DOCSLIB.ORG

Phylo Dcor: Distance Correlation As a Novel Metric for Phylogenetic Profiling Gabriella Sferra, Federica Fratini, Marta Ponzi and Elisabetta Pizzi*

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Phylo Dcor: Distance Correlation As a Novel Metric for Phylogenetic Profiling Gabriella Sferra, Federica Fratini, Marta Ponzi and Elisabetta Pizzi* Sferra et al. BMC Bioinformatics (2017) 18:396 DOI 10.1186/s12859-017-1815-5 SOFTWARE Open Access Phylo_dCor: distance correlation as a novel metric for phylogenetic profiling Gabriella Sferra, Federica Fratini, Marta Ponzi and Elisabetta Pizzi* Abstract Background: Elaboration of powerful methods to predict functional and/or physical protein-protein interactions from genome sequence is one of the main tasks in the post-genomic era. Phylogenetic profiling allows the prediction of protein-protein interactions at a whole genome level in both Prokaryotes and Eukaryotes. For this reason it is considered one of the most promising methods. Results: Here, we propose an improvement of phylogenetic profiling that enables handling of large genomic datasets and infer global protein-protein interactions. This method uses the distance correlation as a new measure of phylogenetic profile similarity. We constructed robust reference sets and developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation that makes it applicable to large genomic data. Using Saccharomyces cerevisiae and Escherichia coli genome datasets, we showed that Phylo-dCor outperforms phylogenetic profiling methods previously described based on the mutual information and Pearson’s correlation as measures of profile similarity. Conclusions: In this work, we constructed and assessed robust reference sets and propose the distance correlation as a measure for comparing phylogenetic profiles. To make it applicable to large genomic data, we developed Phylo-dCor, a parallelized version of the algorithm for calculating the distance correlation. Two R scripts that can be run on a wide range of machines are available upon request. Keywords: Phylogenetic profiling, Distance correlation, Protein-protein interaction Background to gain insights on the function of uncharacterized pro- In the last two decades, several computational ap- teins [see for example [6–8]. These methods are based proaches have been proposed to infer both functional on the detection of orthologs either from sequence simi- and physical protein-protein interactions (PPIs). These larity score or from tree-based algorithms (for a recent methods includes the identification of gene fusion events implementation see [9]). [1, 2], conservation of gene neighborhood [3] or phylo- In general, phylogenetic profiling is based on the as- genetic profiling [4, 5]. Recently, the increasing number sumption that proteins involved in the same biological of fully sequenced genomes led to a renewed interest in pathway or in the same protein complex co-evolve [for a these approaches. Among them, the phylogenetic profil- review see [10]. In a first implementation [4], the phylo- ing is one of the most promising in that it allows to pre- genetic profile of a protein was defined as a binary vec- dict protein-protein interactions at a whole genome tor that describes the occurrence pattern of orthologs in level, while gene fusion and gene neighborhood are rela- a set of fully sequenced genomes, and the Hamming dis- tively rare events found typically in prokaryotic tance was used to score the similarity between profile genomes. pairs. Subsequently, to evaluate different degrees of se- Well implemented methods, based on phylogenetic quence divergence, phylogenetic profiles were recon- profiling, have been developed and successfully applied structed using probabilities derived by the expectation for understanding relationships between proteins and/or values obtained aligning the proteins under study with a genome reference set [5]. Among measures proposed to * Correspondence: [email protected] score the phylogenetic profile similarities [for a review Dipartimento di Malattie Infettive, Parassitarie e Immunomediate, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy see [11], the Mutual Information (MI) was demonstrated © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Sferra et al. BMC Bioinformatics (2017) 18:396 Page 2 of 7 to correlate well in accuracy with genome-wide yeast −∑∑p(a, b)lnp(a, b)represents the summation of the two-hybrid screens or mass spectrometry interaction as- relative entropies of the joint probability distribution says [5]. Although it was largely adopted as a measure of p(a, b)of co-occurrence of gene A and B across the phylogenetic profile similarity, Simon and Tibshirani re- set of reference genomes, in the intervals of the prob- cently debated about the lower power of MI in detecting ability distribution. The mutual information was cal- dependency between two variables compared with cor- culated by using the mutualInfo function available in relation measures [12]. bioDist R package [16] after binning the data into 0.1 In this work, we propose the distance correlation intervals. (dCor) as a novel metric to score phylogenetic profile We calculated dCor according to Szekely and collabo- similarity. dCor measures any dependence between two rators [13, 14]. The original implementation (available in variables, ranges between 0 and 1, and it satisfies all re- the energy package of Bioconductor) allows the calcula- quirements of a distance [13, 14]. tion only between two arrays of data. For this reason, we In order to apply this measure to large genomic data, developed two novel scripts that make possible to per- we developed a novel parallel version of the original al- form dCor NxN phylogenetic profile comparison, where gorithm. Furthermore, we adopted a new strategy of N is the number of genes in a given genome. In genome selection to obtain unbiased and large reference principle, the method is applicable also to binary phylo- sets of genomes. We applied this methodology to con- genetic profiles. struct phylogenetic profiles of two model organisms, First, the matrix of the Euclidean distances was ob- Escherichia coli and Saccharomyces cerevisiae and con- tained calculating the difference between thek-th firmed that correlation measures (dCor and Pearson’s element and thel-th element of the phylogenetic pro- correlation) have a more robust predictive perform- file as ance than the MI. In particular we showed that dCor performs better than Pearson’s correlation (PC) and D ¼ ⌊dkl⌋ MI especially in predicting physical protein-protein interactions. where. dkl =|ak − al|r as the distance between the r-th pairs of Implementation elements of the profiles. d D Phylogenetic profiling Second, each distance kl of the matrix was then da Phylogenetic profiles were obtained as arrays of pro- converted into an element kl of the matrix of the cen- DA bability values according to tered distances , calculated as ¼ − = ðÞ dakl ¼ dkl−dk −dl þ dkl P 1 log10 E − For E-values higher than 10 1, the probability value is where. Pn set to 1, as proposed in [5]. 1 dk ¼ n dkl is the average calculated on the rows of Where E are the E-values obtained from the align- k¼1 ments of S. cerevisiae and E. coli protein sequences the distance matrix; Pn against the four reference sets. To do this, we applied 1 dl ¼ n dkl is the average calculated on the columns the Smith-Watermann alignment algorithm [15]. The l¼1 FASTA package version 36 was implemented as a of the distance matrix; Pn stand-alone software on two Work Stations both dual d ¼ 1 d kl n2 kl is the average calculated on all the ele- core, the first with 12 CPU and the second with 8 k;l¼1 CPU. ments of the distance matrix; where k = l = 1,.…, n =1,…, j. Similarity measures The distance correlation between the profilesApandAq One of the method usually used to establish similarity was calculated as between phylogenetic profiles is the mutual information that is calculated according to CovðDAp; DAqÞ dCorpq ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiÀÁÀÁ MIðÞ¼ A; B HAðÞþHBðÞ−HAðÞ; B Var DAp Var DAq where H(A)= −∑p(a)lnp(a) is the summation of the where Cov and Varrepresent the covariance and the marginal entropies, calculated over the intervals of variance of the matrices of the centered distances and p probability distribution p(a), of the gene A to occur = q =1, … , i. among the organisms in the reference set. H(A, B)= Pearson’s correlation was calculated according to Sferra et al. BMC Bioinformatics (2017) 18:396 Page 3 of 7 P n ðÞx −x ðÞy −y 1133 manually selected genomes were collected and PC ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffii¼1 i i P pPffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi classified as “core” (high quality genomes) and “per- n ðÞx −x n y −yÞ i¼1 i 2 i¼1 i 2 ipheral” (genomes not completely validated) on the Where n is the size of the two arrays x and y, and x y basis of genome coverage, status of gene annotation and are the corresponding means. and gene completeness. The first reference set (RS1) excluded all the strains of Gold standards and predictive performance assessment the same species classified as “peripheral” genomes. A On the basis of KEGG database [17], we considered pro- second reference set (RS2) was generated from RS1 ex- teins belonging to the same metabolic pathway as func- cluding the eukaryotic genomes with a “peripheral” attri- tional related and hence to be included in the True bute till having 45 eukaryotic genomes in a such way to Positive data set (TP-fun). To derive the True Negative pass from a ratio 5:1 to a ratio 13:1. To construct the data set (TN-fun), we developed a graph-based algo- third reference set (RS3), we progressively excluded “per- rithm to identify non-interacting proteins.
Load more

Recommended publications
  • "Phylogenetic Profiling"
    Introductory Review Phylogenetic p rofiling Matteo Pellegrini, Todd O. Yeates and David Eisenberg Howard Hughes Medical Institute, University of California at Los Angeles, Los Angeles, CA, USA Sorel T. Fitz-Gibbon IGPP Center for Astrobiology, University of California at Los Angeles, Los Angeles, CA, USA 1. Introduction Biology has been profoundly changed by the development of techniques to se- quence DNA. The advent of rapid sequencing in conjunction with the capability to assemble sequence fragments into complete genome sequences enables researchers to read and analyze entire genomes of organisms. Parallel progress has been made in algorithms to study the evolutionary history of proteins. The techniques rely on the ability to measure the similarity of protein sequences in order to determine the likelihood that different proteins are descended from a common ancestor. It is therefore possible to reconstruct families of proteins that share a common ancestor. Combining these two capabilities, we can now not only determine which proteins are coded within an organism’s genome but we can also discover the evolutionary relationships between the proteins of multiple organisms. Phylogenetic profiling is the study of which protein types are found in which organisms. In order to perform phylogenetic profiling, one must first establish a classification of proteins into families. An example of such a classification scheme across a broad range of fully sequenced organisms is the Clusters of Orthologous Groups (Tatusov, 1997), where an attempt is made to group together proteins that perform a similar function. Next, each organism is described in terms of which protein families are coded or not coded in its genome.
    [Show full text]
  • Pellegrini M. Using Phylogenetic Profiles To
    Chapter 9 Using Phylogenetic Profiles to Predict Functional Relationships Matteo Pellegrini Abstract Phylogenetic profiling involves the comparison of phylogenetic data across gene families. It is possible to construct phylogenetic trees, or related data structures, for specific gene families using a wide variety of tools and approaches. Phylogenetic profiling involves the comparison of this data to determine which families have correlated or coupled evolution. The underlying assumption is that in certain cases these couplings may allow us to infer that the two families are functionally related: that is their function in the cell is coupled. Although this technique can be applied to noncoding genes, it is more commonly used to assess the function of protein coding genes. Examples of proteins that are functionally related include subunits of protein complexes, or enzymes that perform consecutive steps along biochemical pathways. We hypothesize the deletion of one of the families from a genome would then indirectly affect the function of the other. Dozens of different implementations of the phylogenetic profiling technique have been developed over the past decade. These range from the first simple approaches that describe phylogenetic profiles as binary vectors to the most complex ones that attempt to model to the coevolution of protein families on a phylogenetic tree. We discuss a set of these implementations and present the software and databases that are available to perform phylogenetic profiling. Key words: Phylogenetic profiles, Coevolution, Functional associations, Comparative genomics, Coevolving proteins 1. Introduction The remarkable improvements in sequencing technology that have occurred over the past few decades have made the sequenc- ing of genomes an ever more routine task.
    [Show full text]
  • Integrating Genomic Data to Predict Transcription Factor Binding
    Integrating Genomic Data to Predict Transcription Factor Binding Dustin T. Holloway 1 Mark Kon 2 Charles DeLisi 3 [email protected] [email protected] [email protected] 1 Molecular Biology Cell Biology and Biochemistry, 3 Boston University, Boston, MA 02215, U.S.A. Bioinformatics and Systems Biology, 2 Boston University, Boston, MA 02215, U.S.A. Department of Mathematics and Statistics, Boston University, Boston, MA 02215 , U.S.A. Abstract Transcription factor binding sites (TFBS) in gene promoter regions are often predicted by using position specific scoring matrices (PSSMs), which summarize sequence patterns of experimentally determined TF binding sites. Although PSSMs are more reliable than simple consensus string matching in predicting a true binding site, they generally result in high numbers of false positive hits. This study attempts to reduce the number of false positive matches and generate new predictions by integrating various types of genomic data by two methods: a Bayesian allocation procedure, and support vector machine classification. Several methods will be explored to strengthen the prediction of a true TFBS in the Saccharomyces cerevisiae genome: binding site degeneracy, binding site conservation, phylogenetic profiling, TF binding site clustering, gene expression profiles, GO functional annotation, and k-mer counts in promoter regions. Binding site degeneracy (or redundancy) refers to the number of times a particular transcription factor’s binding motif is discovered in the upstream region of a gene. Phylogenetic conservation takes into account the number of orthologous upstream regions in other genomes that contain a particular binding site. Phylogenetic profiling refers to the presence or absence of a gene across a large set of genomes.
    [Show full text]
  • Scalable Phylogenetic Profiling Using Minhash Uncovers Likely Eukaryotic Sexual Reproduction Genes
    bioRxiv preprint doi: https://doi.org/10.1101/852491; this version posted November 22, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Scalable Phylogenetic Profiling using MinHash Uncovers Likely Eukaryotic Sexual Reproduction Genes 1,2,3,* 1,2,3 4,5 1,2,3,6,7,* David Moi ​ ,​ Laurent Kilchoer ,​ Pablo S. Aguilar ​ and Christophe Dessimoz ​ ​ ​ ​ 1 2 Department​ of Computational Biology, University of Lausanne, Switzerland; Center​ for Integrative Genomics, ​ 3 4 University of Lausanne, Switzerland; SIB​ Swiss Institute of Bioinformatics, Lausanne, Switzerland; Instituto​ de ​ ​ 5 Investigaciones Biotecnologicas (IIBIO), Universidad Nacional de San Martín Buenos Aires, Argentina; Instituto​ de ​ 6 Fisiología, Biología Molecular y Neurociencias (IFIBYNE-CONICET), Department​ of Genetics, Evolution, and ​ 7 Environment, University College London, UK; Department​ of Computer Science, University College London, UK. ​ *Corresponding authors: [email protected] and [email protected] Abstract Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require quadratic time or worse in the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles.
    [Show full text]
  • Review Article Protein-Protein Interaction Detection: Methods and Analysis
    Hindawi Publishing Corporation International Journal of Proteomics Volume 2014, Article ID 147648, 12 pages http://dx.doi.org/10.1155/2014/147648 Review Article Protein-Protein Interaction Detection: Methods and Analysis V. Srinivasa Rao,1 K. Srinivas,1 G. N. Sujini,2 and G. N. Sunand Kumar1 1 Department of CSE, VR Siddhartha Engineering College, Vijayawada 520007, India 2 Department of CSE, Mahatma Gandhi Institute of Technology, Hyderabad 500075, India Correspondence should be addressed to V. Srinivasa Rao; [email protected] Received 26 October 2013; Revised 5 December 2013; Accepted 20 December 2013; Published 17 February 2014 Academic Editor: Yaoqi Zhou Copyright © 2014 V. Srinivasa Rao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways.
    [Show full text]
  • Mapping Global and Local Co-Evolution Across 600 Species to Identify Novel Homologous Recombination Repair Genes
    Downloaded from genome.cshlp.org on October 9, 2021 - Published by Cold Spring Harbor Laboratory Press Mapping global and local co-evolution across 600 species to identify novel homologous recombination repair genes Dana Sherill-Rofe1*, Dolev Rahat1,2*, Steven Findlay3,4,#, Anna Mellul1,#, Irene Guberman1, Maya Braun1, Idit Bloch1, Alon Lalezari1, Arash Samiei 3,4, Ruslan Sadreyev5,6,7, Michal 8 3,4,9,10 2,† 1,†, Goldberg , Alexandre Orthwein , Aviad Zick and Yuval Tabach 1Department of Developmental Biology and Cancer Research, Institute for Medical Research- Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel. 2Sharett Institute of Oncology, Hadassah Medical Center, Ein-Kerem, Jerusalem, Israel. 3Lady Davis Institute for Medical Research, Segal Cancer Centre, Jewish General Hospital, Montreal, Quebec, Canada. 4Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada. 5Department of Molecular Biology, Massachusetts General Hospital, Boston, MA USA. 6Department of Genetics, Harvard Medical School, Boston, MA USA. 7Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA. 8Department of Genetics, Alexander Silberman Institute of Life Sciences, Hebrew University of Jerusalem, Jerusalem, Israel. 9Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada. 10Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada. *These authors contributed equally to this work. # These authors contributed equally to this work.
    [Show full text]
  • Phylogenetic Profiling of the Arabidopsis Thaliana Proteome
    Open Access Research2004Gutiérrezet al.Volume 5, Issue 8, Article R53 Phylogenetic profiling of the Arabidopsis thaliana proteome: what comment proteins distinguish plants from other organisms? Rodrigo A Gutiérrez*†§, Pamela J Green‡, Kenneth Keegstra*† and John B Ohlrogge† Addresses: *Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI 48824-1312, USA. †Department of Plant Biology, Michigan State University, East Lansing, MI 48824-1312, USA. ‡Delaware Biotechnology Institute, University of Delaware, 15 § Innovation Way, Newark, DE 19711, USA. Current address: Department of Biology, New York University, 100 Washington Square East, New reviews York, NY 10003, USA. Correspondence: Rodrigo A Gutiérrez. E-mail: [email protected] Published: 15 July 2004 Received: 16 March 2004 Revised: 10 May 2004 Genome Biology 2004, 5:R53 Accepted: 7 June 2004 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2004/5/8/R53 reports © 2004 Gutiérrez et al.; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL Phylogenetic<p>Theopportunitycodingof life</p> genes availability to profilingof decipher <it>Arabidopsis of theof the the complete genetic Arabidopsis thaliana genomefactors </it>onthaliana that sequence define the proteome: ofbasis plant <it>Arab of form whattheir idopsisand proteinspattern function. thaliana of distingu sequence To </it>together beginish similarityplants this task, from wi to thwe other organismsthose have organisms? of classified other across organisms the the nuc threlear proe domains protein-vides an deposited research Abstract Background: The availability of the complete genome sequence of Arabidopsis thaliana together with those of other organisms provides an opportunity to decipher the genetic factors that define plant form and function.
    [Show full text]
  • Phylogenetic Profiling of the Arabidopsis Genome
    Figure 1 Phylogenetic Profiling of the Arabidopsis Genome Aren Ewing and Gernot Presting Department of Molecular Biosciences and Bioengineering, University of Hawaii, Honolulu, HI 96822, USA Conclusion: Introduction: Results: The goal of this project is to assign genes to Member proteins of several biosynthetic pathways pathways based on co-evolution. Arabidopsis Clustered pathway proteins were found in the hierarchical tree using the greedy outlier removal clustered based on their phylogenetic profiles using several thaliana is the first fully sequenced plant, and program. Tryptophan and isoprenoid biosynthesis pathways were examined in detail. Most of different hierarchical trees and datasets. Within these many (60%) of its genes are functionally the proteins in the pathways do cluster, those that don’t either catalyze the first enzymatic step compact clusters we found new metabolic pathway annotated. This makes it a good candidate for or are misannotated / mispredicted (figure 3). New pathway members were found near known members that were not annotated in Aracyc. This study phlyogenetic studies. Utilizing existing pathway members in the hierarchical tree (figure 4). demonstrates the utility of a novel tool for assigning new information about pathways of Arabidopsis Figure 1 (left and poster border): All of the Arabidopsis genes with at least one match other members to pathways through the use of a high throughput (AraCyc), a tool was created to find novel than Arabidopsis (19324 rows) clustered in a binary tree. Clusters of unique profiles are pathway/gene list search. genes that may be associated with known Figure 1 scattered throughout the tree. Red color signifies presence of that Arabidopsis gene in target pathways.
    [Show full text]
  • A Study on the Phylogenetics of Gene Transfer: from Pathways to Kingdoms
    A STUDY ON THE PHYLOGENETICS OF GENE TRANSFER: FROM PATHWAYS TO KINGDOMS A THESIS SUBMITTED TO THE GRADUATE DIVISION OF UNIVERSITY OF HAWAI'I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN MOLECULAR BIOSCIENCES AND BIOENGINEERING May 2008 By Aren Ewing Thesis Committee: Gemot Presting, Chairperson Anne Alvarez Daniel Rubinoff We certify that we have read this dissertation and that, in our opinion, it is satisfactory in scope and quality as a dissertation for the degree of Masters of Science in Molecular Biosciences and Bioengineering. Thesis Committee 2 Acknowledgments With much gratitude I would like to thank my advisor Dr. Gemot Presting. He provided and outstanding workplace and lab, with the freedom to explore our interests and the facility to follow them. During my time in the Presting lab, I also met some outstanding people and scientists. I would like to thank Thomas Wolfgruber, Anupma Sharma, Kevin Schneider, Jeffery Lai, and Normon Wang. They have provided great support and friends over the years. I would also like to thank my committee who has stuck in there with me for the completion of this degree. It was a struggle to take what was once a PhD project and convert to a MS and while making it as excellent as possible. I would also like to thank my girlfriend Hideko Kasahara for her support and encouragement during the writing of this thesis. 3 Table of Contents Acknowledgments ..........................................................................................................................
    [Show full text]
  • Prediction of Protein-Protein Interactions Using Protein Signature Profiling
    Article Prediction of Protein-Protein Interactions Using Protein Signature Profiling Mahmood A. Mahdavi and Yen-Han Lin* Department of Chemical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada. Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain inter- actions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as “interacting”. In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis ele- gans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area un- der the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs in- creased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on aver- age 4 times better than those predicted by either phylogenetic profiling or gene expression profiling. Key words: protein-protein interaction, protein signature, ROC curve Introduction Protein-protein interaction (PPI) is the key element utilized as input to predict more accurate interac- of any biological process in a living cell.
    [Show full text]
  • Genomics of Energy & Environment User Meeting Abstracts
    DEPARTMENT OF ENERGY JOINT GENOME INSTITUTE (DOE JGI) Genomics of Energy & Environment User Meeting Abstracts March 26–28, 2013 / Walnut Creek Marriott / Walnut Creek, CA An electronic version of this document is available at: http://go.usa.gov/g3P All information current as of March 14, 2013 JGI Contact: Marsha Fenner DOE Joint Genome Institute [email protected] The Joint Genome Institute is a user facility of the Department of Energy Office of Science DOE Joint Genome Institute: www.jgi.doe.gov DOE Office of Science: science.doe.gov Eighth Annual DOE Joint Genome Institute User Meeting Sponsored By U.S. Department of Energy Office of Science March 26-28, 2013 Walnut Creek Marriott Walnut Creek, California Contents Speaker Presentations .................................................................................................................................. 1 Poster Presentations ..................................................................................................................................... 8 Attendees .................................................................................................................................................... 58 Author Index ............................................................................................................................................... 64 Speaker Presentations Abstracts alphabetical by speaker Assembly-driven metagenomics of a hypersaline microbial ecosystem Eric E. Allen ([email protected]) Marine Biology Research Division, Scripps Institution
    [Show full text]
  • Phylogene Server for Identification and Visualization of Co-Evolving Proteins Using Normalized Phylogenetic Profiles
    PhyloGene server for identification and visualization of co-evolving proteins using normalized phylogenetic profiles The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Sadreyev, Ilyas R., Fei Ji, Emiliano Cohen, Gary Ruvkun, and Yuval Tabach. 2015. “PhyloGene server for identification and visualization of co-evolving proteins using normalized phylogenetic profiles.” Nucleic Acids Research 43 (Web Server issue): W154-W159. doi:10.1093/nar/gkv452. http://dx.doi.org/10.1093/nar/gkv452. Published Version doi:10.1093/nar/gkv452 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:17820916 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA W154–W159 Nucleic Acids Research, 2015, Vol. 43, Web Server issue Published online 09 May 2015 doi: 10.1093/nar/gkv452 PhyloGene server for identification and visualization of co-evolving proteins using normalized phylogenetic profiles Ilyas R. Sadreyev1, Fei Ji1,2, Emiliano Cohen3, Gary Ruvkun1,2 and Yuval Tabach3,* 1Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA, 2Department of Genetics, Harvard Medical School, Boston, Boston, MA, USA and 3Department of Developmental Biology and Cancer Research, The Institute For Medical Research-Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem, Israel Received February 19, 2015; Revised April 16, 2015; Accepted April 24, 2015 ABSTRACT tion together, for example members of the same pathway or protein complex, often show similar pattern of conserva- Proteins that function in the same pathways, pro- tion across phylogenetic clades.
    [Show full text]