Sanger Institute Gene Trap Resource (SIGTR)
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
Homology & Alignment
Protein Bioinformatics Johns Hopkins Bloomberg School of Public Health 260.655 Thursday, April 1, 2010 Jonathan Pevsner Outline for today 1. Homology and pairwise alignment 2. BLAST 3. Multiple sequence alignment 4. Phylogeny and evolution Learning objectives: homology & alignment 1. You should know the definitions of homologs, orthologs, and paralogs 2. You should know how to determine whether two genes (or proteins) are homologous 3. You should know what a scoring matrix is 4. You should know how alignments are performed 5. You should know how to align two sequences using the BLAST tool at NCBI 1 Pairwise sequence alignment is the most fundamental operation of bioinformatics • It is used to decide if two proteins (or genes) are related structurally or functionally • It is used to identify domains or motifs that are shared between proteins • It is the basis of BLAST searching (next topic) • It is used in the analysis of genomes myoglobin Beta globin (NP_005359) (NP_000509) 2MM1 2HHB Page 49 Pairwise alignment: protein sequences can be more informative than DNA • protein is more informative (20 vs 4 characters); many amino acids share related biophysical properties • codons are degenerate: changes in the third position often do not alter the amino acid that is specified • protein sequences offer a longer “look-back” time • DNA sequences can be translated into protein, and then used in pairwise alignments 2 Find BLAST from the home page of NCBI and select protein BLAST… Page 52 Choose align two or more sequences… Page 52 Enter the two sequences (as accession numbers or in the fasta format) and click BLAST. -
Assembly Exercise
Assembly Exercise Turning reads into genomes Where we are • 13:30-14:00 – Primer Design to Amplify Microbial Genomes for Sequencing • 14:00-14:15 – Primer Design Exercise • 14:15-14:45 – Molecular Barcoding to Allow Multiplexed NGS • 14:45-15:15 – Processing NGS Data – de novo and mapping assembly • 15:15-15:30 – Break • 15:30-15:45 – Assembly Exercise • 15:45-16:15 – Annotation • 16:15-16:30 – Annotation Exercise • 16:30-17:00 – Submitting Data to GenBank Log onto ILRI cluster • Log in to HPC using ILRI instructions • NOTE: All the commands here are also in the file - assembly_hands_on_steps.txt • If you are like me, it may be easier to cut and paste Linux commands from this file instead of typing them in from the slides Start an interactive session on larger servers • The interactive command will start a session on a server better equipped to do genome assembly $ interactive • Switch to csh (I use some csh features) $ csh • Set up Newbler software that will be used $ module load 454 A norovirus sample sequenced on both 454 and Illumina • The vendors use different file formats unknown_norovirus_454.GACT.sff unknown_norovirus_illumina.fastq • I have converted these files to additional formats for use with the assembly tools unknown_norovirus_454_convert.fasta unknown_norovirus_454_convert.fastq unknown_norovirus_illumina_convert.fasta Set up and run the Newbler de novo assembler • Create a new de novo assembly project $ newAssembly de_novo_assembly • Add read data to the project $ addRun de_novo_assembly unknown_norovirus_454.GACT.sff -
Orthofiller: Utilising Data from Multiple Species to Improve the Completeness of Genome Annotations Michael P
Dunne and Kelly BMC Genomics (2017) 18:390 DOI 10.1186/s12864-017-3771-x SOFTWARE Open Access OrthoFiller: utilising data from multiple species to improve the completeness of genome annotations Michael P. Dunne and Steven Kelly* Abstract Backround: Complete and accurate annotation of sequenced genomes is of paramount importance to their utility and analysis. Differences in gene prediction pipelines mean that genome annotations for a species can differ considerably in the quality and quantity of their predicted genes. Furthermore, genes that are present in genome sequences sometimes fail to be detected by computational gene prediction methods. Erroneously unannotated genes can lead to oversights and inaccurate assertions in biological investigations, especially for smaller-scale genome projects, which rely heavily on computational prediction. Results: Here we present OrthoFiller, a tool designed to address the problem of finding and adding such missing genes to genome annotations. OrthoFiller leverages information from multiple related species to identify those genes whose existence can be verified through comparison with known gene families, but which have not been predicted. By simulating missing gene annotations in real sequence datasets from both plants and fungi we demonstrate the accuracy and utility of OrthoFiller for finding missing genes and improving genome annotations. Furthermore, we show that applying OrthoFiller to existing “complete” genome annotations can identify and correct substantial numbers of erroneously missing genes in these two sets of species. Conclusions: We show that significant improvements in the completeness of genome annotations can be made by leveraging information from multiple species. Keywords: Genome annotation, Gene prediction, Orthology, Orthogroup Background of several effective algorithms for identifying genes in Genome sequences have become fundamental to many de novo sequenced genomes [3]. -
Infravec2 Open Research Data Management Plan
INFRAVEC2 OPEN RESEARCH DATA MANAGEMENT PLAN Authors: Andrea Crisanti, Gareth Maslen, Andy Yates, Paul Kersey, Alain Kohl, Clelia Supparo, Ken Vernick Date: 10th July 2020 Version: 3.0 Overview Infravec2 will align to Open Research Data, as follows: Data Types and Standards Infravec2 will generate a variety of data types, including molecular data types: genome sequence and assembly, structural annotation (gene models, repeats, other functional regions) and functional annotation (protein function assignment), variation data, and transcriptome data; arbovirus and malaria experimental infection data, linked to archived samples; and microbiome data (Operational Taxonomic Units), including natural virome composition. All data will be released according to the appropriate standards and formats for each data type. For example, DNA sequence will be released in FASTA format; variant calls in Variant Call Format; sequence alignments in BAM (Binary Alignment Map) and CRAM (Compressed Read Alignment Map) formats, etc. We will strongly encourage the organisation of linked data sets as Track Hubs, a mechanism for publishing a set of linked genomic data that aids data discovery, sharing, and selection for subsequent analysis. We will develop internal standards within the consortium to define minimal metadata that will accompany all data sets, following the template of the Minimal Information Standards for Biological and Biomedical Investigations (http://www.dcc.ac.uk/resources/metadata-standards/mibbi-minimum-information-biological- and-biomedical-investigations). Data Exploitation, Accessibility, Curation and Preservation All molecular data for which existing public data repositories exist will be submitted to such repositories on or before the publication of written manuscripts, with early release of data (i.e. -
Lab 5: Bioinformatics I Sanger Sequence Analysis
Lab 5: Bioinformatics I Sanger Sequence Analysis Project Guide The Wolbachia Project 1 Arthropod Identification 2 DNA Extraction 3 PCR 4 Gel Electrophoresis 5 Bioinformatics Content is made available under the Creative Commons Attribution-NonCommercial-No Derivatives International License. Contact ([email protected]) if you would like to make adaptations for distribution beyond the classroom. The Wolbachia Project: Discover the Microbes Within! was developed by a collaboration of scientists, educators, and outreach specialists. It is directed by the Bordenstein Lab at Vanderbilt University. https://www.vanderbilt.edu/wolbachiaproject 2 Activity at a Glance Goal To analyze and interpret the quality of Sanger sequences, and generate a consensus DNA sequence for bioinformatics analyses. Learning Objectives Upon completion of this activity, students will (i) understand the Sanger method of sequencing, also known as the chain-termination method; (ii) be able to interpret chromatograms; (iii) evaluate sequencing Quality Scores; and (iv) generate a consensus DNA sequence based on forward and reverse Sanger reactions. Prerequisite Skills While no computer programming skills are necessary to complete this work, prior exposure to personal computers and the Internet is assumed. Teaching Time: One class period Recommended Background Tutorials • DNA Learning Center Animation: Sanger Method of DNA Sequencing (https://www.dnalc.org/view/15479-sanger-method-of-dna-sequencing-3d-animation-with- narration.html) • YouTube video: The Sanger -
Introduction to Bioinformatics (Elective) – SBB1609
SCHOOL OF BIO AND CHEMICAL ENGINEERING DEPARTMENT OF BIOTECHNOLOGY Unit 1 – Introduction to Bioinformatics (Elective) – SBB1609 1 I HISTORY OF BIOINFORMATICS Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biologicaldata. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. Bioinformatics derives knowledge from computer analysis of biological data. These can consist of the information stored in the genetic code, but also experimental results from various sources, patient statistics, and scientific literature. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of biology and is highly interdisciplinary, using techniques and concepts from informatics, statistics, mathematics, chemistry, biochemistry, physics, and linguistics. It has many practical applications in different areas of biology and medicine. Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. "Classical" bioinformatics: "The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.” The National Center for Biotechnology Information (NCBI 2001) defines bioinformatics as: "Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline. -
A Tool to Sanity Check and If Needed Reformat FASTA Files
bioRxiv preprint doi: https://doi.org/10.1101/024448; this version posted August 13, 2015. 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. Fasta-O-Matic: a tool to sanity check and if needed reformat FASTA files Jennifer Shelton Kansas State University August 11, 2015 Abstract As the shear volume of bioinformatic sequence data increases the only way to take advantage of this content is to more completely automate ro- bust analysis workflows. Analysis bottlenecks are often mundane and overlooked processing steps. Idiosyncrasies in reading and/or writing bioinformatics file formats can halt or impair analysis workflows by in- terfering with the transfer of data from one informatics tools to another. Fasta-O-Matic automates handling of common but minor format issues that otherwise may halt pipelines. The need for automation must be balanced by the need for manual confirmation that any formatting error is actually minor rather than indicative of a corrupt data file. To that end Fasta-O-Matic reports any issues detected to the user with optionally color coded and quiet or verbose logs. Fasta-O-Matic can be used as a general pre-processing tool in bioin- formatics workflows (e.g. to automatically wrap FASTA files so that they can be read by BioPerl). It was also developed as a sanity check for bioinformatic core facilities that tend to repeat common analysis steps on FASTA files received from disparate sources. -
The Uniprot Knowledgebase BLAST
Introduction to bioinformatics The UniProt Knowledgebase BLAST UniProtKB Basic Local Alignment Search Tool A CRITICAL GUIDE 1 Version: 1 August 2018 A Critical Guide to BLAST BLAST Overview This Critical Guide provides an overview of the BLAST similarity search tool, Briefly examining the underlying algorithm and its rise to popularity. Several WeB-based and stand-alone implementations are reviewed, and key features of typical search results are discussed. Teaching Goals & Learning Outcomes This Guide introduces concepts and theories emBodied in the sequence database search tool, BLAST, and examines features of search outputs important for understanding and interpreting BLAST results. On reading this Guide, you will Be aBle to: • search a variety of Web-based sequence databases with different query sequences, and alter search parameters; • explain a range of typical search parameters, and the likely impacts on search outputs of changing them; • analyse the information conveyed in search outputs and infer the significance of reported matches; • examine and investigate the annotations of reported matches, and their provenance; and • compare the outputs of different BLAST implementations and evaluate the implications of any differences. finding short words – k-tuples – common to the sequences Being 1 Introduction compared, and using heuristics to join those closest to each other, including the short mis-matched regions Between them. BLAST4 was the second major example of this type of algorithm, From the advent of the first molecular sequence repositories in and rapidly exceeded the popularity of FastA, owing to its efficiency the 1980s, tools for searching dataBases Became essential. DataBase searching is essentially a ‘pairwise alignment’ proBlem, in which the and Built-in statistics. -
Genbank Dennis A
Published online 28 November 2016 Nucleic Acids Research, 2017, Vol. 45, Database issue D37–D42 doi: 10.1093/nar/gkw1070 GenBank Dennis A. Benson, Mark Cavanaugh, Karen Clark, Ilene Karsch-Mizrachi, David J. Lipman, James Ostell and Eric W. Sayers* National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA Received September 15, 2016; Revised October 19, 2016; Editorial Decision October 24, 2016; Accepted November 07, 2016 ABSTRACT data from sequencing centers. The U.S. Patent and Trade- ® mark Office also contributes sequences from issued patents. GenBank (www.ncbi.nlm.nih.gov/genbank/)isa GenBank participates with the EMBL-EBI European Nu- comprehensive database that contains publicly avail- cleotide Archive (ENA) (2) and the DNA Data Bank of able nucleotide sequences for 370 000 formally de- Japan (DDBJ) (3) as a partner in the International Nu- Downloaded from scribed species. These sequences are obtained pri- cleotide Sequence Database Collaboration (INSDC) (4). marily through submissions from individual labora- The INSDC partners exchange data daily to ensure that tories and batch submissions from large-scale se- a uniform and comprehensive collection of sequence infor- quencing projects, including whole genome shotgun mation is available worldwide. NCBI makes GenBank data (WGS) and environmental sampling projects. Most available at no cost over the Internet, through FTP and a submissions are made using the web-based BankIt wide range of Web-based retrieval and analysis services (5). http://nar.oxfordjournals.org/ or the NCBI Submission Portal. GenBank staff assign accession numbers upon data receipt. -
Multiple Alignments, Blast and Clustalw
Multiple alignments, blast and clustalW 1. Blast idea: a. Filter out low complexity regions (tandem repeats… that sort of thing) [optional] b. Compile list of high-scoring strings (words, in BLAST jargon) of fixed length in query (threshold T) c. Extend alignments (highs scoring pairs) d. Report High Scoring pairs: score at least S (or an E value lower than some threshold) 2. Multiple Sequence Alignments: a. Attempts to extend dynamic programming techniques to multiple sequences run into problems after only a few proteins (8 average proteins were a problem early in 2000s) b. Heuristic approach c. Idea (Progressive Approach): i. homologous sequences are evolutionarily related ii. Build multiple alignment by series of pairwise alignments based off some phylogenetic tree (the initial tree or the guide tree ) iii. Add in more distantly related sequences d. Progressive Sequence alignment example: 1. NYLS & NKYLS: N YLS N(K|-)YLS NKYLS 2. NFS & NFLS: N YLS NF S NF(L|-)S NKYLS NFLS 3. N(K|-)YLS & NF(L|-)S N YLS N(K|-)(Y|F)(L|-)S NKYLS N YLS N F S N FLS e. Assessment: i. Works great for fairly similar sequences ii. Not so well for highly divergent ones f. Two Problems: i. local minimum problem: Algorithm greedily adds sequences based off of tree— might miss global solution ii. Alignment parameters: Mistakes (misaligned regions) early in procedure can’t be corrected later. g. ClustalW does multiple alignments and attempts to solve alignment parameter problem i. gap costs are dynamically varied based on position and amino acid ii. weight matrices are changed as the level of divergence between sequence increases (say going from PAM30 -> PAM60) iii. -
The FASTA Program Package Introduction
fasta-36.3.8 December 4, 2020 The FASTA program package Introduction This documentation describes the version 36 of the FASTA program package (see W. R. Pearson and D. J. Lipman (1988), “Improved Tools for Biological Sequence Analysis”, PNAS 85:2444-2448, [17] W. R. Pearson (1996) “Effective protein sequence comparison” Meth. Enzymol. 266:227- 258 [15]; and Pearson et. al. (1997) Genomics 46:24-36 [18]. Version 3 of the FASTA packages contains many programs for searching DNA and protein databases and for evaluating statistical significance from randomly shuffled sequences. This document is divided into four sections: (1) A summary overview of the programs in the FASTA3 package; (2) A guide to using the FASTA programs; (3) A guide to installing the programs and databases. Section (4) provides answers to some Frequently Asked Questions (FAQs). In addition to this document, the changes v36.html, changes v35.html and changes v34.html files list functional changes to the programs. The readme.v30..v36 files provide a more complete revision history of the programs, including bug fixes. The programs are easy to use; if you are using them on a machine that is administered by someone else, you can focus on sections (1) and (2) to learn how to use the programs. If you are installing the programs on your own machine, you will need to read section (3) carefully. FASTA and BLAST – FASTA and BLAST have the same goal: to identify statistically signifi- cant sequence similarity that can be used to infer homology. The FASTA programs offer several advantages over BLAST: 1.