HMMER User's Guide

Total Page:16

File Type:pdf, Size:1020Kb

HMMER User's Guide HMMER User’s Guide Biological sequence analysis using profile hidden Markov models http://hmmer.org/ Version 3.1b2; February 2015 Sean R. Eddy, Travis J. Wheeler and the HMMER development team Copyright (C) 2015 Howard Hughes Medical Institute. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are retained on all copies. HMMER is licensed and freely distributed under the GNU General Public License version 3 (GPLv3). For a copy of the License, see http://www.gnu.org/licenses/. HMMER is a trademark of the Howard Hughes Medical Institute. 1 Contents 1 Introduction 7 How to avoid reading this manual . 7 How to avoid using this software (links to similar software) . 7 What profile HMMs are . 7 Applications of profile HMMs . 8 Design goals of HMMER3 . 9 What’s new in HMMER3.1 . 10 What’s still missing in HMMER3.1 . 11 How to learn more about profile HMMs . 11 2 Installation 13 Quick installation instructions . 13 System requirements . 13 Multithreaded parallelization for multicores is the default . 14 MPI parallelization for clusters is optional . 14 Using build directories . 15 Makefile targets . 15 Why is the output of ’make’ so clean? . 15 What gets installed by ’make install’, and where? . 15 Staged installations in a buildroot, for a packaging system . 16 Workarounds for some unusual configure/compilation problems . 16 3 Tutorial 18 The programs in HMMER . 18 Supported formats . 19 Files used in the tutorial . 19 Searching a protein sequence database with a single protein profile HMM . 20 Step 1: build a profile HMM with hmmbuild . 20 Step 2: search the sequence database with hmmsearch . 21 Single sequence protein queries using phmmer . 27 Iterative protein searches using jackhmmer . 28 Searching a DNA sequence database . 30 Step 1: Optionally build a profile HMM with hmmbuild . 30 Step 2: search the DNA sequence database with nhmmer . 31 Searching a profile HMM database with a query sequence . 33 Step 1: create an HMM database flatfile . 33 Step 2: compress and index the flatfile with hmmpress . 34 Step 3: search the HMM database with hmmscan . 34 Creating multiple alignments with hmmalign . 36 4 The HMMER profile/sequence comparison pipeline 38 Null model. 39 MSV filter. 39 Biased composition filter. 39 Viterbi filter. 40 Forward filter/parser. 41 Domain definition. 41 Modifications to the pipeline as used for DNA search. 43 SSV, not MSV. 43 There are no domains, but there are envelopes. 43 2 Biased composition. 43 5 Tabular output formats 45 The target hits table . 45 The domain hits table (protein search only) . 47 6 Some other topics 49 How do I cite HMMER? . 49 How do I report a bug? . 49 Input files . 50 Reading from a stdin pipe using - (dash) as a filename argument . 50 7 Manual pages 52 alimask - Add mask line to a multiple sequence alignment . 52 Synopsis . 52 Description . 52 Options . 52 Options for Specifying Mask Range . 52 Options for Specifying the Alphabet . 53 Options Controlling Profile Construction . 53 Options Controlling Relative Weights . 54 Other Options . 54 hmmalign - align sequences to a profile HMM . 55 Synopsis . 55 Description . 55 Options . 55 hmmbuild - construct profile HMM(s) from multiple sequence alignment(s) . 56 Synopsis . 56 Description . 56 Options . 56 Options for Specifying the Alphabet . 56 Options Controlling Profile Construction . 57 Options Controlling Relative Weights . 57 Options Controlling Effective Sequence Number . 58 Options Controlling Priors . 58 Options Controlling E-value Calibration . 59 Other Options . 59 hmmconvert - convert profile file to a HMMER format . 61 Synopsis . 61 Description . 61 Options . 61 hmmemit - sample sequences from a profile HMM . 62 Synopsis . 62 Description . 62 Common Options . 62 Options Controlling What to Emit . 62 Options Controlling Emission from Profiles . ..
Recommended publications
  • Current Status and Future Perspectives of Bioinformatics in Tanzania
    CURRENT STATUS AND FUTURE PERSPECTIVES OF BIOINFORMATICS IN TANZANIA Sylvester L. Lyantagaye Department of Molecular Biology and Biotechnology, College of Natural and Applied Sciences, University of Dar es Salaam, P.O. Box 35179, Dar es Salaam, Tanzania E-mail: [email protected], [email protected] ABSTRACT The main bottleneck in advancing genomics in present times is the lack of expertise in using bioinformatics tools and approaches for data mining in raw DNA sequences generated by modern high throughput technologies such as next generation sequencing. Although bioinformatics has been making major progress and contributing to the development in the rest of the world, it has still not yet fully integrated the tertiary education and research sector in Tanzania. This review aims to introduce a summary of recent achievements, trends and success stories of application of bioinformatics in biotechnology. The applications of bioinformatics in the fields such as molecular biology, biotechnology, medicine and agriculture, the global trend of bioinformatics, accessibility bioinformatics products in Tanzania, bioinformatics training initiatives in Tanzania, the future prospects of bioinformatics use in biotechnology globally and Tanzania in particular are reviewed. The paper is of interest and importance to rouse public awareness of the new opportunities that could be brought about by bioinformatics to address many research problems relevant to Tanzania and sub-Sahara Africa. Keywords: Bioinformatics, Biotechnology, Genomics, Tanzania. INTRODUCTION analysis, and management of biological Bioinformatics is conceptualising biology in information through computational terms of molecules (in the sense of physical techniques. It uses mathematics, biology, chemistry) and applying "informatics and computer science to understand the techniques" (derived from disciplines such biological importance of an extensive as applied mathematics, computer science variety of omics data (Reportlinker 2013).
    [Show full text]
  • RDA COVID-19 Recommendations and Guidelines on Data Sharing
    RDA COVID-19 Recommendations and Guidelines on Data Sharing DOI: 10.15497/RDA00052 Authors: RDA COVID-19 Working Group Published: 30th June 2020 Abstract: This is the final version of the Recommendations and Guidelines from the RDA COVID19 Working Group, and has been endorsed through the official RDA process. Keywords: RDA; Recommendations; COVID-19. Language: English License: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication RDA webpage: https://www.rd-alliance.org/group/rda-covid19-rda-covid19-omics-rda-covid19- epidemiology-rda-covid19-clinical-rda-covid19-1 Related resources: - RDA COVID-19 Guidelines and Recommendations – preliminary version, https://doi.org/10.15497/RDA00046 - Data Sharing in Epidemiology, https://doi.org/10.15497/RDA00049 - RDA COVID-19 Zotero Library, https://doi.org/10.15497/RDA00051 Citation and Download: RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing. Research Data Alliance. 2020. DOI: https://doi.org/10.15497/RDA00052 RDA COVID-19 Recommendations and Guidelines on Data Sharing RDA Recommendation (FINAL Release) Produced by: RDA COVID-19 Working Group, 2020 Document Metadata Identifier DOI: https://doi.org/10.15497/rda00052 Citation To cite this document please use: RDA COVID-19 Working Group. Recommendations and Guidelines on data sharing. Research Data Alliance. 2020. DOI: https://doi.org/10.15497/rda00052 Title RDA COVID-19; Recommendations and Guidelines on Data Sharing, Final release 30 June 2020 Description This is the final version of the Recommendations and Guidelines
    [Show full text]
  • Sequencing Alignment I Outline: Sequence Alignment
    Sequencing Alignment I Lectures 16 – Nov 21, 2011 CSE 527 Computational Biology, Fall 2011 Instructor: Su-In Lee TA: Christopher Miles Monday & Wednesday 12:00-1:20 Johnson Hall (JHN) 022 1 Outline: Sequence Alignment What Why (applications) Comparative genomics DNA sequencing A simple algorithm Complexity analysis A better algorithm: “Dynamic programming” 2 1 Sequence Alignment: What Definition An arrangement of two or several biological sequences (e.g. protein or DNA sequences) highlighting their similarity The sequences are padded with gaps (usually denoted by dashes) so that columns contain identical or similar characters from the sequences involved Example – pairwise alignment T A C T A A G T C C A A T 3 Sequence Alignment: What Definition An arrangement of two or several biological sequences (e.g. protein or DNA sequences) highlighting their similarity The sequences are padded with gaps (usually denoted by dashes) so that columns contain identical or similar characters from the sequences involved Example – pairwise alignment T A C T A A G | : | : | | : T C C – A A T 4 2 Sequence Alignment: Why The most basic sequence analysis task First aligning the sequences (or parts of them) and Then deciding whether that alignment is more likely to have occurred because the sequences are related, or just by chance Similar sequences often have similar origin or function New sequence always compared to existing sequences (e.g. using BLAST) 5 Sequence Alignment Example: gene HBB Product: hemoglobin Sickle-cell anaemia causing gene Protein sequence (146 aa) MVHLTPEEKS AVTALWGKVN VDEVGGEALG RLLVVYPWTQ RFFESFGDLS TPDAVMGNPK VKAHGKKVLG AFSDGLAHLD NLKGTFATLS ELHCDKLHVD PENFRLLGNV LVCVLAHHFG KEFTPPVQAA YQKVVAGVAN ALAHKYH BLAST (Basic Local Alignment Search Tool) The most popular alignment tool Try it! Pick any protein, e.g.
    [Show full text]
  • Comparative Analysis of Multiple Sequence Alignment Tools
    I.J. Information Technology and Computer Science, 2018, 8, 24-30 Published Online August 2018 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2018.08.04 Comparative Analysis of Multiple Sequence Alignment Tools Eman M. Mohamed Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected]. Hamdy M. Mousa, Arabi E. keshk Faculty of Computers and Information, Menoufia University, Egypt E-mail: [email protected], [email protected]. Received: 24 April 2018; Accepted: 07 July 2018; Published: 08 August 2018 Abstract—The perfect alignment between three or more global alignment algorithm built-in dynamic sequences of Protein, RNA or DNA is a very difficult programming technique [1]. This algorithm maximizes task in bioinformatics. There are many techniques for the number of amino acid matches and minimizes the alignment multiple sequences. Many techniques number of required gaps to finds globally optimal maximize speed and do not concern with the accuracy of alignment. Local alignments are more useful for aligning the resulting alignment. Likewise, many techniques sub-regions of the sequences, whereas local alignment maximize accuracy and do not concern with the speed. maximizes sub-regions similarity alignment. One of the Reducing memory and execution time requirements and most known of Local alignment is Smith-Waterman increasing the accuracy of multiple sequence alignment algorithm [2]. on large-scale datasets are the vital goal of any technique. The paper introduces the comparative analysis of the Table 1. Pairwise vs. multiple sequence alignment most well-known programs (CLUSTAL-OMEGA, PSA MSA MAFFT, BROBCONS, KALIGN, RETALIGN, and Compare two biological Compare more than two MUSCLE).
    [Show full text]
  • (12) Patent Application Publication (10) Pub. No.: US 2003/0211987 A1 Labat Et Al
    US 2003O21, 1987A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2003/0211987 A1 Labat et al. (43) Pub. Date: Nov. 13, 2003 (54) METHODS AND MATERIALS RELATING TO Apr. 7, 2000 (US)........................................... O9545,714 STEM CELL GROWTH FACTOR-LIKE Apr. 11, 2000 (US)........................................... O9547358 POLYPEPTIDES AND POLYNUCLEOTDES Publication Classification (76) Inventors: Ivan Labat, Mountain View, CA (US); Y Tom Tang, San Jose, CA (US); Radoje T. Drmanac, Palo Alto, CA (51) Int. Cl." ....................... A61K 38/18; CO7K 14/475; (US); Chenghua Liu, San Jose, CA C12O 1/68; CO7H 21/04; (US); Juhi Lee, Fremont, CA (US); C12M 1/34; C12P 21/02; Nancy K Mize, Mountain View, CA C12N 5/08 (US); John Childs, Sunnyvale, CA (52) U.S. Cl. ......... 514/12; 435/69.1; 435/6; 435/320.1; (US); Cheng-Chi Chao, Cupertino, CA 435/366; 530/399; 536/23.5; (US) 435/287.2 Correspondence Address: MARSHALL, GERSTEIN & BORUN LLP (57) ABSTRACT 6300 SEARS TOWER 233 S. WACKER DRIVE The invention provides novel polynucleotides and polypep CHICAGO, IL 60606 (US) tides encoded by Such polynucleotides and mutants or variants thereof that correspond to a novel human Secreted (21) Appl. No.: 10/168,365 Stem cell growth factor-like polypeptide. These polynucle otides comprise nucleic acid Sequences isolated from cDNA (22) PCT Filed: Dec. 23, 2000 libraries prepared from human fetal liver Spleen, ovary, adult (86) PCT No.: PCT/US00/35260 brain, lung tumor, Spinal cord, cervix, ovary, endothelial cells, umbilical cord, lymphocyte, lung fibroblast, fetal (30) Foreign Application Priority Data brain, and testis.
    [Show full text]
  • The ELIXIR Core Data Resources: ​Fundamental Infrastructure for The
    Supplementary Data: The ELIXIR Core Data Resources: fundamental infrastructure ​ for the life sciences The “Supporting Material” referred to within this Supplementary Data can be found in the Supporting.Material.CDR.infrastructure file, DOI: 10.5281/zenodo.2625247 (https://zenodo.org/record/2625247). ​ ​ Figure 1. Scale of the Core Data Resources Table S1. Data from which Figure 1 is derived: Year 2013 2014 2015 2016 2017 Data entries 765881651 997794559 1726529931 1853429002 2715599247 Monthly user/IP addresses 1700660 2109586 2413724 2502617 2867265 FTEs 270 292.65 295.65 289.7 311.2 Figure 1 includes data from the following Core Data Resources: ArrayExpress, BRENDA, CATH, ChEBI, ChEMBL, EGA, ENA, Ensembl, Ensembl Genomes, EuropePMC, HPA, IntAct /MINT , InterPro, PDBe, PRIDE, SILVA, STRING, UniProt ● Note that Ensembl’s compute infrastructure physically relocated in 2016, so “Users/IP address” data are not available for that year. In this case, the 2015 numbers were rolled forward to 2016. ● Note that STRING makes only minor releases in 2014 and 2016, in that the interactions are re-computed, but the number of “Data entries” remains unchanged. The major releases that change the number of “Data entries” happened in 2013 and 2015. So, for “Data entries” , the number for 2013 was rolled forward to 2014, and the number for 2015 was rolled forward to 2016. The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences ​ 1 Figure 2: Usage of Core Data Resources in research The following steps were taken: 1. API calls were run on open access full text articles in Europe PMC to identify articles that ​ ​ mention Core Data Resource by name or include specific data record accession numbers.
    [Show full text]
  • Dual Proteome-Scale Networks Reveal Cell-Specific Remodeling of the Human Interactome
    bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Dual Proteome-scale Networks Reveal Cell-specific Remodeling of the Human Interactome Edward L. Huttlin1*, Raphael J. Bruckner1,3, Jose Navarrete-Perea1, Joe R. Cannon1,4, Kurt Baltier1,5, Fana Gebreab1, Melanie P. Gygi1, Alexandra Thornock1, Gabriela Zarraga1,6, Stanley Tam1,7, John Szpyt1, Alexandra Panov1, Hannah Parzen1,8, Sipei Fu1, Arvene Golbazi1, Eila Maenpaa1, Keegan Stricker1, Sanjukta Guha Thakurta1, Ramin Rad1, Joshua Pan2, David P. Nusinow1, Joao A. Paulo1, Devin K. Schweppe1, Laura Pontano Vaites1, J. Wade Harper1*, Steven P. Gygi1*# 1Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA. 2Broad Institute, Cambridge, MA, 02142, USA. 3Present address: ICCB-Longwood Screening Facility, Harvard Medical School, Boston, MA, 02115, USA. 4Present address: Merck, West Point, PA, 19486, USA. 5Present address: IQ Proteomics, Cambridge, MA, 02139, USA. 6Present address: Vor Biopharma, Cambridge, MA, 02142, USA. 7Present address: Rubius Therapeutics, Cambridge, MA, 02139, USA. 8Present address: RPS North America, South Kingstown, RI, 02879, USA. *Correspondence: [email protected] (E.L.H.), [email protected] (J.W.H.), [email protected] (S.P.G.) #Lead Contact: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/2020.01.19.905109; this version posted January 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder.
    [Show full text]
  • Bioinformatics Study of Lectins: New Classification and Prediction In
    Bioinformatics study of lectins : new classification and prediction in genomes François Bonnardel To cite this version: François Bonnardel. Bioinformatics study of lectins : new classification and prediction in genomes. Structural Biology [q-bio.BM]. Université Grenoble Alpes [2020-..]; Université de Genève, 2021. En- glish. NNT : 2021GRALV010. tel-03331649 HAL Id: tel-03331649 https://tel.archives-ouvertes.fr/tel-03331649 Submitted on 2 Sep 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE Pour obtenir le grade de DOCTEUR DE L’UNIVERSITE GRENOBLE ALPES préparée dans le cadre d’une cotutelle entre la Communauté Université Grenoble Alpes et l’Université de Genève Spécialités: Chimie Biologie Arrêté ministériel : le 6 janvier 2005 – 25 mai 2016 Présentée par François Bonnardel Thèse dirigée par la Dr. Anne Imberty codirigée par la Dr/Prof. Frédérique Lisacek préparée au sein du laboratoire CERMAV, CNRS et du Computer Science Department, UNIGE et de l’équipe PIG, SIB Dans les Écoles Doctorales EDCSV et UNIGE Etude bioinformatique des lectines: nouvelle classification et prédiction dans les génomes Thèse soutenue publiquement le 8 Février 2021, devant le jury composé de : Dr. Alexandre de Brevern UMR S1134, Inserm, Université Paris Diderot, Paris, France, Rapporteur Dr.
    [Show full text]
  • Cryptic Inoviruses Revealed As Pervasive in Bacteria and Archaea Across Earth’S Biomes
    ARTICLES https://doi.org/10.1038/s41564-019-0510-x Corrected: Author Correction Cryptic inoviruses revealed as pervasive in bacteria and archaea across Earth’s biomes Simon Roux 1*, Mart Krupovic 2, Rebecca A. Daly3, Adair L. Borges4, Stephen Nayfach1, Frederik Schulz 1, Allison Sharrar5, Paula B. Matheus Carnevali 5, Jan-Fang Cheng1, Natalia N. Ivanova 1, Joseph Bondy-Denomy4,6, Kelly C. Wrighton3, Tanja Woyke 1, Axel Visel 1, Nikos C. Kyrpides1 and Emiley A. Eloe-Fadrosh 1* Bacteriophages from the Inoviridae family (inoviruses) are characterized by their unique morphology, genome content and infection cycle. One of the most striking features of inoviruses is their ability to establish a chronic infection whereby the viral genome resides within the cell in either an exclusively episomal state or integrated into the host chromosome and virions are continuously released without killing the host. To date, a relatively small number of inovirus isolates have been extensively studied, either for biotechnological applications, such as phage display, or because of their effect on the toxicity of known bacterial pathogens including Vibrio cholerae and Neisseria meningitidis. Here, we show that the current 56 members of the Inoviridae family represent a minute fraction of a highly diverse group of inoviruses. Using a machine learning approach lever- aging a combination of marker gene and genome features, we identified 10,295 inovirus-like sequences from microbial genomes and metagenomes. Collectively, our results call for reclassification of the current Inoviridae family into a viral order including six distinct proposed families associated with nearly all bacterial phyla across virtually every ecosystem.
    [Show full text]
  • To Find Information About Arabidopsis Genes Leonore Reiser1, Shabari
    UNIT 1.11 Using The Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes Leonore Reiser1, Shabari Subramaniam1, Donghui Li1, and Eva Huala1 1Phoenix Bioinformatics, Redwood City, CA USA ABSTRACT The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource of Arabidopsis biology for plant scientists. TAIR curates and integrates information about genes, proteins, gene function, orthologs gene expression, mutant phenotypes, biological materials such as clones and seed stocks, genetic markers, genetic and physical maps, genome organization, images of mutant plants, protein sub-cellular localizations, publications, and the research community. The various data types are extensively interconnected and can be accessed through a variety of Web-based search and display tools. This unit primarily focuses on some basic methods for searching, browsing, visualizing, and analyzing information about Arabidopsis genes and genome, Additionally we describe how members of the community can share data using TAIR’s Online Annotation Submission Tool (TOAST), in order to make their published research more accessible and visible. Keywords: Arabidopsis ● databases ● bioinformatics ● data mining ● genomics INTRODUCTION The Arabidopsis Information Resource (TAIR; http://arabidopsis.org) is a comprehensive Web resource for the biology of Arabidopsis thaliana (Huala et al., 2001; Garcia-Hernandez et al., 2002; Rhee et al., 2003; Weems et al., 2004; Swarbreck et al., 2008, Lamesch, et al., 2010, Berardini et al., 2016). The TAIR database contains information about genes, proteins, gene expression, mutant phenotypes, germplasms, clones, genetic markers, genetic and physical maps, genome organization, publications, and the research community. In addition, seed and DNA stocks from the Arabidopsis Biological Resource Center (ABRC; Scholl et al., 2003) are integrated with genomic data, and can be ordered through TAIR.
    [Show full text]
  • Learning Protein Constitutive Motifs from Sequence Data Je´ Roˆ Me Tubiana, Simona Cocco, Re´ Mi Monasson*
    TOOLS AND RESOURCES Learning protein constitutive motifs from sequence data Je´ roˆ me Tubiana, Simona Cocco, Re´ mi Monasson* Laboratory of Physics of the Ecole Normale Supe´rieure, CNRS UMR 8023 & PSL Research, Paris, France Abstract Statistical analysis of evolutionary-related protein sequences provides information about their structure, function, and history. We show that Restricted Boltzmann Machines (RBM), designed to learn complex high-dimensional data and their statistical features, can efficiently model protein families from sequence information. We here apply RBM to 20 protein families, and present detailed results for two short protein domains (Kunitz and WW), one long chaperone protein (Hsp70), and synthetic lattice proteins for benchmarking. The features inferred by the RBM are biologically interpretable: they are related to structure (residue-residue tertiary contacts, extended secondary motifs (a-helixes and b-sheets) and intrinsically disordered regions), to function (activity and ligand specificity), or to phylogenetic identity. In addition, we use RBM to design new protein sequences with putative properties by composing and ’turning up’ or ’turning down’ the different modes at will. Our work therefore shows that RBM are versatile and practical tools that can be used to unveil and exploit the genotype–phenotype relationship for protein families. DOI: https://doi.org/10.7554/eLife.39397.001 Introduction In recent years, the sequencing of many organisms’ genomes has led to the collection of a huge number of protein sequences, which are catalogued in databases such as UniProt or PFAM Finn et al., 2014). Sequences that share a common ancestral origin, defining a family (Figure 1A), *For correspondence: are likely to code for proteins with similar functions and structures, providing a unique window into [email protected] the relationship between genotype (sequence content) and phenotype (biological features).
    [Show full text]
  • UNIVERSITY of CALIFORNIA, SAN DIEGO Use Solid K-Mers In
    UNIVERSITY OF CALIFORNIA, SAN DIEGO Use Solid K-mers In MinHash-Based Genome Distance Estimation A thesis submitted in partial satisfaction of the requirements for the degree Master of Science in Computer Science by An Zheng Committee in charge: Professor Pavel Pevzner, Chair Professor Vikas Bansal Professor Melissa Gymrek 2017 Copyright An Zheng, 2017 All rights reserved. The thesis of An Zheng is approved, and it is acceptable in quality and form for publication on microfilm and electroni- cally: Chair University of California, San Diego 2017 iii TABLE OF CONTENTS Signature Page . iii Table of Contents . iv List of Figures . v List of Tables . vi Acknowledgements . vii Abstract of the Thesis . viii Chapter 1 Introduction and background . 1 1.1 Genome distance estimation . 1 1.2 Current methods . 2 1.3 MinHash . 3 1.4 Solid k-mer powered MinHash . 5 Chapter 2 Method . 7 2.1 General scheme . 7 2.2 Identification of overlapping read pairs . 8 2.2.1 Workflow . 8 2.2.2 Data . 9 2.2.3 Implementation . 9 2.3 Genome identification . 10 2.3.1 Workflow . 10 2.3.2 Data . 10 2.3.3 Implementation . 10 Chapter 3 Result . 15 3.1 Identification of overlapping read pairs . 15 3.1.1 Performance comparison between solid k-mer pow- ered MinHash and regular MinHash . 15 3.1.2 Selecting the solid k-mer threshold . 17 3.2 Genome identification . 19 Chapter 4 Discussion and future work . 21 Bibliography . 23 iv LIST OF FIGURES Figure 1.1: An example of how to use MinHash to compute the resemblance of two genome sequences.
    [Show full text]