part 4: phenomenological load and biological inference. phenomenological load review types of models. Gαβ = 8π Tαβ. Newton.

Size: px
Start display at page:

Download "part 4: phenomenological load and biological inference. phenomenological load review types of models. Gαβ = 8π Tαβ. Newton."

Transcription

1 part 4: and biological inference review types of models phenomenological Newton F= Gm1m2 r2 mechanistic Einstein Gαβ = 8π Tαβ 1

2 molecular evolution is process and pattern process pattern MutSel models! µ ij N 1 N = µ IJ if neutral Pr = 2s µ ij N ij if selected 1 e 2 Ns ij s ij = Δf ij Halpern(and(Bruno((1998)( GTG CTG TCT CCT GCC GAC AAG ACC AAC GTC AAG GCC GCC TGG GGC AAG GTT GGC GCG CAC G.C T....T GC A C..T A..... A.T AA... A.C... AGC C... G.A.AT.....A A..... AA. TG......G... A....T.GC..T.....C..G GA...T T C....G..A... AT......T.....G..A.GC... GCT GGC GAG TAT GGT GCG GAG GCC CTG GAG AGG ATG TTC CTG TCC TTC CCC ACC ACC AAG.....A.CT.....C..A.....T AG G C..C G T.. GG G...T..A.....C.A A C GCT G C..T.CC..C.CA..T..A..T..T.CC..A.CC.....C T.....A ACC TAC TTC CCG CAC TTC GAC CTG AGC CAC GGC TCT GCC CAG GTT AAG GGC CAC GGC AAG C G C G C T.C.C AG... A.C..A.C T.T... A.T..T G.A....C C....CT T C TC..C......C A.C C....T..T..T... Maximum phenomenological model for sequence data: explains all variation in a particular dataset so-called saturated model (multinomial model) does not generalize to or datasets no information about process highest lnl score (useless?) site pattern 4 GTG CTG TCT CCT GCC GAC AAG ACC AAC GTC AAG GCC GCC TGG GGC AAG GTT GGC GCG CAC G.C T....T GC A C..T A..... A.T AA... A.C... AGC C... G.A.AT.....A A..... AA. TG......G... A....T.GC..T.....C..G GA...T T C....G..A... AT......T.....G..A.GC... Question: Does anyone really care, at all, that site pattern No.4 occurs 33 times in my sample of 5 mammalian mt genomes? 2

3 Review phenomenological models: The good all we have to model are outcomes (site pattern distribution) y can be predictive (e.g., Newtonian models) y can tell us about process (e.g., some codon models) The bad a saturated model is useless must decide how much variability to soak up with model parameters matching variability to mechanistic process is hard traditional statistical methods manage phenomenological variability (NOT process variability) ugly getting it wrong = false biological conclusions new concept: move phenomenological from model to parameter (PL): if a parameter has a mechanistic interpretation, and if process it represents did not actually occur, n when it absorbs significant variance that parameter has taken on (measured via PRD*). two conditions for PL: 1. confounding of model parameters 2. underspecified model * PRD = percent reduction of deviance, and is defined in subsequent slides 3

4 codon models 1. confounding 2. underspecified 0 if i and j differ by > 1 π j for synonymous tv. Q ij = κπ j for synonymous ts. ωπ j for non-synonymous tv. ωκπ j for non-synonymous ts. h Δf Ile Leu h Δf Ile Lys DNA sub-model: κ and π applied to all sites equally mutation sub-model protein level sub-model: ω and π direct selective interpretation affected by mutation process missing model variability: different fitness landscapes for sites different AA echangeabilities (s ij ) different equilibrium for sites independent mutational sub-model mechanistic effect of N e high level non-independence (global epistasis for stability) low-level non-independence (local epistasis for function) 1. sub-models are confounded! 2. models are heavily underspecified a different look at issue true model ( ) fitted model () 4

5 P T = ( X θ T ) Kullback-Leibler divergence P = ( X θ ) ( ) KL = P T X θ T log P T (X θ T ) X P X θ ( ) KL Deviance D = 2{ l ( θ X,T ) l MS ( X) } 5

6 Not to scale! KL Percent Reduction in Deviance (PDR)! PRD = D D D poisson Hyposis tests along THIS PATH have direct connection to mechanism of evolution KL Hyposis tests along THIS PATH have PRD significant LRTs b/c variation is not random interpretation is not direct about mechanism of evolution 6

7 DT: Double and Triple mutations Example double: ATG (Met) è AAA (Lys) [α parameter] Example triple: AAA (Lys) è GGG (GLY) [β parameter] Q matrix New Q matrix 2 parameters (κ and ω) 4 parameters (κ, ω, α, β) DT not allowed DT allowed (via α and β ) Is such a model warranted? white: probability = alignment of 12 genes (3598 codon sites) from 244 mammal species, as reported by Tamuri et al. 226 (2012). The choice to use 20-taxon alignment of mammal mtdna in this article was motivated 227 in part by availability of empirical results with which to make se comparsions p( 2 < sij < 2) p(sij > 2) all mutations Tamuri et al. (2012) p(sij < nonsyn. mutations Tamuri et al. (2012) Let s do a simulation study! Results process (MT): DT mutations were detected in a real alignment 2) all substitutions Tamuri et al. (2012) nonsyn. substitutions Tamuri et al. (2012) outcome (X): African chimpanzee bonobo gorilla orangutan Sumatran orangutan common gibbon harbor seal grey seal cat horse Indian rhinoceros cow fin whale blue whale rat mouse wallaroo opossum platypus AA we need outcomes to match up Table 10: The proposed method for generating vectors of site-specific fitness coefficients produces distributions for scaled selection coefficients sij that are a close match to those derived empirically by Tamuri et al. (2012). BB Figure 6: 6:A A comparison of of observed versus acid frequencies. Frequencies Figure comparison observed versus acid frequencies.a:a: Frequenciesobtained obtainedfrom from real data; B:B: forfor alignment (20(20 taxon, 3331 real data; alignment taxon, 3331 sites). Figure 5:same Asame comparison of observed versus distribution ofsites). number of acids realized at a site for mammal mtdna (20 taxon, 3331 sites) real mtdna data simulation outcome simulation Figure 1: The phylogeny for a concatenation of 12 H-strand mitrochondrial DNA sequences (3331 codon sites) from 20 mamalian species. The topology is that reported in Cao et al. (1998). Branch lengths (expected number of MutSel single nucleotide substitutions per codon) were estimated using best fitting of all models presented in this fh differ for each site article (RaMoSSwDT). The scale on horizontal axis is number of single nucleotide substitution per codon. 27 The alignment was provided by PAML software package Yang (2007). NO DT-mutations 12 mt proteins (3331 codons) 20 mammals 11 heat maps: proportion of sites having a given pair of AAs Figure comparison observed versus relative pairwise acid frequencies.for For any cell, Figure 7: 7: AA comparison of of observed versus relative pairwise acid frequencies. any cell, value proportion sites where acids indicated were both present.a:a: Values obtained from value is is proportion of of sites where acids indicated were both present. Values obtained from real mtdna alignment; values obtained from a alignment taxon, 3331 sites).the The same grayscale real mtdna alignment; B:B: values obtained from a alignment (20(20 taxon, 3331 sites). same grayscale applies both panels. applies to to both panels. Our data LOOKS LIKE REAL DATA!

8 simulation for : MutSel with NO DT-mutations KL LRT: 100% LRT: 97% C3 C3 LRT: 47% PRD PRD since re are NO DT-mutations, PRD is a measure of PL PRD PRD with true DT process PRD for real mtdna dataset PL associated with α and β Conclusions: DT parameters (α and β ) carry PL is evidence for DT process in mtdna in excess of PL estimated level of DT very small in real data C3 8

9 Poisson for DNA JC69 model path for shallow phylogenetics Poisson for codons model path for inference of process model path for deep phylogenetics Poisson for acids Alternative model paths: research objective differs target model differs PL differs impact on inferences differs KL Why should you care? PRD 1. All of molecular evolution depends on models to some extent. 2. All models are wrong (underspecified). 3. Model parameters will carry some PL. 4. Faster computers è more complex models 5. Next Gen sequencing è minor effects detectable 6. Standard model selection tools will NOT inform you about levels of PL. 7. Excessive PL will lead to false biological conclusions. 8. Modelers MUST have biological expertise, and y MUST use that expertise as part of modeling process. 9

10 How can you really tell if you have learned anything relevant to function of your protein? formally combine computational and experimental approaches (B. Chang, next lecture) formally combine phenotypic information within computational analysis of sequence evolution The End. 10

types of codon models

types of codon models part 3: analysis of natural selection pressure omega models! types of codon models if i and j differ by > π j for synonymous tv. Q ij = κπ j for synonymous ts. ωπ j for non-synonymous tv. ωκπ j for non-synonymous

More information

Practical Bioinformatics

Practical Bioinformatics 5/2/2017 Dictionaries d i c t i o n a r y = { A : T, T : A, G : C, C : G } d i c t i o n a r y [ G ] d i c t i o n a r y [ N ] = N d i c t i o n a r y. h a s k e y ( C ) Dictionaries g e n e t i c C o

More information

part 3: analysis of natural selection pressure

part 3: analysis of natural selection pressure part 3: analysis of natural selection pressure markov models are good phenomenological codon models do have many benefits: o principled framework for statistical inference o avoiding ad hoc corrections

More information

SEQUENCE ALIGNMENT BACKGROUND: BIOINFORMATICS. Prokaryotes and Eukaryotes. DNA and RNA

SEQUENCE ALIGNMENT BACKGROUND: BIOINFORMATICS. Prokaryotes and Eukaryotes. DNA and RNA SEQUENCE ALIGNMENT BACKGROUND: BIOINFORMATICS 1 Prokaryotes and Eukaryotes 2 DNA and RNA 3 4 Double helix structure Codons Codons are triplets of bases from the RNA sequence. Each triplet defines an amino-acid.

More information

codon substitution models and the analysis of natural selection pressure

codon substitution models and the analysis of natural selection pressure 2015-07-20 codon substitution models and the analysis of natural selection pressure Joseph P. Bielawski Department of Biology Department of Mathematics & Statistics Dalhousie University introduction morphological

More information

SUPPORTING INFORMATION FOR. SEquence-Enabled Reassembly of β-lactamase (SEER-LAC): a Sensitive Method for the Detection of Double-Stranded DNA

SUPPORTING INFORMATION FOR. SEquence-Enabled Reassembly of β-lactamase (SEER-LAC): a Sensitive Method for the Detection of Double-Stranded DNA SUPPORTING INFORMATION FOR SEquence-Enabled Reassembly of β-lactamase (SEER-LAC): a Sensitive Method for the Detection of Double-Stranded DNA Aik T. Ooi, Cliff I. Stains, Indraneel Ghosh *, David J. Segal

More information

High throughput near infrared screening discovers DNA-templated silver clusters with peak fluorescence beyond 950 nm

High throughput near infrared screening discovers DNA-templated silver clusters with peak fluorescence beyond 950 nm Electronic Supplementary Material (ESI) for Nanoscale. This journal is The Royal Society of Chemistry 2018 High throughput near infrared screening discovers DNA-templated silver clusters with peak fluorescence

More information

Codon-model based inference of selection pressure. (a very brief review prior to the PAML lab)

Codon-model based inference of selection pressure. (a very brief review prior to the PAML lab) Codon-model based inference of selection pressure (a very brief review prior to the PAML lab) an index of selection pressure rate ratio mode example dn/ds < 1 purifying (negative) selection histones dn/ds

More information

Advanced topics in bioinformatics

Advanced topics in bioinformatics Feinberg Graduate School of the Weizmann Institute of Science Advanced topics in bioinformatics Shmuel Pietrokovski & Eitan Rubin Spring 2003 Course WWW site: http://bioinformatics.weizmann.ac.il/courses/atib

More information

Supplementary Information for

Supplementary Information for Supplementary Information for Evolutionary conservation of codon optimality reveals hidden signatures of co-translational folding Sebastian Pechmann & Judith Frydman Department of Biology and BioX, Stanford

More information

Supplemental data. Pommerrenig et al. (2011). Plant Cell /tpc

Supplemental data. Pommerrenig et al. (2011). Plant Cell /tpc Supplemental Figure 1. Prediction of phloem-specific MTK1 expression in Arabidopsis shoots and roots. The images and the corresponding numbers showing absolute (A) or relative expression levels (B) of

More information

Supporting Information for. Initial Biochemical and Functional Evaluation of Murine Calprotectin Reveals Ca(II)-

Supporting Information for. Initial Biochemical and Functional Evaluation of Murine Calprotectin Reveals Ca(II)- Supporting Information for Initial Biochemical and Functional Evaluation of Murine Calprotectin Reveals Ca(II)- Dependence and Its Ability to Chelate Multiple Nutrient Transition Metal Ions Rose C. Hadley,

More information

Why do more divergent sequences produce smaller nonsynonymous/synonymous

Why do more divergent sequences produce smaller nonsynonymous/synonymous Genetics: Early Online, published on June 21, 2013 as 10.1534/genetics.113.152025 Why do more divergent sequences produce smaller nonsynonymous/synonymous rate ratios in pairwise sequence comparisons?

More information

Crick s early Hypothesis Revisited

Crick s early Hypothesis Revisited Crick s early Hypothesis Revisited Or The Existence of a Universal Coding Frame Ryan Rossi, Jean-Louis Lassez and Axel Bernal UPenn Center for Bioinformatics BIOINFORMATICS The application of computer

More information

Characterization of Pathogenic Genes through Condensed Matrix Method, Case Study through Bacterial Zeta Toxin

Characterization of Pathogenic Genes through Condensed Matrix Method, Case Study through Bacterial Zeta Toxin International Journal of Genetic Engineering and Biotechnology. ISSN 0974-3073 Volume 2, Number 1 (2011), pp. 109-114 International Research Publication House http://www.irphouse.com Characterization of

More information

Electronic supplementary material

Electronic supplementary material Applied Microbiology and Biotechnology Electronic supplementary material A family of AA9 lytic polysaccharide monooxygenases in Aspergillus nidulans is differentially regulated by multiple substrates and

More information

SSR ( ) Vol. 48 No ( Microsatellite marker) ( Simple sequence repeat,ssr),

SSR ( ) Vol. 48 No ( Microsatellite marker) ( Simple sequence repeat,ssr), 48 3 () Vol. 48 No. 3 2009 5 Journal of Xiamen University (Nat ural Science) May 2009 SSR,,,, 3 (, 361005) : SSR. 21 516,410. 60 %96. 7 %. (),(Between2groups linkage method),.,, 11 (),. 12,. (, ), : 0.

More information

3. Evolution makes sense of homologies. 3. Evolution makes sense of homologies. 3. Evolution makes sense of homologies

3. Evolution makes sense of homologies. 3. Evolution makes sense of homologies. 3. Evolution makes sense of homologies Richard Owen (1848) introduced the term Homology to refer to structural similarities among organisms. To Owen, these similarities indicated that organisms were created following a common plan or archetype.

More information

Clay Carter. Department of Biology. QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture.

Clay Carter. Department of Biology. QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. Clay Carter Department of Biology QuickTime and a TIFF (LZW) decompressor are needed to see this picture. Ornamental tobacco

More information

SUPPLEMENTARY DATA - 1 -

SUPPLEMENTARY DATA - 1 - - 1 - SUPPLEMENTARY DATA Construction of B. subtilis rnpb complementation plasmids For complementation, the B. subtilis rnpb wild-type gene (rnpbwt) under control of its native rnpb promoter and terminator

More information

Number-controlled spatial arrangement of gold nanoparticles with

Number-controlled spatial arrangement of gold nanoparticles with Electronic Supplementary Material (ESI) for RSC Advances. This journal is The Royal Society of Chemistry 2016 Number-controlled spatial arrangement of gold nanoparticles with DNA dendrimers Ping Chen,*

More information

NSCI Basic Properties of Life and The Biochemistry of Life on Earth

NSCI Basic Properties of Life and The Biochemistry of Life on Earth NSCI 314 LIFE IN THE COSMOS 4 Basic Properties of Life and The Biochemistry of Life on Earth Dr. Karen Kolehmainen Department of Physics CSUSB http://physics.csusb.edu/~karen/ WHAT IS LIFE? HARD TO DEFINE,

More information

Modelling and Analysis in Bioinformatics. Lecture 1: Genomic k-mer Statistics

Modelling and Analysis in Bioinformatics. Lecture 1: Genomic k-mer Statistics 582746 Modelling and Analysis in Bioinformatics Lecture 1: Genomic k-mer Statistics Juha Kärkkäinen 06.09.2016 Outline Course introduction Genomic k-mers 1-Mers 2-Mers 3-Mers k-mers for Larger k Outline

More information

Supplemental Table 1. Primers used for cloning and PCR amplification in this study

Supplemental Table 1. Primers used for cloning and PCR amplification in this study Supplemental Table 1. Primers used for cloning and PCR amplification in this study Target Gene Primer sequence NATA1 (At2g393) forward GGG GAC AAG TTT GTA CAA AAA AGC AGG CTT CAT GGC GCC TCC AAC CGC AGC

More information

Supplemental Figure 1.

Supplemental Figure 1. A wt spoiiiaδ spoiiiahδ bofaδ B C D E spoiiiaδ, bofaδ Supplemental Figure 1. GFP-SpoIVFA is more mislocalized in the absence of both BofA and SpoIIIAH. Sporulation was induced by resuspension in wild-type

More information

6.047 / Computational Biology: Genomes, Networks, Evolution Fall 2008

6.047 / Computational Biology: Genomes, Networks, Evolution Fall 2008 MIT OpenCourseWare http://ocw.mit.edu 6.047 / 6.878 Computational Biology: Genomes, Networks, Evolution Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

Introduction to Molecular Phylogeny

Introduction to Molecular Phylogeny Introduction to Molecular Phylogeny Starting point: a set of homologous, aligned DNA or protein sequences Result of the process: a tree describing evolutionary relationships between studied sequences =

More information

Regulatory Sequence Analysis. Sequence models (Bernoulli and Markov models)

Regulatory Sequence Analysis. Sequence models (Bernoulli and Markov models) Regulatory Sequence Analysis Sequence models (Bernoulli and Markov models) 1 Why do we need random models? Any pattern discovery relies on an underlying model to estimate the random expectation. This model

More information

Table S1. Primers and PCR conditions used in this paper Primers Sequence (5 3 ) Thermal conditions Reference Rhizobacteria 27F 1492R

Table S1. Primers and PCR conditions used in this paper Primers Sequence (5 3 ) Thermal conditions Reference Rhizobacteria 27F 1492R Table S1. Primers and PCR conditions used in this paper Primers Sequence (5 3 ) Thermal conditions Reference Rhizobacteria 27F 1492R AAC MGG ATT AGA TAC CCK G GGY TAC CTT GTT ACG ACT T Detection of Candidatus

More information

Aoife McLysaght Dept. of Genetics Trinity College Dublin

Aoife McLysaght Dept. of Genetics Trinity College Dublin Aoife McLysaght Dept. of Genetics Trinity College Dublin Evolution of genome arrangement Evolution of genome content. Evolution of genome arrangement Gene order changes Inversions, translocations Evolution

More information

Nature Structural & Molecular Biology: doi: /nsmb Supplementary Figure 1

Nature Structural & Molecular Biology: doi: /nsmb Supplementary Figure 1 Supplementary Figure 1 Zn 2+ -binding sites in USP18. (a) The two molecules of USP18 present in the asymmetric unit are shown. Chain A is shown in blue, chain B in green. Bound Zn 2+ ions are shown as

More information

Evolutionary Analysis of Viral Genomes

Evolutionary Analysis of Viral Genomes University of Oxford, Department of Zoology Evolutionary Biology Group Department of Zoology University of Oxford South Parks Road Oxford OX1 3PS, U.K. Fax: +44 1865 271249 Evolutionary Analysis of Viral

More information

Supplementary Information

Supplementary Information Electronic Supplementary Material (ESI) for RSC Advances. This journal is The Royal Society of Chemistry 2014 Directed self-assembly of genomic sequences into monomeric and polymeric branched DNA structures

More information

The Trigram and other Fundamental Philosophies

The Trigram and other Fundamental Philosophies The Trigram and other Fundamental Philosophies by Weimin Kwauk July 2012 The following offers a minimal introduction to the trigram and other Chinese fundamental philosophies. A trigram consists of three

More information

Supporting Information

Supporting Information Supporting Information T. Pellegrino 1,2,3,#, R. A. Sperling 1,#, A. P. Alivisatos 2, W. J. Parak 1,2,* 1 Center for Nanoscience, Ludwig Maximilians Universität München, München, Germany 2 Department of

More information

Using algebraic geometry for phylogenetic reconstruction

Using algebraic geometry for phylogenetic reconstruction Using algebraic geometry for phylogenetic reconstruction Marta Casanellas i Rius (joint work with Jesús Fernández-Sánchez) Departament de Matemàtica Aplicada I Universitat Politècnica de Catalunya IMA

More information

Sequence Divergence & The Molecular Clock. Sequence Divergence

Sequence Divergence & The Molecular Clock. Sequence Divergence Sequence Divergence & The Molecular Clock Sequence Divergence v simple genetic distance, d = the proportion of sites that differ between two aligned, homologous sequences v given a constant mutation/substitution

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION DOI:.8/NCHEM. Conditionally Fluorescent Molecular Probes for Detecting Single Base Changes in Double-stranded DNA Sherry Xi Chen, David Yu Zhang, Georg Seelig. Analytic framework and probe design.. Design

More information

TM1 TM2 TM3 TM4 TM5 TM6 TM bp

TM1 TM2 TM3 TM4 TM5 TM6 TM bp a 467 bp 1 482 2 93 3 321 4 7 281 6 21 7 66 8 176 19 12 13 212 113 16 8 b ATG TCA GGA CAT GTA ATG GAG GAA TGT GTA GTT CAC GGT ACG TTA GCG GCA GTA TTG CGT TTA ATG GGC GTA GTG M S G H V M E E C V V H G T

More information

Evolvable Neural Networks for Time Series Prediction with Adaptive Learning Interval

Evolvable Neural Networks for Time Series Prediction with Adaptive Learning Interval Evolvable Neural Networs for Time Series Prediction with Adaptive Learning Interval Dong-Woo Lee *, Seong G. Kong *, and Kwee-Bo Sim ** *Department of Electrical and Computer Engineering, The University

More information

Building a Multifunctional Aptamer-Based DNA Nanoassembly for Targeted Cancer Therapy

Building a Multifunctional Aptamer-Based DNA Nanoassembly for Targeted Cancer Therapy Supporting Information Building a Multifunctional Aptamer-Based DNA Nanoassembly for Targeted Cancer Therapy Cuichen Wu,, Da Han,, Tao Chen,, Lu Peng, Guizhi Zhu,, Mingxu You,, Liping Qiu,, Kwame Sefah,

More information

Protein Threading. Combinatorial optimization approach. Stefan Balev.

Protein Threading. Combinatorial optimization approach. Stefan Balev. Protein Threading Combinatorial optimization approach Stefan Balev Stefan.Balev@univ-lehavre.fr Laboratoire d informatique du Havre Université du Havre Stefan Balev Cours DEA 30/01/2004 p.1/42 Outline

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION DOI:.38/NCHEM.246 Optimizing the specificity of nucleic acid hyridization David Yu Zhang, Sherry Xi Chen, and Peng Yin. Analytic framework and proe design 3.. Concentration-adjusted

More information

Encoding of Amino Acids and Proteins from a Communications and Information Theoretic Perspective

Encoding of Amino Acids and Proteins from a Communications and Information Theoretic Perspective Jacobs University Bremen Encoding of Amino Acids and Proteins from a Communications and Information Theoretic Perspective Semester Project II By: Dawit Nigatu Supervisor: Prof. Dr. Werner Henkel Transmission

More information

Plan: Evolutionary trees, characters. Perfect phylogeny Methods: NJ, parsimony, max likelihood, Quartet method

Plan: Evolutionary trees, characters. Perfect phylogeny Methods: NJ, parsimony, max likelihood, Quartet method Phylogeny 1 Plan: Phylogeny is an important subject. We have 2.5 hours. So I will teach all the concepts via one example of a chain letter evolution. The concepts we will discuss include: Evolutionary

More information

Codon Distribution in Error-Detecting Circular Codes

Codon Distribution in Error-Detecting Circular Codes life Article Codon Distribution in Error-Detecting Circular Codes Elena Fimmel, * and Lutz Strüngmann Institute for Mathematical Biology, Faculty of Computer Science, Mannheim University of Applied Sciences,

More information

The 3 Genomic Numbers Discovery: How Our Genome Single-Stranded DNA Sequence Is Self-Designed as a Numerical Whole

The 3 Genomic Numbers Discovery: How Our Genome Single-Stranded DNA Sequence Is Self-Designed as a Numerical Whole Applied Mathematics, 2013, 4, 37-53 http://dx.doi.org/10.4236/am.2013.410a2004 Published Online October 2013 (http://www.scirp.org/journal/am) The 3 Genomic Numbers Discovery: How Our Genome Single-Stranded

More information

Sex-Linked Inheritance in Macaque Monkeys: Implications for Effective Population Size and Dispersal to Sulawesi

Sex-Linked Inheritance in Macaque Monkeys: Implications for Effective Population Size and Dispersal to Sulawesi Supporting Information http://www.genetics.org/cgi/content/full/genetics.110.116228/dc1 Sex-Linked Inheritance in Macaque Monkeys: Implications for Effective Population Size and Dispersal to Sulawesi Ben

More information

evoglow - express N kit distributed by Cat.#: FP product information broad host range vectors - gram negative bacteria

evoglow - express N kit distributed by Cat.#: FP product information broad host range vectors - gram negative bacteria evoglow - express N kit broad host range vectors - gram negative bacteria product information distributed by Cat.#: FP-21020 Content: Product Overview... 3 evoglow express N -kit... 3 The evoglow -Fluorescent

More information

evoglow - express N kit Cat. No.: product information broad host range vectors - gram negative bacteria

evoglow - express N kit Cat. No.: product information broad host range vectors - gram negative bacteria evoglow - express N kit broad host range vectors - gram negative bacteria product information Cat. No.: 2.1.020 evocatal GmbH 2 Content: Product Overview... 4 evoglow express N kit... 4 The evoglow Fluorescent

More information

Evolutionary dynamics of abundant stop codon readthrough in Anopheles and Drosophila

Evolutionary dynamics of abundant stop codon readthrough in Anopheles and Drosophila biorxiv preprint first posted online May. 3, 2016; doi: http://dx.doi.org/10.1101/051557. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved.

More information

The role of the FliD C-terminal domain in pentamer formation and

The role of the FliD C-terminal domain in pentamer formation and The role of the FliD C-terminal domain in pentamer formation and interaction with FliT Hee Jung Kim 1,2,*, Woongjae Yoo 3,*, Kyeong Sik Jin 4, Sangryeol Ryu 3,5 & Hyung Ho Lee 1, 1 Department of Chemistry,

More information

"Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky

Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky MOLECULAR PHYLOGENY "Nothing in biology makes sense except in the light of evolution Theodosius Dobzhansky EVOLUTION - theory that groups of organisms change over time so that descendeants differ structurally

More information

ChemiScreen CaS Calcium Sensor Receptor Stable Cell Line

ChemiScreen CaS Calcium Sensor Receptor Stable Cell Line PRODUCT DATASHEET ChemiScreen CaS Calcium Sensor Receptor Stable Cell Line CATALOG NUMBER: HTS137C CONTENTS: 2 vials of mycoplasma-free cells, 1 ml per vial. STORAGE: Vials are to be stored in liquid N

More information

Supplementary information. Porphyrin-Assisted Docking of a Thermophage Portal Protein into Lipid Bilayers: Nanopore Engineering and Characterization.

Supplementary information. Porphyrin-Assisted Docking of a Thermophage Portal Protein into Lipid Bilayers: Nanopore Engineering and Characterization. Supplementary information Porphyrin-Assisted Docking of a Thermophage Portal Protein into Lipid Bilayers: Nanopore Engineering and Characterization. Benjamin Cressiot #, Sandra J. Greive #, Wei Si ^#,

More information

Near-instant surface-selective fluorogenic protein quantification using sulfonated

Near-instant surface-selective fluorogenic protein quantification using sulfonated Electronic Supplementary Material (ESI) for rganic & Biomolecular Chemistry. This journal is The Royal Society of Chemistry 2014 Supplemental nline Materials for ear-instant surface-selective fluorogenic

More information

Understanding relationship between homologous sequences

Understanding relationship between homologous sequences Molecular Evolution Molecular Evolution How and when were genes and proteins created? How old is a gene? How can we calculate the age of a gene? How did the gene evolve to the present form? What selective

More information

Objective: You will be able to justify the claim that organisms share many conserved core processes and features.

Objective: You will be able to justify the claim that organisms share many conserved core processes and features. Objective: You will be able to justify the claim that organisms share many conserved core processes and features. Do Now: Read Enduring Understanding B Essential knowledge: Organisms share many conserved

More information

Phylogenetic Tree Reconstruction

Phylogenetic Tree Reconstruction I519 Introduction to Bioinformatics, 2011 Phylogenetic Tree Reconstruction Yuzhen Ye (yye@indiana.edu) School of Informatics & Computing, IUB Evolution theory Speciation Evolution of new organisms is driven

More information

Re- engineering cellular physiology by rewiring high- level global regulatory genes

Re- engineering cellular physiology by rewiring high- level global regulatory genes Re- engineering cellular physiology by rewiring high- level global regulatory genes Stephen Fitzgerald 1,2,, Shane C Dillon 1, Tzu- Chiao Chao 2, Heather L Wiencko 3, Karsten Hokamp 3, Andrew DS Cameron

More information

SENCA: A codon substitution model to better estimate evolutionary processes

SENCA: A codon substitution model to better estimate evolutionary processes SENCA: A codon substitution model to better estimate evolutionary processes Fanny Pouyet Marc Bailly-Bechet, Dominique Mouchiroud and Laurent Guéguen LBBE - UCB Lyon - UMR 5558 June 2015, Porquerolles,

More information

Pathways and Controls of N 2 O Production in Nitritation Anammox Biomass

Pathways and Controls of N 2 O Production in Nitritation Anammox Biomass Supporting Information for Pathways and Controls of N 2 O Production in Nitritation Anammox Biomass Chun Ma, Marlene Mark Jensen, Barth F. Smets, Bo Thamdrup, Department of Biology, University of Southern

More information

Evidence for Evolution: Change Over Time (Make Up Assignment)

Evidence for Evolution: Change Over Time (Make Up Assignment) Lesson 7.2 Evidence for Evolution: Change Over Time (Make Up Assignment) Name Date Period Key Terms Adaptive radiation Molecular Record Vestigial organ Homologous structure Strata Divergent evolution Evolution

More information

Phylogeny: traditional and Bayesian approaches

Phylogeny: traditional and Bayesian approaches Phylogeny: traditional and Bayesian approaches 5-Feb-2014 DEKM book Notes from Dr. B. John Holder and Lewis, Nature Reviews Genetics 4, 275-284, 2003 1 Phylogeny A graph depicting the ancestor-descendent

More information

Biosynthesis of Bacterial Glycogen: Primary Structure of Salmonella typhimurium ADPglucose Synthetase as Deduced from the

Biosynthesis of Bacterial Glycogen: Primary Structure of Salmonella typhimurium ADPglucose Synthetase as Deduced from the JOURNAL OF BACTERIOLOGY, Sept. 1987, p. 4355-4360 0021-9193/87/094355-06$02.00/0 Copyright X) 1987, American Society for Microbiology Vol. 169, No. 9 Biosynthesis of Bacterial Glycogen: Primary Structure

More information

Supplementary Figure 1. Schematic of split-merger microfluidic device used to add transposase to template drops for fragmentation.

Supplementary Figure 1. Schematic of split-merger microfluidic device used to add transposase to template drops for fragmentation. Supplementary Figure 1. Schematic of split-merger microfluidic device used to add transposase to template drops for fragmentation. Inlets are labelled in blue, outlets are labelled in red, and static channels

More information

Phylogenetic inference

Phylogenetic inference Phylogenetic inference Bas E. Dutilh Systems Biology: Bioinformatic Data Analysis Utrecht University, March 7 th 016 After this lecture, you can discuss (dis-) advantages of different information types

More information

Identification of a Locus Involved in the Utilization of Iron by Haemophilus influenzae

Identification of a Locus Involved in the Utilization of Iron by Haemophilus influenzae INFECrION AND IMMUNITY, OCt. 1994, p. 4515-4525 0019-9567/94/$04.00+0 Copyright 1994, American Society for Microbiology Vol. 62, No. 10 Identification of a Locus Involved in the Utilization of Iron by

More information

Motif Finding Algorithms. Sudarsan Padhy IIIT Bhubaneswar

Motif Finding Algorithms. Sudarsan Padhy IIIT Bhubaneswar Motif Finding Algorithms Sudarsan Padhy IIIT Bhubaneswar Outline Gene Regulation Regulatory Motifs The Motif Finding Problem Brute Force Motif Finding Consensus and Pattern Branching: Greedy Motif Search

More information

7. Tests for selection

7. Tests for selection Sequence analysis and genomics 7. Tests for selection Dr. Katja Nowick Group leader TFome and Transcriptome Evolution Bioinformatics group Paul-Flechsig-Institute for Brain Research www. nowicklab.info

More information

From DNA to protein, i.e. the central dogma

From DNA to protein, i.e. the central dogma From DNA to protein, i.e. the central dogma DNA RNA Protein Biochemistry, chapters1 5 and Chapters 29 31. Chapters 2 5 and 29 31 will be covered more in detail in other lectures. ph, chapter 1, will be

More information

THE MATHEMATICAL STRUCTURE OF THE GENETIC CODE: A TOOL FOR INQUIRING ON THE ORIGIN OF LIFE

THE MATHEMATICAL STRUCTURE OF THE GENETIC CODE: A TOOL FOR INQUIRING ON THE ORIGIN OF LIFE STATISTICA, anno LXIX, n. 2 3, 2009 THE MATHEMATICAL STRUCTURE OF THE GENETIC CODE: A TOOL FOR INQUIRING ON THE ORIGIN OF LIFE Diego Luis Gonzalez CNR-IMM, Bologna Section, Via Gobetti 101, I-40129, Bologna,

More information

An Analytical Model of Gene Evolution with 9 Mutation Parameters: An Application to the Amino Acids Coded by the Common Circular Code

An Analytical Model of Gene Evolution with 9 Mutation Parameters: An Application to the Amino Acids Coded by the Common Circular Code Bulletin of Mathematical Biology (2007) 69: 677 698 DOI 10.1007/s11538-006-9147-z ORIGINAL ARTICLE An Analytical Model of Gene Evolution with 9 Mutation Parameters: An Application to the Amino Acids Coded

More information

Supplementary Information

Supplementary Information Supplementary Information Arginine-rhamnosylation as new strategy to activate translation elongation factor P Jürgen Lassak 1,2,*, Eva Keilhauer 3, Max Fürst 1,2, Kristin Wuichet 4, Julia Gödeke 5, Agata

More information

Title: Robust analysis of synthetic label-free DNA junctions in solution by X-ray scattering and molecular simulation

Title: Robust analysis of synthetic label-free DNA junctions in solution by X-ray scattering and molecular simulation Supplementary Information Title: Robust analysis of synthetic label-free DNA junctions in solution by X-ray scattering and molecular simulation Kyuhyun Im 1,5, Daun Jeong 2,5, Jaehyun Hur 1, Sung-Jin Kim

More information

It is the author's version of the article accepted for publication in the journal "Biosystems" on 03/10/2015.

It is the author's version of the article accepted for publication in the journal Biosystems on 03/10/2015. It is the author's version of the article accepted for publication in the journal "Biosystems" on 03/10/2015. The system-resonance approach in modeling genetic structures Sergey V. Petoukhov Institute

More information

Edinburgh Research Explorer

Edinburgh Research Explorer Edinburgh Research Explorer Codon usage patterns in Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens; a review of the considerable

More information

Using phylogenetics to estimate species divergence times... Basics and basic issues for Bayesian inference of divergence times (plus some digression)

Using phylogenetics to estimate species divergence times... Basics and basic issues for Bayesian inference of divergence times (plus some digression) Using phylogenetics to estimate species divergence times... More accurately... Basics and basic issues for Bayesian inference of divergence times (plus some digression) "A comparison of the structures

More information

Multiple Sequence Alignment. Sequences

Multiple Sequence Alignment. Sequences Multiple Sequence Alignment Sequences > YOR020c mstllksaksivplmdrvlvqrikaqaktasglylpe knveklnqaevvavgpgftdangnkvvpqvkvgdqvl ipqfggstiklgnddevilfrdaeilakiakd > crassa mattvrsvksliplldrvlvqrvkaeaktasgiflpe

More information

Massachusetts Institute of Technology Computational Evolutionary Biology, Fall, 2005 Notes for November 7: Molecular evolution

Massachusetts Institute of Technology Computational Evolutionary Biology, Fall, 2005 Notes for November 7: Molecular evolution Massachusetts Institute of Technology 6.877 Computational Evolutionary Biology, Fall, 2005 Notes for November 7: Molecular evolution 1. Rates of amino acid replacement The initial motivation for the neutral

More information

Timing molecular motion and production with a synthetic transcriptional clock

Timing molecular motion and production with a synthetic transcriptional clock Timing molecular motion and production with a synthetic transcriptional clock Elisa Franco,1, Eike Friedrichs 2, Jongmin Kim 3, Ralf Jungmann 2, Richard Murray 1, Erik Winfree 3,4,5, and Friedrich C. Simmel

More information

160, and 220 bases, respectively, shorter than pbr322/hag93. (data not shown). The DNA sequence of approximately 100 bases of each

160, and 220 bases, respectively, shorter than pbr322/hag93. (data not shown). The DNA sequence of approximately 100 bases of each JOURNAL OF BACTEROLOGY, JUlY 1988, p. 3305-3309 0021-9193/88/073305-05$02.00/0 Copyright 1988, American Society for Microbiology Vol. 170, No. 7 Construction of a Minimum-Size Functional Flagellin of Escherichia

More information

Orthologous loci for phylogenomics from raw NGS data

Orthologous loci for phylogenomics from raw NGS data Orthologous loci for phylogenomics from raw NS data Rachel Schwartz The Biodesign Institute Arizona State University Rachel.Schwartz@asu.edu May 2, 205 Big data for phylogenetics Phylogenomics requires

More information

Probabilistic modeling and molecular phylogeny

Probabilistic modeling and molecular phylogeny Probabilistic modeling and molecular phylogeny Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis Technical University of Denmark (DTU) What is a model? Mathematical

More information

CREATING PHYLOGENETIC TREES FROM DNA SEQUENCES

CREATING PHYLOGENETIC TREES FROM DNA SEQUENCES INTRODUCTION CREATING PHYLOGENETIC TREES FROM DNA SEQUENCES This worksheet complements the Click and Learn developed in conjunction with the 2011 Holiday Lectures on Science, Bones, Stones, and Genes:

More information

Supporting Information. An Electric Single-Molecule Hybridisation Detector for short DNA Fragments

Supporting Information. An Electric Single-Molecule Hybridisation Detector for short DNA Fragments Supporting Information An Electric Single-Molecule Hybridisation Detector for short DNA Fragments A.Y.Y. Loh, 1 C.H. Burgess, 2 D.A. Tanase, 1 G. Ferrari, 3 M.A. Maclachlan, 2 A.E.G. Cass, 1 T. Albrecht*

More information

Chain-like assembly of gold nanoparticles on artificial DNA templates via Click Chemistry

Chain-like assembly of gold nanoparticles on artificial DNA templates via Click Chemistry Electronic Supporting Information: Chain-like assembly of gold nanoparticles on artificial DNA templates via Click Chemistry Monika Fischler, Alla Sologubenko, Joachim Mayer, Guido Clever, Glenn Burley,

More information

CONTEXT-FREE CODON ALIGNMENT

CONTEXT-FREE CODON ALIGNMENT CONTEXT-FREE CODON ALIGNMENT CONTEXT-FREE CODON ALIGNMENT By BIN WU, B.SC. A Thesis Submitted to the School of Graduate Studies in Partial Fulfilment of the Requirements for the Degree Master of Science

More information

AtTIL-P91V. AtTIL-P92V. AtTIL-P95V. AtTIL-P98V YFP-HPR

AtTIL-P91V. AtTIL-P92V. AtTIL-P95V. AtTIL-P98V YFP-HPR Online Resource 1. Primers used to generate constructs AtTIL-P91V, AtTIL-P92V, AtTIL-P95V and AtTIL-P98V and YFP(HPR) using overlapping PCR. pentr/d- TOPO-AtTIL was used as template to generate the constructs

More information

Lecture IV A. Shannon s theory of noisy channels and molecular codes

Lecture IV A. Shannon s theory of noisy channels and molecular codes Lecture IV A Shannon s theory of noisy channels and molecular codes Noisy molecular codes: Rate-Distortion theory S Mapping M Channel/Code = mapping between two molecular spaces. Two functionals determine

More information

Evidence for RNA editing in mitochondria of all major groups of

Evidence for RNA editing in mitochondria of all major groups of Proc. Natl. Acad. Sci. USA Vol. 91, pp. 629-633, January 1994 Plant Biology Evidence for RNA editing in mitochondria of all major groups of land plants except the Bryophyta RUDOLF HIESEL, BRUNO COMBETTES*,

More information

ydci GTC TGT TTG AAC GCG GGC GAC TGG GCG CGC AAT TAA CGG TGT GTA GGC TGG AGC TGC TTC

ydci GTC TGT TTG AAC GCG GGC GAC TGG GCG CGC AAT TAA CGG TGT GTA GGC TGG AGC TGC TTC Table S1. DNA primers used in this study. Name ydci P1ydcIkd3 Sequence GTC TGT TTG AAC GCG GGC GAC TGG GCG CGC AAT TAA CGG TGT GTA GGC TGG AGC TGC TTC Kd3ydcIp2 lacz fusion YdcIendP1 YdcItrgP2 GAC AGC

More information

Lecture 14: Multiple Sequence Alignment (Gene Finding, Conserved Elements) Scribe: John Ekins

Lecture 14: Multiple Sequence Alignment (Gene Finding, Conserved Elements) Scribe: John Ekins Lecture 14: Multiple Sequence Alignment (Gene Finding, Conserved Elements) 2 19 2015 Scribe: John Ekins Multiple Sequence Alignment Given N sequences x 1, x 2,, x N : Insert gaps in each of the sequences

More information

Molecular phylogeny How to infer phylogenetic trees using molecular sequences

Molecular phylogeny How to infer phylogenetic trees using molecular sequences Molecular phylogeny How to infer phylogenetic trees using molecular sequences ore Samuelsson Nov 2009 Applications of phylogenetic methods Reconstruction of evolutionary history / Resolving taxonomy issues

More information

CSCI 4181 / CSCI 6802 Algorithms in Bioinformatics

CSCI 4181 / CSCI 6802 Algorithms in Bioinformatics CSCI 4181 / CSCI 6802 Algorithms in Bioinformatics 1 "In science there is only physics; all the rest is stamp collecting." -Ernest Rutherford 2 Was I a stamp collector? Alan Turing 3 Inte skert, men vad

More information

Pavel Bucek 1, Joaquim Jaumot 2, Anna Aviñó 3, Ramon Eritja 3, Raimundo Gargallo 2

Pavel Bucek 1, Joaquim Jaumot 2, Anna Aviñó 3, Ramon Eritja 3, Raimundo Gargallo 2 ph-modulated Watson-Crick duplex - quadruplex equilibria of guanine-rich and cytosine-rich DNA sequences upstream of the c-kit transcription initiation site Pavel Bucek 1, Joaquim Jaumot 2, Anna Aviñó

More information

Diversity of Chlamydia trachomatis Major Outer Membrane

Diversity of Chlamydia trachomatis Major Outer Membrane JOURNAL OF ACTERIOLOGY, Sept. 1987, p. 3879-3885 Vol. 169, No. 9 0021-9193/87/093879-07$02.00/0 Copyright 1987, American Society for Microbiology Diversity of Chlamydia trachomatis Major Outer Membrane

More information

Symmetry Studies. Marlos A. G. Viana

Symmetry Studies. Marlos A. G. Viana Symmetry Studies Marlos A. G. Viana aaa aac aag aat caa cac cag cat aca acc acg act cca ccc ccg cct aga agc agg agt cga cgc cgg cgt ata atc atg att cta ctc ctg ctt gaa gac gag gat taa tac tag tat gca gcc

More information

evoglow yeast kit distributed by product information Cat.#: FP-21040

evoglow yeast kit distributed by product information Cat.#: FP-21040 evoglow yeast kit product information distributed by Cat.#: FP-21040 Flavin-mononucleotide-based Fluorescent Protein (FbFP) evoglow basic kit Cat.# FP-21010 Quantity 20 µg each General Information Fluorescent

More information

Using an Artificial Regulatory Network to Investigate Neural Computation

Using an Artificial Regulatory Network to Investigate Neural Computation Using an Artificial Regulatory Network to Investigate Neural Computation W. Garrett Mitchener College of Charleston January 6, 25 W. Garrett Mitchener (C of C) UM January 6, 25 / 4 Evolution and Computing

More information