Developing and Applying Heterogeneous Phylogenetic Models with Xrate Oscar Westesson1, Ian Holmes2,∗
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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 . -
Validation and Annotation of Virus Sequence Submissions to Genbank Alejandro A
Schäffer et al. BMC Bioinformatics (2020) 21:211 https://doi.org/10.1186/s12859-020-3537-3 SOFTWARE Open Access VADR: validation and annotation of virus sequence submissions to GenBank Alejandro A. Schäffer1,2, Eneida L. Hatcher2, Linda Yankie2, Lara Shonkwiler2,3,J.RodneyBrister2, Ilene Karsch-Mizrachi2 and Eric P. Nawrocki2* *Correspondence: [email protected] Abstract 2National Center for Biotechnology Background: GenBank contains over 3 million viral sequences. The National Center Information, National Library of for Biotechnology Information (NCBI) previously made available a tool for validating Medicine, National Institutes of Health, Bethesda, MD, 20894 USA and annotating influenza virus sequences that is used to check submissions to Full list of author information is GenBank. Before this project, there was no analogous tool in use for non-influenza viral available at the end of the article sequence submissions. Results: We developed a system called VADR (Viral Annotation DefineR) that validates and annotates viral sequences in GenBank submissions. The annotation system is based on the analysis of the input nucleotide sequence using models built from curated RefSeqs. Hidden Markov models are used to classify sequences by determining the RefSeq they are most similar to, and feature annotation from the RefSeq is mapped based on a nucleotide alignment of the full sequence to a covariance model. Predicted proteins encoded by the sequence are validated with nucleotide-to-protein alignments using BLAST. The system identifies 43 types of “alerts” that (unlike the previous BLAST-based system) provide deterministic and rigorous feedback to researchers who submit sequences with unexpected characteristics. VADR has been integrated into GenBank’s submission processing pipeline allowing for viral submissions passing all tests to be accepted and annotated automatically, without the need for any human (GenBank indexer) intervention. -
RNA Ontology Consortium January 8-9, 2011
UC San Diego UC San Diego Previously Published Works Title Meeting report of the RNA Ontology Consortium January 8-9, 2011. Permalink https://escholarship.org/uc/item/0kw6h252 Journal Standards in genomic sciences, 4(2) ISSN 1944-3277 Authors Birmingham, Amanda Clemente, Jose C Desai, Narayan et al. Publication Date 2011-04-01 DOI 10.4056/sigs.1724282 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Standards in Genomic Sciences (2011) 4:252-256 DOI:10.4056/sigs.1724282 Meeting report of the RNA Ontology Consortium January 8-9, 2011 Amanda Birmingham1, Jose C. Clemente2, Narayan Desai3, Jack Gilbert3,4, Antonio Gonzalez2, Nikos Kyrpides5, Folker Meyer3,6, Eric Nawrocki7, Peter Sterk8, Jesse Stombaugh2, Zasha Weinberg9,10, Doug Wendel2, Neocles B. Leontis11, Craig Zirbel12, Rob Knight2,13, Alain Laederach14 1 Thermo Fisher Scientific, Lafayette, CO, USA 2 Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA 3 Argonne National Laboratory, Argonne, IL, USA 4 Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA 5 DOE Joint Genome Institute, Walnut Creek, CA, USA 6 Computation Institute, University of Chicago, Chicago, IL, USA 7 Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA 8 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK 9 Department of Molecular, Cellular and Developmental, Yale University, New Haven, CT, USA 10 Howard Hughes Medical Institute, Yale University, New -
BMC Bioinformatics Biomed Central
BMC Bioinformatics BioMed Central Software Open Access XRate: a fast prototyping, training and annotation tool for phylo-grammars Peter S Klosterman1, Andrew V Uzilov1, Yuri R Bendaña1, Robert K Bradley1, Sharon Chao1, Carolin Kosiol2,3, Nick Goldman2 and Ian Holmes*1 Address: 1Department of Bioengineering, University of California, Berkeley CA, USA, 2European Bioinformatics Institute, Hinxton, Cambridgeshire, UK and 3Department of Biological Statistics and Computational Biology, Cornell University, Ithaca NY, USA Email: Peter S Klosterman - [email protected]; Andrew V Uzilov - [email protected]; Yuri R Bendaña - [email protected]; Robert K Bradley - [email protected]; Sharon Chao - [email protected]; Carolin Kosiol - [email protected]; Nick Goldman - [email protected]; Ian Holmes* - [email protected] * Corresponding author Published: 03 October 2006 Received: 24 February 2006 Accepted: 03 October 2006 BMC Bioinformatics 2006, 7:428 doi:10.1186/1471-2105-7-428 This article is available from: http://www.biomedcentral.com/1471-2105/7/428 © 2006 Klosterman et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Recent years have seen the emergence of genome annotation methods based on the phylo-grammar, a probabilistic model combining continuous-time Markov chains and stochastic grammars. Previously, phylo-grammars have required considerable effort to implement, limiting their adoption by computational biologists. Results: We have developed an open source software tool, xrate, for working with reversible, irreversible or parametric substitution models combined with stochastic context-free grammars. -
Tools for Simulating Evolution of Aligned Genomic Regions with Integrated Parameter Estimation Avinash Varadarajan*, Robert K Bradley and Ian H Holmes
Open Access Software2008VaradarajanetVolume al. 9, Issue 10, Article R147 Tools for simulating evolution of aligned genomic regions with integrated parameter estimation Avinash Varadarajan*, Robert K Bradley and Ian H Holmes Addresses: *Computer Science Division, University of California, Berkeley, CA 94720-1776, USA. Biophysics Graduate Group, University of California, Berkeley, CA 94720-3200, USA. Department of Bioengineering, University of California, Berkeley, CA 94720-1762, USA. Correspondence: Ian H Holmes. Email: [email protected] Published: 8 October 2008 Received: 20 June 2008 Revised: 21 August 2008 Genome Biology 2008, 9:R147 (doi:10.1186/gb-2008-9-10-r147) Accepted: 8 October 2008 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2008/9/10/R147 © 2008 Varadarajan et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Simulation<p>Threefor richly structured tools of genome for simulating syntenic evolution blocks genome of genomeevolution sequence.</p> are presented: for neutrally evolving DNA, for phylogenetic context-free grammars and Abstract Controlled simulations of genome evolution are useful for benchmarking tools. However, many simulators lack extensibility and cannot measure parameters directly from data. These issues are addressed by three new open-source programs: GSIMULATOR (for neutrally evolving DNA), SIMGRAM (for generic structured features) and SIMGENOME (for syntenic genome blocks). Each offers algorithms for parameter measurement and reconstruction of ancestral sequence. -
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 . -
Developing and Applying Heterogeneous Phylogenetic Models with Xrate
Developing and Applying Heterogeneous Phylogenetic Models with XRate Oscar Westesson, Ian Holmes* Department of Bioengineering, University of California, Berkeley, California, United States of America Abstract Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the ‘‘grammar’’ of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate ’s new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate ’s flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART. Citation: Westesson O, Holmes I (2012) Developing and Applying Heterogeneous Phylogenetic Models with XRate. PLoS ONE 7(6): e36898. doi:10.1371/ journal.pone.0036898 Editor: Christos A. Ouzounis, The Centre for Research and Technology, Hellas, Greece Received October 18, 2011; Accepted April 9, 2012; Published June 5, 2012 Copyright: ß 2012 Westesson, Holmes. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This research was funded from an NIH Research Project Grant Program (R01). -
Sequence Databases Enriched with Computationally Designed Protein
D300–D305 Nucleic Acids Research, 2015, Vol. 43, Database issue Published online 27 September 2014 doi: 10.1093/nar/gku888 NrichD database: sequence databases enriched with computationally designed protein-like sequences aid in remote homology detection Richa Mudgal1, Sankaran Sandhya2,GayatriKumar3, Ramanathan Sowdhamini4, Nagasuma R. Chandra2 and Narayanaswamy Srinivasan3,* 1IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560 012, Karnataka, India, 2Department of Biochemistry, Indian Institute of Science, Bangalore 560 012, Karnataka, India, 3Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, Karnataka, India and 4National Centre for Biological Sciences, Gandhi Krishi Vignan Kendra Campus, Bellary road, Bangalore 560 065, Karnataka, India Received August 03, 2014; Revised September 11, 2014; Accepted September 15, 2014 ABSTRACT INTRODUCTION NrichD (http://proline.biochem.iisc.ernet.in/NRICHD/) Efficiency of protein remote homology detection has been is a database of computationally designed protein- dependent on the availability of sequences which can con- like sequences, augmented into natural sequence vincingly connect distantly related proteins (1,2). With im- databases that can perform hops in protein se- provements in homology detection methods, natural linker quence space to assist in the detection of remote sequences often serve as ‘tools’ that facilitate hops be- tween proteins to connect them (3–6). Despite these ad- relationships. Establishing protein relationships vancements, identification -
Biopython Tutorial and Cookbook
Biopython Tutorial and Cookbook Jeff Chang, Brad Chapman, Iddo Friedberg, Thomas Hamelryck, Michiel de Hoon, Peter Cock, Tiago Antao, Eric Talevich Last Update – 31 August 2010 (Biopython 1.55) Contents 1 Introduction 7 1.1 What is Biopython? ......................................... 7 1.2 What can I find in the Biopython package ............................. 7 1.3 Installing Biopython ......................................... 8 1.4 Frequently Asked Questions (FAQ) ................................. 8 2 Quick Start – What can you do with Biopython? 12 2.1 General overview of what Biopython provides ........................... 12 2.2 Working with sequences ....................................... 12 2.3 A usage example ........................................... 13 2.4 Parsing sequence file formats .................................... 14 2.4.1 Simple FASTA parsing example ............................... 14 2.4.2 Simple GenBank parsing example ............................. 15 2.4.3 I love parsing – please don’t stop talking about it! .................... 15 2.5 Connecting with biological databases ................................ 15 2.6 What to do next ........................................... 16 3 Sequence objects 17 3.1 Sequences and Alphabets ...................................... 17 3.2 Sequences act like strings ...................................... 18 3.3 Slicing a sequence .......................................... 19 3.4 Turning Seq objects into strings ................................... 20 3.5 Concatenating or adding sequences ................................ -
Seqmagick Documentation Release 0.7.0+7.G1642bb8
seqmagick Documentation Release 0.7.0+7.g1642bb8 Matsen Group May 30, 2018 Contents 1 Changes for seqmagick 3 2 Motivation 7 3 Installation 9 4 Use 11 5 List of Subcommands 13 6 Supported File Extensions 23 7 Acknowledgements 25 8 Contributing 27 i ii seqmagick Documentation, Release 0.7.0+7.g1642bb8 Contents • seqmagick – Motivation – Installation – Use – List of Subcommands – Supported File Extensions * Default Format * Compressed file support – Acknowledgements – Contributing Contents 1 seqmagick Documentation, Release 0.7.0+7.g1642bb8 2 Contents CHAPTER 1 Changes for seqmagick 1.1 0.7.0 • Supports Python 3.4+ • Drops support for python 2.7 • requires biopython >= 1.70 • Drops support for bz2 compression [see GH-66] • New option convert --sample-seed to make --sample deterministic. 1.2 0.6.2 • New quality-filter --pct-ambiguous switch [GH-53] • setup.py enforces biopython>=1.58,<=1.66 (1.67 is not compatible) [GH-59] • This is the last release that will support Python 2! 1.3 0.6.1 • Allow string wrapping when input isn’t FASTA. [GH-45] • Fix --pattern-include, --pattern-exclude, and --pattern-replace for sequences without descriptions (e.g., from NEXUS files). [GH-47] • Fix mogrify example. [GH-52] 3 seqmagick Documentation, Release 0.7.0+7.g1642bb8 1.4 0.6.0 • Map .nex extension to NEXUS-format (–alphabet must be specified if writing) • Use reservoir sampling in --sample selector (lower memory use) • Support specifying negative indices to --cut [GH-33] • Optionally allow invalid codons in backtrans-align [GH-34] • Map .fq extension to FASTQ format • Optional multithreaded I/O in info [GH-36] • Print sequence name on length mismatch in backtrans-align [GH-37] • Support for + and - in head and tail to mimick Linux head and tail commands. -
HMMER Web Server Documentation Release 1.0
HMMER web server Documentation Release 1.0 Rob Finn & Simon Potter EMBL-EBI Oct 16, 2019 Contents 1 Target databases 3 1.1 Sequence databases...........................................3 1.2 Profile HMM databases.........................................4 1.3 Search provenance............................................4 2 Searches 5 2.1 Search query...............................................5 2.2 Query examples.............................................6 2.3 Default search parameters........................................6 2.3.1 phmmer.............................................6 2.3.2 hmmscan............................................6 2.3.3 hmmsearch...........................................6 2.3.4 jackhmmer...........................................6 2.4 Databases.................................................7 2.4.1 Sequence databases.......................................7 2.4.2 HMM databases.........................................7 2.5 Thresholds................................................7 2.5.1 Significance thresholds.....................................7 2.5.2 Reporting thresholds......................................8 2.5.3 Gathering thresholds......................................9 2.5.4 Gene3D and Superfamily thresholds..............................9 3 Advanced search options 11 3.1 Taxonomy Restrictions.......................................... 11 3.1.1 Search.............................................. 11 3.1.2 Pre-defined Taxonomic Tree.................................. 11 3.2 Customisation of results........................................ -
Quiz: Past Midterm Exam (2017) 4/7/21, 9:49 PM
Quiz: Past midterm exam (2017) 4/7/21, 9:49 PM Past midterm exam (2017) Started: Apr 7 at 6:47am Quiz Instructions Answer as many questions as you can in the time allowed. Question 1 1 pts Which of the following genomic mutation events would you expect to occur most frequently in non-coding, non-selected regions of the human genome? Single nucleotide mutation of A to T Single nucleotide mutation of A to G Single nucleotide mutation of A to C Single nucleotide mutation of C to G Single nucleotide insertion of a C between an A and a G (AG becomes ACG) Single nucleotide deletion of a C between an A and a G (ACG becomes AG) Question 2 1 pts Which of the following is a common structural basis for DNA-protein recognition by transcription factors containing the helix-turn-helix domain? Complementary base-pairing between nucleotides in transcription factor and nucleotides in binding site https://bcourses.berkeley.edu/courses/1498319/quizzes/2322845/take Page 1 of 19 Quiz: Past midterm exam (2017) 4/7/21, 9:49 PM Phosphorylation of transcription factor by kinase proteins Van der Waals interactions between transcription factor and DNA backbone Insertion of protein alpha-helix into DNA major groove Hydrogen bonding between amino acids and unpaired nucleotides in single-stranded regions Question 3 1 pts In a uniform, independent & identically distributed sequence of nucleotides about the length of the HIV genome, roughly how many times would you expect to see the motif ACGACG? Give your answer to one significant figure.