247 The neighbor-joining tree of SARS-CoV-2 related coronaviruses • CDSs were aligned based on translated amino acid sequences using MUSCLE v3.8.31 … • Phylogenetic relationships were constructed using the neighbor-joining method based on Kimura’s two-parameter model. The origin and underlying driving forces of the SARS-CoV-2 outbreak, Shu-Miaw Chaw, Jui-Hung Tai, Shi-Lun Chen, Chia-Hung Hsieh, Sui-Yuan Chang, Shiou- Hwei Yeh, Wei-Shiung Yang, Pei-Jer Chen, Hurng-Yi Wang bioRxiv 2020.04.12.038554; doi: https://doi.org/10.1101/2020.04.12.038554 Phylogenetic Relationship of CoVs • Sequence alignment was carried out using MUSCLE software. • Gblocks was used to process the gap in the aligned sequence.
    [Show full text]
  • Multiple Alignments: CLUSTALW * Identical : Conserved Substitutions
    Multiple Alignments: CLUSTALW * identical : conserved substitutions . semi-conserved substitutions gi|2213819 CDN-ELKSEAIIEHLCASEFALR-------------MKIKEVKKENGDKK 223 gi|12656123 ----ELKSEAIIEHLCASEFALR-------------MKIKEVKKENGD-- 31 gi|7512442 CKNKNDDDNDIMETLCKNDFALK-------------IKVKEITYINRDTK 211 gi|1344282 QDECKFDYVEVYETSSSGAFSLLGRFCGAEPPPHLVSSHHELAVLFRTDH 400 : . : * ..*:*.:*: Red: AVFPMLW (Small & hydrophobic) Blue: DE (Acidic) Magenta: RHK (Basic) Green: STYHCNGQ (Hydroxyl, Amine, Basic) Gray: Others 1/31/06 CAP5510/CGS5166 1 Multiple Alignments • Family alignment for the ITAM domain (Immunoreceptor tyrosine-based activation motif) • CD3D_MOUSE/1-2 EQLYQPLRDR EDTQ-YSRLG GN Q90768/1-21 DQLYQPLGER NDGQ-YSQLA TA CD3G_SHEEP/1-2 DQLYQPLKER EDDQ-YSHLR KK P79951/1-21 NDLYQPLGQR SEDT-YSHLN SR FCEG_CAVPO/1-2 DGIYTGLSTR NQET-YETLK HE CD3Z_HUMAN/3-0 DGLYQGLSTA TKDT-YDALHMQ C79A_BOVIN/1-2 ENLYEGLNLD DCSM-YEDIS RG C79B_MOUSE/1-2 DHTYEGLNID QTAT-YEDIV TL CD3H_MOUSE/1-2 NQLYNELNLG RREE-YDVLE KK Simple CD3Z_SHEEP/1-2 NPVYNELNVG RREE-YAVLD RR Modular CD3E_HUMAN/1-2 NPDYEPIRKG QRDL-YSGLN QR Architecture CD3H_MOUSE/2-0 EGVYNALQKD KMAEAYSEIG TK Consensus/60% -.lYpsLspc pcsp.YspLs pp Research Tool 1/31/06 CAP5510/CGS5166 2 Multiple Alignment 1/31/06 CAP5510/CGS5166 3 1 How to Score Multiple Alignments? • Sum of Pairs Score (SP) –Optimal alignment: O(dN) [Dynamic Prog] – Approximate Algorithm: Approx Ratio 2 •Locate Center: O(d2N2) • Locate Consensus: O(d2N2) Consensus char: char with min distance sum Consensus string: string of consensus char Center: input
    [Show full text]
  • Lecture 5: Multiple Sequence Alignment
    Introduction to Bioinformatics for Computer Scientists Lecture 5 Course Beers or Coffee ● Who wants to volunteer for organizing this? Plan for next lectures ● Today: Multiple Sequence Alignment ● Lecture 6: Introduction to phylogenetics ● Lecture 7: Phylogenetic search algorithms ● Lecture 8 (Kassian): The phylogenetic Maximum Likelihood Model ● Lecture 8 (Kassian): The phylogenetic Maximum Likelihood Model (continued) Insertions, Deletions & Substitutions ATTGCG CTTGCG T ATTGCG i m e ATGCG ATTGCG CTTGCG CTTGCAAG Insertions, Deletions & Substitutions ATTGCG A → C: substitution CTTGCG T ATTGCG i m e ATGCG ATTGCG CTTGCG CTTGCAAG Insertions, Deletions & Substitutions ATTGCG A → C: substitution CTTGCG T ATTGCG i m e VOID → AA: insertion ATGCG ATTGCG CTTGCG CTTGCAAG Insertions, Deletions & Substitutions ATTGCG A → C: substitution CTTGCG T ATTGCG i m e VOID → AA: insertion ATGCG ATTGCG CTTGCG CTTGCAAG We call this: “an indel” From insertion-deletion The indel length here is 2, longer indels lengths are not uncommon! Insertions, Deletions & Substitutions ATTGCG A → C: substitution CTTGCG T ATTGCG i m e VOID → AA: insertion T → VOID: deletion AT-GCG ATTGCG CTTGCG CTTGCAAG Insertions, Deletions & Substitutions ATTGCG A → C: substitution CTTGCG T ATTGCG i m T e → VOID: deletion VOID → AA: insertion AT-GCG ATTGCG CTTGCG CTTGCAAG AT-GC--G Aligned data: ATTGC--G CTTGC--G CTTGCAAG Insertions, Deletions & Substitutions ATTGCG A → C: substitution CTTGCG T ATTGCG i m T e → VOID: deletion Compute whichVOID characters → AA: shareinsertion a common evolutionary
    [Show full text]