User Manual for MEGAN V6.12.3
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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. -
File Formats Exercises
Introduction to high-throughput sequencing file formats – advanced exercises Daniel Vodák (Bioinformatics Core Facility, The Norwegian Radium Hospital) ( [email protected] ) All example files are located in directory “/data/file_formats/data”. You can set your current directory to that path (“cd /data/file_formats/data”) Exercises: a) FASTA format File “Uniprot_Mammalia.fasta” contains a collection of mammalian protein sequences obtained from the UniProt database ( http://www.uniprot.org/uniprot ). 1) Count the number of lines in the file. 2) Count the number of header lines (i.e. the number of sequences) in the file. 3) Count the number of header lines with “Homo” in the organism/species name. 4) Count the number of header lines with “Homo sapiens” in the organism/species name. 5) Display the header lines which have “Homo” in the organism/species names, but only such that do not have “Homo sapiens” there. b) FASTQ format File NKTCL_31T_1M_R1.fq contains a reduced collection of tumor read sequences obtained from The Sequence Read Archive ( http://www.ncbi.nlm.nih.gov/sra ). 1) Count the number of lines in the file. 2) Count the number of lines beginning with the “at” symbol (“@”). 3) How many reads are there in the file? c) SAM format File NKTCL_31T_1M.sam contains alignments of pair-end reads stored in files NKTCL_31T_1M_R1.fq and NKTCL_31T_1M_R2.fq. 1) Which program was used for the alignment? 2) How many header lines are there in the file? 3) How many non-header lines are there in the file? 4) What do the non-header lines represent? 5) Reads from how many template sequences have been used in the alignment process? c) BED format File NKTCL_31T_1M.bed holds information about alignment locations stored in file NKTCL_31T_1M.sam. -
EMBL-EBI Powerpoint Presentation
Processing data from high-throughput sequencing experiments Simon Anders Use-cases for HTS · de-novo sequencing and assembly of small genomes · transcriptome analysis (RNA-Seq, sRNA-Seq, ...) • identifying transcripted regions • expression profiling · Resequencing to find genetic polymorphisms: • SNPs, micro-indels • CNVs · ChIP-Seq, nucleosome positions, etc. · DNA methylation studies (after bisulfite treatment) · environmental sampling (metagenomics) · reading bar codes Use cases for HTS: Bioinformatics challenges Established procedures may not be suitable. New algorithms are required for · assembly · alignment · statistical tests (counting statistics) · visualization · segmentation · ... Where does Bioconductor come in? Several steps: · Processing of the images and determining of the read sequencest • typically done by core facility with software from the manufacturer of the sequencing machine · Aligning the reads to a reference genome (or assembling the reads into a new genome) • Done with community-developed stand-alone tools. · Downstream statistical analyis. • Write your own scripts with the help of Bioconductor infrastructure. Solexa standard workflow SolexaPipeline · "Firecrest": Identifying clusters ⇨ typically 15..20 mio good clusters per lane · "Bustard": Base calling ⇨ sequence for each cluster, with Phred-like scores · "Eland": Aligning to reference Firecrest output Large tab-separated text files with one row per identified cluster, specifying · lane index and tile index · x and y coordinates of cluster on tile · for each -
A SARS-Cov-2 Sequence Submission Tool for the European Nucleotide
Databases and ontologies Downloaded from https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab421/6294398 by guest on 25 June 2021 A SARS-CoV-2 sequence submission tool for the European Nucleotide Archive Miguel Roncoroni 1,2,∗, Bert Droesbeke 1,2, Ignacio Eguinoa 1,2, Kim De Ruyck 1,2, Flora D’Anna 1,2, Dilmurat Yusuf 3, Björn Grüning 3, Rolf Backofen 3 and Frederik Coppens 1,2 1Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium, 1VIB Center for Plant Systems Biology, 9052 Ghent, Belgium and 2University of Freiburg, Department of Computer Science, Freiburg im Breisgau, Baden-Württemberg, Germany ∗To whom correspondence should be addressed. Associate Editor: XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX Abstract Summary: Many aspects of the global response to the COVID-19 pandemic are enabled by the fast and open publication of SARS-CoV-2 genetic sequence data. The European Nucleotide Archive (ENA) is the European recommended open repository for genetic sequences. In this work, we present a tool for submitting raw sequencing reads of SARS-CoV-2 to ENA. The tool features a single-step submission process, a graphical user interface, tabular-formatted metadata and the possibility to remove human reads prior to submission. A Galaxy wrap of the tool allows users with little or no bioinformatic knowledge to do bulk sequencing read submissions. The tool is also packed in a Docker container to ease deployment. Availability: CLI ENA upload tool is available at github.com/usegalaxy- eu/ena-upload-cli (DOI 10.5281/zenodo.4537621); Galaxy ENA upload tool at toolshed.g2.bx.psu.edu/view/iuc/ena_upload/382518f24d6d and https://github.com/galaxyproject/tools- iuc/tree/master/tools/ena_upload (development) and; ENA upload Galaxy container at github.com/ELIXIR- Belgium/ena-upload-container (DOI 10.5281/zenodo.4730785) Contact: [email protected] 1 Introduction Nucleotide Archive (ENA). -
Sequence Alignment/Map Format Specification
Sequence Alignment/Map Format Specification The SAM/BAM Format Specification Working Group 3 Jun 2021 The master version of this document can be found at https://github.com/samtools/hts-specs. This printing is version 53752fa from that repository, last modified on the date shown above. 1 The SAM Format Specification SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. If present, the header must be prior to the alignments. Header lines start with `@', while alignment lines do not. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. This specification is for version 1.6 of the SAM and BAM formats. Each SAM and BAMfilemay optionally specify the version being used via the @HD VN tag. For full version history see Appendix B. Unless explicitly specified elsewhere, all fields are encoded using 7-bit US-ASCII 1 in using the POSIX / C locale. Regular expressions listed use the POSIX / IEEE Std 1003.1 extended syntax. 1.1 An example Suppose we have the following alignment with bases in lowercase clipped from the alignment. Read r001/1 and r001/2 constitute a read pair; r003 is a chimeric read; r004 represents a split alignment. Coor 12345678901234 5678901234567890123456789012345 ref AGCATGTTAGATAA**GATAGCTGTGCTAGTAGGCAGTCAGCGCCAT +r001/1 TTAGATAAAGGATA*CTG +r002 aaaAGATAA*GGATA +r003 gcctaAGCTAA +r004 ATAGCT..............TCAGC -r003 ttagctTAGGC -r001/2 CAGCGGCAT The corresponding SAM format is:2 1Charset ANSI X3.4-1968 as defined in RFC1345. -
Six-Fold Speed-Up of Smith-Waterman Sequence Database Searches Using Parallel Processing on Common Microprocessors
Six-fold speed-up of Smith-Waterman sequence database searches using parallel processing on common microprocessors Running head: Six-fold speed-up of Smith-Waterman searches Torbjørn Rognes* and Erling Seeberg Institute of Medical Microbiology, University of Oslo, The National Hospital, NO-0027 Oslo, Norway Abstract Motivation: Sequence database searching is among the most important and challenging tasks in bioinformatics. The ultimate choice of sequence search algorithm is that of Smith- Waterman. However, because of the computationally demanding nature of this method, heuristic programs or special-purpose hardware alternatives have been developed. Increased speed has been obtained at the cost of reduced sensitivity or very expensive hardware. Results: A fast implementation of the Smith-Waterman sequence alignment algorithm using SIMD (Single-Instruction, Multiple-Data) technology is presented. This implementation is based on the MMX (MultiMedia eXtensions) and SSE (Streaming SIMD Extensions) technology that is embedded in Intel’s latest microprocessors. Similar technology exists also in other modern microprocessors. Six-fold speed-up relative to the fastest previously known Smith-Waterman implementation on the same hardware was achieved by an optimised 8-way parallel processing approach. A speed of more than 150 million cell updates per second was obtained on a single Intel Pentium III 500MHz microprocessor. This is probably the fastest implementation of this algorithm on a single general-purpose microprocessor described to date. Availability: Online searches with the software are available at http://dna.uio.no/search/ Contact: [email protected] Published in Bioinformatics (2000) 16 (8), 699-706. Copyright © (2000) Oxford University Press. *) To whom correspondence should be addressed. -
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. -
Bioinformatics Courses Lecture 3: (Local) Alignment and Homology
C E N Bioinformatics courses T R E Principles of Bioinformatics (BSc) & F B O I Fundamentals of Bioinformatics R O I I N N (MSc) T F E O G R R M A A T T I I Lecture 3: (local) alignment and V C E S homology searching V U Centre for Integrative Bioinformatics VU (IBIVU) Faculty of Exact Sciences / Faculty of Earth and Life Sciences 1 http://ibi.vu.nl, [email protected], 87649 (Heringa), Room P1.28 Divergent evolution sequence -> structure -> function • Common ancestor (CA) CA • Sequences change over time • Protein structures typically remain the same (robust against Sequence 1 ≠ Sequence 2 multiple mutations) • Therefore, function normally is Structure 1 = Structure 2 preserved within orthologous families Function 1 = Function 2 “Structure more conserved than sequence” 2 Reconstructing divergent evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø) mutation deletion ACCD or ACCD Pairwise Alignment AB─D A─BD 3 Reconstructing divergent evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø) mutation deletion ACCD or ACCD Pairwise Alignment AB─D A─BD true alignment 4 Pairwise alignment examples A protein sequence alignment MSTGAVLIY--TSILIKECHAMPAGNE----- ---GGILLFHRTHELIKESHAMANDEGGSNNS * * * **** *** A DNA sequence alignment attcgttggcaaatcgcccctatccggccttaa att---tggcggatcg-cctctacgggcc---- *** **** **** ** ****** 5 Evolution and three-dimensional protein structure information Multiple alignment Protein structure What do we see if we colour code the space-filling (CPK) protein model? • E.g., red for conserved alignment positions to blue for variable (unconserved) positions. 6 Evolution and three-dimensional protein structure information Isocitrate dehydrogenase: The distance from the active site (in yellow) determines the rate of evolution (red = fast evolution, blue = slow evolution) Dean, A. -
NGS Raw Data Analysis
NGS raw data analysis Introduc3on to Next-Generaon Sequencing (NGS)-data Analysis Laura Riva & Maa Pelizzola Outline 1. Descrip7on of sequencing plaorms 2. Alignments, QC, file formats, data processing, and tools of general use 3. Prac7cal lesson Illumina Sequencing Single base extension with incorporaon of fluorescently labeled nucleodes Library preparaon Automated cluster genera3on DNA fragmenta3on and adapter ligaon Aachment to the flow cell Cluster generaon by bridge amplifica3on of DNA fragments Sequencing Illumina Output Quality Scores Sequence Files Comparison of some exisng plaGorms 454 Ti RocheT Illumina HiSeqTM ABI 5500 (SOLiD) 2000 Amplificaon Emulsion PCR Bridge PCR Emulsion PCR Sequencing Pyrosequencing Reversible Ligaon-based reac7on terminators sequencing Paired ends/sep Yes/3kb Yes/200 bp Yes/3 kb Read length 400 bp 100 bp 75 bp Advantages Short run 7mes. The most popular Good base call Longer reads plaorm accuracy. Good improve mapping in mulplexing repe7ve regions. capability Ability to detect large structural variaons Disadvantages High reagent cost. Higher error rates in repeat sequences The Illumina HiSeq! Stacey Gabriel Libraries, lanes, and flowcells Flowcell Lanes Illumina Each reaction produces a unique Each NGS machine processes a library of DNA fragments for single flowcell containing several sequencing. independent lanes during a single sequencing run Applications of Next-Generation Sequencing Basic data analysis concepts: • Raw data analysis= image processing and base calling • Understanding Fastq • Understanding Quality scores • Quality control and read cleaning • Alignment to the reference genome • Understanding SAM/BAM formats • Samtools • Bedtools Understanding FASTQ file format FASTQ format is a text-based format for storing a biological sequence and its corresponding quality scores. -
Large Scale Genomic Rearrangements in Selected
Pucker et al. BMC Genomics (2021) 22:599 https://doi.org/10.1186/s12864-021-07877-8 RESEARCH ARTICLE Open Access Large scale genomic rearrangements in selected Arabidopsis thaliana T-DNA lines are caused by T-DNA insertion mutagenesis Boas Pucker1,2† , Nils Kleinbölting3† and Bernd Weisshaar1* Abstract Background: Experimental proof of gene function assignments in plants is based on mutant analyses. T-DNA insertion lines provided an invaluable resource of mutants and enabled systematic reverse genetics-based investigation of the functions of Arabidopsis thaliana genes during the last decades. Results: We sequenced the genomes of 14 A. thaliana GABI-Kat T-DNA insertion lines, which eluded flanking sequence tag-based attempts to characterize their insertion loci, with Oxford Nanopore Technologies (ONT) long reads. Complex T-DNA insertions were resolved and 11 previously unknown T-DNA loci identified, resulting in about 2 T-DNA insertions per line and suggesting that this number was previously underestimated. T-DNA mutagenesis caused fusions of chromosomes along with compensating translocations to keep the gene set complete throughout meiosis. Also, an inverted duplication of 800 kbp was detected. About 10 % of GABI-Kat lines might be affected by chromosomal rearrangements, some of which do not involve T-DNA. Local assembly of selected reads was shown to be a computationally effective method to resolve the structure of T-DNA insertion loci. We developed an automated workflow to support investigation of long read data from T-DNA insertion lines. All steps from DNA extraction to assembly of T-DNA loci can be completed within days. Conclusions: Long read sequencing was demonstrated to be an effective way to resolve complex T-DNA insertions and chromosome fusions. -
L3: Short Read Alignment to a Reference Genome
L3: Short Read Alignment to a Reference Genome Shamith Samarajiwa CRUK Autumn School in Bioinformatics Cambridge, September 2017 Where to get help! http://seqanswers.com http://www.biostars.org http://www.bioconductor.org/help/mailing-list Read the posting guide before sending email! Overview ● Understand the difference between reference genome builds ● Introduction to Illumina sequencing ● Short read aligners ○ BWA ○ Bowtie ○ STAR ○ Other aligners ● Coverage and Depth ● Mappability ● Use of decoy and sponge databases ● Alignment Quality, SAMStat, Qualimap ● Samtools and Picard tools, ● Visualization of alignment data ● A very brief look at long reads, graph genome aligners and de novo genome assembly Reference Genomes ● A haploid representation of a species genome. ● The human genome is a haploid mosaic derived from 13 volunteer donors from Buffalo, NY. USA. ● In regions where there is known large scale population variation, sets of alternate loci (178 in GRCh38) are assembled alongside the reference locus. ● The current build has around 500 gaps, whereas the first version had ~150,000 gaps. Genome Reference Consortium: https://www.ncbi.nlm.nih.gov/grc £600 30X Sequencers http://omicsmap.com Illumina sequencers Illumina Genome Analyzer Illumina sequencing technology ● Illumina sequencing is based on the Solexa technology developed by Shankar Balasubramanian and David Klenerman (1998) at the University of Cambridge. ● Multiple steps in “Sequencing by synthesis” (explained in next slide) ○ Library Preparation ○ Bridge amplification and Cluster generation ○ Sequencing using reversible terminators ○ Image acquisition and Fastq generation ○ Alignment and data analysis Illumina Flowcell Sequencing By Synthesis technology Illumina Sequencing Bridge Amplification Sequencing Cluster growth Incorporation of fluorescence, reversibly terminated tagged nt Multiplexing • Multiplexing gives the ability to sequence multiple samples at the same time. -
Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by Pickeringhutchins Ltd
European Bioinformatics Institute EMBL-EBI Annual Scientific Report 2011 Annual Scientific Report 2011 Designed and Produced by PickeringHutchins Ltd www.pickeringhutchins.com EMBL member states: Austria, Croatia, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom. Associate member state: Australia EMBL-EBI is a part of the European Molecular Biology Laboratory (EMBL) EMBL-EBI EMBL-EBI EMBL-EBI EMBL-European Bioinformatics Institute Wellcome Trust Genome Campus, Hinxton Cambridge CB10 1SD United Kingdom Tel. +44 (0)1223 494 444, Fax +44 (0)1223 494 468 www.ebi.ac.uk EMBL Heidelberg Meyerhofstraße 1 69117 Heidelberg Germany Tel. +49 (0)6221 3870, Fax +49 (0)6221 387 8306 www.embl.org [email protected] EMBL Grenoble 6, rue Jules Horowitz, BP181 38042 Grenoble, Cedex 9 France Tel. +33 (0)476 20 7269, Fax +33 (0)476 20 2199 EMBL Hamburg c/o DESY Notkestraße 85 22603 Hamburg Germany Tel. +49 (0)4089 902 110, Fax +49 (0)4089 902 149 EMBL Monterotondo Adriano Buzzati-Traverso Campus Via Ramarini, 32 00015 Monterotondo (Rome) Italy Tel. +39 (0)6900 91402, Fax +39 (0)6900 91406 © 2012 EMBL-European Bioinformatics Institute All texts written by EBI-EMBL Group and Team Leaders. This publication was produced by the EBI’s Outreach and Training Programme. Contents Introduction Foreword 2 Major Achievements 2011 4 Services Rolf Apweiler and Ewan Birney: Protein and nucleotide data 10 Guy Cochrane: The European Nucleotide Archive 14 Paul Flicek: