Pan-genomics: theory & practice
|
|
|
- Cody Foster
- 6 years ago
- Views:
Transcription
1 Pan-genomics: theory & practice Michael Schatz Sept 20, 2014 GRC Assembly Workshop #gi2014
2 Part 1: Theory
3
4 Advances in Assembly! Perfect Human Assembly First PacBio CSHL Perfect Microbes First Hybrid Assembly Perfect Simple Ag. Genomes Perfect Model Orgs. Perfect Fungi Perfect Higher Euk. Error correction and assembly complexity of single molecule sequencing reads. Lee, H*, Gurtowski, J*, Yoo, S, Marcus, S, McCombie, WR, Schatz, MC
5 Pan-Genome Alignment & Assembly A" B" C" D" Time to start considering problems for which N complete genomes are the input to study the pan-genome! Available today for many microbial species, near future for higher eukaryotes! Pan-genome colored de Bruijn graph! Encodes all the sequence relationships between the genomes! How well conserved is a given sequence?! What are the pan-genome network properties?! SplitMEM: Graphical pan-genome analysis with suffix skips Marcus, S, Lee, H, Schatz, MC
6 Graphical pan-genome analysis Colored de Bruijn graph Node for each distinct kmer Directed edge connects consecutive kmers Nodes overlap by k-1 bp AGA TCC AGAAGTCC!! ATAAGTTA! GAA TAA AAG AGT GTC GTT ATA TTA
7 Graphical pan-genome analysis Colored de Bruijn graph Node for each distinct kmer Directed edge connects consecutive kmers Nodes overlap by k-1 bp AGAAGTCC!! ATAAGTTA! AGAA ATAA AAGT GTCC GTTA More specifically:! We aim to build the compressed de Bruijn graph as quickly as possible without considering every distinct kmer!
8 Maximal Exact Matches (MEMs)" to de Bruijn Graphs! TGCAC GGCAA GCA!
9 Overlapping MEMs TGCCATCGCCAACCAT TGCCATCGCCAACCAT
10 Suffix Trees & de Bruijn Graphs Key concepts: Shared sequences form repeats called maximal exact matches (MEM) Easy to identify MEMs in a suffix tree, but may be nested within other MEMs Use suffix skips to quickly decompose MEMs, add in the missing nodes and edges
11 B. anthracis pan-genome (9 strains) k=25 k=1000
12 Microbial Pan-Genomes E. coli (62) and B. anthracis (9) pan-genome analysis! Analyzed all available strains in Genbank! Space and time are effectively linear in the number of genomes! O(n log g) where g is the length of the longest genome!! Many possible applications:! Identifying core genes present in all strains! Characterizing highly variable regions (+ flanking shared)! Cataloging sequences shared by pathogenic varieties! 62 strain E. coli Pan-Genome Node Sharing!
13 Part 2: Practice
14 Genetics of Autism Spectrum Disorders 1. Constructed database of >1M transmitted and de novo indels in ~1000 families 2. For practical reasons, analysis is computed relative to the (unpatched) reference genome We use population statistics to clean problematic regions We believe we are missing and/or misinterpreting some interesting variants Accurate de novo and transmitted indel detection in exome-capture data using microassembly. Narzisi et al. (2014) Nature Methods. doi: /nmeth.3069
15 Population structure of Oryza sativa Indica Total Span: Mbp Contig N50: 22.2kbp Aus Total Span: 344.9Mbp Contig N50: 25.5kbp Nipponbare Total Span: 354.9Mbp Contig N50: 21.9kbp Whole genome de novo assemblies of three divergent strains of rice (O. sativa) documents novel gene space of aus and indica Schatz, Maron, Stein et al (2014)
16 Pan-genomics of draft assemblies Strategy:! 1. Align the genomes to each other (MUMmer)! 2. Identify segments of genome A that do not align anywhere to genome B (BEDTools)!! Megabases specific to each genome!!!!! Aus 3. Screen regions that fail to align with their k-mer frequencies (jellyfish)! Genome specific regions averaged over 10,000x kmer coverage while unique regions were ~50x! Indica! 100s of KB specific to each genome!!!!
17 Genome-specific Regions Successfully able to identify many regions specific to each genome (30/30 PCR validation)! Enriched for genes for disease resistance & other interesting phenotypes!
18 Pan-genomics Summary Now is the time to study pan-genomes Perfect assemblies of microbes and many smaller eukaryotic genomes are now routine Expect to rapidly scale up these results to larger genomes soon Algorithms must scale to large collections, be robust to errors, gaps, and ambiguity Large body of assembly and alignment theory can be repurposed Simple refinements, like k-mer screening, can be very effective even if the sequence is lacking The right approach will depend on the questions you ask We all agree we need to work from a graph, but there is not a clear consensus of what the graph should represent or how it should be encoded. Ultimately the needs will be driven by applications graph-blast, -BWA, -SAMTools, -TopHat/Cufflinks, -IGV, -UCSC, -MAKER,
19 Acknowledgements Schatz Lab Rahul Amin Tyler Gavin James Gurtowski Han Fang Hayan Lee Maria Nattestad Aspyn Palatnick Srividya Ramakrishnan Eric Biggers Ke Jiang Shoshana Marcus Giuseppe Narzisi Rachel Sherman Greg Vurture Alejandro Wences CSHL Hannon Lab Gingeras Lab Jackson Lab Hicks Lab Iossifov Lab Levy Lab Lippman Lab Lyon Lab Martienssen Lab McCombie Lab Tuveson Lab Ware Lab Wigler Lab Pacific Biosciences Oxford Nanopore
20 Biological Data Sciences Anne Carpenter, Michael Schatz, Matt Wood Nov 5-8, 2014 Thank you
Genome Sequencing & Assembly Michael Schatz. May 2, 2013 Human Microbiome Consortium
Genome Sequencing & Assembly Michael Schatz May 2, 2013 Human Microbiome Consortium Outline 1. Assembly theory 1. Assembly by analogy 2. De Bruijn and Overlap graph 3. Coverage, read length, errors, and
Graphs and Genomes Michael Schatz. July 26, 2013 CSHL Undergraduate Research Program
Graphs and Genomes Michael Schatz July 26, 2013 CSHL Undergraduate Research Program Outline 1. Graph Searching 2. Assembly by analogy 3. Genome Assembly Biological Networks Graphs A B Nodes People, Proteins,
Whole Genome Alignment. Adam Phillippy University of Maryland, Fall 2012
Whole Genome Alignment Adam Phillippy University of Maryland, Fall 2012 Motivation cancergenome.nih.gov Breast cancer karyotypes www.path.cam.ac.uk Goal of whole-genome alignment } For two genomes, A and
Comparative genomics: Overview & Tools + MUMmer algorithm
Comparative genomics: Overview & Tools + MUMmer algorithm Urmila Kulkarni-Kale Bioinformatics Centre University of Pune, Pune 411 007. urmila@bioinfo.ernet.in Genome sequence: Fact file 1995: The first
De novo assembly and genotyping of variants using colored de Bruijn graphs
De novo assembly and genotyping of variants using colored de Bruijn graphs Iqbal et al. 2012 Kolmogorov Mikhail 2013 Challenges Detecting genetic variants that are highly divergent from a reference Detecting
High-throughput sequence alignment. November 9, 2017
High-throughput sequence alignment November 9, 2017 a little history human genome project #1 (many U.S. government agencies and large institute) started October 1, 1990. Goal: 10x coverage of human genome,
Annotation of Plant Genomes using RNA-seq. Matteo Pellegrini (UCLA) In collaboration with Sabeeha Merchant (UCLA)
Annotation of Plant Genomes using RNA-seq Matteo Pellegrini (UCLA) In collaboration with Sabeeha Merchant (UCLA) inuscu1-35bp 5 _ 0 _ 5 _ What is Annotation inuscu2-75bp luscu1-75bp 0 _ 5 _ Reconstruction
Paired-End Read Length Lower Bounds for Genome Re-sequencing
1/11 Paired-End Read Length Lower Bounds for Genome Re-sequencing Rayan Chikhi ENS Cachan Brittany PhD student in the Symbiose team, Irisa, France 2/11 NEXT-GENERATION SEQUENCING Next-gen vs. traditional
Practical Bioinformatics
5/2/2017 Dictionaries d i c t i o n a r y = { A : T, T : A, G : C, C : G } d i c t i o n a r y [ G ] d i c t i o n a r y [ N ] = N d i c t i o n a r y. h a s k e y ( C ) Dictionaries g e n e t i c C o
Genomes Comparision via de Bruijn graphs
Genomes Comparision via de Bruijn graphs Student: Ilya Minkin Advisor: Son Pham St. Petersburg Academic University June 4, 2012 1 / 19 Synteny Blocks: Algorithmic challenge Suppose that we are given two
Towards More Effective Formulations of the Genome Assembly Problem
Towards More Effective Formulations of the Genome Assembly Problem Alexandru Tomescu Department of Computer Science University of Helsinki, Finland DACS June 26, 2015 1 / 25 2 / 25 CENTRAL DOGMA OF BIOLOGY
MiGA: The Microbial Genome Atlas
December 12 th 2017 MiGA: The Microbial Genome Atlas Jim Cole Center for Microbial Ecology Dept. of Plant, Soil & Microbial Sciences Michigan State University East Lansing, Michigan U.S.A. Where I m From
Supplemental Table 1. Primers used for cloning and PCR amplification in this study
Supplemental Table 1. Primers used for cloning and PCR amplification in this study Target Gene Primer sequence NATA1 (At2g393) forward GGG GAC AAG TTT GTA CAA AAA AGC AGG CTT CAT GGC GCC TCC AAC CGC AGC
Assembly improvement: based on Ragout approach. student: Anna Lioznova scientific advisor: Son Pham
Assembly improvement: based on Ragout approach student: Anna Lioznova scientific advisor: Son Pham Plan Ragout overview Datasets Assembly improvements Quality overlap graph paired-end reads Coverage Plan
BMI/CS 776 Lecture #20 Alignment of whole genomes. Colin Dewey (with slides adapted from those by Mark Craven)
BMI/CS 776 Lecture #20 Alignment of whole genomes Colin Dewey (with slides adapted from those by Mark Craven) 2007.03.29 1 Multiple whole genome alignment Input set of whole genome sequences genomes diverged
Y chromosome dynamics in Drosophila. Amanda Larracuente Department of Biology
Y chromosome dynamics in Drosophila Amanda Larracuente Department of Biology Sex chromosomes J. Graves X X X Y Sex chromosome evolution Autosomes Proto-sex chromosomes Sex determining Suppressed recombination
Regulatory Sequence Analysis. Sequence models (Bernoulli and Markov models)
Regulatory Sequence Analysis Sequence models (Bernoulli and Markov models) 1 Why do we need random models? Any pattern discovery relies on an underlying model to estimate the random expectation. This model
Microbes usually have few distinguishing properties that relate them, so a hierarchical taxonomy mainly has not been possible.
Microbial Taxonomy Traditional taxonomy or the classification through identification and nomenclature of microbes, both "prokaryote" and eukaryote, has been in a mess we were stuck with it for traditional
Microbial Taxonomy. Slowly evolving molecules (e.g., rrna) used for large-scale structure; "fast- clock" molecules for fine-structure.
Microbial Taxonomy Traditional taxonomy or the classification through identification and nomenclature of microbes, both "prokaryote" and eukaryote, has been in a mess we were stuck with it for traditional
Crick s early Hypothesis Revisited
Crick s early Hypothesis Revisited Or The Existence of a Universal Coding Frame Ryan Rossi, Jean-Louis Lassez and Axel Bernal UPenn Center for Bioinformatics BIOINFORMATICS The application of computer
Supplementary Material for Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation
Supplementary Material for Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation Raw assemblies and polished results at http://gembox.cbcb.umd.edu/shared/canu/index.html
Mathangi Thiagarajan Rice Genome Annotation Workshop May 23rd, 2007
-2 Transcript Alignment Assembly and Automated Gene Structure Improvements Using PASA-2 Mathangi Thiagarajan mathangi@jcvi.org Rice Genome Annotation Workshop May 23rd, 2007 About PASA PASA is an open
Applications of genome alignment
Applications of genome alignment Comparing different genome assemblies Locating genome duplications and conserved segments Gene finding through comparative genomics Analyzing pathogenic bacteria against
Tandem Mass Spectrometry: Generating function, alignment and assembly
Tandem Mass Spectrometry: Generating function, alignment and assembly With slides from Sangtae Kim and from Jones & Pevzner 2004 Determining reliability of identifications Can we use Target/Decoy to estimate
Mapping-free and Assembly-free Discovery of Inversion Breakpoints from Raw NGS Reads
1st International Conference on Algorithms for Computational Biology AlCoB 2014 Tarragona, Spain, July 1-3, 2014 Mapping-free and Assembly-free Discovery of Inversion Breakpoints from Raw NGS Reads Claire
Giri Narasimhan. CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools. Evaluation. Course Homepage.
CAP 5510: Introduction to Bioinformatics CGS 5166: Bioinformatics Tools Giri Narasimhan ECS 389; Phone: x3748 giri@cis.fiu.edu www.cis.fiu.edu/~giri/teach/bioinfs06.html 1/12/06 CAP5510/CGS5166 1 Evaluation
Chapters AP Biology Objectives. Objectives: You should know...
Objectives: You should know... Notes 1. Scientific evidence supports the idea that evolution has occurred in all species. 2. Scientific evidence supports the idea that evolution continues to occur. 3.
Assembly and Repeats: An Epic Struggle
Assembly and Repeats: An Epic Struggle Towards efficient assemblers for reference quality, de novo reconstructions MPI CBG Gene Myers Tschira Chair of Systems Biology MPI for Cell Biology and Genetics
Introduction to Sequence Alignment. Manpreet S. Katari
Introduction to Sequence Alignment Manpreet S. Katari 1 Outline 1. Global vs. local approaches to aligning sequences 1. Dot Plots 2. BLAST 1. Dynamic Programming 3. Hash Tables 1. BLAT 4. BWT (Burrow Wheeler
Microbial Taxonomy. Microbes usually have few distinguishing properties that relate them, so a hierarchical taxonomy mainly has not been possible.
Microbial Taxonomy Traditional taxonomy or the classification through identification and nomenclature of microbes, both "prokaryote" and eukaryote, has been in a mess we were stuck with it for traditional
Algorithmics and Bioinformatics
Algorithmics and Bioinformatics Gregory Kucherov and Philippe Gambette LIGM/CNRS Université Paris-Est Marne-la-Vallée, France Schedule Course webpage: https://wikimpri.dptinfo.ens-cachan.fr/doku.php?id=cours:c-1-32
Sequence Database Search Techniques I: Blast and PatternHunter tools
Sequence Database Search Techniques I: Blast and PatternHunter tools Zhang Louxin National University of Singapore Outline. Database search 2. BLAST (and filtration technique) 3. PatternHunter (empowered
The minimal prokaryotic genome. The minimal prokaryotic genome. The minimal prokaryotic genome. The minimal prokaryotic genome
Dr. Dirk Gevers 1,2 1 Laboratorium voor Microbiologie 2 Bioinformatics & Evolutionary Genomics The bacterial species in the genomic era CTACCATGAAAGACTTGTGAATCCAGGAAGAGAGACTGACTGGGCAACATGTTATTCAG GTACAAAAAGATTTGGACTGTAACTTAAAAATGATCAAATTATGTTTCCCATGCATCAGG
Introduction to de novo RNA-seq assembly
Introduction to de novo RNA-seq assembly Introduction Ideal day for a molecular biologist Ideal Sequencer Any type of biological material Genetic material with high quality and yield Cutting-Edge Technologies
Classical Selection, Balancing Selection, and Neutral Mutations
Classical Selection, Balancing Selection, and Neutral Mutations Classical Selection Perspective of the Fate of Mutations All mutations are EITHER beneficial or deleterious o Beneficial mutations are selected
Computational approaches for functional genomics
Computational approaches for functional genomics Kalin Vetsigian October 31, 2001 The rapidly increasing number of completely sequenced genomes have stimulated the development of new methods for finding
Small RNA in rice genome
Vol. 45 No. 5 SCIENCE IN CHINA (Series C) October 2002 Small RNA in rice genome WANG Kai ( 1, ZHU Xiaopeng ( 2, ZHONG Lan ( 1,3 & CHEN Runsheng ( 1,2 1. Beijing Genomics Institute/Center of Genomics and
Nature Biotechnology: doi: /nbt Supplementary Figure 1. Detailed overview of the primer-free full-length SSU rrna library preparation.
Supplementary Figure 1 Detailed overview of the primer-free full-length SSU rrna library preparation. Detailed overview of the primer-free full-length SSU rrna library preparation. Supplementary Figure
Algorithms in Bioinformatics FOUR Pairwise Sequence Alignment. Pairwise Sequence Alignment. Convention: DNA Sequences 5. Sequence Alignment
Algorithms in Bioinformatics FOUR Sami Khuri Department of Computer Science San José State University Pairwise Sequence Alignment Homology Similarity Global string alignment Local string alignment Dot
Alignment Strategies for Large Scale Genome Alignments
Alignment Strategies for Large Scale Genome Alignments CSHL Computational Genomics 9 November 2003 Algorithms for Biological Sequence Comparison algorithm value scoring gap time calculated matrix penalty
Reducing storage requirements for biological sequence comparison
Bioinformatics Advance Access published July 15, 2004 Bioinfor matics Oxford University Press 2004; all rights reserved. Reducing storage requirements for biological sequence comparison Michael Roberts,
Bacillus anthracis. Last Lecture: 1. Introduction 2. History 3. Koch s Postulates. 1. Prokaryote vs. Eukaryote 2. Classifying prokaryotes
Last Lecture: Bacillus anthracis 1. Introduction 2. History 3. Koch s Postulates Today s Lecture: 1. Prokaryote vs. Eukaryote 2. Classifying prokaryotes 3. Phylogenetics I. Basic Cell structure: (Fig.
1 of 13 8/11/2014 10:32 AM Units: Teacher: APBiology, CORE Course: APBiology Year: 2012-13 Chemistry of Life Chapters 1-4 Big Idea 1, 2 & 4 Change in the genetic population over time is feedback mechanisms
Designing and Testing a New DNA Fragment Assembler VEDA-2
Designing and Testing a New DNA Fragment Assembler VEDA-2 Mark K. Goldberg Darren T. Lim Rensselaer Polytechnic Institute Computer Science Department {goldberg, limd}@cs.rpi.edu Abstract We present VEDA-2,
Whole Genome Alignments and Synteny Maps
Whole Genome Alignments and Synteny Maps IINTRODUCTION It was not until closely related organism genomes have been sequenced that people start to think about aligning genomes and chromosomes instead of
Predicting Protein Functions and Domain Interactions from Protein Interactions
Predicting Protein Functions and Domain Interactions from Protein Interactions Fengzhu Sun, PhD Center for Computational and Experimental Genomics University of Southern California Outline High-throughput
Tiffany Samaroo MB&B 452a December 8, Take Home Final. Topic 1
Tiffany Samaroo MB&B 452a December 8, 2003 Take Home Final Topic 1 Prior to 1970, protein and DNA sequence alignment was limited to visual comparison. This was a very tedious process; even proteins with
17 Non-collinear alignment Motivation A B C A B C A B C A B C D A C. This exposition is based on:
17 Non-collinear alignment This exposition is based on: 1. Darling, A.E., Mau, B., Perna, N.T. (2010) progressivemauve: multiple genome alignment with gene gain, loss and rearrangement. PLoS One 5(6):e11147.
Other tools for typing and phylogeny
Other tools for typing and phylogeny Workshop on Whole Genome Sequencing and Analysis, 27-29 Mar. 2017 Learning objective: After this lecture you should be able to account for tools for typing Salmonella
Clay Carter. Department of Biology. QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture.
QuickTime and a TIFF (Uncompressed) decompressor are needed to see this picture. Clay Carter Department of Biology QuickTime and a TIFF (LZW) decompressor are needed to see this picture. Ornamental tobacco
High-throughput sequencing: Alignment and related topic
High-throughput sequencing: Alignment and related topic Simon Anders EMBL Heidelberg HTS Platforms E s ta b lis h e d p la tfo rm s Illu m in a H is e q, A B I S O L id, R o c h e 4 5 4 N e w c o m e rs
Essential knowledge 1.A.2: Natural selection
Appendix C AP Biology Concepts at a Glance Big Idea 1: The process of evolution drives the diversity and unity of life. Enduring understanding 1.A: Change in the genetic makeup of a population over time
Repeat resolution. This exposition is based on the following sources, which are all recommended reading:
Repeat resolution This exposition is based on the following sources, which are all recommended reading: 1. Separation of nearly identical repeats in shotgun assemblies using defined nucleotide positions,
chapter 5 the mammalian cell entry 1 (mce1) operon of Mycobacterium Ieprae and Mycobacterium tuberculosis
chapter 5 the mammalian cell entry 1 (mce1) operon of Mycobacterium Ieprae and Mycobacterium tuberculosis chapter 5 Harald G. Wiker, Eric Spierings, Marc A. B. Kolkman, Tom H. M. Ottenhoff, and Morten
7 Multiple Genome Alignment
94 Bioinformatics I, WS /3, D. Huson, December 3, 0 7 Multiple Genome Alignment Assume we have a set of genomes G,..., G t that we want to align with each other. If they are short and very closely related,
AP Biology Essential Knowledge Cards BIG IDEA 1
AP Biology Essential Knowledge Cards BIG IDEA 1 Essential knowledge 1.A.1: Natural selection is a major mechanism of evolution. Essential knowledge 1.A.4: Biological evolution is supported by scientific
PanTools: representation, storage and exploration of pan-genomic data
Bioinformatics,, 2016, i487 i3 doi: 10.10/bioinformatics/btw455 ECCB 2016 PanTools: representation, storage and exploration of pan-genomic data Siavash Sheikhizadeh 1, M. Eric Schranz 2, Mehmet Akdel 1,
Valley Central School District 944 State Route 17K Montgomery, NY Telephone Number: (845) ext Fax Number: (845)
Valley Central School District 944 State Route 17K Montgomery, NY 12549 Telephone Number: (845)457-2400 ext. 18121 Fax Number: (845)457-4254 Advance Placement Biology Presented to the Board of Education
Genome Assembly Results, Protocol & Demo
Genome Assembly Results, Protocol & Demo Monday, March 7, 2016 BIOL 7210: Genome Assembly Group Aroon Chande, Cheng Chen, Alicia Francis, Alli Gombolay, Namrata Kalsi, Ellie Kim, Tyrone Lee, Wilson Martin,
Bioinformatics Chapter 1. Introduction
Bioinformatics Chapter 1. Introduction Outline! Biological Data in Digital Symbol Sequences! Genomes Diversity, Size, and Structure! Proteins and Proteomes! On the Information Content of Biological Sequences!
Genome Assembly. Sequencing Output. High Throughput Sequencing
Genome High Throughput Sequencing Sequencing Output Example applications: Sequencing a genome (DNA) Sequencing a transcriptome and gene expression studies (RNA) ChIP (chromatin immunoprecipitation) Example
Advanced topics in bioinformatics
Feinberg Graduate School of the Weizmann Institute of Science Advanced topics in bioinformatics Shmuel Pietrokovski & Eitan Rubin Spring 2003 Course WWW site: http://bioinformatics.weizmann.ac.il/courses/atib
Do Read Errors Matter for Genome Assembly?
Do Read Errors Matter for Genome Assembly? Ilan Shomorony C Berkeley ilan.shomorony@berkeley.edu Thomas Courtade C Berkeley courtade@berkeley.edu David Tse Stanford niversity dntse@stanford.edu arxiv:50.0694v
Characterization of Structural Variants with Single Molecule and Hybrid Sequencing Approaches
Bioinformatics Advance Access published October 28, 2014 Characterization of Structural Variants with Single Molecule and Hybrid Sequencing Approaches Anna Ritz 1,,, Ali Bashir 2,3, Suzanne Sindi 4, David
Map of AP-Aligned Bio-Rad Kits with Learning Objectives
Map of AP-Aligned Bio-Rad Kits with Learning Objectives Cover more than one AP Biology Big Idea with these AP-aligned Bio-Rad kits. Big Idea 1 Big Idea 2 Big Idea 3 Big Idea 4 ThINQ! pglo Transformation
10-810: Advanced Algorithms and Models for Computational Biology. microrna and Whole Genome Comparison
10-810: Advanced Algorithms and Models for Computational Biology microrna and Whole Genome Comparison Central Dogma: 90s Transcription factors DNA transcription mrna translation Proteins Central Dogma:
SSR ( ) Vol. 48 No ( Microsatellite marker) ( Simple sequence repeat,ssr),
48 3 () Vol. 48 No. 3 2009 5 Journal of Xiamen University (Nat ural Science) May 2009 SSR,,,, 3 (, 361005) : SSR. 21 516,410. 60 %96. 7 %. (),(Between2groups linkage method),.,, 11 (),. 12,. (, ), : 0.
Whole genome sequencing (WGS) - there s a new tool in town. Henrik Hasman DTU - Food
Whole genome sequencing (WGS) - there s a new tool in town Henrik Hasman DTU - Food Welcome to the NGS world TODAY Welcome Introduction to Next Generation Sequencing DNA purification (Hands-on) Lunch (Sandwishes
Accelerating Biomolecular Nuclear Magnetic Resonance Assignment with A*
Accelerating Biomolecular Nuclear Magnetic Resonance Assignment with A* Joel Venzke, Paxten Johnson, Rachel Davis, John Emmons, Katherine Roth, David Mascharka, Leah Robison, Timothy Urness and Adina Kilpatrick
CSE182-L7. Protein Sequence Analysis Patterns (regular expressions) Profiles HMM Gene Finding CSE182
CSE182-L7 Protein Sequence Analysis Patterns (regular expressions) Profiles HMM Gene Finding 10-07 CSE182 Bell Labs Honors Pattern matching 10-07 CSE182 Just the Facts Consider the set of all substrings
25 : Graphical induced structured input/output models
10-708: Probabilistic Graphical Models 10-708, Spring 2013 25 : Graphical induced structured input/output models Lecturer: Eric P. Xing Scribes: Meghana Kshirsagar (mkshirsa), Yiwen Chen (yiwenche) 1 Graph
Encoding and Decoding 3D Genome Organization
Encoding and Decoding 3D Genome Organization Noam Kaplan Dekker Lab Program in Systems Biology University of Massachusetts Medical School Chromatin organization 10000nm Human genome: 2x3000 Mbp Human chromosome:
objective functions...
objective functions... COFFEE (Notredame et al. 1998) measures column by column similarity between pairwise and multiple sequence alignments assumes that the pairwise alignments are optimal assumes a set
Supplementary Information for Discovery and characterization of indel and point mutations
Supplementary Information for Discovery and characterization of indel and point mutations using DeNovoGear Avinash Ramu 1 Michiel J. Noordam 1 Rachel S. Schwartz 2 Arthur Wuster 3 Matthew E. Hurles 3 Reed
Using algebraic geometry for phylogenetic reconstruction
Using algebraic geometry for phylogenetic reconstruction Marta Casanellas i Rius (joint work with Jesús Fernández-Sánchez) Departament de Matemàtica Aplicada I Universitat Politècnica de Catalunya IMA
Sara C. Madeira. Universidade da Beira Interior. (Thanks to Ana Teresa Freitas, IST for useful resources on this subject)
Bioinformática Sequence Alignment Pairwise Sequence Alignment Universidade da Beira Interior (Thanks to Ana Teresa Freitas, IST for useful resources on this subject) 1 16/3/29 & 23/3/29 27/4/29 Outline
Electronic supplementary material
Applied Microbiology and Biotechnology Electronic supplementary material A family of AA9 lytic polysaccharide monooxygenases in Aspergillus nidulans is differentially regulated by multiple substrates and
Supplementary Information for: The genome of the extremophile crucifer Thellungiella parvula
Supplementary Information for: The genome of the extremophile crucifer Thellungiella parvula Maheshi Dassanayake 1,9, Dong-Ha Oh 1,9, Jeffrey S. Haas 1,2, Alvaro Hernandez 3, Hyewon Hong 1,4, Shahjahan
Graduate Funding Information Center
Graduate Funding Information Center UNC-Chapel Hill, The Graduate School Graduate Student Proposal Sponsor: Program Title: NESCent Graduate Fellowship Department: Biology Funding Type: Fellowship Year:
RGP finder: prediction of Genomic Islands
Training courses on MicroScope platform RGP finder: prediction of Genomic Islands Dynamics of bacterial genomes Gene gain Horizontal gene transfer Gene loss Deletion of one or several genes Duplication
Lecture 14: Multiple Sequence Alignment (Gene Finding, Conserved Elements) Scribe: John Ekins
Lecture 14: Multiple Sequence Alignment (Gene Finding, Conserved Elements) 2 19 2015 Scribe: John Ekins Multiple Sequence Alignment Given N sequences x 1, x 2,, x N : Insert gaps in each of the sequences
Using Phylogenomics to Predict Novel Fungal Pathogenicity Genes
Using Phylogenomics to Predict Novel Fungal Pathogenicity Genes David DeCaprio, Ying Li, Hung Nguyen (sequenced Ascomycetes genomes courtesy of the Broad Institute) Phylogenomics Combining whole genome
BLAST. Varieties of BLAST
BLAST Basic Local Alignment Search Tool (1990) Altschul, Gish, Miller, Myers, & Lipman Uses short-cuts or heuristics to improve search speed Like speed-reading, does not examine every nucleotide of database
Campbell Biology AP Edition 11 th Edition, 2018
A Correlation and Narrative Summary of Campbell Biology AP Edition 11 th Edition, 2018 To the AP Biology Curriculum Framework AP is a trademark registered and/or owned by the College Board, which was not
Genome sequence of Plasmopara viticola and insight into the pathogenic mechanism
Genome sequence of Plasmopara viticola and insight into the pathogenic mechanism Ling Yin 1,3,, Yunhe An 1,2,, Junjie Qu 3,, Xinlong Li 1, Yali Zhang 1, Ian Dry 5, Huijun Wu 2*, Jiang Lu 1,4** 1 College
Title ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Title ghost-tree: creating hybrid-gene phylogenetic trees for diversity analyses
Phylogenetic Tree Generation using Different Scoring Methods
International Journal of Computer Applications (975 8887) Phylogenetic Tree Generation using Different Scoring Methods Rajbir Singh Associate Prof. & Head Department of IT LLRIET, Moga Sinapreet Kaur Student
Computational Structural Bioinformatics
Computational Structural Bioinformatics ECS129 Instructor: Patrice Koehl http://koehllab.genomecenter.ucdavis.edu/teaching/ecs129 koehl@cs.ucdavis.edu Learning curve Math / CS Biology/ Chemistry Pre-requisite
Nature Structural & Molecular Biology: doi: /nsmb Supplementary Figure 1
Supplementary Figure 1 Zn 2+ -binding sites in USP18. (a) The two molecules of USP18 present in the asymmetric unit are shown. Chain A is shown in blue, chain B in green. Bound Zn 2+ ions are shown as
Motivating the need for optimal sequence alignments...
1 Motivating the need for optimal sequence alignments... 2 3 Note that this actually combines two objectives of optimal sequence alignments: (i) use the score of the alignment o infer homology; (ii) use
Discrete Probability Distributions: Poisson Applications to Genome Assembly
Discrete Probability Distributions: Poisson Applications to Genome Assembly Binomial Distribution Binomially distributed random variable X equals sum of n independent Bernoulli trials The probability mass
Big Idea 1: The process of evolution drives the diversity and unity of life.
Big Idea 1: The process of evolution drives the diversity and unity of life. understanding 1.A: Change in the genetic makeup of a population over time is evolution. 1.A.1: Natural selection is a major
AP Curriculum Framework with Learning Objectives
Big Ideas Big Idea 1: The process of evolution drives the diversity and unity of life. AP Curriculum Framework with Learning Objectives Understanding 1.A: Change in the genetic makeup of a population over
CGS 5991 (2 Credits) Bioinformatics Tools
CAP 5991 (3 Credits) Introduction to Bioinformatics CGS 5991 (2 Credits) Bioinformatics Tools Giri Narasimhan 8/26/03 CAP/CGS 5991: Lecture 1 1 Course Schedules CAP 5991 (3 credit) will meet every Tue
SEQUENCE ALIGNMENT BACKGROUND: BIOINFORMATICS. Prokaryotes and Eukaryotes. DNA and RNA
SEQUENCE ALIGNMENT BACKGROUND: BIOINFORMATICS 1 Prokaryotes and Eukaryotes 2 DNA and RNA 3 4 Double helix structure Codons Codons are triplets of bases from the RNA sequence. Each triplet defines an amino-acid.
Bio 119 Bacterial Genomics 6/26/10
BACTERIAL GENOMICS Reading in BOM-12: Sec. 11.1 Genetic Map of the E. coli Chromosome p. 279 Sec. 13.2 Prokaryotic Genomes: Sizes and ORF Contents p. 344 Sec. 13.3 Prokaryotic Genomes: Bioinformatic Analysis
Metabolic modelling. Metabolic networks, reconstruction and analysis. Esa Pitkänen Computational Methods for Systems Biology 1 December 2009
Metabolic modelling Metabolic networks, reconstruction and analysis Esa Pitkänen Computational Methods for Systems Biology 1 December 2009 Department of Computer Science, University of Helsinki Metabolic
Introduction to the SNP/ND concept - Phylogeny on WGS data
Introduction to the SNP/ND concept - Phylogeny on WGS data Johanne Ahrenfeldt PhD student Overview What is Phylogeny and what can it be used for Single Nucleotide Polymorphism (SNP) methods CSI Phylogeny
Gene expression in prokaryotic and eukaryotic cells, Plasmids: types, maintenance and functions. Mitesh Shrestha
Gene expression in prokaryotic and eukaryotic cells, Plasmids: types, maintenance and functions. Mitesh Shrestha Plasmids 1. Extrachromosomal DNA, usually circular-parasite 2. Usually encode ancillary
Linear-Space Alignment
Linear-Space Alignment Subsequences and Substrings Definition A string x is a substring of a string x, if x = ux v for some prefix string u and suffix string v (similarly, x = x i x j, for some 1 i j x