Ebook plant reverse genetics methods and protocols – part 1

aMbavaRaM • Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA GynheunG an • Department of Plant Molecular Systems Biotechnology and Crop Biotech Institute, Kyung He

Me t h o d s i n Mo l e c u l a r Bi o l o g y ™

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Plant Reverse Genetics

Methods and Protocols

Edited by

Andy Pereira

Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA

Andy Pereira, Ph.D.

Virginia Bioinformatics Institute

Virginia Tech

Blacksburg, VA

USA

pereiraa@vbi.vt.edu

DOI 10.1007/978-1-60761-682-5

Springer New York Dordrecht Heidelberg London

Library of Congress Control Number: 2010935805

© Springer Science+Business Media, LLC 2011

All rights reserved This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden.

The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified

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be made The publisher makes no warranty, express or implied, with respect to the material contained herein Printed on acid-free paper

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Preface

Plant biology is at the crossroads, integrating the data from genomics into knowledge and understanding of important biological processes With the generation of genome sequence data from model and other plants, databases are filled with sequence information of genes with no known biological function While bioinformatics tools can help analyze genome sequences and predict gene structures, experimental approaches to discover gene func-tions need to be widely implemented This book deals with plant functional genomics using reverse genetics methods, namely, from gene sequence to plant gene functions The methods developed and described by leading researchers in the field are high-throughput and genome-wide in the models Arabidopsis and rice as well as other plants to provide comparative functional genomics information

This book describes methods for the analysis of high-throughput genome sequence data, the identification of noncoding RNA from sequence information, the comprehen-sive analysis of gene expression by microarrays, and Metabolomic analysis, all of which are supported by scripts to aid their computational use A series of mutational approaches to ascribe gene function are described using insertion sequences such as T-DNA and trans-posons as well as methods for the silencing and overexpression of genes The cataloging

of developmental mutant phenotypes as well as analysis of functions using specific phe-nome screens described can be adapted to any lab conditions The integration of the diverse comparative functional genomics information in a database, such as Gramene, provides the capabilities for an understanding of how plant genes work together in a sys-tems biology view

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Contents

Preface v Contributors ix

1 Analysis of High-Throughput Sequencing Data 1

Shrinivasrao P Mane, Thero Modise, and Bruno W Sobral

2 Identification of Plant microRNAs Using Expressed

Sequence Tag Analysis 13

Taylor P Frazier and Baohong Zhang

3 Microarray Data Analysis 27

Saroj K Mohapatra and Arjun Krishnan

4 Setting Up Reverse Transcription Quantitative-PCR Experiments 45

Madana M.R Ambavaram and Andy Pereira

5 Virus-Induced Gene Silencing in Nicotiana benthamiana

and Other Plant Species 55

Andrew Hayward, Meenu Padmanabhan, and S.P Dinesh-Kumar

6 Agroinoculation and Agroinfiltration: Simple Tools

for Complex Gene Function Analyses 65

Zarir Vaghchhipawala, Clemencia M Rojas, Muthappa Senthil-Kumar,

and Kirankumar S Mysore

7 Full-Length cDNA Overexpressor Gene Hunting System

(FOX Hunting System) 77

Mieko Higuchi, Youichi Kondou, Takanari Ichikawa,

and Minami Matsui

8 Activation Tagging with En/Spm-I/dSpm Transposons in Arabidopsis 91

Nayelli Marsch-Martínez and Andy Pereira

9 Activation Tagging and Insertional Mutagenesis in Barley 107

Michael A Ayliffe and Anthony J Pryor

10 Methods for Rice Phenomics Studies 129

Chyr-Guan Chern, Ming-Jen Fan, Sheng-Chung Huang, Su-May Yu,

Fu-Jin Wei,Cheng-Chieh Wu, Arunee Trisiriroj, Ming-Hsing Lai,

Shu Chen, and Yue-Ie C Hsing

11 Development of an Efficient Inverse PCR Method

for Isolating Gene Tags from T-DNA Insertional Mutants in Rice 139

Sung-Ryul Kim, Jong-Seong Jeon, and Gynheung An

12 Transposon Insertional Mutagenesis in Rice 147

Narayana M Upadhyaya, Qian-Hao Zhu, and Ramesh S Bhat

13 Reverse Genetics in Medicago truncatula

Using Tnt1 Insertion Mutants 179

Xiaofei Cheng, Jiangqi Wen, Million Tadege, Pascal Ratet,

and Kirankumar S Mysore

viii Contents

14 Screening Arabidopsis Genotypes for Drought Stress Resistance 191

Amal Harb and Andy Pereira

15 Protein Tagging for Chromatin Immunoprecipitation

from Arabidopsis 199

Stefan de Folter

16 Yeast One-Hybrid Screens for Detection

of Transcription Factor DNA Interactions 211

Pieter B.F Ouwerkerk and Annemarie H Meijer

17 Plant Metabolomics by GC-MS and Differential Analysis 229

Joel L Shuman, Diego F Cortes, Jenny M Armenta,

Revonda M Pokrzywa, Pedro Mendes, and Vladimir Shulaev

18 Gramene Database: A Hub for Comparative Plant Genomics 247

Pankaj Jaiswal

Index 277

Contributors

Madana M R aMbavaRaM • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

GynheunG an • Department of Plant Molecular Systems Biotechnology and Crop

Biotech Institute, Kyung Hee University, Yongin 446-701, Republic of Korea

Jenny M aRMenta • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

Michael a ayliffe • CSIRO Plant Industry, Canberra, ACT, Australia

RaMesh s bhat • University of Agricultural Sciences, Dharwad, Karnataka, India

shu chen • Taiwan Agricultural Research Institute, Wufeng, Taichung, Taiwan

Xiaofei chenG • Plant Biology Division, The Samuel Roberts Noble Foundation,

Ardmore, OK, USA

chyR-Guan cheRn • Taiwan Agricultural Research Institute,

Wufeng, Taichung, Taiwan

dieGo f coRtes • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

s P dinesh-KuMaR • UC Davis Genome Center, 1319 Genome and Biomedical

Sciences Facility, 451 Health Sciences Drive, Davis, CA 95616, USA

MinG-Jen fan • Department of Biotechnology, Asia University, Wufeng, Taichung,

Taiwan

stefan de folteR • Laboratorio Nacional de Genómica para la Biodiversidad

(Langebio), Centro de Investigación y de Estudios Avanzados del Instituto

Politécnico Nacional (CINVESTAV-IPN), Irapuato, Mexico

tayloR P fRazieR • Department of Biology, East Carolina University,

Greenville, NC, USA

aMal haRb • Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA

andRew haywaRd • Department of Molecular, Cellular, and Developmental Biology,

Yale University, New Haven, CT, USA

MieKo hiGuchi • RIKEN Plant Science Center, Yokohama Kanagawa, Japan

yue-ie c hsinG • Institute of Plant and Microbial Biology, Academia Sinica,

Taipei, Taiwan

shenG-chunG huanG • Taiwan Agricultural Research Institute, Wufeng, Taichung,

Taiwan

taKanaRi ichiKawa • RIKEN Plant Science Center, YokohamaKanagawa, Japan;

Gene Research Center, Tsukuba University, Tsukuba, Japan

PanKaJ Jaiswal • Department of Botany and Plant Pathology,

Oregon State University, Corvallis, OR, USA

JonG-seonG Jeon • Graduate School of Biotechnology & Plant Metabolism Research

Center, Kyung Hee University, Yongin, Korea

x Contributors

sunG-Ryul KiM • National Research Laboratory of Plant Functional Genomics,

Division of Molecular and Life Sciences, POSTECH Biotech Center,

Pohang University of Science and Technology, Pohang, Korea

youichi Kondou • RIKEN Plant Science Center, Yokohama, Kanagawa, Japan

aRJun KRishnan • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

MinG-hsinG lai • Taiwan Agricultural Research Institute, Wufeng, Taichung,

Taiwan

shRinivasRao P Mane • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

nayelli MaRsch-MaRtínez • Laboratorio Nacional de Genómica para la

Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), Irapuato, México

MinaMi Matsui • RIKEN Plant Science Center, Yokohama, Kanagawa, Japan

anneMaRie h MeiJeR • Clusius Laboratory, Institute of Biology (IBL),

Leiden University, Leiden, The Netherlands

PedRo Mendes • Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA,

USA; School of Computer Science, University of Manchester, Manchester, UK; Department of Cancer Biology, Wake Forest University School of Medicine,

Winston-Salem, NC, USA

theRo Modise • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

saRoJ K MohaPatRa • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

KiRanKuMaR s MysoRe • Plant Biology Division, The Samuel Roberts

Noble Foundation, Ardmore, OK, USA

PieteR b f ouweRKeRK • Sylvius Laboratory, Institute of Biology (IBL), Leiden

Uni-versity, Leiden, The Netherlands

Meenu PadManabhan • Department of Molecular, Cellular,

and Developmental Biology, Yale University, New Haven, CT, USA

andy PeReiRa • Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA

Revonda M PoKRzywa • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

anthony J PRyoR • CSIRO Plant Industry, Canberra, ACT, Australia

Pascal Ratet • Institut des Sciences du Vegetal, CNRS, Gif sur Yvette Cedex, France

cleMencia M RoJas • Plant Biology Division, The Samuel Roberts Noble

Foundation, Ardmore, OK, USA

MuthaPPa senthil-KuMaR • Plant Biology Division, The Samuel Roberts Noble

Foundation, Ardmore, OK, USA

vladiMiR shulaev • Department of Horticulture, Virginia Bioinformatics Institute,

Virginia Tech, BlacksburgVA, USA; Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC, USA

Joel l shuMan • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

bRuno w sobRal • Virginia Bioinformatics Institute, Virginia Tech,

Blacksburg, VA, USA

Million tadeGe • Plant Biology Division, The Samuel Roberts Noble Foundation,

Ardmore, OK, USA

aRunee tRisiRiRoJ • Institute of Plant and Microbial Biology, Academia Sinica,

Taipei, Taiwan

naRayana M uPadhyaya • CSIRO Plant Industry, Canberra, ACT, Australia

zaRiR vaGhchhiPawala • Plant Biology Division, The Samuel Roberts Noble

Foundation, Ardmore, OK, USA

fu-Jin wei • Institute of Plant and Microbial Biology, Academia Sinica,

Taipei, Taiwan

JianGqi wen • Plant Biology Division, The Samuel Roberts Noble Foundation,

Ardmore, OK, USA

chenG-chieh wu • Institute of Plant and Microbial Biology, Academia Sinica,

Taipei, Taiwan

su-May yu • Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan

baohonG zhanG • Department of Biology, East Carolina University,

Greenville, NC, USA

qian-hao zhu • CSIRO Plant Industry, Canberra, ACT, Australia

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Chapter 1

Analysis of High-Throughput Sequencing Data

Shrinivasrao P Mane, Thero Modise, and Bruno W Sobral

Abstract

Next-generation sequencing has revolutionized biology by exponentially increasing sequencing output while dramatically lowering costs High-throughput sequence data with shorter reads has opened up new applications such as whole genome resequencing, indel and SNP detection, transcriptome sequencing, etc Several tools are available for the analysis of high-throughput sequencing data In this chapter, we describe the use of an ultrafast alignment program, Bowtie, to align short-read sequence (SRS) data against the Arabidopsis reference genome The alignment files generated from Bowtie will be used to identify SNPs and indels using Maq.

Key words: Next-generation sequencing, Short-read sequences, Alignment programs, Bowtie, Maq

Next-generation sequencers from Roche/454, Illumina, Applied Biosystems and Helicos have revolutionized biological research

by greatly increasing sequencing output while dramatically lower-ing costs Roche/454 produces ~400 bp sequence reads suitable for de novo sequencing and medium throughput applications, while Illumina and ABI produce short-read sequences (SRSs) typically ranging from 35 to 80 bp in length suitable for rese-quencing and high-throughput applications SRS technologies provide endless opportunities for genomics, comparative genome biology, medical diagnostics, etc Some of the examples include genome resequencing to detect SNPs and mutations within pop-ulations (SNP-seq), sequencing of closely related species, methylome profiling, DNA-protein interactions (ChIP-seq), transcriptome sequencing (RNA-seq), mRNA expression profiling (DGE), and small RNA identification and profiling

Since SRS technology produces enormous amounts of very short reads, assembly tools developed for Sanger sequencing data

1 Introduction

Andy Pereira (ed.), Plant Reverse Genetics: Methods and Protocols, Methods in Molecular Biology, vol 678,

DOI 10.1007/978-1-60761-682-5_1, © Springer Science+Business Media, LLC 2011

2 Mane, Modise, and Sobral

cannot be directly applied to assemble SRS data because the algorithms rely on longer reads and different sequencing error characteristics Although several assemblers have been developed

to assemble smaller genomes, they are not well suited to handle large eukaryotic genomes Recently, several tools for efficiently mapping/aligning the SRSs to reference genomes of any arbi-trary length have been developed Table 1 provides a list of tools currently available for mapping These tools can be used for rese-quencing, identification of SNPs and indels, identification of small RNA, mRNA transcripts, and alternate splicing

In this chapter, we focus on analyzing resequencing data using Bowtie and Maq Bowtie is an ultrafast, memory-efficient short-read aligner It aligns SRSs to the human genome at a rate

of over 25 million 35-bp reads per hour It works best with short reads although it can support reads up to 1,024 bp in length Currently, Bowtie does not support colorspace data (from ABI SOLiD), but this will be added in future releases Bowtie provides alignment parameters similar to Maq and SOAP but can run at much faster speeds than both Although Maq is much slower than Bowtie at mapping reads to a reference sequence, it has more sequence analytical tools For example, Maq can produce consensus sequences from alignments and also has tools for SNP discovery

This section contains a list of prerequisite hardware and software for mapping the reads to the reference genome In addition to requirements, the formats of the input and output files are described As mentioned previously, we use Bowtie and Maq These software are open source and free to use under the GNU public license

Bowtie can be downloaded from http://bowtie-bio.sourceforge net/ Maq can be downloaded from http://maq.sourceforge net/ Source code and binary releases are available for Windows, Linux/Unix, and Mac platforms

The software was tested on a 2.66 GHz Two Dual-Core Intel Xeon Mac Pro with 4 GB RAM and 8 core AMD Opteron Linux machine with 64 GB RAM The software system requires the following:

(a) A regular desktop computer should be sufficient for bacterial genomes For eukaryotic genomes, at least 2 GB of RAM is needed

(b) Available disk space should be more than approximately five times the size of input files

2 Materials

2.1 Downloading

the Software

2.2 Installing Bowtie