DOCSLIB.ORG

What Can the Miseq & NGS Core Do for Your Research?

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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) ! Sequencing performed in the flow cell on the clusters 5 Sequencing by synthesis 3’ 5’ DNA (0.1-1.0 ug) A G T C A G C T T A C C G G A T A A C T C C C G G A T T C Sample G A preparation SingleCluster molecule growth array T 5’ Sequencing 1 2 3 4 5 6 7 8 9 T G C T A C G A T … Image acquisition Base calling 6 Illumina Paired End Sequencing Overview of automated process on the flowcell Cluster amplification 1st Read 1 cut FLOWCELL Normal sequencing FLOWCELL process Linearize DNA Sequence 1st strand Because we amplify on the flowcell surface, we can resynthesize the DNA in the cluster, and regenerate both FLOWCELL strands again Strand re-synthesis 2nd Read 2 cut FLOWCELL FLOWCELL Linearize Sequence 2nd strand 7 de novo Sequencing gap contig contig long read mate pair 8 Re-Sequencing Alignment 3X 2X reference Variant Calling G/A SNV GCTATGCATTGGCATGGCATGCTAGCTACGGGATGCTGATCGATTTCGAAACTGACT! CTATGCATTGGCATGGCATGCTAGCTACAGGATGCTGATCGATTTCGAAACTGACTG! random TATGCATTGGCATGGCATGCTAGCTACGGGATGCTGATCGATTTCGAAACTGACTGT! errors ATGCATTGGCATGTCATGCTAGCTACAGGATGCTGATCGATTTCGAAACTGACTGTT! TGCATTGGCATGGCATGCTAGCTACGGGATGCTGATCGATTTCGAAACTGACTGTTA! GCATTGGCATGGCATGCTAGCTACGGGATGCTGATCGATTTCGAAACTGACTGTTAG! CATTGGCATGGCATGCTAGCTACAGGATGCTGATCGATTTCGAAACTGACTGTTAGC! ATTGGCATGGCATGCTAGCTACGGGATGCTGATCGATATCGAAACTGACTGTTAGCC! TTGGCATGGCATGCTAGCTACAGGATGCTGATCGATTTCGAAACTGACTGTTAGCCA! TGGCATGGCATGCTAGCTACAGGATGCTGATCGATTTCGAAACTGACTGTTAGCCAT! reference GCTATGCATTGGCATGGCATGCTAGCTACAGGATGCTGATCGATTTCGAAACTGACTGTTAGCCAT! 9 Paired-End Sequencing Extends the Power of the Technology Known sequence Sequence reads (single-end) New sequence 80-90% 10 Paired-End Sequencing Extends the Power of the Technology Known sequence Sequence reads (paired-end) Unique placement of one end can resolve ambiguous placement of other New sequence 95 to >99% 11 Indexing pooling samples DNA/RNA samples ! unique sequence “barcode” create library & add index ! increase throughput indexed ! maximize capacity libraries TAAGGCGAGTA….. CGTACTAGTGG….. informatic AGGCAGAAAGT….. pool de-multiplex TCCTGAGCTGC….. GGACTCCTGTG….. TAGGCATGTAG….. CTCTCTACGCA….. FASTQ CAGAGAGGAGT….. GCTACGCTGTG….. CGAGGCTGTAG….. AAGAGGCAGCA….. GTAGAGGAAGT….. 12 Coverage 4X 2X 3X average (read length) x (# of clusters) = coverage genome size (target) Examples Human on HiSeq 2500 (Rapid) (2x150bp) x (600 Million clusters) = 200 X average coverage 3 Billion bp (human) E. coli on MiSeq – 96 samples (2x300bp) x (25 Million clusters) = 52 X average coverage 96 samples x 3 Million bp (E. coli) 13 Coverage Optimizing Sequence Capacity High Performers Consume Unnecessary Coverage Low Performers Need More Coverage Minimum Coverage 14 Applications Guidelines Whole Human Genome ChIP seq Exomes Shotgun Metagenomics RNA seq Targeted Panels Microbial & 16S Metagenomics Focused Power Flexible Power Production Power Population Power MiSeq Series NextSeq HiSeq Series HiSeq X Series 1-25M 130-400M 300-2,500M 3,000M 15 MiSeq Desktop Sequencer- Speed and Simplicity ! Economical personal sequencer ! Easy to use workflow ! Rapid turnaround time ! Proven sequencing chemistry ! Multiple applications 16 MiSeq Offers Scalable Sequencing MiSeq Core Consumables Version 3 25 •" 600 cycles Million Reads •" 150 cycles MiSeq Core Consumables Version 2 •" 500 cycles 15 •" 300 cycles Million Reads •" 150 cycles MiSeq Core Consumables Version 2 Micro 4 •" 300 cycles (Micro) Million Reads MiSeq Core Consumables Version 2 Nano 1 •" 500 cycles (Nano) Million Reads •" 300 cycles (Nano) 17 Multiple Kits Available to Maximize Utility Chemistry Number of Sequencing Cycles Output (Gb) Version Reads (mil) Time (hrs) 3 25 600 15 56 3 25 150 3.8 21 2 15 500 8.5 39 2 15 300 5.1 24 2 15 50 0.85 5.5 2 4 (Micro) 300 1.2 28 2 1 (Nano) 500 0.5 19 2 1 (Nano) 300 0.3 19 18 MiSeq Applications Portfolio Integrated. Optimized. Simplified. Amplicon Custom Targeted Custom Small RNA Clone Sequencing Amplicon Resequencing Enrichment sequencing checking ChIP-Seq Library QC Plasmid Regulation RNA-Seq Resequencing Small RNA De novo 16S genome sequencing sequencing Metagenomics 19 For Research Use Only. Not for use in diagnostic procedures MiSeq Reporter (MSR) Apps De Novo Enrichment Generate LibraryQC PCR Metagenomics Small Resequencing TruSeq Assembly FASTQ Amplicon RNA Amplicon ! Streamlined on-board analysis workflows ! Most workflows also available on BaseSpace ! No user intervention from sample loading to report generation ! Accessible from any computer on the same local network as instrument rd ! All workflows generate FASTQ files that can be analyzed by most 3 party apps 20 For Research Use Only. Not for use in diagnostic procedures BaseSpace Core Apps BaseSpace Labs Apps 16S TopHat Cufflinks RNA VariantStudio FastQC Kraken NextBio VCAT Metagenomics Alignment Assembly Express Metagenomics Annotates & DE BWA Isaac Broad IGV TruSeq Amplicon DS NextBio SRST2 FASTQ Velvet Enrichment Enrichment Amplicon Transporter Toolkit Assembly BWA Isaac Tumor Long Read Long Read PicardSpace SRA Prokka WGS WGS Normal Assembly Phasing Import Annotation Third-Party Apps SPAdes Novoalign Advaita DNASTAR SCIEX SCIEX SCIEX EDGC SWATHAtlas Pipeline Annotator MetaPhlAn N-of-One MyFLQ LoFreq eGB Genomatix Genome OncoMD GeneTalk Profiler PathGEN Melanoma Tute DeepChek® PEDANT CosmosID Dx PathSEQ Profiler HIV, HBV, HCV 21 For Research Use Only. Not for use in diagnostic procedures. Nextera XT workflow and applications 22 For Research Use Only. Not for use in diagnostic procedures Library Prep Approaches ! Shotgun – random fragmentation –" mechanical shearing –" enzymatic shearing (Nextera) ! Considerations –" fragment length –" uniformity of coverage –" workflow 23 A Typical MiSeq DNA Workflow Nextera XT MiSeq MSR or BaseSpace BaseSpace Prep Sequence Analyze Share 15 minutes hands-on 20 minutes hands-on Fully automated Secure and store ! Rapid library preparation –" Complete library prep in as little as 90 minutes with only 15 minutes of hands-on time ! Optimized for small genomes, PCR amplicons and plasmids –" One library prep kit for many applications ! Ultra low input –" Only a single nanogram of input DNA needed ! Innovative sample normalization –" Eliminates the need for library quantification before sample pooling and sequencing 24 For Research Use Only. Not for use in diagnostic procedures Nextera XT, how does it work? 25 Nextera XT, how does it work? FLEXIBLE: Genomic DNA Environmental sample (metagenomics) Long range PCR amplicon Plasmid ds-cDNA 26 Nextera XT, how does it work? FAST: 5 minutes 27 Nextera XT, how does it work? COST EFFECTIVE: Under $35 28 Nextera XT workflow applications ! Assemble and compare genomes of bacterial isolates –" Develop and infection control program ! Perform whole genome metagenomics –" Monitor evolution of bacterial populations under specific conditions ! Single cell RNA-seq analysis –" Sequence amplified ds-cDNA from single cells from different tissue compartments ! Resequence long range PCR products to detect variation between samples –" Manage highly similar genes by leveraging the LR PCR specificity 29 Using NGS to Assess Food Pathogen Outbreak Samples First example: FDA/CDC Listeria whole genome sequencing project Compared with pulsed-field gel electrophoresis (PFGE), WGS provides clearer distinction between cases and foods that are likely part of a given outbreak and those that are not. Whole-genome sequences of the Listeria First time EVER that microbial whole strains isolated from Roos Foods cheese genome sequencing was used by the products were available after the recall and US Federal Government in real time were found to be highly related to sequences to link an outbreak to a company and of the Listeria strains isolated from the affect legal action. patients. 30 PFGE Protocol 31 CDC Protocol, PulseNet Application: Bacterial WGS from culture Displacing PFGE MiSeq & Nextera Sample Prep Primary Library Prep Analysis •" Grow •" Lyse cells from •" Nextera XT •" MiSeq workflow: culture cultured •" 16 samples
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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,
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • Metaspades: a New Versatile Metagenomics Assembler
    metaSPAdes: a new versatile metagenomics assembler 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] Abstract While metagenomics has emerged as a technology of choice for analyzing bacterial populations, as- sembly of metagenomic data remains challenging thus stifling biological discoveries. Moreover, re- cent studies revealed that complex bacterial populations may be composed from dozens of related strains thus further amplifying the challenge metagenomics assembly. metaSPAdes addresses various challenges of metagenomics assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes. We benchmark metaSPAdes against other state-of-the-art metagenome assemblers and demonstrate that it results in high-quality assemblies across diverse datasets. Introduction Metagenome sequencing has emerged as a technology of choice for analyzing bacterial popula- tions and discovery of novel organisms and genes (Venter et al. 2004; Tyson et al. 2004; Yooseph et al. 2007; Arumugam et al. 2011). In one of the early metagenomics studies, Venter et al. (2004) at- tempted to assemble the complex Sargasso Sea microbial community but, as the paper stated, failed. On the other side of the spectrum of metagenomics studies, Tyson et al. (2004) succeeded in assem- bling a simple metagenomic community from a biofilm consisting of a few species.
    [Show full text]
  • Scaling Metagenome Sequence Assembly with Probabilistic De Bruijn Graphs
    Scaling metagenome sequence assembly with probabilistic de Bruijn graphs Jason Pella, Arend Hintzea, Rosangela Canino-Koninga, Adina Howeb, James M. Tiedjeb,c, and C. Titus Browna,b,1 aComputer Science and Engineering, Michigan State University, East Lansing, MI 48824; bMicrobiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824; and cCrop and Soil Sciences, Michigan State University, East Lansing, MI 48824 Edited by Dan Gusfield, UC Davis, and accepted by the Editorial Board June 26, 2012 (received for review December 27, 2011) Deep sequencing has enabled the investigation of a wide range of more complex samples such as soil metagenomes, which may environmental microbial ecosystems, but the high memory require- possess millions or more species, terabytes of memory would be ments for de novo assembly of short-read shotgun sequencing required to store the graph. Moreover, the wide variation in spe- data from these complex populations are an increasingly large cies abundance limits the utility of standard memory-reduction practical barrier. Here we introduce a memory-efficient graph re- practices such as abundance-based error-correction (15). presentation with which we can analyze the k-mer connectivity In this work, we describe a simple probabilistic representation of metagenomic samples. The graph representation is based on for storing de Bruijn graphs in memory, based on Bloom filters a probabilistic data structure, a Bloom filter, that allows us to effi- (16). Bloom filters are fixed-memory probabilistic data structures ciently store assembly graphs in as little as 4 bits per k-mer, albeit for storing sparse sets; essentially hash tables without collision inexactly.
    [Show full text]
  • Efficient De Novo Assembly of Large Genomes Using Compressed Data Structures
    Downloaded from genome.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press Resource Efficient de novo assembly of large genomes using compressed data structures Jared T. Simpson and Richard Durbin1 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom De novo genome sequence assembly is important both to generate new sequence assemblies for previously uncharac- terized genomes and to identify the genome sequence of individuals in a reference-unbiased way. We present memory efficient data structures and algorithms for assembly using the FM-index derived from the compressed Burrows-Wheeler transform, and a new assembler based on these called SGA (String Graph Assembler). We describe algorithms to error- correct, assemble, and scaffold large sets of sequence data. SGA uses the overlap-based string graph model of assembly, unlike most de novo assemblers that rely on de Bruijn graphs, and is simply parallelizable. We demonstrate the error correction and assembly performance of SGA on 1.2 billion sequence reads from a human genome, which we are able to assemble using 54 GB of memory. The resulting contigs are highly accurate and contiguous, while covering 95% of the reference genome (excluding contigs <200 bp in length). Because of the low memory requirements and parallelization without requiring inter-process communication, SGA provides the first practical assembler to our knowledge for a mammalian-sized genome on a low-end computing cluster. [Supplemental material is available for this article.] The cost of DNA sequencing continues to fall rapidly, faster than Most short read assemblers rely on the de Bruijn graph model Moore’s law for computing costs (Stein 2010).
    [Show full text]
  • Next-Generation DNA Sequencing Informatics, 2Nd Edition
    This is a free sample of content from Next-Generation DNA Sequencing Informatics, 2nd edition. Click here for more information on how to buy the book. Index Page references followed by f denote figures. Page references followed by t denote tables. A Needleman–Wunsch (NW) algorithm, 49, 54, 110–113 overview, 109–110 Abeel, Thomas, 103 – – – ABI. See Applied Biosystems Inc. Smith Waterman (SW) algorithm, 38, 49, 62 63, 111 113 Ab initio genome annotation, 172, 178, 180t–181t Splign, 182 – TopHat, 43, 182 ab1PeakReporter software, 52 53 – A-Bruijn graph, 133–134 Alignment score, FASTA, 64 65 ABySS (Assembly by Short Sequencing), 134, 142, 147–153 Allele, 52, 354 Allele frequency, 76, 94, 193 effect of k-mer size and minimum pair number on assembly, fi 148–149, 149f Allele-speci c expression, 155, 298 overview of, 147–148 ALLPATHS, 134 quality of assembly, 149–153, 150t, 151f–152f ALN format, 92 α transcriptome assembly (Trans-ABySS), 158t, 160–161, 166 -diversity indices, 319 – – AceView database, 294, 295f Alternative splicing, 182, 293 296, 294f 295f Acrylamide gels Altschul, Stephen, 65 capillary tube, 4 Amazon Elastic Compute Cloud (EC2), 43, 254, 300, 315, – Sanger sequencing and, 2, 3–4 362 364, 366, 369 – ACT, 179t Amino acids, pairwise comparisons, 48 49 Adapter removal, 37–39, 39f, 43 Amplicons, 8, 30, 89, 204, 309, 312 Adapter Removal program, 38 Amplicon Variant Analyzer, 101 Affine gaps, 42, 110, 111–112 AmpliSeq Cancer Panel (Ion Torrent), 206 Algorithms Annotation, 75. See also Genome annotation – – – alignment, 49, 109–124, 129, 223, 338, 344 ChIP-seq peak, 240 242, 255, 259, 262 263, 262f 263f – assembly, 59, 127–129, 133–134, 338 proteogenomics and, 327 328, 328f – database searching, 113–115 of variants, 208 212 development, 364 ANNOVAR, 211 DNA fragment/genome assembly, 127–129, 133–134, 142 Anthrax, 141 dynamic programming, 110–124 Anti-sense RNA, 281 file compression, 79 Application programming interface (API), 368 Golay error-correcting, 31 Applied Biosystems Inc.
    [Show full text]
  • Assembling Metagenomes, One Community at a Time
    bioRxiv preprint doi: https://doi.org/10.1101/120154; this version posted March 24, 2017. 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. 1 Assembling metagenomes, one community at a time 2 Andries J. van der Walt1,2†, Marc W. Van Goethem1†, Jean-Baptiste Ramond1, Thulani P. 3 Makhalanyane1, Oleg Reva2 & Don A. Cowan1 4 1 Centre for Microbial Ecology and Genomics (CMEG), Department of Genetics, University of 5 Pretoria, Pretoria, South Africa 6 2 Centre for Bioinformatics and Computational Biology, Department of Biochemistry, University of 7 Pretoria, Pretoria, South Africa 8 † Contributed equally 9 † Corresponding authors: Andries van der Walt or Marc Van Goethem 10 Centre for Microbial Ecology and Genomics (CMEG) 11 Natural Sciences Building 2 12 Lynnwood Road 13 University of Pretoria 14 Pretoria 0028 15 South Africa 16 Email: [email protected] / [email protected] 17 Abstract 18 Metagenomics allows unprecedented access to uncultured environmental microorganisms. The 19 assembly of metagenomic sequences facilitates gene prediction and annotation, and enables the 20 assembly of draft genomes of dominant taxa, including uncultured members of a community. 21 However, while numerous platforms have been developed for this critical step, there is currently no 22 strict standard for the assembly of metagenomic sequence data. To assist with selection of an 23 appropriate metagenome assembler we evaluated the capabilities of eight prominent assembly tools 24 on nine publicly-available environmental metagenomes.
    [Show full text]
  • Cultivar-Specific Transcriptome and Pan-Transcriptome Reconstruction of Tetraploid Potato
    www.nature.com/scientificdata oPEN Cultivar-specifc transcriptome and Data DescriptoR pan-transcriptome reconstruction of tetraploid potato Marko Petek 1,6 ✉ , Maja Zagorščak1,6 ✉ , Živa Ramšak 1, Sheri Sanders3, Špela Tomaž1,2, Elizabeth Tseng4, Mohamed Zouine 5, Anna Coll1 & Kristina Gruden1 Although the reference genome of Solanum tuberosum Group Phureja double-monoploid (DM) clone is available, knowledge on the genetic diversity of the highly heterozygous tetraploid Group Tuberosum, representing most cultivated varieties, remains largely unexplored. This lack of knowledge hinders further progress in potato research. In conducted investigation, we frst merged and manually curated the two existing partially-overlapping DM genome-based gene models, creating a union of genes in Phureja scafold. Next, we compiled available and newly generated RNA-Seq datasets (cca. 1.5 billion reads) for three tetraploid potato genotypes (cultivar Désirée, cultivar Rywal, and breeding clone PW363) with diverse breeding pedigrees. Short-read transcriptomes were assembled using several de novo assemblers under diferent settings to test for optimal outcome. For cultivar Rywal, PacBio Iso- Seq full-length transcriptome sequencing was also performed. EvidentialGene redundancy-reducing pipeline complemented with in-house developed scripts was employed to produce accurate and complete cultivar-specifc transcriptomes, as well as to attain the pan-transcriptome. The generated transcriptomes and pan-transcriptome represent a valuable resource for potato gene variability
    [Show full text]
  • Genomics Tutorial 2019
    Genomics Tutorial 2019. Instructors: ● Josie Paris & the Workshop Team! ● Konrad Paszkiewicz in absentia (don’t worry he’s not dead). ​ ​ Important Notes username: genomics password: see the whiteboard Objectives: Part 1: Short Read Genomics: An Introduction ● Understand how short reads are generated. ● Understand paired-end reads ● See possible sources of errors ● Learn about adaptors Part 2: QC, Alignment and Variant Calling ● Interpret FASTQ quality metrics ● Remove poor quality data ● Trim adaptor/contaminant sequences from FASTQ data ● Count the number of reads before and after trimming and quality control ● Align reads to a reference sequence to form a SAM file (Sequence AlignMent file) using BWA ● Convert the SAM file to BAM format (Binary AlignMent format) ● Identify and select high quality SNPs and Indels using SAMtools ● Identify missing or truncated genes with respect to the reference genome ● Identify SNPs which overlap with known coding regions Part 3: Assembly of Unmapped Reads ● Extract reads which do not map to the reference sequence ● Assemble these reads de novo using SPAdes ● Generate summary statistics for the assembly ● Identify potential genes within the assembly ● Search for matches within the NCBI database via BLAST and against the Pfam database 1 of 88 ● Visualize the taxonomic distribution of BLAST hits ● Perform gene prediction and annotation using RAST Part 4: De-novo Assembly Using Short Reads ● Perform QC and adaptor-trim Illumina reads. ● Assemble these reads de novo using SPAdes ● Generate summary statistics
    [Show full text]
  • Efficient De Novo Assembly of Large Genomes Using Compressed Data Structures
    Downloaded from genome.cshlp.org on September 30, 2021 - Published by Cold Spring Harbor Laboratory Press Efficient de novo assembly of large genomes using compressed data structures Jared T Simpson, Richard Durbin1 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge. CB10 1SA UK 1To whom correspondence should be addressed. Richard Durbin [email protected] Running title: Genome assembly using compressed data structures Keywords: Sequence assembly, string graphs, short read sequencing, FM-index 1 Downloaded from genome.cshlp.org on September 30, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract De novo genome sequence assembly is important both to generate new sequence assemblies for previously uncharacterized genomes and to identify the genome sequence of individuals in a reference- unbiased way. We present memory efficient data structures and algorithms for assembly using the FM- index derived from the compressed Burrows-Wheeler transform, and a new assembler based on these called SGA (String Graph Assembler). We describe algorithms to error correct, assemble and scaffold large sets of sequence data. SGA uses the overlap-based string graph model of assembly, unlike most de novo assemblers that rely on de Bruijn graphs, and is simply parallelizable. We demonstrate the error correction and assembly performance of SGA on 1.2 billion sequence reads from a human genome, which we are able to assemble using 54 GB of memory. The resulting contigs are highly accurate and contiguous, while covering 95% of the reference genome (excluding contigs less than 200bp in length). Because of the low memory requirements and parallelization without requiring inter- process communication, SGA provides the first practical assembler to our knowledge for a mammalian-sized genome on a low-end computing cluster.
    [Show full text]