DOCSLIB.ORG

Sequence Assembly Demystified

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. The length of the almost identical form throughout a genome — yield sequences generated by modern sequencing instru- fragments with highly similar sequences that originate Mate-pair data Data from a pair of reads ments is considerably shorter (hundreds to thousands of from different places in the genome. Similarly, in tran- sequenced from the same base pairs) than that of the genomes or genomic features scriptome or metagenomic samples, nearly identical circularized DNA fragment. being studied (which commonly span tens of thousands sequences may originate from different transcripts or The circularization step allows to billions of base pairs). Thus, many analyses start with genomes within the sample. How much an assembler for larger fragments sizes to be used. They provide the the computational process of sequence assembly that is confused by such artefacts primarily depends on the same information as paired- joins together the many sequence fragments generated length of the sequences that are read by the sequenc- end reads to the assembler. by the instrument. Biologists who need to assemble ing instrument, as repetitive regions shorter than a the sequencing data generated in their experiments are sequencing read can be automatically resolved14. The Contiguous sequence faced with the challenge of choosing, from a myriad of accuracy of the sequence data also has an important (Contig). A sequence reconstructed by assembling options, the assembly strategy and software best suited role: the more errors an assembler is willing to tolerate together multiple reads. for their experiment. This choice is made harder by within the data, the more similar distinct regions of a the rapid development of new assembly tools, which genome will appear to the assembler. Broadly speaking, is driven by advances in sequencing technologies and segments of the genome that diverge by less than the 1Computational and Systems Biology, Genome Institute the broader scope of applications for these technolo- error rate of the sequencing instrument cannot easily of Singapore, 138672 gies. Among recent innovations in sequence assem- be distinguished by an assembler. Singapore. bly are the use of memory-efficient data structures1,2, In this Review, we begin by providing an overview 2Department of Computer the development of new de novo assembly strategies of the principles underlying modern assembly tools as Science, University of 3–5 6,7 Maryland, College Park, for data derived from metagenomic , single-cell or well as the engineering trade-offs involved in designing 8,9 Maryland 20742, USA. transcriptome experiments and the effective use of them. We discuss the value of experimental design for Correspondence to M.P. complementary information derived from multiple successful assembly as well as the ongoing work in the e-mail: [email protected]. sequencing technologies10,11 and/or paired-end data or important field of assembly evaluation. We then discuss edu mate-pair data12,13. trade-offs in the context of the most common applica- doi:10.1038/nrg3367 Published online 29 January All assembly approaches rely on the simple assump- tions of sequence assembly. Our goal is to provide non- 2013; corrected online tion that highly similar DNA fragments originate from specialist readers with sufficient background to make 5 February 2013 the same position within a genome. The similarity informed choices in the use of assembly techniques. NATURE REVIEWS | GENETICS VOLUME 14 | MARCH 2013 | 157 © 2013 Macmillan Publishers Limited. All rights reserved REVIEWS Modern sequence assemblers assemblers have successfully been used with longer reads Mathematical analyses of sequence assembly, which by using a pre-processing stage to correct sequencing dates back to the pioneering work of Esko Ukkonen errors17,18, and efficient overlap-based assemblers for in the 1980s15,16, revealed the fundamental difficulty of short reads have also been developed19. The choice of reconstructing a genome from sequenced fragments. assembly paradigm on its own plays a marginal part in Depending on the relationship between the length of defining the performance and efficiency of an assembler. reads and the length of repeats in the DNA being assem- As sequencing technologies evolve, assembly tools bled (BOX 1), genome assembly can range from trivial are adapting to cope with the features and scale of the (when all repeats are shorter than the read length) to data (TABLE 1). Whereas early sequence assemblers had to computationally intractable (that is, finding the cor- make do with sparse coverage, modern assemblers have rect answer requires trying an exponential number of to deal with the problems of plenty. The success of modern arrangements of reads, a task that cannot be achieved sequence assemblers has thus primarily been determined even on the best supercomputers) to impossible14 (that by their ability to face the twin challenges of engineering is, the information contained within the reads is insuf- (that is, dealing with the scale of data) and analysis (that ficient to identify the correct sequence reconstruction is, adapting to and exploiting specific features of the data). from an exponential number of equally good alterna- tives). Given the characteristics of currently available Engineering challenges. Modern assemblers must be able sequencing technologies (BOX 1), few projects fall into to analyse large data sets efficiently, to handle sequencing the category of ‘trivial’ (primarily small viral genomes or errors and to capture the repeat structure of the genome transcriptomes). In most cases, full genome sequences correctly. Meeting these challenges has been key in defin- cannot be efficiently and reliably reconstructed from the ing assembly tools that have had an important impact data produced by the sequencing experiment; rather, on the field. For example, one of the first widely used assemblers produce a fragmented and often error-prone short-read assemblers, Velvet20, made a mark by show- picture of the sequence being assembled. ing that high-quality assemblies could be obtained from ultra-short reads (~30 bp) and high-coverage data sets. Assembly paradigms. Assemblers are based on one of This approach was extended to the assembly of large several different paradigms, such as greedy, overlap– genomes in the program ABySS21 and for the first de novo layout–consensus (OLC), de Bruijn graph and string assembly of a mammalian genome entirely using short graph (introduced in BOX 2). The choice of approach reads with the program SOAPdenovo22. SOAPdenovo is depends on the characteristics of the data being assem- a memory-efficient assembler that also includes robust bled. For example, de Bruijn-graph-based approaches error correction (to reduce sequencing errors) and scaf- have been successful in assembling highly accurate short folding modules (to leverage mate-pair data; see ‘Analysis reads (<~100 bp, such as those generated by the Illumina challenges’ below). Solexa technology; BOX 1), whereas overlap-based The importance of error correction for de Bruijn- approaches (such as OLC or string graph) are mostly graph-based assembly, in particular, has led to the used for longer, more inaccurate data (>200 bp, such as recent development of tools for this task18,23,24 that serve Roche 454 and Sanger sequencing data; BOX 1). However, as useful pre-processors in genome assembly applica- this is not an exclusive arrangement: de Bruijn graph tions. Whereas early short-read assemblers focused on Box 1 | Sequencing and mapping technologies A full survey of sequencing technologies is beyond the scope of this article. The table below compares the currently available technologies in terms of several characteristics of importance for genome assembly: read
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • De Novo Genome Assembly Versus Mapping to a Reference Genome
    De novo genome assembly versus mapping to a reference genome Beat Wolf PhD. Student in Computer Science University of Würzburg, Germany University of Applied Sciences Western Switzerland [email protected] 1 Outline ● Genetic variations ● De novo sequence assembly ● Reference based mapping/alignment ● Variant calling ● Comparison ● Conclusion 2 What are variants? ● Difference between a sample (patient) DNA and a reference (another sample or a population consensus) ● Sum of all variations in a patient determine his genotype and phenotype 3 Variation types ● Small variations ( < 50bp) – SNV (Single nucleotide variation) – Indel (insertion/deletion) 4 Structural variations 5 Sequencing technologies ● Sequencing produces small overlapping sequences 6 Sequencing technologies ● Difference read lengths, 36 – 10'000bp (150-500bp is typical) ● Different sequencing technologies produce different data And different kinds of errors – Substitutions (Base replaced by other) – Homopolymers (3 or more repeated bases) ● AAAAA might be read as AAAA or AAAAAA – Insertion (Non existent base has been read) – Deletion (Base has been skipped) – Duplication (cloned sequences during PCR) – Somatic cells sequenced 7 Sequencing technologies ● Standardized output format: FASTQ – Contains the read sequence and a quality for every base http://en.wikipedia.org/wiki/FASTQ_format 8 Recreating the genome ● The problem: – Recreate the original patient genome from the sequenced reads ● For which we dont know where they came from and are noisy ● Solution: – Recreate the genome
    [Show full text]
  • Sequencing and Comparative Analysis of <I>De Novo</I> Genome
    University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Dissertations and Theses in Biological Sciences Biological Sciences, School of 7-2016 Sequencing and Comparative Analysis of de novo Genome Assemblies of Streptomyces aureofaciens ATCC 10762 Julien S. Gradnigo University of Nebraska - Lincoln, [email protected] Follow this and additional works at: http://digitalcommons.unl.edu/bioscidiss Part of the Bacteriology Commons, Bioinformatics Commons, and the Genomics Commons Gradnigo, Julien S., "Sequencing and Comparative Analysis of de novo Genome Assemblies of Streptomyces aureofaciens ATCC 10762" (2016). Dissertations and Theses in Biological Sciences. 88. http://digitalcommons.unl.edu/bioscidiss/88 This Article is brought to you for free and open access by the Biological Sciences, School of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Dissertations and Theses in Biological Sciences by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. SEQUENCING AND COMPARATIVE ANALYSIS OF DE NOVO GENOME ASSEMBLIES OF STREPTOMYCES AUREOFACIENS ATCC 10762 by Julien S. Gradnigo A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science Major: Biological Sciences Under the Supervision of Professor Etsuko Moriyama Lincoln, Nebraska July, 2016 SEQUENCING AND COMPARATIVE ANALYSIS OF DE NOVO GENOME ASSEMBLIES OF STREPTOMYCES AUREOFACIENS ATCC 10762 Julien S. Gradnigo, M.S. University of Nebraska, 2016 Advisor: Etsuko Moriyama Streptomyces aureofaciens is a Gram-positive Actinomycete used for commercial antibiotic production. Although it has been the subject of many biochemical studies, no public genome resource was available prior to this project.
    [Show full text]
  • Improving Metagenomic Assemblies Through Data Partitioning: a GC Content Approach
    bioRxiv preprint doi: https://doi.org/10.1101/261784; this version posted February 8, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Improving Metagenomic Assemblies Through Data Partitioning: a GC content approach F´abioMiranda1, Cassio Batista1, Artur Silva2;3, Jefferson Morais1, Nelson Neto1, and Rommel Ramos1;2;3 1Computer Science Graduate Program { Federal University of Par´a,Bel´em,Brazil {fabiomm,cassiotb,jmorais,nelsonneto,rommelramos}@ufpa.br 2Institute of Biological Sciences { Federal University of Par´a,Bel´em,Brazil [email protected] 3Center of Genomics and Systems Biology { Federal University of Par´a,Bel´em,Brazil Abstract. Assembling metagenomic data sequenced by NGS platforms poses significant computational challenges, especially due to large vol- umes of data, sequencing errors, and variations in size, complexity, di- versity and abundance of organisms present in a given metagenome. To overcome these problems, this work proposes an open-source, bioinfor- matic tool called GCSplit, which partitions metagenomic sequences into subsets using a computationally inexpensive metric: the GC content. Ex- periments performed on real data show that preprocessing short reads with GCSplit prior to assembly reduces memory consumption and gen- erates higher quality results, such as an increase in the N50 metric and the reduction in both the L50 value and the total number of contigs produced in the assembly. GCSplit is available at https://github.com/ mirand863/gcsplit. Keywords: DNA sequencing · Metagenomics · Data partitioning · Bioin- formatic tools · Metagenomic data preprocessing 1 Introduction Metagenomics consists in determining the collective DNA of microorganisms that coexist as communities in a variety of environments, such as soil, sea and even the human body [1{3].
    [Show full text]
  • Extreme Scale De Novo Metagenome Assembly
    Extreme Scale De Novo Metagenome Assembly Evangelos Georganas∗, Rob Egany, Steven Hofmeyrz, Eugene Goltsmany, Bill Arndt{, Andrew Tritt{ Aydın Buluc¸z, Leonid Olikerz, Katherine Yelickz ∗ Parallel Computing Lab, Intel Corp. zComputational Research Division / yJoint Genome Institute, Lawrence Berkeley National Laboratory, USA { National Energy Research Scientific Computing Center, USA Abstract—Metagenome assembly is the process of transforming are not parallelized for distributed-memory systems. As a a set of short, overlapping, and potentially erroneous DNA result, they require specialized, large shared-memory machines segments from environmental samples into the accurate rep- with hundreds of GB of memory in order to deal even with resentation of the underlying microbiomes’s genomes. State-of- the-art tools require big shared memory machines and cannot modestly-sized metagenomic datasets. At the same time, the handle contemporary metagenome datasets that exceed Terabytes concurrency of these tools is limited by the core count of in size. In this paper, we introduce the MetaHipMer pipeline, a a single node (typically 10s of cores) and consequently the high-quality and high-performance metagenome assembler that execution times even for small datasets are in the order of employs an iterative de Bruijn graph approach. MetaHipMer days. The only exception among the metagenome assemblers leverages a specialized scaffolding algorithm that produces long scaffolds and accommodates the idiosyncrasies of metagenomes. is Ray Meta, which is a tool designed for distributed-memory MetaHipMer is end-to-end parallelized using the Unified Parallel systems. However, Ray Meta is not scalable to massive con- C language and therefore can run seamlessly on shared and currencies [13] and its assembly quality has been shown to be distributed-memory systems.
    [Show full text]
  • DNA Sequence Assembly and Genetic Algorithms New Results and Puzzling Insights
    From: ISMB-95 Proceedings. Copyright © 1995, AAAI (www.aaai.org). All rights reserved. DNA Sequence Assembly and Genetic Algorithms New Results and Puzzling Insights Rebecca Parsons Mark E. Johnson Department of Computer Science Statistics Department University of Central Florida University of Central Florida Orlando, FL USA Orlando, FL USA [email protected] [email protected] Abstract performance.Several critical issues affect the perfor- manceof a genetic algorithm: problemrepresentation, Applyinggenetic algorithmsto DNAsequence assem- operator selection, population size, and parameter se- bly is not a straightforwardprocess. Significantlyim- lection. Our previous workindicated that the perfor- provedresults in termsof performance,quality of re- manceof the genetic algorithm was sensitive to the sults, andthe scaling of applicability havebeen real- ized throughnon-standard and even counter-intuitive setting of the rate parameters. This issue seemedim- parametersettings. Specifically,the solutiontime for portant to address first. Weapplied the techniques of a 10kbdata set wasreduced by an order of magnitude, experimentaldesign and response surface analysis (Box and a 20kbdata set that waspreviously unsolved by & Draper 1987; Box, Hunter, & Hunter 1978) to the the genetic algorithmwas solved in a time that rep- rate parameterscontrolling the application of the var- resents only a linear increasefrom the 10kbdata set. ious operators in the genetic algorithm. This analysis Additionally, significant progress has been madeon promptedus to set the operator rates at significantly a 35kbdata set representingreal biological data. A different levels than those we had previously tried and single contig solution wasfound for a 752 fragment those typically used by the genetic algorithm commu- subset of the data set, and a 15 contig solution was foundfor the full data set.
    [Show full text]
  • Efficient and High Quality Hybrid De Novo Assembly of Human Genomes
    bioRxiv preprint doi: https://doi.org/10.1101/840447; this version posted November 25, 2019. 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-NC-ND 4.0 International license. WENGAN: Efficient and high quality hybrid de novo as- sembly of human genomes Alex Di Genova1;2;∗, Elena Buena-Atienza3;4, Stephan Ossowski3;4 and Marie-France Sagot1;2;∗ 1Inria Grenoble Rhone-Alpes,ˆ 655, Avenue de l’Europe, 38334 Montbonnot, France. 2CNRS, UMR5558, Universite´ Claude Bernard Lyon 1, 43, Boulevard du 11 Novembre 1918, 69622 Villeurbanne, France. 3Institute of Medical Genetics and Applied Genomics, University of Tubingen,¨ Tubingen,¨ Ger- many. 4NGS Competence Center Tubingen¨ (NCCT), University of Tubingen,¨ Tubingen,¨ Germany. *Correspondence should be addressed to Alex Di Genova (email: [email protected]) and Marie-France Sagot (email: [email protected]). The continuous improvement of long-read sequencing technologies along with the development of ad-doc algorithms has launched a new de novo assembly era that promises high-quality genomes. However, it has proven difficult to use only long reads to generate accurate genome assemblies of large, repeat-rich human genomes. To date, most of the human genomes assembled from long error-prone reads add accurate short reads to further polish the consensus quality. Here, we report the development of a novel algorithm for hybrid assembly, WENGAN, and the de novo assembly of four human genomes using a combination of sequencing data generated on ONT PromethION, PacBio Sequel, Illumina and MGI technology.
    [Show full text]
  • Cancer Whole-Genome Sequencing: Present and Future
    Oncogene (2015) 34, 5943–5950 © 2015 Macmillan Publishers Limited All rights reserved 0950-9232/15 www.nature.com/onc REVIEW Cancer whole-genome sequencing: present and future H Nakagawa, CP Wardell, M Furuta, H Taniguchi and A Fujimoto Recent explosive advances in next-generation sequencing technology and computational approaches to massive data enable us to analyze a number of cancer genome profiles by whole-genome sequencing (WGS). To explore cancer genomic alterations and their diversity comprehensively, global and local cancer genome-sequencing projects, including ICGC and TCGA, have been analyzing many types of cancer genomes mainly by exome sequencing. However, there is limited information on somatic mutations in non- coding regions including untranslated regions, introns, regulatory elements and non-coding RNAs, and rearrangements, sometimes producing fusion genes, and pathogen detection in cancer genomes remain widely unexplored. WGS approaches can detect these unexplored mutations, as well as coding mutations and somatic copy number alterations, and help us to better understand the whole landscape of cancer genomes and elucidate functions of these unexplored genomic regions. Analysis of cancer genomes using the present WGS platforms is still primitive and there are substantial improvements to be made in sequencing technologies, informatics and computer resources. Taking account of the extreme diversity of cancer genomes and phenotype, it is also required to analyze much more WGS data and integrate these with multi-omics data,
    [Show full text]
  • Basics on Molecular Biology ‡Ŷ
    BASICS ON MOLECULAR BIOLOGY Ŷ Ŷ Cell – DNA – RNA – protein Ŷ Sequencing methods Ŷ arising questions for handling the data, making sense of it Ŷ next two week lectures: sequence alignment and genome assembly Cells • Fundamental working units of every living system. • Every organism is composed of one of two radically different types of cells: – prokaryotic cells – eukaryotic cells which have DNA inside a nucleus. • Prokaryotes and Eukaryotes are descended from primitive cells and the results of 3.5 billion years of evolution. 2 Prokaryotes and Eukaryotes • According to the most recent evidence, there are three main branches to the tree of life • Prokaryotes include Archaea (“ancient ones”) and bacteria • Eukaryotes are kingdom Eukarya and includes plants, animals, fungi and certain algae Lecture: Phylogenetic trees, this topic in more detail 3 All Cells have common Cycles • Born, eat, replicate, and die 4 Common features of organisms • Chemical energy is stored in ATP • Genetic information is encoded by DNA • Information is transcribed into RNA • There is a common triplet genetic code – some variations are known, however • Translation into proteins involves ribosomes • Shared metabolic pathways • Similar proteins among diverse groups of organisms 5 All Life depends on 3 critical molecules • DNAs (Deoxyribonucleic acid) – Hold information on how cell works • RNAs (Ribonucleic acid) – Act to transfer short pieces of information to different parts of cell – Provide templates to synthesize into protein • Proteins – Form enzymes that send signals
    [Show full text]
  • Information Theory of DNA Shotgun Sequencing Abolfazl S
    IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 59, NO. 10, OCTOBER 2013 6273 Information Theory of DNA Shotgun Sequencing Abolfazl S. Motahari, Guy Bresler, Student Member, IEEE, and David N. C. Tse, Fellow, IEEE Abstract—DNA sequencing is the basic workhorse of modern day biology and medicine. Shotgun sequencing is the dominant technique used: many randomly located short fragments called reads are extracted from the DNA sequence, and these reads are assembled to reconstruct the original sequence. A basic question is: given a sequencing technology and the statistics of the DNA sequence, what is the minimum number of reads required for reliable reconstruction? This number provides a fundamental Fig. 1. Schematic for shotgun sequencing. limit to the performance of any assembly algorithm. For a simple statistical model of the DNA sequence and the read process, we show that the answer admits a critical phenomenon in the asymp- totic limit of long DNA sequences: if the read length is below Shotgun sequencing is the dominant method currently used a threshold, reconstruction is impossible no matter how many to sequence long strands of DNA, including entire genomes. reads are observed, and if the read length is above the threshold, The basic shotgun DNA sequencing setup is shown in Fig. 1. having enough reads to cover the DNA sequence is sufficient to Starting with a DNA molecule, the goal is to obtain the sequence reconstruct. The threshold is computed in terms of the Renyi entropy rate of the DNA sequence. We also study the impact of of nucleotides ( , , ,or ) comprising it. (For humans, the noise in the read process on the performance.
    [Show full text]
  • Assembly Algorithms for Next-Generation Sequence Data
    The Pennsylvania State University The Graduate School College of Engineering ASSEMBLY ALGORITHMS FOR NEXT-GENERATION SEQUENCE DATA A Dissertation in Computer Science and Engineering by Aakrosh Ratan c 2009 Aakrosh Ratan Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy December 2009 The dissertation of Aakrosh Ratan was reviewed and approved* by the following: Webb Miller Professor of Biology and Computer Science and Engineering Dissertation Adviser, Chair of Committee Piotr Berman Associate Professor of Computer Science and Engineering Stephan Schuster Professor of Biochemistry and Molecular Biology Raj Acharya Professor of Computer Science and Engineering Head of the Department of Computer Science and Engineering *Signatures are on file in the Graduate School. ii Abstract Next-generation sequencing is revolutionizing genomics, promising higher coverage at a lower cost per base when compared to Sanger sequencing. Shorter reads and higher error rates from these new instruments necessitate the development of new algorithms and software. This dissertation describes approaches to tackle some problems related to genome assembly with these short frag- ments. We describe YASRA (Yet Another Short Read Assembler), that performs comparative assem- bly of short reads using a reference genome, which can differ substantially from the genome being sequenced. We explain the algorithm and present the results of assembling one ancient- mitochondrial and one plastid dataset. Comparing the performance of YASRA with the AMOScmp- shortReads and Newbler mapping assemblers (version 2.0.00.17) as template genomes are var- ied, we find that YASRA generates fewer contigs with higher coverage and fewer errors. We also analyze situations where the use of comparative assembly outperforms de novo assembly, and vice-versa, and compare the performance of YASRA with that of the Velvet (version 0.7.53) and Newbler de novo assemblers (version 2.0.00.17).
    [Show full text]