de novo assembly Simon Rasmussen 36626: Next Generation Sequencing analysis DTU Bioinformatics Next Generation Sequencing Analysis
|
|
|
- Walter Blair
- 5 years ago
- Views:
Transcription
1 de novo assembly Simon Rasmussen 36626: Next Generation Sequencing analysis DTU Bioinformatics Next Generation Sequencing Analysis
2 Generalized NGS analysis Data size Application Assembly: Compare Raw Pre- specific: Question Alignment / samples / Answer? reads processing Variant calling, de novo methods count matrix, Next Generation Sequencing Analysis
3 Generalized NGS analysis Data size Application Assembly: Compare Raw Pre- specific: Question Alignment / samples / Answer? reads processing Variant calling, de novo methods count matrix, Next Generation Sequencing Analysis
4 What is de novo assembly? Merge small DNA fragments together so they form a previously unknown sequence
5 What is de novo assembly? Merge millions reads together so they form previously unknown sequences
6 de novo assembly Assemble reads into longer fragments Find overlap between reads Many approaches reads& con*gs& scaffolds&
7 de novo assembly Assemble reads into longer fragments Find overlap between reads Many approaches reads& con*gs& scaffolds&
8 Lets try to assemble some reads! Rules: a minimum of 7-bp overlap overlap must not include any N bases same orientation so that the sequence can be read left to right there may be 1-bp differences simplified - no double stranded DNA..NNNNGGACTATGATTCG TGATTCGAGGCTAANN....NNNNNNNNCGATTCTGATCCGA GTCCTCGATTCTNNNNNNNN....NNNNCGGACTATGATT ATGATTCGAGGCTAANN....NNNNNNNNCGCTACTGATCCGA GTCCTCGATTCTGNNNNNNN..
9 Which are valid?..nnnncggactatgatt..nnnnggactatgattcg ATGATTCGAGGCTAANN.. TGATTCGAGGCTAANN....NNNNNNNNCGCTACTGATCCGA..NNNNNNNNCGATTCTGATCCGA GTCCTCGATTCTGNNNNNNN.. GTCCTCGATTCTNNNNNNNN..
10 Which are valid?..nnnncggactatgatt..nnnnggactatgattcg ATGATTCGAGGCTAANN.. TGATTCGAGGCTAANN....NNNNNNNNCGCTACTGATCCGA..NNNNNNNNCGATTCTGATCCGA GTCCTCGATTCTGNNNNNNN.. GTCCTCGATTCTNNNNNNNN..
11 Which approaches? Greedy ( Simple approach) Overlap-Layout-Consensus (OLC) de Bruijn graphs
12 Simple approach - Greedy Pseudo code: 1. Pairwise alignment of all reads 2. Identify fragments that have largest overlap 3. Merge these 4. Repeat until all overlaps are used Can only resolve repeats smaller than read length High computational cost with increasing no. reads
13 Reads > Contigs > Scaffolds Overlap Layout Consensus and de Bruijn use a similar general approach. 1. Try to correct sequence errors in reads with high coverage 2. Assemble reads to contiguous sequence fragments contigs 3. Identify repeat contigs 4. Combine and order contigs to scaffolds, with gaps representing regions of uncertainty
14 Overlap-Layout-Consensus Create overlap graph by all-vs-all alignment (Overlap) Build graph where each node is a read, edges are overlaps between reads (Layout) Example Schatz et al., Genome Res, 2010
15 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
16 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
17 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
18 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
19 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
20 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
21 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
22 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
23 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
24 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
25 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
26 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
27 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
28 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
29 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
30 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
31 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
32 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
33 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
34 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
35 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once
36 Overlap-Layout-Consensus Create consensus sequence We need to use graph theory to solve the graph Walk the Hamiltonian path Eg. visit each node exactly once Imagine trying to solve this for a graph of hundred of thousands of nodes (=reads) - this is an NP-complete problem
37 Overlap-Layout-Consensus Not good with many short reads -> lots of alignment! With short read lengths, hard to resolve repeats Good for large read lengths: PacBio, Oxford Nanopore, 10X Genomics, 454, Ion Torrent, Sanger Example assemblers: Canu, Celera, Newbler
38 de Bruijn graph Directed graph of overlapping items (here DNA sequences) Instead of comparing reads, decompose reads into k- mers Graph is created by mapping the k-mers to the graph Each k-mer only exists once in the graph Problem is reduced to walking Eulerian path (visiting each edge once) - this is a solve-able problem
39 Drawbacks... Lots of RAM required ( GB!) Optimal k can not be identified a priori, must be experimentally tested for each dataset small k: very complex graph, large k: limited overlap in low coverage areas Iterative approach to find best assembly
40 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3:
41 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3:
42 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: GAC
43 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: 1 GAC
44 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: 1 1 GAC ACC
45 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: GAC ACC CCT
46 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: GAC ACC CCT CTA TAC ACA
47 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: GAC ACC CCT CTA TAC ACA
48 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: GAC ACC CCT CTA TAC ACA
49 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: GAC ACC CCT CTA TAC ACA CAA
50 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: GAC ACC CCT CTA TAC ACA CAA
51 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: GAC ACC CCT CTA TAC ACA CAA AAG AGT
52 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: GAC ACC CCT CTA TAC ACA CAA AAG AGT
53 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: 1 2 TAG 3 TTA GTT GAC ACC CCT CTA TAC ACA CAA AAG AGT
54 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: 1 2 TAG 3 TTA GTT GAC ACC CCT CTA TAC ACA CAA AAG AGT
55 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: 1 2 TAG 3 TTA GTT GAC ACC CCT CTA TAC ACA CAA AAG AGT 3 GTC 2 TCC 1 CCG
56 How is the graph constructed? Same 10 reads, extract k-mers from reads and map onto graph, k = 3: 1 2 TAG 3 TTA GTT GAC ACC CCT CTA TAC ACA CAA AAG AGT 3 GTC 2 TCC 1 No alignment is used! CCG Different assemblers uses different modifications of the de Bruijn graphs
57 Complicated graphs TAG TTA GTT GAC ACC CCT CTA TAC ACA CAA AAG AGT GTC TCC CCG
58 Complicated graphs TAG TTA GTT GAC ACC CCT CTA TAC ACA CAA AAG AGT GTC TCC CCG T G to T
59 Complicated graphs TAG TTA GTT GAC ACC CCT CTA TAC ACA CAA AAG AGT GTC TCC CCG T G to T
60 Complicated graphs TAG TTA GTT GAC ACC CCT CTA TAC ACA CAA AAG AGT GTC TCC T G to T
61 Complicated graphs TAG TTA GTT CTA TAC ACA CAA AAG AGT CCT TCC GTC T ACC GAC G to T
62 Complicated graphs TAG TTA GTT CTA TAC ACA CAA AAG AGT CCT TCC GTC T ACC GAC G to T Large genomes with many repeats/errors creates very large graphs
63 Create the de Bruijn graph of this genome using k=3 AAGACTCCGACTGGGACTTT
64 After building: Simplify 1 Clip tips 2 (seq err, end) 30 2 Pinch bubbles (seq err, middle, SNP) Remove low cov. links 27 2
65 After building: Simplify Clip tips (seq err, end) 30 2 Pinch bubbles (seq err, middle, SNP) Remove low cov. links 27 2
66 After building: Simplify Clip tips (seq err, end) 30 Pinch bubbles (seq err, middle, SNP) Remove low cov. links 27 2
67 After building: Simplify Clip tips (seq err, end) 30 Pinch bubbles (seq err, middle, SNP) Remove low cov. links 27
68 ... Create contigs and scaffolds C1 C2... Repeat... C3... C4
69 ... Create contigs and scaffolds C1 C2... Repeat... C3... C4
70 ... Create contigs and scaffolds C1 C2... Repeat... C3... C4
71 ... Create contigs and scaffolds Cut graph at repeat boundaries to create contigs C1 C2... Repeat... C3... C4
72 Create contigs and scaffolds Cut graph at repeat boundaries to create contigs C1... Repeat C2... C1 C2 C3 C4... C3... C4
73 Create contigs and scaffolds Cut graph at repeat boundaries to create contigs Use paired end information to resolve repeats and combine to scaffolds C1... Repeat C2... C1 C2 C3 C4... C3... C4
74 Create contigs and scaffolds Cut graph at repeat boundaries to create contigs Use paired end information to resolve repeats and combine to scaffolds C1... Repeat C2... C1 C2 C3 C4... C3... C4
75 Create contigs and scaffolds Cut graph at repeat boundaries to create contigs Use paired end information to resolve repeats and combine to scaffolds C1... Repeat C2... C1 C2 C3 C4... C3... C4 Fill potential gaps using PE reads S1 S2
76 Create contigs and scaffolds Cut graph at repeat boundaries to create contigs Use paired end information to resolve repeats and combine to scaffolds C1... Repeat C2... C1 C2 C3 C4... C3... C4 Fill potential gaps using PE reads S1 S2 The assembly is done
77 Iterate parameters Re-run with different k-sizes, find optimum Run with multiple k-mers at the same time! (eg. SPAdes) Compare assembly statistics such as, assembly length, N50, no. contigs Assembly refinement Break contigs not supported by PE/MP reads Analyze assembly using REAPR or QUAST
78 Successful de novo assembly Success is a factor of: Genome size, genomic repeats(!), ploidy High coverage, long read lengths, PE/MP libraries Repeats in E. coli
79 Improving de novo assemblies Paired end & Mate pair for long range continuity Hybrid approaches (combine Illumina with PacBio/ Oxford Nanopore) Synthetic long reads: Illumina Synthetic Reads (Moleculo) or 10X Genomics Hi-C contact maps
80 Two bacterial genomes de Bruijn graphs Few repeats more repeats b Flicek & Birney, Nat.Methods 2009 Zerbino, 2009
81 N50: Assembly quality N50: What is the smallest piece in the largest half of the assembly? Calculate sum of assembly Order contigs by size Sum contigs starting by largest When half the sum is reached, N50 is the length of the contig
82 N50 example 5 scaffolds, calculate N50: 200kb 150kb 140kb 125kb 95kb Sum: = 710kb Half: 710 / 2 = 355kb 200kb + 150kb = 350kb Start adding: 350kb + 140kb = 490kb 490kb > 355kb => N50: 140kb
83 Some assemblers OLC: Canu, Newbler de Bruijn: Allpaths-LG, SPAdes, Velvet, SOAPdenovo, Megahit, other: MIRA, SGA, Used in exercises today
Genome Assembly and De Novo RNAseq
Genome Assembly and De Novo RNAseq BMI 7830 Kun Huang Department of Biomedical Informatics The Ohio State University Outline Problem formulation Hamiltonian path formulation Euler path and de Bruijin graph
Read Mapping. de Novo Assembly. Genomics: Lecture #2 WS 2014/2015
Mapping de Novo Assembly Institut für Medizinische Genetik und Humangenetik Charité Universitätsmedizin Berlin Genomics: Lecture #2 WS 2014/2015 Today Genome assembly: the basics Hamiltonian and Eulerian
Omega: an Overlap-graph de novo Assembler for Metagenomics
Omega: an Overlap-graph de novo Assembler for Metagenomics B a h l e l H a i d e r, Ta e - H y u k A h n, B r i a n B u s h n e l l, J u a n j u a n C h a i, A l e x C o p e l a n d, C h o n g l e Pa n
Description of a genome assembler: CABOG
Theo Zimmermann Description of a genome assembler: CABOG CABOG (Celera Assembler with the Best Overlap Graph) is an assembler built upon the Celera Assembler, which, at first, was designed for Sanger sequencing,
Genome Reconstruction: A Puzzle with a Billion Pieces Phillip E. C. Compeau and Pavel A. Pevzner
Genome Reconstruction: A Puzzle with a Billion Pieces Phillip E. C. Compeau and Pavel A. Pevzner Outline I. Problem II. Two Historical Detours III.Example IV.The Mathematics of DNA Sequencing V.Complications
Scalable Solutions for DNA Sequence Analysis
Scalable Solutions for DNA Sequence Analysis Michael Schatz Dec 4, 2009 JHU/UMD Joint Sequencing Meeting The Evolution of DNA Sequencing Year Genome Technology Cost 2001 Venter et al. Sanger (ABI) $300,000,000
Assembly in the Clouds
Assembly in the Clouds Michael Schatz October 13, 2010 Beyond the Genome Shredded Book Reconstruction Dickens accidentally shreds the first printing of A Tale of Two Cities Text printed on 5 long spools
I519 Introduction to Bioinformatics, Genome assembly. Yuzhen Ye School of Informatics & Computing, IUB
I519 Introduction to Bioinformatics, 2014 Genome assembly Yuzhen Ye (yye@indiana.edu) School of Informatics & Computing, IUB Contents Genome assembly problem Approaches Comparative assembly The string
Sequence Assembly. BMI/CS 576 Mark Craven Some sequencing successes
Sequence Assembly BMI/CS 576 www.biostat.wisc.edu/bmi576/ Mark Craven craven@biostat.wisc.edu Some sequencing successes Yersinia pestis Cannabis sativa The sequencing problem We want to determine the identity
Next Generation Sequencing Workshop De novo genome assembly
Next Generation Sequencing Workshop De novo genome assembly Tristan Lefébure TNL7@cornell.edu Stanhope Lab Population Medicine & Diagnostic Sciences Cornell University April 14th 2010 De novo assembly
de novo assembly Rayan Chikhi Pennsylvania State University Workshop On Genomics - Cesky Krumlov - January /73
1/73 de novo assembly Rayan Chikhi Pennsylvania State University Workshop On Genomics - Cesky Krumlov - January 2014 2/73 YOUR INSTRUCTOR IS.. - Postdoc at Penn State, USA - PhD at INRIA / ENS Cachan,
Data Preprocessing. Next Generation Sequencing analysis DTU Bioinformatics Next Generation Sequencing Analysis
Data Preprocessing Next Generation Sequencing analysis DTU Bioinformatics Generalized NGS analysis Data size Application Assembly: Compare Raw Pre- specific: Question Alignment / samples / Answer? reads
Computational Methods for de novo Assembly of Next-Generation Genome Sequencing Data
1/39 Computational Methods for de novo Assembly of Next-Generation Genome Sequencing Data Rayan Chikhi ENS Cachan Brittany / IRISA (Genscale team) Advisor : Dominique Lavenier 2/39 INTRODUCTION, YEAR 2000
Performance analysis of parallel de novo genome assembly in shared memory system
IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS Performance analysis of parallel de novo genome assembly in shared memory system To cite this article: Syam Budi Iryanto et al 2018
Introduction to Genome Assembly. Tandy Warnow
Introduction to Genome Assembly Tandy Warnow 2 Shotgun DNA Sequencing DNA target sample SHEAR & SIZE End Reads / Mate Pairs 550bp 10,000bp Not all sequencing technologies produce mate-pairs. Different
Sequencing. Short Read Alignment. Sequencing. Paired-End Sequencing 6/10/2010. Tobias Rausch 7 th June 2010 WGS. ChIP-Seq. Applied Biosystems.
Sequencing Short Alignment Tobias Rausch 7 th June 2010 WGS RNA-Seq Exon Capture ChIP-Seq Sequencing Paired-End Sequencing Target genome Fragments Roche GS FLX Titanium Illumina Applied Biosystems SOLiD
Data Preprocessing : Next Generation Sequencing analysis CBS - DTU Next Generation Sequencing Analysis
Data Preprocessing 27626: Next Generation Sequencing analysis CBS - DTU Generalized NGS analysis Data size Application Assembly: Compare Raw Pre- specific: Question Alignment / samples / Answer? reads
Genome 373: Genome Assembly. Doug Fowler
Genome 373: Genome Assembly Doug Fowler What are some of the things we ve seen we can do with HTS data? We ve seen that HTS can enable a wide variety of analyses ranging from ID ing variants to genome-
Finishing Circular Assemblies. J Fass UCD Genome Center Bioinformatics Core Thursday April 16, 2015
Finishing Circular Assemblies J Fass UCD Genome Center Bioinformatics Core Thursday April 16, 2015 Assembly Strategies de Bruijn graph Velvet, ABySS earlier, basic assemblers IDBA, SPAdes later, multi-k
IDBA - A Practical Iterative de Bruijn Graph De Novo Assembler
IDBA - A Practical Iterative de Bruijn Graph De Novo Assembler Yu Peng, Henry Leung, S.M. Yiu, Francis Y.L. Chin Department of Computer Science, The University of Hong Kong Pokfulam Road, Hong Kong {ypeng,
Mar%n Norling. Uppsala, November 15th 2016
Mar%n Norling Uppsala, November 15th 2016 Sequencing recap This lecture is focused on illumina, but the techniques are the same for all short-read sequencers. Short reads are (generally) high quality and
Reducing Genome Assembly Complexity with Optical Maps
Reducing Genome Assembly Complexity with Optical Maps Lee Mendelowitz LMendelo@math.umd.edu Advisor: Dr. Mihai Pop Computer Science Department Center for Bioinformatics and Computational Biology mpop@umiacs.umd.edu
IDBA A Practical Iterative de Bruijn Graph De Novo Assembler
IDBA A Practical Iterative de Bruijn Graph De Novo Assembler Yu Peng, Henry C.M. Leung, S.M. Yiu, and Francis Y.L. Chin Department of Computer Science, The University of Hong Kong Pokfulam Road, Hong Kong
de Bruijn graphs for sequencing data
de Bruijn graphs for sequencing data Rayan Chikhi CNRS Bonsai team, CRIStAL/INRIA, Univ. Lille 1 SMPGD 2016 1 MOTIVATION - de Bruijn graphs are instrumental for reference-free sequencing data analysis:
Introduction and tutorial for SOAPdenovo. Xiaodong Fang Department of Science and BGI May, 2012
Introduction and tutorial for SOAPdenovo Xiaodong Fang fangxd@genomics.org.cn Department of Science and Technology @ BGI May, 2012 Why de novo assembly? Genome is the genetic basis for different phenotypes
Purpose of sequence assembly
Sequence Assembly Purpose of sequence assembly Reconstruct long DNA/RNA sequences from short sequence reads Genome sequencing RNA sequencing for gene discovery Amplicon sequencing But not for transcript
Sequence Assembly Required!
Sequence Assembly Required! 1 October 3, ISMB 20172007 1 Sequence Assembly Genome Sequenced Fragments (reads) Assembled Contigs Finished Genome 2 Greedy solution is bounded 3 Typical assembly strategy
RESEARCH TOPIC IN BIOINFORMANTIC
RESEARCH TOPIC IN BIOINFORMANTIC GENOME ASSEMBLY Instructor: Dr. Yufeng Wu Noted by: February 25, 2012 Genome Assembly is a kind of string sequencing problems. As we all know, the human genome is very
Genome Reconstruction: A Puzzle with a Billion Pieces. Phillip Compeau Carnegie Mellon University Computational Biology Department
http://cbd.cmu.edu Genome Reconstruction: A Puzzle with a Billion Pieces Phillip Compeau Carnegie Mellon University Computational Biology Department Eternity II: The Highest-Stakes Puzzle in History Courtesy:
CS 68: BIOINFORMATICS. Prof. Sara Mathieson Swarthmore College Spring 2018
CS 68: BIOINFORMATICS Prof. Sara Mathieson Swarthmore College Spring 2018 Outline: Jan 31 DBG assembly in practice Velvet assembler Evaluation of assemblies (if time) Start: string alignment Candidate
Taller práctico sobre uso, manejo y gestión de recursos genómicos de abril de 2013 Assembling long-read Transcriptomics
Taller práctico sobre uso, manejo y gestión de recursos genómicos 22-24 de abril de 2013 Assembling long-read Transcriptomics Rocío Bautista Outline Introduction How assembly Tools assembling long-read
BLAST & Genome assembly
BLAST & Genome assembly Solon P. Pissis Tomáš Flouri Heidelberg Institute for Theoretical Studies May 15, 2014 1 BLAST What is BLAST? The algorithm 2 Genome assembly De novo assembly Mapping assembly 3
Computational Architecture of Cloud Environments Michael Schatz. April 1, 2010 NHGRI Cloud Computing Workshop
Computational Architecture of Cloud Environments Michael Schatz April 1, 2010 NHGRI Cloud Computing Workshop Cloud Architecture Computation Input Output Nebulous question: Cloud computing = Utility computing
DNA Fragment Assembly
Algorithms in Bioinformatics Sami Khuri Department of Computer Science San José State University San José, California, USA khuri@cs.sjsu.edu www.cs.sjsu.edu/faculty/khuri DNA Fragment Assembly Overlap
IDBA - A practical Iterative de Bruijn Graph De Novo Assembler
IDBA - A practical Iterative de Bruijn Graph De Novo Assembler Speaker: Gabriele Capannini May 21, 2010 Introduction De Novo Assembly assembling reads together so that they form a new, previously unknown
CS681: Advanced Topics in Computational Biology
CS681: Advanced Topics in Computational Biology Can Alkan EA224 calkan@cs.bilkent.edu.tr Week 7 Lectures 2-3 http://www.cs.bilkent.edu.tr/~calkan/teaching/cs681/ Genome Assembly Test genome Random shearing
Sequencing. Computational Biology IST Ana Teresa Freitas 2011/2012. (BACs) Whole-genome shotgun sequencing Celera Genomics
Computational Biology IST Ana Teresa Freitas 2011/2012 Sequencing Clone-by-clone shotgun sequencing Human Genome Project Whole-genome shotgun sequencing Celera Genomics (BACs) 1 Must take the fragments
Reducing Genome Assembly Complexity with Optical Maps Mid-year Progress Report
Reducing Genome Assembly Complexity with Optical Maps Mid-year Progress Report Lee Mendelowitz LMendelo@math.umd.edu Advisor: Dr. Mihai Pop Computer Science Department Center for Bioinformatics and Computational
CSCI2950-C Lecture 4 DNA Sequencing and Fragment Assembly
CSCI2950-C Lecture 4 DNA Sequencing and Fragment Assembly Ben Raphael Sept. 22, 2009 http://cs.brown.edu/courses/csci2950-c/ l-mer composition Def: Given string s, the Spectrum ( s, l ) is unordered multiset
DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies
DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies Chengxi Ye 1, Christopher M. Hill 1, Shigang Wu 2, Jue Ruan 2, Zhanshan (Sam) Ma
Techniques for de novo genome and metagenome assembly
1 Techniques for de novo genome and metagenome assembly Rayan Chikhi Univ. Lille, CNRS séminaire INRA MIAT, 24 novembre 2017 short bio 2 @RayanChikhi http://rayan.chikhi.name - compsci/math background
Manual of SOAPdenovo-Trans-v1.03. Yinlong Xie, Gengxiong Wu, Jingbo Tang,
Manual of SOAPdenovo-Trans-v1.03 Yinlong Xie, 2013-07-19 Gengxiong Wu, 2013-07-19 Jingbo Tang, 2013-07-19 ********** Introduction SOAPdenovo-Trans is a de novo transcriptome assembler basing on the SOAPdenovo
(for more info see:
Genome assembly (for more info see: http://www.cbcb.umd.edu/research/assembly_primer.shtml) Introduction Sequencing technologies can only "read" short fragments from a genome. Reconstructing the entire
Genome Sequencing Algorithms
Genome Sequencing Algorithms Phillip Compaeu and Pavel Pevzner Bioinformatics Algorithms: an Active Learning Approach Leonhard Euler (1707 1783) William Hamilton (1805 1865) Nicolaas Govert de Bruijn (1918
Computational models for bionformatics
Computational models for bionformatics De-novo assembly and alignment-free measures Michele Schimd Department of Information Engineering July 8th, 2015 Michele Schimd (DEI) PostDoc @ DEI July 8th, 2015
Algorithms for Bioinformatics
Adapted from slides by Alexandru Tomescu, Leena Salmela and Veli Mäkinen, which are partly from http://bix.ucsd.edu/bioalgorithms/slides.php 582670 Algorithms for Bioinformatics Lecture 3: Graph Algorithms
Graph Algorithms in Bioinformatics
Graph Algorithms in Bioinformatics Computational Biology IST Ana Teresa Freitas 2015/2016 Sequencing Clone-by-clone shotgun sequencing Human Genome Project Whole-genome shotgun sequencing Celera Genomics
Reducing Genome Assembly Complexity with Optical Maps
Reducing Genome Assembly Complexity with Optical Maps AMSC 663 Mid-Year Progress Report 12/13/2011 Lee Mendelowitz Lmendelo@math.umd.edu Advisor: Mihai Pop mpop@umiacs.umd.edu Computer Science Department
Genome Assembly Using de Bruijn Graphs. Biostatistics 666
Genome Assembly Using de Bruijn Graphs Biostatistics 666 Previously: Reference Based Analyses Individual short reads are aligned to reference Genotypes generated by examining reads overlapping each position
Adam M Phillippy Center for Bioinformatics and Computational Biology
Adam M Phillippy Center for Bioinformatics and Computational Biology WGS sequencing shearing sequencing assembly WGS assembly Overlap reads identify reads with shared k-mers calculate edit distance Layout
BLAST & Genome assembly
BLAST & Genome assembly Solon P. Pissis Tomáš Flouri Heidelberg Institute for Theoretical Studies November 17, 2012 1 Introduction Introduction 2 BLAST What is BLAST? The algorithm 3 Genome assembly De
CS 173, Lecture B Introduction to Genome Assembly (using Eulerian Graphs) Tandy Warnow
CS 173, Lecture B Introduction to Genome Assembly (using Eulerian Graphs) Tandy Warnow 2 Shotgun DNA Sequencing DNA target sample SHEAR & SIZE End Reads / Mate Pairs 550bp 10,000bp Not all sequencing technologies
Meraculous De Novo Assembly of the Ariolimax dolichophallus Genome. Charles Cole, Jake Houser, Kyle McGovern, and Jennie Richardson
Meraculous De Novo Assembly of the Ariolimax dolichophallus Genome Charles Cole, Jake Houser, Kyle McGovern, and Jennie Richardson Meraculous Assembler Published by the US Department of Energy Joint Genome
de novo assembly & k-mers
de novo assembly & k-mers Rayan Chikhi CNRS Workshop on Genomics - Cesky Krumlov January 2018 1 YOUR INSTRUCTOR IS.. - Junior CNRS bioinformatics researcher in Lille, France - Postdoc: Penn State, USA;
DNA Sequencing. Overview
BINF 3350, Genomics and Bioinformatics DNA Sequencing Young-Rae Cho Associate Professor Department of Computer Science Baylor University Overview Backgrounds Eulerian Cycles Problem Hamiltonian Cycles
Assembling short reads from jumping libraries with large insert sizes
Bioinformatics, 31(20), 2015, 3262 3268 doi: 10.1093/bioinformatics/btv337 Advance Access Publication Date: 3 June 2015 Original Paper Sequence analysis Assembling short reads from jumping libraries with
7.36/7.91 recitation. DG Lectures 5 & 6 2/26/14
7.36/7.91 recitation DG Lectures 5 & 6 2/26/14 1 Announcements project specific aims due in a little more than a week (March 7) Pset #2 due March 13, start early! Today: library complexity BWT and read
Under the Hood of Alignment Algorithms for NGS Researchers
Under the Hood of Alignment Algorithms for NGS Researchers April 16, 2014 Gabe Rudy VP of Product Development Golden Helix Questions during the presentation Use the Questions pane in your GoToWebinar window
Appendix A. Example code output. Chapter 1. Chapter 3
Appendix A Example code output This is a compilation of output from selected examples. Some of these examples requires exernal input from e.g. STDIN, for such examples the interaction with the program
Next generation sequencing: de novo assembly. Overview
Next generation sequencing: de novo assembly Laurent Falquet, Vital-IT Helsinki, June 4, 2010 Overview What is de novo assembly? Methods Greedy OLC de Bruijn Tools Issues File formats Paired-end vs mate-pairs
AMemoryEfficient Short Read De Novo Assembly Algorithm
Original Paper AMemoryEfficient Short Read De Novo Assembly Algorithm Yuki Endo 1,a) Fubito Toyama 1 Chikafumi Chiba 2 Hiroshi Mori 1 Kenji Shoji 1 Received: October 17, 2014, Accepted: October 29, 2014,
Towards a de novo short read assembler for large genomes using cloud computing
Towards a de novo short read assembler for large genomes using cloud computing Michael Schatz April 21, 2009 AMSC664 Advanced Scientific Computing Outline 1.! Genome assembly by analogy 2.! DNA sequencing
Practical Bioinformatics for Life Scientists. Week 4, Lecture 8. István Albert Bioinformatics Consulting Center Penn State
Practical Bioinformatics for Life Scientists Week 4, Lecture 8 István Albert Bioinformatics Consulting Center Penn State Reminder Before any serious work re-check the documentation for small but essential
HiPGA: A High Performance Genome Assembler for Short Read Sequence Data
2014 IEEE 28th International Parallel & Distributed Processing Symposium Workshops HiPGA: A High Performance Genome Assembler for Short Read Sequence Data Xiaohui Duan, Kun Zhao, Weiguo Liu* School of
A Genome Assembly Algorithm Designed for Single-Cell Sequencing
SPAdes A Genome Assembly Algorithm Designed for Single-Cell Sequencing Bankevich A, Nurk S, Antipov D, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput
Building approximate overlap graphs for DNA assembly using random-permutations-based search.
An algorithm is presented for fast construction of graphs of reads, where an edge between two reads indicates an approximate overlap between the reads. Since the algorithm finds approximate overlaps directly,
[v1.2] SIMBA docs LGCM Federal University of Minas Gerais 2015
![[v1.2] SIMBA docs LGCM Federal University of Minas Gerais 2015](/thumbs/81/84357512.jpg "[v1.2] SIMBA docs LGCM Federal University of Minas Gerais 2015")
[v1.2] SIMBA docs LGCM Federal University of Minas Gerais 2015 2 Summary 1. Introduction... 3 1.1 Why use SIMBA?... 3 1.2 How does SIMBA work?... 4 1.3 How to download SIMBA?... 4 1.4 SIMBA VM... 4 2.
De novo sequencing and Assembly. Andreas Gisel International Institute of Tropical Agriculture (IITA) Ibadan, Nigeria
De novo sequencing and Assembly Andreas Gisel International Institute of Tropical Agriculture (IITA) Ibadan, Nigeria The Principle of Mapping reads good, ood_, d_mo, morn, orni, ning, ing_, g_be, beau,
Read Mapping and Assembly
Statistical Bioinformatics: Read Mapping and Assembly Stefan Seemann seemann@rth.dk University of Copenhagen April 9th 2019 Why sequencing? Why sequencing? Which organism does the sample comes from? Assembling
Gap Filling as Exact Path Length Problem
Gap Filling as Exact Path Length Problem RECOMB 2015 Leena Salmela 1 Kristoffer Sahlin 2 Veli Mäkinen 1 Alexandru I. Tomescu 1 1 University of Helsinki 2 KTH Royal Institute of Technology April 12th, 2015
MITOCW watch?v=zyw2aede6wu
MITOCW watch?v=zyw2aede6wu The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To
Algorithms for Bioinformatics
Adapted from slides by Alexandru Tomescu, Leena Salmela and Veli Mäkinen, which are partly from http://bix.ucsd.edu/bioalgorithms/slides.php 58670 Algorithms for Bioinformatics Lecture 5: Graph Algorithms
Pyramidal and Chiral Groupings of Gold Nanocrystals Assembled Using DNA Scaffolds
Pyramidal and Chiral Groupings of Gold Nanocrystals Assembled Using DNA Scaffolds February 27, 2009 Alexander Mastroianni, Shelley Claridge, A. Paul Alivisatos Department of Chemistry, University of California,
DNA Sequencing The Shortest Superstring & Traveling Salesman Problems Sequencing by Hybridization
Eulerian & Hamiltonian Cycle Problems DNA Sequencing The Shortest Superstring & Traveling Salesman Problems Sequencing by Hybridization The Bridge Obsession Problem Find a tour crossing every bridge just
How to apply de Bruijn graphs to genome assembly
PRIMER How to apply de Bruijn graphs to genome assembly Phillip E C Compeau, Pavel A Pevzner & lenn Tesler A mathematical concept known as a de Bruijn graph turns the formidable challenge of assembling
Assembly in the Clouds
Assembly in the Clouds Michael Schatz November 12, 2010 GENSIPS'10 Outline 1. Genome Assembly by Analogy 2. DNA Sequencing and Genomics 3. Sequence Analysis in the Clouds 1. Mapping & Genotyping 2. De
Hybrid Parallel Programming
Hybrid Parallel Programming for Massive Graph Analysis KameshMdd Madduri KMadduri@lbl.gov ComputationalResearch Division Lawrence Berkeley National Laboratory SIAM Annual Meeting 2010 July 12, 2010 Hybrid
BIOINFORMATICS. Integrating genome assemblies with MAIA
BIOINFORMATICS Vol. 26 ECCB 2010, pages i433 i439 doi:10.1093/bioinformatics/btq366 Integrating genome assemblies with MAIA Jurgen Nijkamp 1,2,3,, Wynand Winterbach 1,4, Marcel van den Broek 2,3, Jean-Marc
02-711/ Computational Genomics and Molecular Biology Fall 2016
Literature assignment 2 Due: Nov. 3 rd, 2016 at 4:00pm Your name: Article: Phillip E C Compeau, Pavel A. Pevzner, Glenn Tesler. How to apply de Bruijn graphs to genome assembly. Nature Biotechnology 29,
User's Guide to DNASTAR SeqMan NGen For Windows, Macintosh and Linux
User's Guide to DNASTAR SeqMan NGen 12.0 For Windows, Macintosh and Linux DNASTAR, Inc. 2014 Contents SeqMan NGen Overview...7 Wizard Navigation...8 Non-English Keyboards...8 Before You Begin...9 The
ABySS. Assembly By Short Sequences
ABySS Assembly By Short Sequences ABySS Developed at Canada s Michael Smith Genome Sciences Centre Developed in response to memory demands of conventional DBG assembly methods Parallelizability Illumina
1 Abstract. 2 Introduction. 3 Requirements
1 Abstract 2 Introduction This SOP describes the HMP Whole- Metagenome Annotation Pipeline run at CBCB. This pipeline generates a 'Pretty Good Assembly' - a reasonable attempt at reconstructing pieces
RNA-Seq in Galaxy: Tuxedo protocol. Igor Makunin, UQ RCC, QCIF
RNA-Seq in Galaxy: Tuxedo protocol Igor Makunin, UQ RCC, QCIF Acknowledgments Genomics Virtual Lab: gvl.org.au Galaxy for tutorials: galaxy-tut.genome.edu.au Galaxy Australia: galaxy-aust.genome.edu.au
Constrained traversal of repeats with paired sequences
RECOMB 2011 Satellite Workshop on Massively Parallel Sequencing (RECOMB-seq) 26-27 March 2011, Vancouver, BC, Canada; Short talk: 2011-03-27 12:10-12:30 (presentation: 15 minutes, questions: 5 minutes)
Parallelization of MIRA Whole Genome and EST Sequence Assembler [1]
![Parallelization of MIRA Whole Genome and EST Sequence Assembler [1]](/thumbs/89/99619875.jpg "Parallelization of MIRA Whole Genome and EST Sequence Assembler [1]")
Parallelization of MIRA Whole Genome and EST Sequence Assembler [1] Abhishek Biswas *, Desh Ranjan, Mohammad Zubair Department of Computer Science, Old Dominion University, Norfolk, VA 23529 USA * abiswas@cs.odu.edu
10/15/2009 Comp 590/Comp Fall
Lecture 13: Graph Algorithms Study Chapter 8.1 8.8 10/15/2009 Comp 590/Comp 790-90 Fall 2009 1 The Bridge Obsession Problem Find a tour crossing every bridge just once Leonhard Euler, 1735 Bridges of Königsberg
A Fast Sketch-based Assembler for Genomes
A Fast Sketch-based Assembler for Genomes Priyanka Ghosh School of EECS Washington State University Pullman, WA 99164 pghosh@eecs.wsu.edu Ananth Kalyanaraman School of EECS Washington State University
Assembly of the Ariolimax dolicophallus genome with Discovar de novo. Chris Eisenhart, Robert Calef, Natasha Dudek, Gepoliano Chaves
Assembly of the Ariolimax dolicophallus genome with Discovar de novo Chris Eisenhart, Robert Calef, Natasha Dudek, Gepoliano Chaves Overview -Introduction -Pair correction and filling -Assembly theory
Aligners. J Fass 21 June 2017
Aligners J Fass 21 June 2017 Definitions Assembly: I ve found the shredded remains of an important document; put it back together! UC Davis Genome Center Bioinformatics Core J Fass Aligners 2017-06-21
From Sequencing to Sequences:
From Sequencing to Sequences: Algorithms and Metrics for Accurate Genome Assembly Bud Mishra Courant Institute of Mathematical Sciences New York University & Cold Spring Harbor Laboratory August 26, 2011
EAGLER - Eliminating Assembly Gaps by Long Extending Reads
UNIVERSITY OF ZAGREB FACULTY OF ELECTRICAL ENGINEERING AND COMPUTING MASTER s THESIS No. 1196 EAGLER - Eliminating Assembly Gaps by Long Extending Reads Luka Šterbić Zagreb, December 2015 iii CONTENTS
AMOS Assembly Validation and Visualization
AMOS Assembly Validation and Visualization Michael Schatz Center for Bioinformatics and Computational Biology University of Maryland April 7, 2006 Outline AMOS Introduction Getting Data into AMOS AMOS
RNA-seq Data Analysis
Seyed Abolfazl Motahari RNA-seq Data Analysis Basics Next Generation Sequencing Biological Samples Data Cost Data Volume Big Data Analysis in Biology تحلیل داده ها کنترل سیستمهای بیولوژیکی تشخیص بیماریها
Accelerating Genomic Sequence Alignment Workload with Scalable Vector Architecture
Accelerating Genomic Sequence Alignment Workload with Scalable Vector Architecture Dong-hyeon Park, Jon Beaumont, Trevor Mudge University of Michigan, Ann Arbor Genomics Past Weeks ~$3 billion Human Genome
MacVector for Mac OS X. The online updater for this release is MB in size
MacVector 17.0.3 for Mac OS X The online updater for this release is 143.5 MB in size You must be running MacVector 15.5.4 or later for this updater to work! System Requirements MacVector 17.0 is supported
Genome 373: Mapping Short Sequence Reads I. Doug Fowler
Genome 373: Mapping Short Sequence Reads I Doug Fowler Two different strategies for parallel amplification BRIDGE PCR EMULSION PCR Two different strategies for parallel amplification BRIDGE PCR EMULSION
Darwin: A Hardware-acceleration Framework for Genomic Sequence Alignment
Darwin: A Hardware-acceleration Framework for Genomic Sequence Alignment Yatish Turakhia EE PhD candidate Stanford University Prof. Bill Dally (Electrical Engineering and Computer Science) Prof. Gill Bejerano
DNA / RNA sequencing
Outline Ways to generate large amounts of sequence Understanding the contents of large sequence files Fasta format Fastq format Sequence quality metrics Summarizing sequence data quality/quantity Using
A THEORETICAL ANALYSIS OF SCALABILITY OF THE PARALLEL GENOME ASSEMBLY ALGORITHMS
A THEORETICAL ANALYSIS OF SCALABILITY OF THE PARALLEL GENOME ASSEMBLY ALGORITHMS Munib Ahmed, Ishfaq Ahmad Department of Computer Science and Engineering, University of Texas At Arlington, Arlington, Texas
Sequence Analysis Pipeline
Sequence Analysis Pipeline Transcript fragments 1. PREPROCESSING 2. ASSEMBLY (today) Removal of contaminants, vector, adaptors, etc Put overlapping sequence together and calculate bigger sequences 3. Analysis/Annotation