Assembling Metagenomes, One Community at a Time
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De Bruijn Graph Based De Novo Genome Assembly
2160 JOURNAL OF SOFTWARE, VOL. 9, NO. 8, AUGUST 2014 De Bruijn Graph based De novo Genome Assembly Mohammad Ibrahim Khan Computer Science and Engineering, Chittagong University of Engineering and Technology Chittagong, Bangladesh [email protected] Md. Sarwar Kamal Computer Science and Engineering, Chittagong University of Engineering and Technology Chittagong, Bangladesh [email protected] Abstract—The Next Generation Sequencing (NGS) is an and microorganism. Now –a-days, numbers of unique important process which assures inexpensive organization idea and vocational attempt have been established on the of vast size of raw sequence data set over any traditional road to the evolution of new techniques to overcome the sequencing systems or methods. Various aspects of NGS like problems of Sanger. The continuing innovation of DNA, template preparation, sequencing imaging and genome RNA and Proteins sequencing techniques have alignment and assembly outlines the genome sequencing and accompanied the development of sequencing system alignment .Consequently, deBruijn Graph (dBG) is an important mathematical tool that graphically analyzes how under the environment of sudden reduced expenditures the orientations are constructed in groups of nucleotides. and better outcomes in performance comparing with the Basically, de Bruijn graph describe the formation of the systems of tow three years back. Various sequencers genome segments in a circular iterative fashions. Some from some established methods like 454 Life Science or pivotal de Bruijn graph based de novo algorithms and Roche or Illumina and Applied Biosystems are in software package like T-IDBA, Oases, IDBA-tran, Euler, developments and Helicos have appeared in application. Velvet, ABySS, AllPaths, SOAPdenovo and SOAPdenovo2 All these sequencers will produce more and more data have illustrated here. -
Methods for De-Novo Genome Assembly
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2020 doi:10.20944/preprints202006.0324.v1 Methods for De-novo Genome Assembly Arash Bayat∗1,3, Hasindu Gamaarachchi1, Nandan P Deshpande2, Marc R Wilkins2, and Sri Parameswaran1 1School of Computer Science and Engineering, UNSW, Australia 2Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, UNSW, Australia 3Health and Biosecurity, CSIRO, Australia June 25, 2020 Abstract Despite advances in algorithms and computational platforms, de-novo genome assembly remains a chal- lenging process. Due to the constant innovation in sequencing technologies (Sanger, SOLiD, Illumina, 454 , PacBio and Oxford Nanopore), genome assembly has evolved to respond to the changes in input data type. This paper includes a broad and comparative review of the most recent short-read, long-read and hybrid assembly techniques. In this review, we provide (1) an algorithmic description of the important processes in the workflow that introduces fundamental concepts and improvements; (2) a review of existing software that explains possible options for genome assembly; and (3) a comparison of the accuracy and the performance of existing methods executed on the same computer using the same processing capabilities and using the same set of real and synthetic datasets. Such evaluation allows a fair and precise comparison of accuracy in all aspects. As a result, this paper identifies both the strengths and weaknesses of each method. This com- parative review is unique in providing a detailed comparison of a broad spectrum of cutting-edge algorithms and methods. Availability: https://arashbayat.github.io/asm ∗To whom correspondence should be addressed. Email: [email protected] 1 © 2020 by the author(s). -
Sequence Assembly Demystified
REVIEWS COMPUTATIONAL TOOLS Sequence assembly demystified Niranjan Nagarajan1 and Mihai Pop2 Abstract | Advances in sequencing technologies and increased access to sequencing services have led to renewed interest in sequence and genome assembly. Concurrently, new applications for sequencing have emerged, including gene expression analysis, discovery of genomic variants and metagenomics, and each of these has different needs and challenges in terms of assembly. We survey the theoretical foundations that underlie modern assembly and highlight the options and practical trade-offs that need to be considered, focusing on how individual features address the needs of specific applications. We also review key software and the interplay between experimental design and efficacy of assembly. Paired-end data Previously the province of large genome centres, DNA between DNA sequences is then used to ‘stitch’ together Data from a pair of reads sequencing is now a key component of research carried the individual fragments into larger contiguous sequenced from ends of out in many laboratories. A number of new applica- sequences (contigs), thereby recovering the informa- the same DNA fragment. The tions of sequencing technologies have been spurred tion lost during the sequencing process. The assembly genomic distance between the reads is approximately by rapidly decreasing costs, including the study of process is complicated by the fact that, in many cases, known and is used to constrain microbial communities (metagenomics), the discovery this underlying assumption is incorrect. For example, assembly solutions. See also of structural variants in genomes and the analysis of genomic repeats — segments of DNA repeated in an ‘mate-pair read’. gene structure and expression. -
What Can the Miseq & NGS Core Do for Your Research?
What Can the MiSeq & NGS Core Do for Your Research? Grant Hill Library Prep Specialist [email protected] © 2013 Illumina, Inc. All rights reserved. Illumina, IlluminaDx, BaseSpace, BeadArray, BeadXpress, cBot, CSPro, DASL, DesignStudio, Eco, GAIIx, Genetic Energy, Genome Analyzer, GenomeStudio, GoldenGate, HiScan, HiSeq, Infinium, iSelect, MiSeq, Nextera, NuPCR, SeqMonitor, Solexa, TruSeq, TruSight, VeraCode, the pumpkin orange color, and the Genetic Energy streaming bases design are trademarks or registered trademarks of Illumina, Inc. All other brands and names contained herein are the property of their respective owners. Agenda ! Into/basic principles of NGS (terms & technology) ! Small genome sequencing ! Targeted resequencing (custom panels) ! 16s metagenomics & amplicon sequencing ! Small RNA & targeted rna expression ! Experimental design & local resources 2 Enhanced Focus on the Sample to Answer Integration From library prep to downstream informatics & knowledge generation Library Prep Sequence Answer 3 For Research Use Only. Not for use in diagnostic procedures. The Flow Cell Where the magic happens ! Everything except sample preparation is completed on the flow cell •" Template annealing (1 - 384 samples) •" Template amplification •" Sequencing primer hybridization •" Sequencing-by-synthesis reaction •" Generation of fluorescent signal MiSeq NextSeq HiSeq 4 Flow Cell Surface 8 channels Simplified workflow Surface of flow cell ! Clusters in a coated with a lawn of oligo pairs contained environment (no need for clean rooms) ! -
DNA Sequencing
Contig Assembly ATCGATGCGTAGCAGACTACCGTTACGATGCCTT… TAGCTACGCATCGTCTGATGGCAATGCTACGGAA.. C T AG AGCAGA TAGCTACGCATCGT GT CTACCG GC TT AT CG GTTACGATGCCTT AT David Wishart, Ath 3-41 [email protected] DNA Sequencing 1 Principles of DNA Sequencing Primer DNA fragment Amp PBR322 Tet Ori Denature with Klenow + ddNTP heat to produce + dNTP + primers ssDNA The Secret to Sanger Sequencing 2 Principles of DNA Sequencing 5’ G C A T G C 3’ Template 5’ Primer dATP dATP dATP dATP dCTP dCTP dCTP dCTP dGTP dGTP dGTP dGTP dTTP dTTP dTTP dTTP ddCTP ddATP ddTTP ddGTP GddC GCddA GCAddT ddG GCATGddC GCATddG Principles of DNA Sequencing G T _ _ short C A G C A T G C + + long 3 Capillary Electrophoresis Separation by Electro-osmotic Flow Multiplexed CE with Fluorescent detection ABI 3700 96x700 bases 4 High Throughput DNA Sequencing Large Scale Sequencing • Goal is to determine the nucleic acid sequence of molecules ranging in size from a few hundred bp to >109 bp • The methodology requires an extensive computational analysis of raw data to yield the final sequence result 5 Shotgun Sequencing • High throughput sequencing method that employs automated sequencing of random DNA fragments • Automated DNA sequencing yields sequences of 500 to 1000 bp in length • To determine longer sequences you obtain fragmentary sequences and then join them together by overlapping • Overlapping is an alignment problem, but different from those we have discussed up to now Shotgun Sequencing Isolate ShearDNA Clone into Chromosome into Fragments Seq. Vectors Sequence 6 Shotgun Sequencing Sequence Send to Computer Assembled Chromatogram Sequence Analogy • You have 10 copies of a movie • The film has been cut into short pieces with about 240 frames per piece (10 seconds of film), at random • Reconstruct the film 7 Multi-alignment & Contig Assembly ATCGATGCGTAGCAGACTACCGTTACGATGCCTT… TAGCTACGCATCGTCTGATGGCAATGCTACGGAA. -
A Galaxy-Based Virus Genome Assembly Pipeline A
The Pennsylvania State University The Graduate School VIRAMP: A GALAXY-BASED VIRUS GENOME ASSEMBLY PIPELINE A Thesis in Integrative Biosciences by Yinan Wan © 2014 Yinan Wan Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science December 2014 The thesis of Yinan Wan was reviewed and approved* by the following: Istvan Albert Associate Professor, Bioinformatics, Biochemistry and Molecular Biology Thesis Advisor Moriah L. Szpara Assistant Professor of Biochemistry & Molecular Biology Cooduvalli S. Shashikant Associate Professor of Molecular and Development Biology Co-Director, IBIOS Graduate Program Option in Bioinformatics and Genomics Peter Hudson Willaman Professor of Biology Director, Huck Institutes of the Life Sciences *Signatures are on file in the Graduate School iii ABSTRACT Background Advances in next generation sequencing make it possible to obtain high-coverage sequence data for large numbers of viral strains in a short time. However, since most bioinformatics tools are developed for command line use, the selection and accessibility of computational tools for genome assembly and variation analysis limits the ability of individual scientist to perform further bioinformatics analysis. Findings We have developed a multi-step viral genome assembly pipeline named VirAmp that combines existing tools and techniques and presents them to end users via a web-enabled Galaxy interface. Our pipeline allows users to assemble, analyze and interpret high coverage viral sequencing data with an ease and efficiency that previously was not feasible. Our software makes a large number of genome assembly and related tools available to life scientists and automates the currently recommended best practices into a single, easy to use interface. -
Computational Methods for De Novo Assembly of Next-Generation Genome Sequencing Data Rayan Chikhi
Computational methods for de novo assembly of next-generation genome sequencing data Rayan Chikhi To cite this version: Rayan Chikhi. Computational methods for de novo assembly of next-generation genome sequencing data. Other [cs.OH]. École normale supérieure de Cachan - ENS Cachan, 2012. English. NNT : 2012DENS0033. tel-00752033 HAL Id: tel-00752033 https://tel.archives-ouvertes.fr/tel-00752033 Submitted on 14 Nov 2012 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 / ENS CACHAN - BRETAGNE présentée par sous le sceau de l’Université européenne de Bretagne Rayan Chikhi pour obtenir le titre de Préparée à l’Unité Mixte de Recherche 6074 DOCTEUR DE L’école norMALE suPÉRIEURE de CACHAN Institut de recherche en informatique Mention : Informatique École doctorale MATISSE et systèmes aléatoires Thèse soutenue le 2 juillet 2012 Computational Methods devant le jury composé de : Éric RIVALS, for de novo Assembly of DR, LIRMM / rapporteur Sante GNERRE, Next-Generation Genome Group leader, Broad Institute of MIT and Harvard / rapporteur Marie-France SAGOT, -
Metagenomic Assembly Using Coverage and Evolutionary Distance (MACE)
Metagenomic Assembly using Coverage and Evolutionary distance (MACE) Introduction Recent advances in accuracy and cost of whole-genome shotgun sequencing have opened up various fields of study for biologists. One such field is that of metagenomics – the study of the bacterial biome in a given sample. The human body, for example, has approximately 10 bacterial cells for every native cell[1], and lack of certain strains may contribute to prevalence of diseases such as obesity, Crohn’s disease, and type 2 diabetes[2,3,4]. Although the field of metagenomics is very promising, as with any new field, there are many difficulties with such analyses. Arguably, the greatest obstacle to sequencing a sample’s biome is the existence of a large number of strains of bacteria, many of which do not have an assembled genome, and an unknown prevalence of each strain. Assembly is further complicated by inherent inaccuracy in reading DNA fragments, which makes it difficult to distinguish between noise and true variants either within a strain or between strains. Additionally, in a given biotic sample, there are bound to be many clusters of closely related strains. When related bacteria coexist in a sample, any conserved regions can easily cause misassembly of the genomes. For example, two genomic sequences with a single region of homology (of length greater than the read length) will be easily distinguished from each other both before and after the homologous region. However, if given only short reads from the two genomes, it becomes impossible just based on the sequences to infer which sequence before the homologous region belongs to which sequence after the homologous region (Figure 2). -
Whole-Genome Sequencing of Bacterial Pathogens
REVIEW crossm Whole-Genome Sequencing of Bacterial Downloaded from Pathogens: the Future of Nosocomial Outbreak Analysis Scott Quainoo,a Jordy P. M. Coolen,b Sacha A. F. T. van Hijum,c,d Martijn A. Huynen,c Willem J. G. Melchers,b Willem van Schaik,e http://cmr.asm.org/ Heiman F. L. Wertheimb Department of Microbiology, Radboud University, Nijmegen, The Netherlandsa; Department of Medical Microbiology, Radboud University Medical Centre, Nijmegen, The Netherlandsb; Centre for Molecular and Biomolecular Informatics, Radboud University Medical Centre, Nijmegen, The Netherlandsc; NIZO, Ede, The Netherlandsd; Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdome SUMMARY ...................................................................................1016 Published 30 August 2017 on August 31, 2017 by TECH KNOWLEDGE CTR OF DENMARK INTRODUCTION .............................................................................1017 Citation Quainoo S, Coolen JPM, van Hijum OUTBREAK DEFINITION ....................................................................1018 SAFT, Huynen MA, Melchers WJG, van Schaik CONVENTIONAL MOLECULAR CHARACTERIZATION METHODS ......................1018 W, Wertheim HFL. 2017. Whole-genome Non-Amplification-Based Typing Technologies ..........................................1018 sequencing of bacterial pathogens: the future Restriction fragment length polymorphism methods ................................1018 of nosocomial outbreak analysis. Clin Microbiol Matrix-assisted laser desorption -
De Novo Transcriptome Assembly of Cucurbita Pepo L. Leaf Tissue Infested by Aphis Gossypii
data Data Descriptor De Novo Transcriptome Assembly of Cucurbita Pepo L. Leaf Tissue Infested by Aphis Gossypii Alessia Vitiello 1, Rosa Rao 1 , Giandomenico Corrado 1, Pasquale Chiaiese 1, Maria Cristina Digilio 1, Riccardo Aiese Cigliano 2 and Nunzio D’Agostino 3,* 1 Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Portici, Italy; [email protected] (A.V.); [email protected] (R.R.); [email protected] (G.C.); [email protected] (P.C.); [email protected] (M.C.D.) 2 Sequentia Biotech SL, Campus UAB, Av. de Can Domènech s/n, 08193 Bellaterra, Barcelona, Spain; [email protected] 3 CREA Research Centre for Vegetable and Ornamental Crops, via dei Cavalleggeri 25, 84098 Pontecagnano Faiano, Italy * Correspondence: [email protected]; Tel.: +39-89-386243 Received: 24 July 2018; Accepted: 14 September 2018; Published: 16 September 2018 Abstract: Zucchini (Cucurbita pepo L.), extensively cultivated in temperate areas, belongs to the Cucurbitaceae family and it is a species with great economic value. One major threat related to zucchini cultivation is the damage imposed by the cotton/melon aphid Aphis gossypii Glover (Homoptera: Aphididae). We performed RNA-sequencing on cultivar “San Pasquale” leaves, uninfested and infested by A. gossypii, that were collected at three time points (24, 48, and 96 h post infestation). Then, we combined all high-quality reads for de novo assembly of the transcriptome. This resource was primarily established to be used as a reference for gene expression studies in order to investigate the transcriptome reprogramming of zucchini plants following aphid infestation. In addition, raw reads will be valuable for new experiments based on the latest bioinformatic tools and analytical approaches. -
Culturing Ancient Bacteria Carrying Resistance Genes from Permafrost and Comparative Genomics with Modern Isolates
microorganisms Article Culturing Ancient Bacteria Carrying Resistance Genes from Permafrost and Comparative Genomics with Modern Isolates Pamela Afouda 1,2, Grégory Dubourg 1,2, Anthony Levasseur 1,2, Pierre-Edouard Fournier 2,3 , Jeremy Delerce 1, Oleg Mediannikov 1 , Seydina M. Diene 1,2 , Daniel Nahon 4, Didier Bourlès 4, Jean-Marc Rolain 1,2 and Didier Raoult 1,2,* 1 Aix Marseille Université, IRD, AP-HM, MEPHI, 13005 Marseille, France; [email protected] (P.A.); [email protected] (G.D.); [email protected] (A.L.); [email protected] (J.D.); [email protected] (O.M.); [email protected] (S.M.D.); [email protected] (J.-M.R.) 2 IHU Méditerranée Infection, 13005 Marseille, France; [email protected] 3 UMR VITROME, SSA, Aix-Marseille Université, IRD, AP-HM, IHU-Méditerranée-Infection, 13005 Marseille, France 4 Aix-Marseille University, CNRS, IRD, INRAE, Coll France, UM 34 CEREGE, Technopôle de l’Environnement Arbois-Méditerranée, BP80, 13545 Aix-en-Provence, France; [email protected] (D.N.); [email protected] (D.B.) * Correspondence: [email protected]; Tel.: +33-413-732-401; Fax: +33-413-732-402 Received: 29 March 2020; Accepted: 1 October 2020; Published: 3 October 2020 Abstract: Long considered to be a consequence of human antibiotics use by deduction, antibiotic resistance mechanisms appear to be in fact a much older phenomenon as antibiotic resistance genes have previously been detected from millions of year-old permafrost samples. As these specimens guarantee the viability of archaic bacteria, we herein propose to apply the culturomics approach to recover the bacterial content of a Siberian permafrost sample dated, using the in situ-produced cosmogenic nuclide chlorine36 (36Cl), at 2.7 million years to study the dynamics of bacterial evolution in an evolutionary perspective. -
A New Versatile Metagenomic Assembler (Supplementary Material)
metaSPAdes: a new versatile metagenomic assembler (Supplementary Material) Sergey Nurk1,*,**, Dmitry Meleshko1,*, Anton Korobeynikov1,2 and Pavel A Pevzner1,3 1Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia 2Department of Statistical Modelling, St. Petersburg State University, St. Petersburg, Russia 3Department of Computer Science and Engineering, University of California, San Diego, USA *These authors contributed equally to this work **corresponding author, [email protected] Data preprocessing 2 Modifying the decision rule in exSPAnder for metagenomic data 3 Reducing running time and memory footprint of metaSPAdes 4 Bulge projection approach 6 Nx statistics 7 Analysis of the SYNTH dataset 8 CAMI datasets 10 Analysis of the CAMI datasets 14 BenchmarKing SPAdes against metaSPAdes 17 Effect of novel algorithmic approaches in metaSPAdes on assembly quality 21 Analysis of the HMP dataset 25 References 27 1 Supplemental Material: Data preprocessing The SYNTH, HMP, MARINE, and SOIL datasets were pre-processed to remove adaptors and trim low- quality segments of the reads. We used cutadapt software v 1.9.1 (Martin 2011), trimming bases with PHRED quality < 20 from 3’ end (parameter -q 20). Adaptor sequences were identified for each dataset individually, using FastQC v0.11.3 and manual reads inspection: SYNTH: -q 20 -a GAACTCCAGTCACTGACCAATCTCGTATGCCGTCTTCTGCTTG -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTATCATT HMP: -q 20 -a AGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGACCGATCTCGTATGCCGTCTTCTGCTTG