<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ivanchenko, Maria G</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">NanoCAGE-XL: An Approach to High-Confidence Transcription Start Site Sequencing</style></title><secondary-title><style face="normal" font="default" size="100%">Methods in Molecular Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/protocol/10.1007/978-1-4939-8657-6_13</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1830</style></volume><pages><style face="normal" font="default" size="100%">225-237</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Identifying the transcription start sites (TSS) of genes is essential for characterizing promoter regions. Several protocols have been developed to capture the 5&amp;prime; end of transcripts via Cap-Analysis of Gene Expression (CAGE) or linker-ligation strategies such as Paired-End Analysis of Transcription Start Sites (PEAT), but often require large amounts of tissue. More recently, nanoCAGE was developed for sequencing on the Illumina GAIIx to overcome this limitation. In this chapter, we present the nanoCAGE-XL protocol, the first publicly available adaptation of nanoCAGE for sequencing on recent ultra-high-throughput platforms such as Illumina HiSeq-2000. NanoCAGE-XL provides a method for precise transcription start site identification in large eukaryotic genomes, even in cases where input total RNA quantity is very limited.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joanna Friesner</style></author><author><style face="normal" font="default" size="100%">Sarah M. Assmann</style></author><author><style face="normal" font="default" size="100%">Ruth Bastow</style></author><author><style face="normal" font="default" size="100%">Julia Bailey-Serres</style></author><author><style face="normal" font="default" size="100%">Jim Beynon</style></author><author><style face="normal" font="default" size="100%">Volker Brendel</style></author><author><style face="normal" font="default" size="100%">C. Robin Buell</style></author><author><style face="normal" font="default" size="100%">Alexander Bucksch</style></author><author><style face="normal" font="default" size="100%">Wolfgang Busch</style></author><author><style face="normal" font="default" size="100%">Taku Demura</style></author><author><style face="normal" font="default" size="100%">Jose R. Dinneny</style></author><author><style face="normal" font="default" size="100%">Colleen J. Doherty</style></author><author><style face="normal" font="default" size="100%">Andrea L. Eveland</style></author><author><style face="normal" font="default" size="100%">Pascal Falter-Braun</style></author><author><style face="normal" font="default" size="100%">Malia A. Gehan</style></author><author><style face="normal" font="default" size="100%">Michael Gonzales</style></author><author><style face="normal" font="default" size="100%">Erich Grotewold</style></author><author><style face="normal" font="default" size="100%">Rodrigo Gutierrez</style></author><author><style face="normal" font="default" size="100%">Ute Kramer</style></author><author><style face="normal" font="default" size="100%">Gabriel Krouk</style></author><author><style face="normal" font="default" size="100%">Shisong Ma</style></author><author><style face="normal" font="default" size="100%">R.J. Cody Markelz</style></author><author><style face="normal" font="default" size="100%">Molly Megraw</style></author><author><style face="normal" font="default" size="100%">Blake C. Meyers</style></author><author><style face="normal" font="default" size="100%">James A.H. Murray</style></author><author><style face="normal" font="default" size="100%">Nicholas J. Provart</style></author><author><style face="normal" font="default" size="100%">Sue Rhee</style></author><author><style face="normal" font="default" size="100%">Roger Smith</style></author><author><style face="normal" font="default" size="100%">Edgar P. Spalding</style></author><author><style face="normal" font="default" size="100%">Crispin Taylor</style></author><author><style face="normal" font="default" size="100%">Tracy K. Teal</style></author><author><style face="normal" font="default" size="100%">Keiko U. Torii</style></author><author><style face="normal" font="default" size="100%">Chris Town</style></author><author><style face="normal" font="default" size="100%">Matthew Vaughn</style></author><author><style face="normal" font="default" size="100%">Richard Vierstra</style></author><author><style face="normal" font="default" size="100%">Doreen Ware</style></author><author><style face="normal" font="default" size="100%">Olivia Wilkins</style></author><author><style face="normal" font="default" size="100%">Cranos Williams</style></author><author><style face="normal" font="default" size="100%">Siobhan M. Brady</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Physiology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.plantphysiol.org/content/175/4/1499</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">175</style></volume><pages><style face="normal" font="default" size="100%">1499-1509</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div&gt;It has been more than 50 years since Arabidopsis (Arabidopsis thaliana) was first introduced as a model organism to understand basic processes in plant biology. A well-organized scientific community has used this small reference plant species to make numerous fundamental plant biology discoveries (Provart et al., 2016). Due to an extremely well-annotated genome and advances in high-throughput sequencing, our understanding of this organism and other plant species has become even more intricate and complex. Computational resources, including CyVerse,3 Araport,4 The Arabidopsis Information Resource (TAIR),5 and BAR,6 have further facilitated novel findings with just the click of a mouse. As we move toward understanding biological systems, Arabidopsis researchers will need to use more quantitative and computational approaches to extract novel biological findings from these data. Here, we discuss guidelines, skill sets, and core competencies that should be considered when developing curricula or training undergraduate or graduate students, postdoctoral researchers, and faculty. A selected case study provides more specificity as to the concrete issues plant biologists face and how best to address such challenges.&lt;/div&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cumbie, Jason S</style></author><author><style face="normal" font="default" size="100%">Ivanchenko, Maria G</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">NanoCAGE-XL and CapFilter: an approach to genome wide identification of high confidence transcription start sites.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Nanotechnology</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Roots</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Initiation Site</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">597</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Identifying the transcription start sites (TSS) of genes is essential for characterizing promoter regions. Several protocols have been developed to capture the 5&amp;#39; end of transcripts via Cap Analysis of Gene Expression (CAGE) or linker-ligation strategies such as Paired-End Analysis of Transcription Start Sites (PEAT), but often require large amounts of tissue. More recently, nanoCAGE was developed for sequencing on the Illumina GAIIx to overcome these difficulties.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here we present the first publicly available adaptation of nanoCAGE for sequencing on recent ultra-high throughput platforms such as Illumina HiSeq-2000, and CapFilter, a computational pipeline that greatly increases confidence in TSS identification. We report excellent gene coverage, reproducibility, and precision in transcription start site discovery for samples from Arabidopsis thaliana roots.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;nanoCAGE-XL together with CapFilter allows for genome wide identification of high confidence transcription start sites in large eukaryotic genomes.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;http://megraw-dev.cgrb.oregonstate.edu/nanocage&quot;&gt;[Link to Protocol, Additional Data, and Supplementary Materials]&lt;/a&gt;&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;http://megraw-dev.cgrb.oregonstate.edu/capfilter&quot;&gt;[Link to CapFilter Software]&lt;/a&gt;&lt;/p&gt;</style></abstract></record></records></xml>