DOCSLIB.ORG

Bioinformatics 2 − 2 Nd Lecture

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Bioinformatics 2 − 2 Nd Lecture Bioinformatics 2 − 2nd lecture Prof. László Poppe BME Dept. Organic Chemistry and Technology Bioinformatics – proteomcs Lecture and computer room practice 10/1/2019 Bioinformatics 2 N.M. Luscombe, D. Greenbaum, M. Gerstein: International Medical Informatics Association Yearbook , 2001, 83-100. The bioinformatics space 2 10/1/2019 Bioinformatics 2 A. Kremer,R. Schneider, G.C. Terstappen, Biosci. Rep., 2005, 25, 95-106 Relationships in proteomics 3 10/1/2019 Bioinformatics 2 Bioinformatics databases Databases: DNA−sequences: gene identification and gene structure; Genome databases, genome maps; Gene expression databases; Proteins: protein sequences, protein sequence patterns, Structure databases, Proteom analysis; Enzyme databases, Metabolic databases; Molecular interactions: protein-protein, ligand databases, pharmaceuticals databases 1. Nucleotide sequence databases - Primary DNA−sequence databases - Specialized databases 2. Protein sequence databases - Primary protein sequence databases - Secondary and ternary (sequence motifs) databases - Integrated protein sequence databases 3. Structure databases 4. Protein family databases - Clustering - Sequence family databases - Structure family databases 4 10/1/2019 Bioinformatics 2 Sequence analysis Most important process of bioinformatics: search for sequences similar to a novel sequence (with unknown structure / function) among the known sequences with known structure / function. Sequence alignment: Sequence identity: percent ratio of the identical amino acids in the aligned sequences Transferability of function / structure is decreasing with decreasing sequence identity 5 10/1/2019 Bioinformatics 2 Sequence analysis Pairwise sequence alignment 6 10/1/2019 Bioinformatics 2 Sequence analysis problems Questions of orthology and paralogy: By analysis of novel sequences, a serious question how the functional information is applicable to the new protein (a similar sequence in a different organism may be a paralogue of the orthologous protein of the other organism, i.e. common origin but development of a novel function during evolution). This may result in errors during automated function predictions (be careful with the data from automated function annotations)! In the case of modular proteins sequence similarity is often valid only for a part of the sequences. 7 10/1/2019 Bioinformatics 2 Modular proteins Modules: protein domains can serve as exchangeable building blocks (e.g. integration of a module of the A membrane protein into the B protein forms a novel structure) During evolution, the function of modules may change as a part of different proteins => similarity in sequence but different function 8 10/1/2019 Bioinformatics 2 Sequence analysis problems Even in case of large sequence or structural similarity the function can be totally different. E.g. the sequence identity is 50% between lysozyme and alpha-lactalbumin and the spatial structures are also very similar, but these two proteins have completely different functions (−lactalbumin: lactose synthase regulatory protein, lysozyme: gastrointestinal bacterial cell wall hydrolases) −lactalbumine lysozyme => In case of approx. one-third of the uncharacterized sequences their function can not be inferred on the basis of proteins of known function 9 10/1/2019 Bioinformatics 2 Sequence analysis problems Sequence comparison means locating significantly similar zones between two or more sequences. The main problem is to decide what is significant when you are talking about biological sequences. There are several different approaches for many purposes. 10 10/1/2019 Bioinformatics 2 Nucleotide sequence databases Primary DNA sequence databases (International Nucleotide Sequence Database Collaboration http://www.insdc.org/) DDBJ (Japan, DNA Data Base of Japan - National Institute of Genetics) EMBL (Europe, European Bioinformatics Institute) GenBank (USA, National Center for Biotechnology Information) Sequence data collection: • directly from researchers • from literature data • from patent data • from genome sequencing projects 11 10/1/2019 Bioinformatics 2 Sequence analysis problems Start with a simple sequence-pair (the vertical lines represent the agreement): A conserved region is apparent. Is there a better match? Slide the two sequences! The conserved regions are enlarged. Is there an even better match? Insert gaps! The conserved region is even larger. 12 10/1/2019 Bioinformatics 2 Sequence analysis problems Insert gaps! The conserved region is even larger. Even full identity can be achieved in the alignment if there is no limit for gaps (deletions) / inserts! => Limit should be set. Low identity is shown between two sequences: The situation changes dramatically when the bottom chain is mirrored horizontally (5‘ and 3' ends reversal) => Relationships need to be analyzed by computers 13 10/1/2019 Bioinformatics 2 Identity matrices Unlimited insertion of gaps has no biological sense. Creation of gaps should be limited - this can be solved by penalty points: - By inserting a novel gap: (gap opening penalty) - By enlargement of an existing gap: (gap extension penalty) If only the identities are taking into account by judging during sequnce alignements, the identity matrix is used: Nukleotide identity matrix 14 10/1/2019 Bioinformatics 2 Identity matrices Protein identity matrix The identity matrix is a sparse matrix. As the full matches are considered with equal weights, these matrices are not favorable for similarity search. 15 10/1/2019 Bioinformatics 2 Amino acid similarity matrices By alignments of real biological meaning different amino acids can be placed under each other, so there is an importance of what is exchanged to what: "Looser" amino acid similarity matrices are required in which amino acid similarity is scored. Disadvantage: increased „noise" (more false hits of unrelated proteins) As the signal-to-noise ratio depends on the similarity matrix, creating good amino acid similarity matrices is an independent research area. Similarity matrix can be made on a statistical basis (e.g. mutation frequency) or based on the physicochemical properties of amino acids. The two most common matrices (PAM / BLOSUM) were created by the aid of mutation statistics. By using similarity matrices for the alignment, a similarity value may be calculated in addition to the identity (e.g. the similarity % is the % value of the amino acid pairs with positive similarity scores) 16 10/1/2019 Bioinformatics 2 Dayhoff’s PAM matrices Hydrophilic Amino Acids Dr. Margaret Oakley Dayhoff et al: Sulfhydryl Aliphatic In 70’s, the probabilities of amino acid exchanges were calculated from Basic normalized probabilities multiplied by 10000 Aromatic comparisons of hand-aligned sequences of >85% identity. Special Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val A R N D C Q E G H I L K M F P S T W Y V A 9867 2 9 10 3 8 17 21 2 6 4 2 6 2 22 35 32 0 2 18 R 1 9913 1 0 1 10 0 0 10 3 1 19 4 1 4 6 1 8 0 1 N 4 1 9822 36 0 4 6 6 21 3 1 13 0 1 2 20 9 1 4 1 D 6 0 42 9859 0 6 53 6 4 1 0 3 0 0 1 5 3 0 0 1 C 1 1 0 0 9973 0 0 0 1 1 0 0 0 0 1 5 1 0 3 2 Q 3 9 4 5 0 9876 27 1 23 1 3 6 4 0 6 2 2 0 0 1 E 10 0 7 56 0 35 9865 4 2 3 1 4 1 0 3 4 2 0 1 2 G 21 1 12 11 1 3 7 9935 1 0 1 2 1 1 3 21 3 0 0 5 H 1 8 18 3 1 20 1 0 9912 0 1 1 0 2 3 1 1 1 4 1 I 2 2 3 1 2 1 2 0 0 9872 9 2 12 7 0 1 7 0 1 33 L 3 1 3 0 0 6 1 1 4 22 9947 2 45 13 3 1 3 4 2 15 K 2 37 25 6 0 12 7 2 2 4 1 9926 20 0 3 8 11 0 1 1 M 1 1 0 0 0 2 0 0 0 5 8 4 9874 1 0 1 2 0 0 4 F 1 1 1 0 0 0 0 1 2 8 6 0 4 9946 0 2 1 3 28 0 P 13 5 2 1 1 8 3 2 5 1 2 2 1 1 9926 12 4 0 0 2 S 28 11 34 7 11 4 6 16 2 2 1 7 4 3 17 9840 38 5 2 2 T 22 2 13 4 1 3 2 2 1 11 2 8 6 1 5 32 9871 0 2 9 W 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 9976 1 0 Y 1 0 3 0 3 0 1 0 4 1 1 0 0 21 0 1 1 2 9945 1 V 13 2 1 1 3 2 2 3 3 57 11 1 17 1 3 2 10 0 2 9901 17 10/1/2019 Bioinformatics 2 Dayhoff’s PAM matrices PAM Accepted Point Mutation (“accepted” mutations do not negatively affect a protein’s fitness) PAM is a measure of the evolutionary distance: 1 PAM is the evolutionary distance (~ time) which results in 1% difference between two originally identical sequnces. PAM matrices converted to log-odds matrix - Calculate odds ratio for each substitution: Taking scores in previous matrix Divide by frequency of amino acid - Convert ratio to log10 and multiply by 10 - Take average of log odds ratio for converting A to B and converting B to A Result: Symmetric matrix "log odds" matrix −−> adding the logarithms instead of multiplication is simpler 18 10/1/2019 Bioinformatics 2 „Log odds" matrix (250 PAM) Positive values: conservative substitutions, negative values: unlikely exchanges. The amino acids listed are grouped according to their properties, and therefore scores are 19 10/1/2019greater close to the diagonal Bioinformatics 2 „Log odds" matrix (250 PAM) PAM 1: 1 accepted mutation event per 100 amino acids PAM 250: 250 mutation events per 100 … PAM 250: 20% identity PAM 120: 40% identity PAM 80: 50% identity PAM 60: 60% identity The PAM 250 matrix is often used as this corresponds to the critical approx.
Load more

Recommended publications
  • Bioinformatics 1: Lecture 3
    Bioinformatics 1: Lecture 3 •Pairwise alignment •Substitution •Dynamic Programming algorithm Scoring matrix To prepare an alignment, we first consider the score for aligning (associating) any one character of the first sequence with any one character of the second sequence. A A G A C G T T T A G A C T 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 Exact match 0 0 1 0 0 1 0 0 0 0 1/0 0 0 0 0 0 0 1 1 1 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 The cost of mutation is not a constant DNA: A change in the 3rd base in a codon, and sometimes the first base, sometimes conserves the amino acid. No selective pressure. Protein: A change in amino acids that are in the same chemical class conserve their chemical environment. For example: Lys to Arg is conservative because both a positively charged. Conservative amino acid changes N Lys <--> Arg C + N` N N` C N C + C C C N` C C C C O C O C C N N Ile <--> Leu C C C C C C C C O C O C C Ser <--> Thr Asp <--> Glu Asn <--> Gln If the “chemistry” of the sidechain is conserved, then the mutation is less likely to change structure/function.
    [Show full text]
  • BASS: Approximate Search on Large String Databases
    BASS: Approximate Search on Large String Databases Jiong Yang Wei Wang Philip Yu UIUC UNC Chapel Hill IBM [email protected] [email protected] [email protected] Abstract Similarity search on a string database can be classified into two categories: exact match and approximate match. The In this paper, we study the problem on how to build an index struc- search of exact match looks for substrings in the database, ture for large string databases to efficiently support various types of which is exactly identical to the query pattern while the search string matching without the necessity of mapping the substrings to of approximate match allows some types of imperfection such a numerical space (e.g., string B-tree and MRS-index) nor the re- as substitutions between certain symbols, some degree of mis- striction of in-memory practice (e.g., suffix tree and suffix array). alignment, and the presence of “wild-card” in the query pat- Towards this goal, we propose a new indexing scheme, BASS-tree, tern. We shall mention that supporting approximate match is to efficiently support general approximate substring match (in terms very important to many applications. For instance, biologists of certain symbol substitutions and misalignments) in sublinear time have observed that mutations between certain pair of amino on a large string database. The key idea behind the design is that all acids may occur at a noticeable probability in some proteins positions in each string are grouped recursively into a fully balanced and such a mutation usually does not alter the biological func- tree according to the similarities of the subsequent segments starting tion of the proteins.
    [Show full text]
  • Computational Biology Lecture 8: Substitution Matrices Saad Mneimneh
    Computational Biology Lecture 8: Substitution matrices Saad Mneimneh As we have introduced last time, simple scoring schemes like +1 for a match, -1 for a mismatch and -2 for a gap are not justifiable biologically, especially for amino acid sequences (proteins). Instead, more elaborated scoring functions are used. These scores are usually obtained as a result of analyzing chemical properties and statistical data for amino acids and DNA sequences. For example, it is known that same size amino acids are more likely to be substituted by one another. Similarly, amino acids with same affinity to water are likely to serve the same purpose in some cases. On the other hand, some mutations are not acceptable (may lead to demise of the organism). PAM and BLOSUM matrices are amongst results of such analysis. We will see the techniques through which PAM and BLOSUM matrices are obtained. Substritution matrices Chemical properties of amino acids govern how the amino acids substitue one another. In principle, a substritution matrix s, where sij is used to score aligning character i with character j, should reflect the probability of two characters substituing one another. The question is how to build such a probability matrix that closely maps reality? Different strategies result in different matrices but the central idea is the same. If we go back to the concept of a high scoring segment pair, theory tells us that the alignment (ungapped) given by such a segment is governed by a limiting distribution such that ¸sij qij = pipje where: ² s is the subsitution matrix used ² qij is the probability of observing character i aligned with character j ² pi is the probability of occurrence of character i Therefore, 1 qij sij = ln ¸ pipj This formula for sij suggests a way to constrcut the matrix s.
    [Show full text]
  • Lie Therory and Hermite Polynomials
    International Journal For Research In Advanced Computer Science And Engineering ISSN: 2208-2107 A Computational Method for Sequence analyzing Rekardo Fosalli, Teriso Joloobi School of Computer Science, University of De Cyprus, Cyprus Abstract: To study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures. Common uses of bioinformatics include the identification of candidate genes and nucleotides (SNPs). Often, such identification is made with the aim of better understanding the genetic basis of disease, unique adaptations, desirable properties (esp. in agricultural species), or differences between populations. In a less formal way, bioinformatics also tries to understand the organisational principles within nucleic acid and protein sequences. Keywords: Clustering, RNA sequences, RNA-Seq, Data Mining, Bioinformatics, Graph Mining. 1. INTRODUCTION Bioinformatics has become an important part of many areas of biology. In experimental molecular biology, bioinformatics techniques such as image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics and genomics, it aids in sequencing and annotating genomes and their observed mutations. It plays a role in the text mining of biological literature and the development of biological and gene ontologies to organize and query biological data. It also plays a role in the analysis of gene and protein expression and regulation.
    [Show full text]
  • Sophie Dumont
    Sophie Dumont University of California, San Francisco 513 Parnassus Ave, HSW-613, San Francisco, CA 94143-0512, USA Office: (415) 502-1229 / Cell: (510) 229-9846 [email protected] ; http://dumontlab.ucsf.edu/ EDUCATION 9/2000-12/2005 Ph.D., Biophysics, University of California, Berkeley, CA 10/1999-8/2000 Candidate for D.Phil., Theoretical Physics, University of Oxford, UK 9/1995-6/1999 B.A., Physics, magna cum laude, Princeton University, Princeton, NJ RESEARCH POSITIONS & TRAINING 7/2012-present Assistant Professor, University of California, San Francisco Dept of Cell & Tissue Biology and Dept of Cellular & Molecular Pharmacology Member, QB3 California Institute for Quantitative Biosciences Member, NSF Center for Cellular Construction Affiliate Member, UCSF Cancer Center Graduate program member: Bioengineering, Biomedical Sciences, Biophysics, Tetrad 7/2006-5/2012 Postdoctoral Fellow, Harvard Medical School Mentor: Prof. Timothy Mitchison (Systems Biology) 7/2006-6/2009 Junior Fellow, Harvard Society of Fellows 9/2000-12/2005 Graduate Student, University of California, Berkeley Advisor: Prof. Carlos Bustamante (Physics and Molecular & Cell Biology) 10/1999-8/2000 Graduate Student, University of Oxford Advisor: Prof. Douglas Abraham (Theoretical Physics) 6/1997-5/1999 Undergraduate Student, Princeton University Advisor: Prof. Stanislas Leibler (Physics) AWARDS 2016-2021 NSF CAREER Award 2016 Margaret Oakley Dayhoff Award of the Biophysical Society 2015-2020 NIH New Innovator Award (DP2) 2013-2018 Rita Allen Foundation and Milton
    [Show full text]
  • B.Sc. (Hons.) Biotech BIOT 3013 Unit-5 Satarudra P Singh
    B.Sc. (Hons.) Biotechnology Core Course 13: Basics of Bioinformatics and Biostatistics (BIOT 3013 ) Unit 5: Sequence Alignment and database searching Dr. Satarudra Prakash Singh Department of Biotechnology Mahatma Gandhi Central University, Motihari Challenges in bioinformatics 1. Obtain the genome of an organism. 2. Identify and annotate genes. 3. Find the sequences, three dimensional structures, and functions of proteins. 4. Find sequences of proteins that have desired three dimensional structures. 5. Compare DNA sequences and proteins sequences for similarity. 6. Study the evolution of sequences and species. Sequence alignments lie at the heart of all bioinformatics Definition of Sequence Alignment • Sequence alignment is the procedure of comparing two or more sequences by searching for a series of individual characters or character patterns that are in the same order in the sequences. LGPSSKQTGKGS - SRIWDN Global Alignment LN – ITKSAGKGAIMRLFDA --------TGKG --------- Local Alignment --------AGKG --------- • In global alignment, an attempt is made to align the entire sequences, as many characters as possible. • In local alignment, stretches of sequence with the highest density of matches are given the highest priority, thus generating one or more islands of matches in the aligned sequences. • Eg: problem of locating the famous TATAAT - box (a bacterial promoter) in a piece of DNA. Method for pairwise sequence Alignment: Dynamic Programming • Global Alignment: Needleman- Wunsch Algorithm • Local Alignment: Smith-Waterman Algorithm Needleman & Wunsch algorithm : Global alignment • There are three major phases: 1. initialization 2. Fill 3. Trace back. • Initialization assign values for the first row and column. • The score of each cell is set to the gap score multiplied by the distance from the origin.
    [Show full text]
  • Parameter Advising for Multiple Sequence Alignment
    PARAMETER ADVISING FOR MULTIPLE SEQUENCE ALIGNMENT by Daniel Frank DeBlasio Copyright c Daniel Frank DeBlasio 2016 A Dissertation Submitted to the Faculty of the DEPARTMENT OF COMPUTER SCIENCE In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY In the Graduate College THE UNIVERSITY OF ARIZONA 2016 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the disser- tation prepared by Daniel Frank DeBlasio, entitled Parameter Advising for Multiple Sequence Alignment and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy. Date: 15 April 2016 John Kececioglu Date: 15 April 2016 Alon Efrat Date: 15 April 2016 Stephen Kobourov Date: 15 April 2016 Mike Sanderson Final approval and acceptance of this dissertation is contingent upon the candidate's submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. Date: 15 April 2016 Dissertation Director: John Kececioglu 3 STATEMENT BY AUTHOR This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this dissertation are allowable without special permission, pro- vided that accurate acknowledgment of the source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the copyright holder.
    [Show full text]
  • Pairwise Statistical Significance of Local Sequence Alignment Using Sequence-Specific and Position-Specific Substitution Matrices
    194 IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL. 8, NO. 1, JANUARY/FEBRUARY 2011 Pairwise Statistical Significance of Local Sequence Alignment Using Sequence-Specific and Position-Specific Substitution Matrices Ankit Agrawal and Xiaoqiu Huang Abstract—Pairwise sequence alignment is a central problem in bioinformatics, which forms the basis of various other applications. Two related sequences are expected to have a high alignment score, but relatedness is usually judged by statistical significance rather than by alignment score. Recently, it was shown that pairwise statistical significance gives promising results as an alternative to database statistical significance for getting individual significance estimates of pairwise alignment scores. The improvement was mainly attributed to making the statistical significance estimation process more sequence-specific and database-independent. In this paper, we use sequence-specific and position-specific substitution matrices to derive the estimates of pairwise statistical significance, which is expected to use more sequence-specific information in estimating pairwise statistical significance. Experiments on a benchmark database with sequence-specific substitution matrices at different levels of sequence-specific contribution were conducted, and results confirm that using sequence-specific substitution matrices for estimating pairwise statistical significance is significantly better than using a standard matrix like BLOSUM62, and than database statistical significance estimates
    [Show full text]
  • Deriving Amino Acid Exchange Matrices (II) and Multiple Sequence Alignment (I) Summarysummary Dayhoff’Sdayhoff’S PAMPAM--Matricesmatrices
    IntroductionIntroduction toto bioinformaticsbioinformatics lecturelecture 88 Deriving amino acid exchange matrices (II) and Multiple sequence alignment (I) SummarySummary Dayhoff’sDayhoff’s PAMPAM--matricesmatrices Derived from global alignments of closely related sequences. Matrices for greater evolutionary distances are extrapolated from those for lesser ones. The number with the matrix (PAM40, PAM100) refers to the evolutionary distance; greater numbers are greater distances. Several later groups have attempted to extend Dayhoff's methodology or re-apply her analysis using later databases with more examples. Extensions of Dayhoff’s methodology: > Jones, Thornton and coworkers used the same methodology as Dayhoff but with modern databases (CABIOS 8:275). > Gonnett and coworkers (Science 256:1443) used a slightly different (but theoretically equivalent) methodology. > Henikoff & Henikoff (Proteins 17:49) compared these two newer versions of the PAM matrices with Dayhoff's originals. TheThe BLOSUMBLOSUM matricesmatrices ((BLOcksBLOcks SUbstitutionSUbstitution Matrix)Matrix) The BLOSUM series of matrices were created by Steve Henikoff and colleagues (PNAS 89:10915). Derived from local, un-gapped alignments of distantly related sequences. All matrices are directly calculated; no extrapolations are used. Again: the observed frequency of each pair is compared to the expected frequency (which is essentially the product of the frequencies of each residue in the dataset). Then: Log-odds matrix. TheThe BlocksBlocks DatabaseDatabase The Blocks Database contains multiple alignments of conserved regions in protein families. Blocks are multiply aligned un-gapped segments corresponding to the most highly conserved regions of proteins. 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.
    [Show full text]
  • Sophie Dumont
    Sophie Dumont University of California, San Francisco 513 Parnassus Ave, HSW-613, San Francisco, CA 94143-0512, USA Office: (415) 502-1229 / Cell: (510) 229-9846 [email protected] ; http://dumontlab.ucsf.edu/ EDUCATION 9/2000-12/2005 Ph.D., Biophysics, University of California, Berkeley, CA 10/1999-8/2000 Candidate for D.Phil., Theoretical Physics, University of Oxford, UK 9/1995-6/1999 B.A., Physics, magna cum laude, Princeton University, Princeton, NJ RESEARCH POSITIONS & TRAINING 7/2012-present Assistant Professor, University of California, San Francisco Dept of Cell & Tissue Biology Dept of Cellular & Molecular Pharmacology Member, NSF Center for Cellular Construction Member, QB3 California Institute for Quantitative Biosciences Associate Member, UCSF Cancer Center Graduate program member: Biomedical Sciences Biophysics Bioengineering Tetrad 7/2006-5/2012 Postdoctoral Fellow, Harvard Medical School 7/2006-6/2009 Junior Fellow, Harvard Society of Fellows Mentor: Prof. Timothy Mitchison (Systems Biology) Collaborator: Prof. Edward Salmon (UNC Chapel Hill) Topic: Mechanochemistry of cell division 9/2000-12/2005 Graduate Student, University of California, Berkeley Advisor: Prof. Carlos Bustamante (Physics and Molecular & Cell Biology) Collaborators: Prof. Ignacio Tinoco & Prof. Jan Liphardt (UC Berkeley) Prof. Anna Marie Pyle (Yale University) Thesis: Force and helicase-catalyzed mechanical unfolding of single RNA molecules 10/1999-8/2000 Graduate Student, University of Oxford Advisor: Prof. Douglas Abraham (Theoretical Physics) Topic: Statistical mechanics of neural networks 6/1997-5/1999 Undergraduate Student, Princeton University Advisor: Prof. Stanislas Leibler (Physics) Thesis: Thermal sensing network of E. coli Page 1 of 5 AWARDS 2016-2021 NSF CAREER Award 2016 Margaret Oakley Dayhoff Award of the Biophysical Society 2015-2020 NIH New Innovator Award (DP2) 2013-2018 Rita Allen Foundation and Milton E.
    [Show full text]
  • Pairwise Sequence Alignment Algorithm by a New Measure Based
    Hara et al. BMC Bioinformatics 2010, 11:235 http://www.biomedcentral.com/1471-2105/11/235 SOFTWARE Open Access MTRAP: Pairwise sequence alignment algorithm by a new measure based on transition probability between two consecutive pairs of residues Toshihide Hara*, Keiko Sato, Masanori Ohya Abstract Background: Sequence alignment is one of the most important techniques to analyze biological systems. It is also true that the alignment is not complete and we have to develop it to look for more accurate method. In particular, an alignment for homologous sequences with low sequence similarity is not in satisfactory level. Usual methods for aligning protein sequences in recent years use a measure empirically determined. As an example, a measure is usually defined by a combination of two quantities (1) and (2) below: (1) the sum of substitutions between two residue segments, (2) the sum of gap penalties in insertion/deletion region. Such a measure is determined on the assumption that there is no an intersite correlation on the sequences. In this paper, we improve the alignment by taking the correlation of consecutive residues. Results: We introduced a new method of alignment, called MTRAP by introducing a metric defined on compound systems of two sequences. In the benchmark tests by PREFAB 4.0 and HOMSTRAD, our pairwise alignment method gives higher accuracy than other methods such as ClustalW2, TCoffee, MAFFT. Especially for the sequences with sequence identity less than 15%, our method improves the alignment accuracy significantly. Moreover, we also showed that our algorithm works well together with a consistency-based progressive multiple alignment by modifying the TCoffee to use our measure.
    [Show full text]
  • Scoring Matrices for Sequence Comparisons
    Scoring Matrices for Sequence Comparisons DEKM book Notes from Dr. Bino John and Dr. Takis Benos 1 Why compare sequences? • Given a new sequence, infer its funcHon based on similarity to another sequence • Find important molecular regions – conserved across species 2 Sequence -> Structure -> FuncHon MALRAK… SEQUENCE ? FUNCTION Cytochrome protein STRUCTURE 3 Important molecular regions conserved across species Human (C11A_HUMAN; P05108) vs. pig (C11A_PIG; P10612) Query: 1 MLAKGLPPRSVLVKGYQTFLSAPREGLGRLRVPTGEGAGISTRSPRPFNEIPSPGDNGWL 60 MLA+GL RSVLVKG Q FLSAPRE G RV TGEGA IST++PRPF+EIPSPGDNGW+ Sbjct: 1 MLARGLALRSVLVKGCQPFLSAPRECPGHPRVGTGEGACISTKTPRPFSEIPSPGDNGWI 60 Query: 61 NLYHFWRETGTHKVHLHHVQNFQKYGPIYREKLGNVESVYVIDPEDVALLFKSEGPNPER 120 NLY FW+E GT K+H HHVQNFQKYGPIYREKLGN+ESVY+IDPEDVALLFK EGPNPER Sbjct: 61 NLYRFWKEKGTQKIHYHHVQNFQKYGPIYREKLGNLESVYIIDPEDVALLFKFEGPNPER 120 Query: 121 FLIPPWVAYHQYYQRPIGVLLKKSAAWKKDRVALNQEVMAPEATKNFLPLLDAVSRDFVS 180 + IPPWVAYHQ+YQ+P+GVLLKKS AWKKDR+ LN EVMAPEA KNF+PLLD VS+DFV Sbjct: 121 YNIPPWVAYHQHYQKPVGVLLKKSGAWKKDRLVLNTEVMAPEAIKNFIPLLDTVSQDFVG 180 Query: 181 VLHRRIKKAGSGNYSGDISDDLFRFAFESITNVIFGERQGMLEEVVNPEAQRFIDAIYQM 240 VLHRRIK+ GSG +SGDI +DLFRFAFESITNVIFGER GMLEE+V+PEAQ+FIDA+YQM Sbjct: 181 VLHRRIKQQGSGKFSGDIREDLFRFAFESITNVIFGERLGMLEEIVDPEAQKFIDAVYQM 240 Query: 241 FHTSVPMLNLPPDLFRLFRTKTWKDHVAAWDVIFSKADIYTQNFYWELRQKGSVHHDYRG 300 FHTSVPMLNLPPDLFRLFRTKTW+DHVAAWD IF+KA+ YTQNFYW+LR+K ++Y G Sbjct: 241 FHTSVPMLNLPPDLFRLFRTKTWRDHVAAWDTIFNKAEKYTQNFYWDLRRKRE-FNNYPG 299 Query: 301 MLYRLLGDSKMSFEDIKANVTEMLAGGVDTTSMTLQWHLYEMARNLKVQDMLRAEVLAAR
    [Show full text]