DOCSLIB.ORG

INFERNAL User's Guide

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
INFERNAL User's Guide INFERNAL User’s Guide Sequence analysis using profiles of RNA sequence and secondary structure consensus http://eddylab.org/infernal Version 1.1.4; Dec 2020 Eric Nawrocki and Sean Eddy for the INFERNAL development team https://github.com/EddyRivasLab/infernal/ Copyright (C) 2020 Howard Hughes Medical Institute. Infernal and its documentation are freely distributed under the 3-Clause BSD open source license. For a copy of the license, see http://opensource.org/licenses/BSD-3-Clause. Infernal development is supported by the Intramural Research Program of the National Library of Medicine at the US National Institutes of Health, and also by the National Human Genome Research Institute of the US National Institutes of Health under grant number R01HG009116. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 1 Contents 1 Introduction 6 How to avoid reading this manual . 6 What covariance models are . 6 Applications of covariance models . 7 Infernal and HMMER, CMs and profile HMMs . 7 What’s new in Infernal 1.1 . 8 How to learn more about CMs and profile HMMs . 8 2 Installation 10 Quick installation instructions . 10 System requirements . 10 Multithreaded parallelization for multicores is the default . 11 MPI parallelization for clusters is optional . 11 Using build directories . 12 Makefile targets . 12 Why is the output of ’make’ so clean? . 12 What gets installed by ’make install’, and where? . 12 Staged installations in a buildroot, for a packaging system . 13 Workarounds for some unusual configure/compilation problems . 13 3 Tutorial 15 The programs in Infernal . 15 Files used in the tutorial . 15 Searching a sequence database with a single covariance model . 16 Step 1: build a covariance model with cmbuild . 16 Step 2: calibrate the model with cmcalibrate . 17 Step 3: search a sequence database with cmsearch . 18 Truncated RNA detection . 24 Searching a CM database with a query sequence . 24 Step 1: create an CM database flatfile . 24 Step 2: compress and index the flatfile with cmpress . 25 Step 3: search the CM database with cmscan . 25 Truncated hit and local end alignment example . 27 Searching the Rfam CM database with a query sequence . 28 Creating multiple alignments with cmalign . 30 cmalign assumes sequences may be truncated . 31 Searching a sequence database for RNAs with unknown or no secondary structure . 32 Forcing global CM alignment with the -g option . 34 Specifying and annotating match positions with cmbuild –hand . 34 4 Infernal 1.1’s profile/sequence comparison pipeline 37 Filter thresholds are dependent on database size . 38 Manually setting filter thresholds . 39 In more detail: profile HMM filter stages . 40 Null model. 40 SSV filter. 41 Local Viterbi filter. 41 Biased composition filter. 42 Local Forward filter. 42 Glocal Forward filter. 43 2 Envelope definition. 44 In more detail: CM stages of the pipeline . 45 HMM band definition for CM stages. 45 HMM banded CM CYK filter. 45 HMM banded CM Inside filter/parser. 46 Optimal accuracy alignment. 46 Biased composition CM score correction: the null3 model. 46 Truncated hit detection using variants of the pipeline . 49 Differences between the standard pipeline and the truncated variants . 49 Modifying how truncated hits are detected using command-line options . 50 HMM-only pipeline variant for models without structure . 50 5 Profile SCFG construction: the cmbuild program 52 Technical description of a covariance model . 52 Definition of a stochastic context free grammar . 52 SCFG productions allowed in CMs . 52 From consensus structural alignment to guide tree . 53 From guide tree to covariance model . 54 Parameterization . 55 Comparison to profile HMMs . 55 The cmbuild program, step by step . 57 Alignment input file . 57 Parsing secondary structure annotation . 57 Sequence weighting . 58 Architecture construction . 58 Parameterization . 59 Naming the model . 59 Saving the model . 59 6 Tabular output formats 60 Target hits tables . 60 Target hits table format 1 . 60 Target hits table format 2 . 61 7 Some other topics 63 How do I cite Infernal? . 63 How do I report a bug? . 63 Input files . 64 Reading from a stdin pipe using - (dash) as a filename argument . 64 8 Manual pages 66 cmalign - align sequences to a covariance model . 66 Synopsis . 66 Description . 66 Options . 67 Options for Controlling the Alignment Algorithm . 67 Options for Controlling Speed and Memory Requirements . 68 Optional Output Files . 69 Other Options . 69 cmbuild - construct covariance model(s) from structurally annotated . 71 Synopsis . 71 Description . ..
Load more

Recommended publications
  • 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]
  • 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]
  • 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]
  • 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]
  • DECIPHER: Harnessing Local Sequence Context to Improve Protein Multiple Sequence Alignment Erik S
    Wright BMC Bioinformatics (2015) 16:322 DOI 10.1186/s12859-015-0749-z RESEARCH ARTICLE Open Access DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment Erik S. Wright1,2 Abstract Background: Alignment of large and diverse sequence sets is a common task in biological investigations, yet there remains considerable room for improvement in alignment quality. Multiple sequence alignment programs tend to reach maximal accuracy when aligning only a few sequences, and then diminish steadily as more sequences are added. This drop in accuracy can be partly attributed to a build-up of error and ambiguity as more sequences are aligned. Most high-throughput sequence alignment algorithms do not use contextual information under the assumption that sites are independent. This study examines the extent to which local sequence context can be exploited to improve the quality of large multiple sequence alignments. Results: Two predictors based on local sequence context were assessed: (i) single sequence secondary structure predictions, and (ii) modulation of gap costs according to the surrounding residues. The results indicate that context-based predictors have appreciable information content that can be utilized to create more accurate alignments. Furthermore, local context becomes more informative as the number of sequences increases, enabling more accurate protein alignments of large empirical benchmarks. These discoveries became the basis for DECIPHER, a new context-aware program for sequence alignment, which outperformed other programs on largesequencesets. Conclusions: Predicting secondary structure based on local sequence context is an efficient means of breaking the independence assumption in alignment. Since secondary structure is more conserved than primary sequence, it can be leveraged to improve the alignment of distantly related proteins.
    [Show full text]
  • Methods in and Applications of the Sequencing of Short Non-Coding Rnas" (2013)
    University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2013 Methods in and Applications of the Sequencing of Short Non- Coding RNAs Paul Ryvkin University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Genetics Commons, and the Molecular Biology Commons Recommended Citation Ryvkin, Paul, "Methods in and Applications of the Sequencing of Short Non-Coding RNAs" (2013). Publicly Accessible Penn Dissertations. 922. https://repository.upenn.edu/edissertations/922 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/922 For more information, please contact [email protected]. Methods in and Applications of the Sequencing of Short Non-Coding RNAs Abstract Short non-coding RNAs are important for all domains of life. With the advent of modern molecular biology their applicability to medicine has become apparent in settings ranging from diagonistic biomarkers to therapeutics and fields angingr from oncology to neurology. In addition, a critical, recent technological development is high-throughput sequencing of nucleic acids. The convergence of modern biotechnology with developments in RNA biology presents opportunities in both basic research and medical settings. Here I present two novel methods for leveraging high-throughput sequencing in the study of short non- coding RNAs, as well as a study in which they are applied to Alzheimer's Disease (AD). The computational methods presented here include High-throughput Annotation of Modified Ribonucleotides (HAMR), which enables researchers to detect post-transcriptional covalent modifications ot RNAs in a high-throughput manner. In addition, I describe Classification of RNAs by Analysis of Length (CoRAL), a computational method that allows researchers to characterize the pathways responsible for short non-coding RNA biogenesis.
    [Show full text]
  • Comparing Tools for Non-Coding RNA Multiple Sequence Alignment Based On
    Downloaded from rnajournal.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press ES Wright 1 1 TITLE 2 RNAconTest: Comparing tools for non-coding RNA multiple sequence alignment based on 3 structural consistency 4 Running title: RNAconTest: benchmarking comparative RNA programs 5 Author: Erik S. Wright1,* 6 1 Department of Biomedical Informatics, University of Pittsburgh (Pittsburgh, PA) 7 * Corresponding author: Erik S. Wright ([email protected]) 8 Keywords: Multiple sequence alignment, Secondary structure prediction, Benchmark, non- 9 coding RNA, Consensus secondary structure 10 Downloaded from rnajournal.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press ES Wright 2 11 ABSTRACT 12 The importance of non-coding RNA sequences has become increasingly clear over the past 13 decade. New RNA families are often detected and analyzed using comparative methods based on 14 multiple sequence alignments. Accordingly, a number of programs have been developed for 15 aligning and deriving secondary structures from sets of RNA sequences. Yet, the best tools for 16 these tasks remain unclear because existing benchmarks contain too few sequences belonging to 17 only a small number of RNA families. RNAconTest (RNA consistency test) is a new 18 benchmarking approach relying on the observation that secondary structure is often conserved 19 across highly divergent RNA sequences from the same family. RNAconTest scores multiple 20 sequence alignments based on the level of consistency among known secondary structures 21 belonging to reference sequences in their output alignment. Similarly, consensus secondary 22 structure predictions are scored according to their agreement with one or more known structures 23 in a family.
    [Show full text]
  • 1 Codon-Level Information Improves Predictions of Inter-Residue Contacts in Proteins 2 by Correlated Mutation Analysis 3
    1 Codon-level information improves predictions of inter-residue contacts in proteins 2 by correlated mutation analysis 3 4 5 6 7 8 Etai Jacob1,2, Ron Unger1,* and Amnon Horovitz2,* 9 10 11 1The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat- 12 Gan, 52900, 2Department of Structural Biology 13 Weizmann Institute of Science, Rehovot 7610001, Israel 14 15 16 *To whom correspondence should be addressed: 17 Amnon Horovitz ([email protected]) 18 Ron Unger ([email protected]) 19 1 20 Abstract 21 Methods for analysing correlated mutations in proteins are becoming an increasingly 22 powerful tool for predicting contacts within and between proteins. Nevertheless, 23 limitations remain due to the requirement for large multiple sequence alignments (MSA) 24 and the fact that, in general, only the relatively small number of top-ranking predictions 25 are reliable. To date, methods for analysing correlated mutations have relied exclusively 26 on amino acid MSAs as inputs. Here, we describe a new approach for analysing 27 correlated mutations that is based on combined analysis of amino acid and codon MSAs. 28 We show that a direct contact is more likely to be present when the correlation between 29 the positions is strong at the amino acid level but weak at the codon level. The 30 performance of different methods for analysing correlated mutations in predicting 31 contacts is shown to be enhanced significantly when amino acid and codon data are 32 combined. 33 2 34 The effects of mutations that disrupt protein structure and/or function at one site are often 35 suppressed by mutations that occur at other sites either in the same protein or in other 36 proteins.
    [Show full text]
  • Annual Scientific Report 2013 on the Cover Structure 3Fof in the Protein Data Bank, Determined by Laponogov, I
    EMBL-European Bioinformatics Institute Annual Scientific Report 2013 On the cover Structure 3fof in the Protein Data Bank, determined by Laponogov, I. et al. (2009) Structural insight into the quinolone-DNA cleavage complex of type IIA topoisomerases. Nature Structural & Molecular Biology 16, 667-669. © 2014 European Molecular Biology Laboratory This publication was produced by the External Relations team at the European Bioinformatics Institute (EMBL-EBI) A digital version of the brochure can be found at www.ebi.ac.uk/about/brochures For more information about EMBL-EBI please contact: [email protected] Contents Introduction & overview 3 Services 8 Genes, genomes and variation 8 Molecular atlas 12 Proteins and protein families 14 Molecular and cellular structures 18 Chemical biology 20 Molecular systems 22 Cross-domain tools and resources 24 Research 26 Support 32 ELIXIR 36 Facts and figures 38 Funding & resource allocation 38 Growth of core resources 40 Collaborations 42 Our staff in 2013 44 Scientific advisory committees 46 Major database collaborations 50 Publications 52 Organisation of EMBL-EBI leadership 61 2013 EMBL-EBI Annual Scientific Report 1 Foreword Welcome to EMBL-EBI’s 2013 Annual Scientific Report. Here we look back on our major achievements during the year, reflecting on the delivery of our world-class services, research, training, industry collaboration and European coordination of life-science data. The past year has been one full of exciting changes, both scientifically and organisationally. We unveiled a new website that helps users explore our resources more seamlessly, saw the publication of ground-breaking work in data storage and synthetic biology, joined the global alliance for global health, built important new relationships with our partners in industry and celebrated the launch of ELIXIR.
    [Show full text]
  • EMBL-EBI Now and in the Future
    SureChEMBL: Open Patent Data Chemaxon UGM, Budapest 21/05/2014 Mark Davies ChEMBL Group, EMBL-EBI EMBL-EBI Resources Genes, genomes & variation European Nucleotide Ensembl European Genome-phenome Archive Archive Ensembl Genomes Metagenomics portal 1000 Genomes Gene, protein & metabolite expression ArrayExpress Metabolights Expression Atlas PRIDE Literature & Protein sequences, families & motifs ontologies InterPro Pfam UniProt Europe PubMed Central Gene Ontology Experimental Factor Molecular structures Ontology Protein Data Bank in Europe Electron Microscopy Data Bank Chemical biology ChEMBL ChEBI Reactions, interactions & pathways Systems BioModels BioSamples IntAct Reactome MetaboLights Enzyme Portal ChEMBL – Data for Drug Discovery 1. Scientific facts 3. Insight, tools and resources for translational drug discovery >Thrombin MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLE Compound RECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGT NYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYT TDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVT THGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGY CDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLF EKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDR WVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWR ENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTA NVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGG Ki = 4.5nM PFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE Bioactivity data Assay/Target APTT = 11 min. 2. Organization, integration,
    [Show full text]
  • A Unicellular Relative of Animals Generates a Layer of Polarized Cells
    RESEARCH ARTICLE A unicellular relative of animals generates a layer of polarized cells by actomyosin- dependent cellularization Omaya Dudin1†*, Andrej Ondracka1†, Xavier Grau-Bove´ 1,2, Arthur AB Haraldsen3, Atsushi Toyoda4, Hiroshi Suga5, Jon Bra˚ te3, In˜ aki Ruiz-Trillo1,6,7* 1Institut de Biologia Evolutiva (CSIC-Universitat Pompeu Fabra), Barcelona, Spain; 2Department of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, United Kingdom; 3Section for Genetics and Evolutionary Biology (EVOGENE), Department of Biosciences, University of Oslo, Oslo, Norway; 4Department of Genomics and Evolutionary Biology, National Institute of Genetics, Mishima, Japan; 5Faculty of Life and Environmental Sciences, Prefectural University of Hiroshima, Hiroshima, Japan; 6Departament de Gene`tica, Microbiologia i Estadı´stica, Universitat de Barcelona, Barcelona, Spain; 7ICREA, Barcelona, Spain Abstract In animals, cellularization of a coenocyte is a specialized form of cytokinesis that results in the formation of a polarized epithelium during early embryonic development. It is characterized by coordinated assembly of an actomyosin network, which drives inward membrane invaginations. However, whether coordinated cellularization driven by membrane invagination exists outside animals is not known. To that end, we investigate cellularization in the ichthyosporean Sphaeroforma arctica, a close unicellular relative of animals. We show that the process of cellularization involves coordinated inward plasma membrane invaginations dependent on an *For correspondence: actomyosin network and reveal the temporal order of its assembly. This leads to the formation of a [email protected] (OD); polarized layer of cells resembling an epithelium. We show that this stage is associated with tightly [email protected] (IR-T) regulated transcriptional activation of genes involved in cell adhesion.
    [Show full text]
  • HMMER User's Guide
    HMMER User’s Guide Biological sequence analysis using profile hidden Markov models http://hmmer.org/ Version 3.0rc1; February 2010 Sean R. Eddy for the HMMER Development Team Janelia Farm Research Campus 19700 Helix Drive Ashburn VA 20147 USA http://eddylab.org/ Copyright (C) 2010 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 5 How to avoid reading this manual . 5 How to avoid using this software (links to similar software) . 5 What profile HMMs are . 5 Applications of profile HMMs . 6 Design goals of HMMER3 . 7 What’s still missing in HMMER3 . 8 How to learn more about profile HMMs . 9 2 Installation 10 Quick installation instructions . 10 System requirements . 10 Multithreaded parallelization for multicores is the default . 11 MPI parallelization for clusters is optional . 11 Using build directories . 12 Makefile targets . 12 3 Tutorial 13 The programs in HMMER . 13 Files used in the tutorial . 13 Searching a sequence database with a single profile HMM . 14 Step 1: build a profile HMM with hmmbuild . 14 Step 2: search the sequence database with hmmsearch . 16 Searching a profile HMM database with a query sequence . 22 Step 1: create an HMM database flatfile . 22 Step 2: compress and index the flatfile with hmmpress .
    [Show full text]