Computational assembly for prokaryotic sequencing projects
|
|
|
- Meagan Lee
- 6 years ago
- Views:
Transcription
1 Computational assembly for prokaryotic sequencing projects Lee Katz, Ph.D. Bioinformatician, Enteric Diseases Laboratory Branch (EDLB) Enteric Diseases Bioinformatics Team (EDBiT) January 30, 2017 Disclaimers The findings and conclusions in this presentation have not been formally disseminated by the Centers for Disease Control and Prevention and should not be construed to represent any agency determination or policy. The findings and conclusions in this [report/presentation] are those of the author(s) and do not necessarily represent the official position of CDC
2 April 16,
3 3
4 April 23,
5 2011 to present Vibrio, Campylobacter, Escherichia, Shigella, Yersinia, Salmonella ENTERIC DISEASES LABORATORY BRANCH 5
6 August 29, 2014 Thanks to John Besser for letting me borrow this slide 6
7 PulseNet Detected Outbreaks 2014 (so far) Thanks to John Besser for letting me borrow this slide 7
8 Illumina sequencing Reads Quality control (Q/C) Read metrics Read-cleaning Assembly Algorithms Assembly metrics Genome-based typing Outline Please also see my talk last year at 8
9 Dilbert
10 Prokaryotic Sequencing Projects Stages Sequencing Assembly Feature prediction Functional annotation analysis Display (Genome Browser) Examples Haemophilus influenzae Neisseria meningitidis Bordetella bronchisceptica Vibrio cholerae Listeria monocytogenes Sequencing Assembly prediction annotation Display Fleischman et al. (1995) Whole-Genome Random Sequencing and Assembly of Haemophilus influenzae Rd Science 269:5223 Kislyuk et al. (2010) A computational genomics pipeline for prokaryotic sequencing projects Bioinformatics 26:15
11 Illumina sequencing (2 nd Gen) You have sequences from the Illumina HiSeq 250bp Paired end 11
12 Paired end Illumina reads Ideal Still good Bad 12
13 Q/C + cleaning READS 13
14 Q/C You need to know if your data are good! Example software that give you read metrics FastQC FastqQC (from AMOS) Computational Genomics Pipeline (CG-Pipeline) run_assembly_readmetrics.pl BamUtils stats 14
15 Quality Control 15 FastQC output
16 Quality Control 16 FastQC output
17 The CG-Pipeline way run_assembly_readmetrics.pl File avgreadlength totalbases minreadlength maxreadlength avgquality tmp.fastq
18 READ CLEANING 18
19 Why clean my reads? Your reads have errors These errors can hurt downstream analysis Can be categorized into Trimming Filtering Correcting 19
20 More read cleaning and correction Software CGP documentation: Fastx toolkit EA-utils AMOS amos.sourceforge.net Sickle Quake BayesHammer PrinSeq Blue - and more is out there! Evaluation Fabbro et al 2013, An extensive evaluation of read trimming effects on Illumina NGS data analysis 20
21 ASSEMBLY CONCEPTS 21
22 Assembly Overlaps between reads or between nodes in a graph Generate contigs (contiguous sequences) Generate scaffolds NNN N 22
23 Further reading: Zhang et al 2011 Plos One; Miller et al 2011 Genomics 23
24 Overlap-layout-consensus Great at longer, fewer, imperfect reads, e.g., Ion Torrent, 454, PacBio Stephen Schlebusch, Nicola Illing. Next generation shotgun sequencing and the challenges of de novo genome assembly. 24 South African Journal of Science. Vol. 108
25 Derive consensus sequence TAGATTACACAGATTACTGA-TTGATGGCGTAA-CTA TAGATTACACAGATTACTGACTTGATGGCGTAAACTA TAG-TTACACAGATTATTGACTTCATGGCGTAA-CTA TAGATTACACAGATTACTGACTTGATGGCGTAA-CTA TAGATTACACAGATTACTGACTTGATGGGGTAA-CTA TAGATTACACAGATTACTGACTTGATGGCGTAA-CTA Derive each consensus base by weighted voting Slide adapted from Andrey Kislyuk, compgenomics-kislyuk.pdf 25
26 De bruijn (kmer) Great at shorter, numerous, less error-prone reads, e.g., Illumina 26
27 Another way to look at de bruijn assembly A (1) D (1) B (2) C (3) F (1) E (2) G (1) Unipath graph of the 1.8-Mb genome of C. jejuni Possible paths: ABCDBCEFCEG ABCEFCDBCEG 27 Butler J et al. (2008) ALLPATHS: De novo assembly of whole-genome shotgun microreads. Genome Res. 18:
28 Another way to look at de bruijn assembly A (1) D (1) B (2) C (3) F (1) E (2) G (1) Unipath graph of the 1.8-Mb genome of C. jejuni Possible paths: ABCDBCEFCEG ABCEFCDBCEG 28 Butler J et al. (2008) ALLPATHS: De novo assembly of whole-genome shotgun microreads. Genome Res. 18:
29 Recap of assembly reads Paired end reads contigs Scaffold NNNNNNNNNN 29
30 ASSEMBLY EVALUATION 30
31 Assembly Metrics How do you tell if your assembly is good? Metric description reference Assembly Length Number of Contigs N50 Longest Contig Average contig length The size of the concatenated assembly The count of contigs The size of the contig at where half the genome is located in size >N50 and half is located in size <N50 Kmer21 Frequency of kmers with k=21 /2012/06/26/what-is-wrong-withn50-how-can-we-make-it-betterpart-ii/ GC-content Assembly score Percentage of the genome that is either G or C log N50 percentgenomecovered numcontigs CG-Pipeline/Lee Katz 31
32 Please also see my talk last year at ech.edu/index.php/lectures 32
33 The Far Side Totally changing the topic GENOME-BASED TYPING 33
34 Can we use WGS with epidemiology? Phylogeny* approximates** epidemiology***! * Depending on your evolutionary model ** In a monoclonal outbreak *** A trace back diagram Epidemiology - linking cases through a common food vehicle Phylogeny how things are genetically related through evolution
35 Listeria real-time project Sequenced every Listeria monocytogenes in the US Starting August 2013 Clinical & environmental (CDC/FDA/USDA) Put into large tree (NCBI) Mile-high view 7,800 Listeria monocytogenes genomes in this tree NCBI tree and Genome Workbench. ftp://ftp.ncbi.nlm.nih.gov/pathogen/results/listeria/pdg / 35
36 The flood of data is upon us Sept 23, 2016: 91k genomes 10 taxonomic groups Jan 27, 2015: 121k genomes 19 taxonomic groups * for foodborne and hospital-acquired pathogens 36
37 Are two isolates in the same outbreak? Need to refine NCBI analysis Can use many methods MinHash clustering with kmers wgmlst SNPs Assembly-based (faster) Raw-read based (resolution) 37
38 MinHash Kmer: a length of DNA k nucleotides long 1. Shred all reads into kmers 2. How many kmers are in common? 3. Transform into a jaccard distance Gives a genomic distance With the distances, make tree Fast, not phylogenetic Analogy: comparing equal-sized shreds of paper from two books Image credits: DEATH OF A SHREDDER NCBI: Mashtree: Mash: Sourmash: MinHash algorithm explained: Ondov et al (2016) Mash: fast genome and metagenome distance estimation using MinHash Genome Biology 38
39 MLST MLST: multilocus sequence typing Locus: a place in a genome. Plural: loci Identify a set of loci in the genome Compare each locus in a genome against the set of loci Count differences and the number of loci compared Analogy: comparing two books, page by page Different kinds 7-gene MLST rmlst wgmlst (whole genome MLST) cgmlst (core genome MLST) and more StringMLST SRST2 BioNumerics mlst Salmonella 7-gene scheme Moura, Alexandra, et al. "Whole genome-based population biology and epidemiological surveillance of Listeria monocytogenes." Nature Microbiology 2 (2016): Jackson, Brendan R., et al. "Implementation of nationwide real-time whole-genome sequencing to enhance listeriosis outbreak detection and investigation." Clinical Infectious Diseases 63.3 (2016): Maiden, Martin CJ, et al. "Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms." Proceedings of the National Academy of Sciences 95.6 (1998):
40 SNPs SNP: Single nucleotide polymorphism hqsnp: high-quality SNP Compare individual letters in a query genome against the reference genome Analogy: comparing individual letters between two books Lyve-SET github.com/lskatz/lyve-set SNP-Pipeline (CFSAN) ksnp SPANDx Snippy NASP Red Dog Harvest MUMmer SNVPhyl 40 RealPhy
41 Which algorithm should you use? MinHash wgmlst hqsnp Diversity Outbreak-level resolution Further genomic information Minimal upfront effort Fast Easy-to-use for anyone Easy-to-use for bioinformaticians 41
42 SOFTWARE BY LEE 42
43 Lyve-SET Lyve Listeria, Yersinia, Vibrio, and Enterobacteriaceae reference lab SET Snp Extraction Tool Some details on Lyve-SET: For LINUX Extensive documentation Help options are embedded in each script --fast option, takes ¼ the normal time Easy to use Modular Most stable version: 1.1.4f Newest version:
44 An animation of Lyve-SET 0. Pre-processing a) phage discovery/masking b) Manual identification of troublesome regions c) Read cleaning (Poster Wagner et al) 1. Mapping - SMALT phage Manual identification Reference genome Genome 1 SNP profile a) 95% read identity b) Unambiguous mapping 2. SNP calling - VarScan Genome 2 SNP profile Genome 3 SNP profile Genome 4 SNP profile a) 75% consensus b) 10x depth 3. Phylogeny inferring RAxML v8 a) Removal of clustered SNPs b) Ascertainment bias model C C C C- 2014C- 2014C C C C-3 Phylogeny c) Maximum likelihood Koboldt, D., Zhang, Q., Larson, D., Shen, D., McLellan, M., Lin, L., Miller, C., Mardis, E., Ding, L., & Wilson, R. (2012). VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing Genome Research DOI: /gr Stamatakis, A. (2014) RAxML version8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. doi: /bioinformatics/btu033
45 Lyve-SET method with more details Katz et al 2017, Frontiers in Microbiology. In review. 45
46 Comparison with other SNP pipelines Each data point is a SNP distance as determined by Lyve-SET (x-axis) and the distance of an alternative SNP pipeline (y-axis). The slope indicates the number of SNPs per Lyve-SET SNP. 46 Katz et al 2017, Frontiers in Microbiology. In review.
47 Comparison with whole-genome MLST (Listeria monocytogenes only) Katz et al 2017, Frontiers in Microbiology. In review. 47
48 Comparison with other SNP Pipelines and wgmlst Lyve-SET ksnp RealPhy Snp-Pipeline SNVPhyl wgmlst Tree sensitivity (Sn) a 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% Tree specificity (Sp) a 100.0% 90.2% 100.0% 100.0% 100.0% 100.0% average of Sn and Sp 100.0% 95.1% 100.0% 100.0% 100.0% 100.0% Kendall-Colijn (λ = 0) b E E E E E-04 Robinson-Foulds b E E E E E-147 Mantel SNP ratio c,d , , , , , 0.72 goodness-of-fit (R 2 ) d , , , , , 0.72 Genome analyzed e 25.9% 0.1% 84.8% 0.3% 82.1% 88.2% a) Average percentage from 11 outbreaks. The S. enterica outbreak 1203NYJAP-1 was removed as an outlier because all pipelines except wgmlst produced errors with grouping outbreak vs non-outbreak isolates. Therefore this dataset was removed from the Sn and Sp calculations as an outlier. b) Geometric mean. c) Number of SNPs per Lyve-SET SNP, averaged across 12 outbreaks. For wgmlst, this is the number of alleles per Lyve-SET SNP. d) The average for 12 outbreaks. First value is for all data points; second value is for distances between only outbreakassociated genomes. e) The average for 12 outbreaks. Percentage of the reference genome included for analysis. For wgmlst, the average percentage was calculated by obtaining each GenBank-formatted file with annotated wgmlst loci and calculating the breadth of coverage for all loci. Katz et al 2017, Frontiers in Microbiology. In review. Comparison metrics: Kendall and Colijn 2015, A tree metric using structure and length to capture distinct phylogenetic signals. arxiv Mantel 1967, The detection of disease clustering and a generalized regression approach Cancer Res Robinson and Foulds 1981, Comparison of phylogenetic trees. Mathematical biosciences 48
49 Lyve-SET Lyve Listeria, Yersinia, Vibrio, and Enterobacteriaceae reference lab SET Snp Extraction Tool Who uses Lyve-SET? Used in labs at CDC: Foodborne Diseases Laboratory Branch - EDBIT, Reference Lab (NERO), PulseNet, NARMS, Botulism Lab Clinical and Environmental Microbiology Branch Special Bacterial Pathogens Branch Respiratory Diseases Branch Meningitis and Vaccine Preventable Diseases Branch State health labs - Colorado, Washington USDA - FSIS, ARS 49
50 Mashtree Useful for creating a dendrogram quickly Does not create a phylogeny mashtree 50
51 Mashtree comparison RealPhy v112 Snp-Pipeline v0.5.2 SNVPhyl v1.0 wgmlst BioNumeric s v7.5 ksnp v3.0.0 Lyve-SET v1.1.4f VA-WGS CFSAN VA-WGS VA-WGS VA-WGS PNUSAL CFSAN CFSAN CFSAN PNUSAL PNUSAL CFSAN VA-WGS VA-WGS CFSAN CFSAN VA-WGS VA-WGS VA-WGS VA-WGS PNUSAL PNUSAL VA-WGS VA-WGS VA-WGS VA-WGS CFSAN VA-WGS FS-100 VA-WGS CFSAN CFSAN VA-WGS VA-WGS VA-WGS VA-WGS FS-106 NYAG12B PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL VA-WGS CFSAN VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS FS-106 CFSAN CFSAN VA-WGS VA-WGS CFSAN VA-WGS PNUSAL PNUSAL CFSAN PNUSAL VA-WGS CFSAN VA-WGS CFSAN CFSAN VA-WGS CFSAN VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS CFSAN FS-100 PNUSAL VA-WGS NYAG12B PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL VA-WGS VA-WGS VA-WGS CFSAN CFSAN FS-100 NYAG12B CFSAN VA-WGS VA-WGS VA-WGS CFSAN VA-WGS VA-WGS CFSAN VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS CFSAN VA-WGS CFSAN CFSAN CFSAN FS-106 VA-WGS CFSAN VA-WGS PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL CFSAN VA-WGS FS-106 CFSAN CFSAN PNUSAL VA-WGS PNUSAL VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS CFSAN PNUSAL VA-WGS CFSAN CFSAN FS-100 PNUSAL VA-WGS VA-WGS CFSAN VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS CFSAN VA-WGS CFSAN VA-WGS VA-WGS VA-WGS PNUSAL CFSAN NYAG12B PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL VA-WGS CFSAN VA-WGS VA-WGS CFSAN VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS CFSAN CFSAN CFSAN CFSAN FS-106 CFSAN VA-WGS CFSAN VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS CFSAN FS-100 PNUSAL PNUSAL VA-WGS CFSAN VA-WGS VA-WGS VA-WGS VA-WGS PNUSAL PNUSAL PNUSAL PNUSAL NYAG12B PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL VA-WGS VA-WGS VA-WGS PNUSAL CFSAN VA-WGS VA-WGS CFSAN CFSAN VA-WGS VA-WGS CFSAN VA-WGS VA-WGS VA-WGS CFSAN VA-WGS VA-WGS VA-WGS PNUSAL PNUSAL CFSAN VA-WGS CFSAN VA-WGS VA-WGS CFSAN FS-106 VA-WGS CFSAN VA-WGS VA-WGS CFSAN FS-100 VA-WGS PNUSAL PNUSAL PNUSAL NYAG12B PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL Mashtree v minutes, 41 seconds CFSAN CFSAN CFSAN VA-WGS CFSAN CFSAN CFSAN VA-WGS CFSAN CFSAN CFSAN VA-WGS VA-WGS CFSAN VA-WGS VA-WGS VA-WGS VA-WGS FS-100 FS-106 VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS NYAG12B VA-WGS VA-WGS VA-WGS PNUSAL PNUSAL VA-WGS PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL Mashtree v0.08 with min_abunda nce_filer 35 minutes VA-WGS VA-WGS CFSAN VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS CFSAN PNUSAL PNUSAL VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS VA-WGS CFSAN CFSAN CFSAN CFSAN CFSAN CFSAN CFSAN VA-WGS PNUSAL VA-WGS PNUSAL PNUSAL VA-WGS VA-WGS VA-WGS VA-WGS CFSAN VA-WGS FS-100 FS-106 NYAG12B PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL PNUSAL Green indicates outbreak association Removed one outlier genome in the min_abundance_filter tree that flattened the entire tree 51
52 Can t we just estimate a trace back TransPhylo (Imperial College/bcCDC): xiv/early/2016/07/22/ full.pdf Outbreaker (Imperial College): mpbiol/article?id= /jou rnal.pcbi SCOTTI (University of Oxford): mpbiol/article?id= %2f journal.pcbi tree? 52
53 Acknowledgements Every single compgenomics class since 2008 My branch at CDC 53
54 Questions? Lee github.com/lskatz The findings and conclusions in this presentation have not been formally disseminated by the Centers for Disease Control and Prevention and should not be construed to represent any agency determination or policy. The findings and conclusions in this presentation are those of the author and do not necessarily represent the official position of CDC 54
Computational assembly for prokaryotic sequencing projects
Computational assembly for prokaryotic sequencing projects Lee Katz, Ph.D. Bioinformatician, Enteric Diseases Laboratory Branch January 21, 2015 Disclaimers The findings and conclusions in this presentation
Introduction to NGS Analysis Tools
National Center for Emerging and Zoonotic Infectious Diseases Introduction to NGS Analysis Tools Heather Carleton, PhD, MPH Team Lead, Enteric Diseases Bioinformatics, Enteric Diseases Laboratory Branch,
De Novo Assembly of High-throughput Short Read Sequences
De Novo Assembly of High-throughput Short Read Sequences Chuming Chen Center for Bioinformatics and Computational Biology (CBCB) University of Delaware NECC Third Skate Genome Annotation Workshop May 23,
CDC s Advanced Molecular Detection (AMD) Sequence Data Analysis and Management
CDC s Advanced Molecular Detection (AMD) Sequence Data Analysis and Management Scott Sammons Technology Officer Office of Advanced Molecular Detection National Center for Emerging and Zoonotic Infectious
IFSH WHOLE GENOME SEQUENCING FOR FOOD INDUSTRY SYMPOSIUM May 22-23, 2017
1 USDA, Food Safety and Inspection Service IFSH WHOLE GENOME SEQUENCING FOR FOOD INDUSTRY SYMPOSIUM May 22-23, 2017 Chicago Marriott Southwest at Burr Ridge 1200 Burr Ridge Parkway, Burr Ridge, IL 60527
Mate-pair library data improves genome assembly
De Novo Sequencing on the Ion Torrent PGM APPLICATION NOTE Mate-pair library data improves genome assembly Highly accurate PGM data allows for de Novo Sequencing and Assembly For a draft assembly, generate
Sequence assembly. Jose Blanca COMAV institute bioinf.comav.upv.es
Sequence assembly Jose Blanca COMAV institute bioinf.comav.upv.es Sequencing project Unknown sequence { experimental evidence result read 1 read 4 read 2 read 5 read 3 read 6 read 7 Computational requirements
2014 APHL Next Generation Sequencing (NGS) Survey
APHL would like you to complete the Next Generation Sequencing (NGS) in Public Health Laboratories Survey. The purpose of this survey is to collect information on current capacities for NGS testing and
Genome Assembly. J Fass UCD Genome Center Bioinformatics Core Friday September, 2015
Genome Assembly J Fass UCD Genome Center Bioinformatics Core Friday September, 2015 From reads to molecules What s the Problem? How to get the best assemblies for the smallest expense (sequencing) and
Introductie en Toepassingen van Next-Generation Sequencing in de Klinische Virologie. Sander van Boheemen Medical Microbiology
Introductie en Toepassingen van Next-Generation Sequencing in de Klinische Virologie Sander van Boheemen Medical Microbiology Next-generation sequencing Next-generation sequencing (NGS), also known as
Sequencing technologies. Jose Blanca COMAV institute bioinf.comav.upv.es
Sequencing technologies Jose Blanca COMAV institute bioinf.comav.upv.es Outline Sequencing technologies: Sanger 2nd generation sequencing: 3er generation sequencing: 454 Illumina SOLiD Ion Torrent PacBio
Sequencing Theory. Brett E. Pickett, Ph.D. J. Craig Venter Institute
Sequencing Theory Brett E. Pickett, Ph.D. J. Craig Venter Institute Applications of Genomics and Bioinformatics to Infectious Diseases GABRIEL Network Agenda Sequencing Instruments Sanger Illumina Ion
Sequencing technologies. Jose Blanca COMAV institute bioinf.comav.upv.es
Sequencing technologies Jose Blanca COMAV institute bioinf.comav.upv.es Outline Sequencing technologies: Sanger 2nd generation sequencing: 3er generation sequencing: 454 Illumina SOLiD Ion Torrent PacBio
Gap Filling for a Human MHC Haplotype Sequence
American Journal of Life Sciences 2016; 4(6): 146-151 http://www.sciencepublishinggroup.com/j/ajls doi: 10.11648/j.ajls.20160406.12 ISSN: 2328-5702 (Print); ISSN: 2328-5737 (Online) Gap Filling for a Human
Introduction to Bioinformatics
Introduction to Bioinformatics Alla L Lapidus, Ph.D. SPbSU St. Petersburg Term Bioinformatics Term Bioinformatics was invented by Paulien Hogeweg (Полина Хогевег) and Ben Hesper in 1970 as "the study of
New York State s experience with analyzing, interpreting, and sharing whole genome sequence data for surveillance of enteric organisms.
New York State s experience with analyzing, interpreting, and sharing whole genome sequence data for surveillance of enteric organisms. InForm 11/18/15 William Wolfgang, PhD Wadsworth Center, NYSDOH william.wolfgang@health.ny.gov
Introduction to RNA sequencing
Introduction to RNA sequencing Bioinformatics perspective Olga Dethlefsen NBIS, National Bioinformatics Infrastructure Sweden November 2017 Olga (NBIS) RNA-seq November 2017 1 / 49 Outline Why sequence
Antisera QC and IQCP and Associated Challenges
Antisera QC and IQCP and Associated Challenges Patti Fields Enteric Diseases Laboratory Branch (EDLB) CDC 2017 APHL Annual Meeting Providence, Rhode Island June 14, 2017 National Center for Emerging and
Infectious Disease Omics
Infectious Disease Omics Metagenomics Ernest Diez Benavente LSHTM ernest.diezbenavente@lshtm.ac.uk Course outline What is metagenomics? In situ, culture-free genomic characterization of the taxonomic and
De novo Genome Assembly
De novo Genome Assembly A/Prof Torsten Seemann Winter School in Mathematical & Computational Biology - Brisbane, AU - 3 July 2017 Introduction The human genome has 47 pieces MT (or XY) The shortest piece
Sanger vs Next-Gen Sequencing
Tools and Algorithms in Bioinformatics GCBA815/MCGB815/BMI815, Fall 2017 Week-8: Next-Gen Sequencing RNA-seq Data Analysis Babu Guda, Ph.D. Professor, Genetics, Cell Biology & Anatomy Director, Bioinformatics
De novo whole genome assembly
De novo whole genome assembly Lecture 1 Qi Sun Minghui Wang Bioinformatics Facility Cornell University DNA Sequencing Platforms Illumina sequencing (100 to 300 bp reads) Overlapping reads ~180bp fragment
Genomics AGRY Michael Gribskov Hock 331
Genomics AGRY 60000 Michael Gribskov gribskov@purdue.edu Hock 331 Computing Essentials Resources In this course we will assemble and annotate both genomic and transcriptomic sequence assemblies We will
ALGORITHMS IN BIO INFORMATICS. Chapman & Hall/CRC Mathematical and Computational Biology Series A PRACTICAL INTRODUCTION. CRC Press WING-KIN SUNG
Chapman & Hall/CRC Mathematical and Computational Biology Series ALGORITHMS IN BIO INFORMATICS A PRACTICAL INTRODUCTION WING-KIN SUNG CRC Press Taylor & Francis Group Boca Raton London New York CRC Press
Assembly of Ariolimax dolichophallus using SOAPdenovo2
Assembly of Ariolimax dolichophallus using SOAPdenovo2 Charles Markello, Thomas Matthew, and Nedda Saremi Image taken from Banana Slug Genome Project, S. Weber SOAPdenovo Assembly Tool Short Oligonucleotide
From Variants to Pathways: Agilent GeneSpring GX s Variant Analysis Workflow
From Variants to Pathways: Agilent GeneSpring GX s Variant Analysis Workflow Technical Overview Import VCF Introduction Next-generation sequencing (NGS) studies have created unanticipated challenges with
Sequence Assembly and Alignment. Jim Noonan Department of Genetics
Sequence Assembly and Alignment Jim Noonan Department of Genetics james.noonan@yale.edu www.yale.edu/noonanlab The assembly problem >>10 9 sequencing reads 36 bp - 1 kb 3 Gb Outline Basic concepts in genome
Introduction: Methods:
Eason 1 Introduction: Next Generation Sequencing (NGS) is a term that applies to many new sequencing technologies. The drastic increase in speed and cost of these novel methods are changing the world of
A Roadmap to the De-novo Assembly of the Banana Slug Genome
A Roadmap to the De-novo Assembly of the Banana Slug Genome Stefan Prost 1 1 Department of Integrative Biology, University of California, Berkeley, United States of America April 6th-10th, 2015 Outline
Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Supplementary Material
Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions Joshua N. Burton 1, Andrew Adey 1, Rupali P. Patwardhan 1, Ruolan Qiu 1, Jacob O. Kitzman 1, Jay Shendure 1 1 Department
De novo genome assembly with next generation sequencing data!! "
De novo genome assembly with next generation sequencing data!! " Jianbin Wang" HMGP 7620 (CPBS 7620, and BMGN 7620)" Genomics lectures" 2/7/12" Outline" The need for de novo genome assembly! The nature
MEEPTOOLS: A maximum expected error based FASTQ read filtering and trimming toolkit
MEEPTOOLS: A maximum expected error based FASTQ read filtering and trimming toolkit Vishal N. Koparde 1* (vnkoparde@vcu.edu) Hardik I. Parikh 2 (parikhhi@vcu.edu) Steven P. Bradley 2 (bradleysp@vcu.edu)
A shotgun introduction to sequence assembly (with Velvet) MCB Brem, Eisen and Pachter
A shotgun introduction to sequence assembly (with Velvet) MCB 247 - Brem, Eisen and Pachter Hot off the press January 27, 2009 06:00 AM Eastern Time llumina Launches Suite of Next-Generation Sequencing
Course Presentation. Ignacio Medina Presentation
Course Index Introduction Agenda Analysis pipeline Some considerations Introduction Who we are Teachers: Marta Bleda: Computational Biologist and Data Analyst at Department of Medicine, Addenbrooke's Hospital
Mapping strategies for sequence reads
Mapping strategies for sequence reads Ernest Turro University of Cambridge 21 Oct 2013 Quantification A basic aim in genomics is working out the contents of a biological sample. 1. What distinct elements
WGS Analysis and Interpretation in Clinical and Public Health Microbiology Laboratories: What Are the Requirements and How Do Existing Tools Compare?
Pathogens 2014, 3, 437-458; doi:10.3390/pathogens3020437 Review OPEN ACCESS pathogens ISSN 2076-0817 www.mdpi.com/journal/pathogens WGS Analysis and Interpretation in Clinical and Public Health Microbiology
Lees J.A., Vehkala M. et al., 2016 In Review
Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes Lees J.A., Vehkala M. et al., 2016 In Review Journal Club Triinu Kõressaar 16.03.2016 Introduction Bacterial
NCBI web resources I: databases and Entrez
NCBI web resources I: databases and Entrez Yanbin Yin Most materials are downloaded from ftp://ftp.ncbi.nih.gov/pub/education/ 1 Homework assignment 1 Two parts: Extract the gene IDs reported in table
Welcome to the NGS webinar series
Welcome to the NGS webinar series Webinar 1 NGS: Introduction to technology, and applications NGS Technology Webinar 2 Targeted NGS for Cancer Research NGS in cancer Webinar 3 NGS: Data analysis for genetic
Next Gen Sequencing. Expansion of sequencing technology. Contents
Next Gen Sequencing Contents 1 Expansion of sequencing technology 2 The Next Generation of Sequencing: High-Throughput Technologies 3 High Throughput Sequencing Applied to Genome Sequencing (TEDed CC BY-NC-ND
The first generation DNA Sequencing
The first generation DNA Sequencing Slides 3 17 are modified from faperta.ugm.ac.id/newbie/download/pak_tar/.../instrument20072.ppt slides 18 43 are from Chengxiang Zhai at UIUC. The strand direction http://en.wikipedia.org/wiki/dna
Leonardo Mariño-Ramírez, PhD NCBI / NLM / NIH. BIOL 7210 A Computational Genomics 2/18/2015
Leonardo Mariño-Ramírez, PhD NCBI / NLM / NIH BIOL 7210 A Computational Genomics 2/18/2015 The $1,000 genome is here! http://www.illumina.com/systems/hiseq-x-sequencing-system.ilmn Bioinformatics bottleneck
RADSeq Data Analysis. Through STACKS on Galaxy. Yvan Le Bras Anthony Bretaudeau Cyril Monjeaud Gildas Le Corguillé
RADSeq Data Analysis Through STACKS on Galaxy Yvan Le Bras Anthony Bretaudeau Cyril Monjeaud Gildas Le Corguillé RAD sequencing: next-generation tools for an old problem INTRODUCTION source: Karim Gharbi
Next-Generation Sequencing. Technologies
Next-Generation Next-Generation Sequencing Technologies Sequencing Technologies Nicholas E. Navin, Ph.D. MD Anderson Cancer Center Dept. Genetics Dept. Bioinformatics Introduction to Bioinformatics GS011062
Practical quality control for whole genome sequencing in clinical microbiology
Practical quality control for whole genome sequencing in clinical microbiology John WA Rossen, PhD, MMM Department of Medical Microbiology, University of Groningen, UMCG, Groningen, The Netherlands Disclosure
Pathogenic organisms no thanks: Use of next generation sequencing techniques in risk assessment and HACCP
Pathogenic organisms no thanks: Use of next generation sequencing techniques in risk assessment and HACCP Lisbeth Truelstrup Hansen Professor Microbial Food Safety and Environmental Hygiene Mail: litr@food.dtu.dk
RNA-seq Data Analysis
Lecture 3. Clustering; Function/Pathway Enrichment analysis RNA-seq Data Analysis Qi Sun Bioinformatics Facility Biotechnology Resource Center Cornell University Lecture 1. Map RNA-seq read to genome Lecture
Genome-wide association studies (GWAS) Part 1
Genome-wide association studies (GWAS) Part 1 Matti Pirinen FIMM, University of Helsinki 03.12.2013, Kumpula Campus FIMM - Institiute for Molecular Medicine Finland www.fimm.fi Published Genome-Wide Associations
An Investigation of Palindromic Sequences in the Pseudomonas fluorescens SBW25 Genome Bachelor of Science Honors Thesis
An Investigation of Palindromic Sequences in the Pseudomonas fluorescens SBW25 Genome Bachelor of Science Honors Thesis Lina L. Faller Department of Computer Science University of New Hampshire June 2008
Efficiency in Next-Generation Sequencing for Public Health
Efficiency in Next-Generation Sequencing for Public Health Patrick Van Roey June 27, 2016 June 27, 2016 2 Overview Introduction Applied Genomic Technologies Core Implementing NGS for Public Health NGS
Genome Assembly Workshop Titles and Abstracts
Genome Assembly Workshop Titles and Abstracts TUESDAY, MARCH 15, 2011 08:15 AM Richard Durbin, Wellcome Trust Sanger Institute A generic sequence graph exchange format for assembly and population variation
Informatics of Clinical Genomics
Informatics of Clinical Genomics Lynn Bry, MD, PhD. Director, Center for Clinical and Translational Metagenomics Associate Pathologist, Center for Advanced Molecular Diagnostics Dept. Pathology, Brigham
Ecole de Bioinforma(que AVIESAN Roscoff 2014 GALAXY INITIATION. A. Lermine U900 Ins(tut Curie, INSERM, Mines ParisTech
GALAXY INITIATION A. Lermine U900 Ins(tut Curie, INSERM, Mines ParisTech How does Next- Gen sequencing work? DNA fragmentation Size selection and clonal amplification Massive parallel sequencing ACCGTTTGCCG
Bioinformatics Advice on Experimental Design
Bioinformatics Advice on Experimental Design Where do I start? Please refer to the following guide to better plan your experiments for good statistical analysis, best suited for your research needs. Statistics
MicroSEQ Rapid Microbial Identification System
MicroSEQ Rapid Microbial Identification System Giving you complete control over microbial identification using the gold-standard genotypic method The MicroSEQ ID microbial identification system, based
Long and short/small RNA-seq data analysis
Long and short/small RNA-seq data analysis GEF5, 4.9.2015 Sami Heikkinen, PhD, Dos. Topics 1. RNA-seq in a nutshell 2. Long vs short/small RNA-seq 3. Bioinformatic analysis work flows GEF5 / Heikkinen
Top 5 Lessons Learned From MAQC III/SEQC
Top 5 Lessons Learned From MAQC III/SEQC Weida Tong, Ph.D Division of Bioinformatics and Biostatistics, NCTR/FDA Weida.tong@fda.hhs.gov; 870 543 7142 1 MicroArray Quality Control (MAQC) An FDA led community
Next Generation Sequencing Technologies
Next Generation Sequencing Technologies What is first generation? Sanger Sequencing DNA Polymerase Base-adding reaction +H + http://chemwiki.ucdavis.edu/organic_chemistry/organic_chemistry_with_a_biological_emphasis/chapter_10%3a_phosphoryl_transfer_reactions/section_10.4%3a_phosphate_diesters
Illumina (Solexa) Throughput: 4 Tbp in one run (5 days) Cheapest sequencing technology. Mismatch errors dominate. Cost: ~$1000 per human genme
Illumina (Solexa) Current market leader Based on sequencing by synthesis Current read length 100-150bp Paired-end easy, longer matepairs harder Error ~0.1% Mismatch errors dominate Throughput: 4 Tbp in
Title: Genome sequence of lineage III Listeria monocytogenes strain HCC23
JB Accepts, published online ahead of print on 20 May 2011 J. Bacteriol. doi:10.1128/jb.05236-11 Copyright 2011, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved.
Variant detection analysis in the BRCA1/2 genes from Ion torrent PGM data
Variant detection analysis in the BRCA1/2 genes from Ion torrent PGM data Bruno Zeitouni Bionformatics department of the Institut Curie Inserm U900 Mines ParisTech Ion Torrent User Meeting 2012, October
BLAST. compared with database sequences Sequences with many matches to high- scoring words are used for final alignments
BLAST 100 times faster than dynamic programming. Good for database searches. Derive a list of words of length w from query (e.g., 3 for protein, 11 for DNA) High-scoring words are compared with database
Typically, to be biologically related means to share a common ancestor. In biology, we call this homologous
Typically, to be biologically related means to share a common ancestor. In biology, we call this homologous. Two proteins sharing a common ancestor are said to be homologs. Homologyoften implies structural
Targeted Sequencing in the NBS Laboratory
Targeted Sequencing in the NBS Laboratory Christopher Greene, PhD Newborn Screening and Molecular Biology Branch Division of Laboratory Sciences Gene Sequencing in Public Health Newborn Screening February
Genomic DNA ASSEMBLY BY REMAPPING. Course overview
ASSEMBLY BY REMAPPING Laurent Falquet, The Bioinformatics Unravelling Group, UNIFR & SIB MA/MER @ UniFr Group Leader @ SIB Course overview Genomic DNA PacBio Illumina methylation de novo remapping Annotation
Bioinformatics. Ingo Ruczinski. Some selected examples... and a bit of an overview
Bioinformatics Some selected examples... and a bit of an overview Department of Biostatistics Johns Hopkins Bloomberg School of Public Health July 19, 2007 @ EnviroHealth Connections Bioinformatics and
Next Generation Sequencing Technologies
Next Generation Sequencing Technologies Julian Pierre, Jordan Taylor, Amit Upadhyay, Bhanu Rekepalli Abstract: The process of generating genome sequence data is constantly getting faster, cheaper, and
Hybrid Error Correction and De Novo Assembly with Oxford Nanopore
Hybrid Error Correction and De Novo Assembly with Oxford Nanopore Michael Schatz Jan 13, 2015 PAG Bioinformatics @mike_schatz / #PAGXXIII Oxford Nanopore MinION Thumb drive sized sequencer powered over
Next Generation Sequencing: An Overview
Next Generation Sequencing: An Overview Cavan Reilly November 13, 2017 Table of contents Next generation sequencing NGS and microarrays Study design Quality assessment Burrows Wheeler transform Next generation
Technical meeting on the Impact of Whole Genome Sequencing on food safety management
Technical meeting on the Impact of Whole Genome Sequencing on food safety management Poster abstracts 23-25 May 2016 Food and Agriculture Organization of the United Nations Rome, Italy The views expressed
CNV and variant detection for human genome resequencing data - for biomedical researchers (II)
CNV and variant detection for human genome resequencing data - for biomedical researchers (II) Chuan-Kun Liu 劉傳崑 Senior Maneger National Center for Genome Medican bioit@ncgm.sinica.edu.tw Abstract Common
Why can GBS be complicated? Tools for filtering, error correction and imputation.
Why can GBS be complicated? Tools for filtering, error correction and imputation. Edward Buckler USDA-ARS Cornell University http://www.maizegenetics.net Many Organisms Are Diverse Humans are at the lower
1.1 Post Run QC Analysis
Post Run QC Analysis 100 339 200 01 1. Post Run QC Analysis 1.1 Post Run QC Analysis Welcome to Pacific Biosciences' Post Run QC Analysis Overview. This training module will describe the workflow to assess
Analysis Report. Institution : Macrogen Japan Name : Macrogen Japan Order Number : 1501APB-0004 Sample Name : 8380 Type of Analysis : De novo assembly
Analysis Report Institution : Macrogen Japan Name : Macrogen Japan Order Number : 1501APB-0004 Sample Name : 8380 Type of Analysis : De novo assembly 1 Table of Contents 1. Result of Whole Genome Assembly
Bioinformatics in Public Health Laboratories Duncan MacCannell PhD
Bioinformatics in Public Health Laboratories Duncan MacCannell PhD National Center for Emerging and Zoonotic Infectious Diseases Office of Advanced Molecular Detection What is bioinformatics? 2 The use
Runs of Homozygosity Analysis Tutorial
Runs of Homozygosity Analysis Tutorial Release 8.7.0 Golden Helix, Inc. March 22, 2017 Contents 1. Overview of the Project 2 2. Identify Runs of Homozygosity 6 Illustrative Example...............................................
Introduction to Bioinformatics
Introduction to Bioinformatics Richard Corbett Canada s Michael Smith Genome Sciences Centre Vancouver, British Columbia June 28, 2017 Our mandate is to advance knowledge about cancer and other diseases
ELE4120 Bioinformatics. Tutorial 5
ELE4120 Bioinformatics Tutorial 5 1 1. Database Content GenBank RefSeq TPA UniProt 2. Database Searches 2 Databases A common situation for alignment is to search through a database to retrieve the similar
ABSTRACT COMPUTATIONAL METHODS TO IMPROVE GENOME ASSEMBLY AND GENE PREDICTION. David Kelley, Doctor of Philosophy, 2011
ABSTRACT Title of dissertation: COMPUTATIONAL METHODS TO IMPROVE GENOME ASSEMBLY AND GENE PREDICTION David Kelley, Doctor of Philosophy, 2011 Dissertation directed by: Professor Steven Salzberg Department
Genome Sequencing. I: Methods. MMG 835, SPRING 2016 Eukaryotic Molecular Genetics. George I. Mias
Genome Sequencing I: Methods MMG 835, SPRING 2016 Eukaryotic Molecular Genetics George I. Mias Department of Biochemistry and Molecular Biology gmias@msu.edu Sequencing Methods Cost of Sequencing Wetterstrand
Fundamentals of Next-Generation Sequencing: Technologies and Applications
Fundamentals of Next-Generation Sequencing: Technologies and Applications Society for Hematopathology European Association for Haematopathology 2017 Workshop Eric Duncavage, MD Washington University in
NOW GENERATION SEQUENCING. Monday, December 5, 11
NOW GENERATION SEQUENCING 1 SEQUENCING TIMELINE 1953: Structure of DNA 1975: Sanger method for sequencing 1985: Human Genome Sequencing Project begins 1990s: Clinical sequencing begins 1998: NHGRI $1000
Bioinformatics and computational tools
Bioinformatics and computational tools Etienne P. de Villiers (PhD) International Livestock Research Institute Nairobi, Kenya International Livestock Research Institute Nairobi, Kenya ILRI works at the
Axiom mydesign Custom Array design guide for human genotyping applications
TECHNICAL NOTE Axiom mydesign Custom Genotyping Arrays Axiom mydesign Custom Array design guide for human genotyping applications Overview In the past, custom genotyping arrays were expensive, required
BIOINFORMATICS 1 SEQUENCING TECHNOLOGY. DNA story. DNA story. Sequencing: infancy. Sequencing: beginnings 26/10/16. bioinformatic challenges
BIOINFORMATICS 1 or why biologists need computers SEQUENCING TECHNOLOGY bioinformatic challenges http://www.bioinformatics.uni-muenster.de/teaching/courses-2012/bioinf1/index.hbi Prof. Dr. Wojciech Makałowski"
Workflow of de novo assembly
Workflow of de novo assembly Experimental Design Clean sequencing data (trim adapter and low quality sequences) Run assembly software for contiging and scaffolding Evaluation of assembly Several iterations:
Glossary of Commonly used Annotation Terms
Glossary of Commonly used Annotation Terms Akela a general use server for the annotation group as well as other groups throughout TIGR. Annotation Notebook a link from the gene list page that is associated
Title: High-quality genome assembly of channel catfish, Ictalurus punctatus
Author s response to reviews Title: High-quality genome assembly of channel catfish, Ictalurus punctatus Authors: Qiong Shi (shiqiong@genomics.cn) Xiaohui Chen (xhchenffri@hotmail.com) Liqiang Zhong (lqzhongffri@hotmail.com)
arxiv: v1 [q-bio.gn] 25 Nov 2015
![arxiv: v1 [q-bio.gn] 25 Nov 2015](/thumbs/76/73490664.jpg "arxiv: v1 [q-bio.gn] 25 Nov 2015")
MetaScope - Fast and accurate identification of microbes in metagenomic sequencing data Benjamin Buchfink 1, Daniel H. Huson 1,2 & Chao Xie 2,3 arxiv:1511.08753v1 [q-bio.gn] 25 Nov 2015 1 Department of
Whole Transcriptome Analysis of Illumina RNA- Seq Data. Ryan Peters Field Application Specialist
Whole Transcriptome Analysis of Illumina RNA- Seq Data Ryan Peters Field Application Specialist Partek GS in your NGS Pipeline Your Start-to-Finish Solution for Analysis of Next Generation Sequencing Data
Using the Potato Genome Sequence! Robin Buell! Michigan State University! Department of Plant Biology! August 15, 2010!
Using the Potato Genome Sequence! Robin Buell! Michigan State University! Department of Plant Biology! August 15, 2010! buell@msu.edu! 1 Whole Genome Shotgun Sequencing 2 New Technologies Revolutionize
Phylogenetic methods for taxonomic profiling
Phylogenetic methods for taxonomic profiling Siavash Mirarab University of California at San Diego (UCSD) Joint work with Tandy Warnow, Nam-Phuong Nguyen, Mike Nute, Mihai Pop, and Bo Liu Phylogeny reconstruction
What is the Difference between a Human and a Chimp? T. M. Murali
What is the Difference between a Human and a Chimp? T. M. Murali murali@cs.vt.edu http://bioinformatics.cs.vt.edu/ murali Introduction to Computational Biology and Bioinformatics (CS 3824) March 25 and
What the Genome of Raffaelea lauricola Can Tell Us About Laurel Wilt
What the Genome of Raffaelea lauricola Can Tell Us About Laurel Wilt Laurel Wilt Summit November 3-4, 2016 Dr. Jeffrey Rollins Associate Professor Plant Pathology Department University of Florida Gainesville,
NGS sequence preprocessing. José Carbonell Caballero
NGS sequence preprocessing José Carbonell Caballero jcarbonell@cipf.es Contents Data Format Quality Control Sequence capture Fasta and fastq formats Sequence quality encoding Evaluation of sequence quality
COPE: An accurate k-mer based pair-end reads connection tool to facilitate genome assembly
Bioinformatics Advance Access published October 8, 2012 COPE: An accurate k-mer based pair-end reads connection tool to facilitate genome assembly Binghang Liu 1,2,, Jianying Yuan 2,, Siu-Ming Yiu 1,3,
Next Generation Sequencing Lecture Saarbrücken, 19. March Sequencing Platforms
Next Generation Sequencing Lecture Saarbrücken, 19. March 2012 Sequencing Platforms Contents Introduction Sequencing Workflow Platforms Roche 454 ABI SOLiD Illumina Genome Anlayzer / HiSeq Problems Quality
B I O I N F O R M A T I C S
B I O I N F O R M A T I C S Kristel Van Steen, PhD 2 Montefiore Institute - Systems and Modeling GIGA - Bioinformatics ULg kristel.vansteen@ulg.ac.be SUPPLEMENTARY CHAPTER: DATA BASES AND MINING 1 What