<?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%">Valerie N. Fraser</style></author><author><style face="normal" font="default" size="100%">Benjamin Philmus</style></author><author><style face="normal" font="default" size="100%">Molly Megraw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Metabolomics analysis reveals both plant variety and choice of hormone treatment modulate vinca alkaloid production in Catharanthus roseus</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Direct</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://onlinelibrary.wiley.com/doi/10.1002/pld3.267</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;color: rgb(28, 29, 30); font-family: &amp;quot;Open Sans&amp;quot;, icomoon, sans-serif; font-size: 16px;&quot;&gt;The medicinal plant&amp;nbsp;&lt;/span&gt;&lt;i style=&quot;box-sizing: border-box; color: rgb(28, 29, 30); font-family: &amp;quot;Open Sans&amp;quot;, icomoon, sans-serif; font-size: 16px;&quot;&gt;Catharanthus roseus&lt;/i&gt;&lt;span style=&quot;color: rgb(28, 29, 30); font-family: &amp;quot;Open Sans&amp;quot;, icomoon, sans-serif; font-size: 16px;&quot;&gt;&amp;nbsp;produces numerous secondary metabolites of interest for the treatment of many diseases &amp;ndash; most notably for the terpene indole alkaloid (TIA) vinblastine, which is used in the treatment of leukemia and Hodgkin&amp;#39;s lymphoma. Historically, methyl jasmonate (MeJA) has been used to induce TIA production, but in the past, this has only been investigated in whole seedlings, cell culture, or hairy root culture. This study examines the effects of the phytohormones MeJA and ethylene on the induction of TIA biosynthesis and accumulation in the shoots and roots of 8‐day‐old seedlings of two varieties of&amp;nbsp;&lt;/span&gt;&lt;i style=&quot;box-sizing: border-box; color: rgb(28, 29, 30); font-family: &amp;quot;Open Sans&amp;quot;, icomoon, sans-serif; font-size: 16px;&quot;&gt;C. roseus&lt;/i&gt;&lt;span style=&quot;color: rgb(28, 29, 30); font-family: &amp;quot;Open Sans&amp;quot;, icomoon, sans-serif; font-size: 16px;&quot;&gt;. Using LCMS and RT‐qPCR, we demonstrate the importance of variety selection, as we observe markedly different induction patterns of important TIA precursor compounds. Additionally, both phytohormone choice and concentration have significant effects on TIA biosynthesis. Finally, our study suggests that several early‐induction pathway steps as well as pathway‐specific genes are likely to be transcriptionally regulated. Our findings highlight the need for a complete set of&amp;#39;omics resources in commonly used&amp;nbsp;&lt;/span&gt;&lt;i style=&quot;box-sizing: border-box; color: rgb(28, 29, 30); font-family: &amp;quot;Open Sans&amp;quot;, icomoon, sans-serif; font-size: 16px;&quot;&gt;C. roseus&lt;/i&gt;&lt;span style=&quot;color: rgb(28, 29, 30); font-family: &amp;quot;Open Sans&amp;quot;, icomoon, sans-serif; font-size: 16px;&quot;&gt;&amp;nbsp;varieties and the need for caution when extrapolating results from one cultivar to another.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></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%">Colleen Doherty</style></author><author><style face="normal" font="default" size="100%">Joanna Friesner</style></author><author><style face="normal" font="default" size="100%">Brian Gregory</style></author><author><style face="normal" font="default" size="100%">Ann Loraine</style></author><author><style face="normal" font="default" size="100%">Molly Megraw</style></author><author><style face="normal" font="default" size="100%">Nicholas Provart</style></author><author><style face="normal" font="default" size="100%">R Keith Slotkin</style></author><author><style face="normal" font="default" size="100%">Chris Town</style></author><author><style face="normal" font="default" size="100%">Sarah M Assmann</style></author><author><style face="normal" font="default" size="100%">Michael Axtell</style></author><author><style face="normal" font="default" size="100%">Tanya Berardini</style></author><author><style face="normal" font="default" size="100%">Sixue Chen</style></author><author><style face="normal" font="default" size="100%">Malia Gehan</style></author><author><style face="normal" font="default" size="100%">Eva Huala</style></author><author><style face="normal" font="default" size="100%">Pankaj Jaiswal</style></author><author><style face="normal" font="default" size="100%">Stephen Larson</style></author><author><style face="normal" font="default" size="100%">Song Li</style></author><author><style face="normal" font="default" size="100%">Sean May</style></author><author><style face="normal" font="default" size="100%">Todd Michael</style></author><author><style face="normal" font="default" size="100%">Chris Pires</style></author><author><style face="normal" font="default" size="100%">Chris Topp</style></author><author><style face="normal" font="default" size="100%">Justin Walley</style></author><author><style face="normal" font="default" size="100%">Eve Wurtele</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Arabidopsis bioinformatics resources: The current state, challenges, and priorities for the future</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Direct</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://onlinelibrary.wiley.com/doi/full/10.1002/pld3.109</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">3</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></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%">Sparks, E. E.</style></author><author><style face="normal" font="default" size="100%">Drapek, C.</style></author><author><style face="normal" font="default" size="100%">Gaudinier, A.</style></author><author><style face="normal" font="default" size="100%">Li, S.</style></author><author><style face="normal" font="default" size="100%">Ansariola, M.</style></author><author><style face="normal" font="default" size="100%">Shen, N.</style></author><author><style face="normal" font="default" size="100%">Hennacy, J. H.</style></author><author><style face="normal" font="default" size="100%">Zhang, J.</style></author><author><style face="normal" font="default" size="100%">Turco, G.</style></author><author><style face="normal" font="default" size="100%">Petricka, J. J.</style></author><author><style face="normal" font="default" size="100%">Foret, J.</style></author><author><style face="normal" font="default" size="100%">Hartemink, A. J.</style></author><author><style face="normal" font="default" size="100%">Gordan, R.</style></author><author><style face="normal" font="default" size="100%">Megraw, M.</style></author><author><style face="normal" font="default" size="100%">Brady, S. M.</style></author><author><style face="normal" font="default" size="100%">Benfey, P. N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Establishment of Expression in the SHORTROOT-SCARECROW Transcriptional Cascade through Opposing Activities of Both Activators and Repressors</style></title><secondary-title><style face="normal" font="default" size="100%">Dev Cell</style></secondary-title><short-title><style face="normal" font="default" size="100%">Developmental cell</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis Proteins/ genetics/ metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Arabidopsis/ genetics/growth &amp; development/ metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Reporter</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Synthetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Roots/cytology/metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Plants, Genetically Modified</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Repressor Proteins/genetics/metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Trans-Activators/genetics/metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors/ genetics/ metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Two-Hybrid System Techniques</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.devcel.2016.09.031</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">585-596</style></pages><isbn><style face="normal" font="default" size="100%">1878-1551 (Electronic)1534-5807 (Linking)</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Tissue-specific gene expression is often thought to arise from spatially restricted transcriptional cascades. However, it is unclear how expression is established at the top of these cascades in the absence of pre-existing specificity. We generated a transcriptional network to explore how transcription factor expression is established in the Arabidopsis thaliana root ground tissue. Regulators of the SHORTROOT-SCARECROW transcriptional cascade were validated in planta. At the top of this cascade, we identified both activators and repressors of SHORTROOT. The aggregate spatial expression of these regulators is not sufficient to predict transcriptional specificity. Instead, modeling, transcriptional reporters, and synthetic promoters support a mechanism whereby expression at the top of the SHORTROOT-SCARECROW cascade is established through opposing activities of activators and repressors.&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%">Filichkin, Sergei</style></author><author><style face="normal" font="default" size="100%">Priest, Henry D</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Mockler, Todd C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Alternative splicing in plants: directing traffic at the crossroads of adaptation and environmental stress.</style></title><secondary-title><style face="normal" font="default" size="100%">Curr Opin Plant Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Curr. Opin. Plant Biol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adaptation, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">Alternative Splicing</style></keyword><keyword><style  face="normal" font="default" size="100%">Circadian Clocks</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Physiological Phenomena</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Stress, Physiological</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 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">125-35</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In recent years, high-throughput sequencing-based analysis of plant transcriptomes has suggested that up to &amp;sim;60% of plant gene loci encode alternatively spliced mature transcripts. These studies have also revealed that alternative splicing in plants can be regulated by cell type, developmental stage, the environment, and the circadian clock. Alternative splicing is coupled to RNA surveillance and processing mechanisms, including nonsense mediated decay. Recently, non-protein-coding transcripts have also been shown to undergo alternative splicing. These discoveries collectively describe a robust system of post-transcriptional regulatory feedback loops which influence RNA abundance. In this review, we summarize recent studies describing the specific roles alternative splicing and RNA surveillance play in plant adaptation to environmental stresses and the regulation of the circadian clock.&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%">Filichkin, Sergei A</style></author><author><style face="normal" font="default" size="100%">Cumbie, Jason S</style></author><author><style face="normal" font="default" size="100%">Dharmawardhana, Palitha</style></author><author><style face="normal" font="default" size="100%">Jaiswal, Pankaj</style></author><author><style face="normal" font="default" size="100%">Chang, Jeff H</style></author><author><style face="normal" font="default" size="100%">Palusa, Saiprasad G</style></author><author><style face="normal" font="default" size="100%">Reddy, A S N</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Mockler, Todd C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Environmental stresses modulate abundance and timing of alternatively spliced circadian transcripts in Arabidopsis.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Plant</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Plant</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alternative Splicing</style></keyword><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Arabidopsis Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Circadian Clocks</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Introns</style></keyword><keyword><style  face="normal" font="default" size="100%">Nonsense Mediated mRNA Decay</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 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">207-27</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Environmental stresses profoundly altered accumulation of nonsense mRNAs including intron-retaining (IR) transcripts in Arabidopsis. Temporal patterns of stress-induced IR mRNAs were dissected using both oscillating and non-oscillating transcripts. Broad-range thermal cycles triggered a sharp increase in the long IR CCA1 isoforms and altered their phasing to different times of day. Both abiotic and biotic stresses such as drought or Pseudomonas syringae infection induced a similar increase. Thermal stress induced a time delay in accumulation of CCA1 I4Rb transcripts, whereas functional mRNA showed steady oscillations. Our data favor a hypothesis that stress-induced instabilities of the central oscillator can be in part compensated through fluctuations in abundance and out-of-phase oscillations of CCA1 IR transcripts. Taken together, our results support a concept that mRNA abundance can be modulated through altering ratios between functional and nonsense/IR transcripts. SR45 protein specifically bound to the retained CCA1 intron in&amp;nbsp;vitro, suggesting that this splicing factor could be involved in regulation of intron retention. Transcriptomes of nonsense-mediated mRNA decay (NMD)-impaired and heat-stressed plants shared a set of retained introns associated with stress- and defense-inducible transcripts. Constitutive activation of certain stress response networks in an NMD mutant could be linked to disequilibrium between functional and nonsense mRNAs.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></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%">Filichkin, Sergei A</style></author><author><style face="normal" font="default" size="100%">Cumbie, Jason S</style></author><author><style face="normal" font="default" size="100%">Dharmawadhana, J Palitha</style></author><author><style face="normal" font="default" size="100%">Jaiswal, Pankaj</style></author><author><style face="normal" font="default" size="100%">Chang, Jeff H</style></author><author><style face="normal" font="default" size="100%">Palusa, Saiprasad G</style></author><author><style face="normal" font="default" size="100%">Reddy, A S N</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Mockler, Todd C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Environmental Stresses Modulate Abundance and Timing of Alternatively Spliced Circadian Transcripts in Arabidopsis.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Plant</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Plant</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Nov 3</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Environmental stresses profoundly altered accumulation of nonsense mRNAs including intron retaining (IR) transcripts in Arabidopsis. Temporal patterns of stress-induced IR mRNAs were dissected using both oscillating and non-oscillating transcripts. Broad range thermal cycles triggered a sharp increase in the long intron retaining CCA1 isoforms and altered their phasing to different times of day. Both abiotic and biotic stresses such as drought or P. syringae infection induced similar increase. Thermal stress induced a time delay in accumulation of CCA1 I4Rb transcripts whereas functional mRNA showed steady oscillations. Our data favor a hypothesis that stress-induced instabilities of the central oscillator can be in part compensated through fluctuations in abundance and out of phase oscillations of CCA1 IR transcripts. Altogether, our results support a concept that mRNA abundance can be modulated through altering ratios between functional and nonsense/IR transcripts. SR45 protein specifically bound to the retained CCA1 intron in vitro, suggesting that this splicing factor could be involved in regulation of intron retention. Transcriptomes of NMD-impaired and heat-stressed plants shared a set of retained introns associated with stress- and defense-inducible transcripts. Constitutive activation of certain stress response networks in an NMD mutant could be linked to disequilibrium between functional and nonsense mRNAs.&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%">Morton, Taj</style></author><author><style face="normal" font="default" size="100%">Petricka, Jalean</style></author><author><style face="normal" font="default" size="100%">Corcoran, David L</style></author><author><style face="normal" font="default" size="100%">Li, Song</style></author><author><style face="normal" font="default" size="100%">Winter, Cara M</style></author><author><style face="normal" font="default" size="100%">Carda, Alexa</style></author><author><style face="normal" font="default" size="100%">Benfey, Philip N</style></author><author><style face="normal" font="default" size="100%">Ohler, Uwe</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%">Paired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures.</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Cell</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Plant Cell</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Arabidopsis Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Binding Sites</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Nucleotide Motifs</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%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Species Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">TATA Box</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</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%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">2746-60</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding plant gene promoter architecture has long been a challenge due to the lack of relevant large-scale data sets and analysis methods. Here, we present a publicly available, large-scale transcription start site (TSS) data set in plants using a high-resolution method for analysis of 5&amp;#39; ends of mRNA transcripts. Our data set is produced using the paired-end analysis of transcription start sites (PEAT) protocol, providing millions of TSS locations from wild-type Columbia-0 Arabidopsis thaliana whole root samples. Using this data set, we grouped TSS reads into &amp;quot;TSS tag clusters&amp;quot; and categorized clusters into three spatial initiation patterns: narrow peak, broad with peak, and weak peak. We then designed a machine learning model that predicts the presence of TSS tag clusters with outstanding sensitivity and specificity for all three initiation patterns. We used this model to analyze the transcription factor binding site content of promoters exhibiting these initiation patterns. In contrast to the canonical notions of TATA-containing and more broad &amp;quot;TATA-less&amp;quot; promoters, the model shows that, in plants, the vast majority of transcription start sites are TATA free and are defined by a large compendium of known DNA sequence binding elements. We present results on the usage of these elements and provide our Plant PEAT Peaks (3PEAT) model that predicts the presence of TSSs directly from sequence.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;http://megraw-dev.cgrb.oregonstate.edu/3PEAT&quot;&gt;[Link to Additional Data and Supplementary Materials]&lt;/a&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue></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%">Petricka, Jalean J</style></author><author><style face="normal" font="default" size="100%">Schauer, Monica A</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Breakfield, Natalie W</style></author><author><style face="normal" font="default" size="100%">Thompson, J Will</style></author><author><style face="normal" font="default" size="100%">Georgiev, Stoyan</style></author><author><style face="normal" font="default" size="100%">Soderblom, Erik J</style></author><author><style face="normal" font="default" size="100%">Ohler, Uwe</style></author><author><style face="normal" font="default" size="100%">Moseley, Martin Arthur</style></author><author><style face="normal" font="default" size="100%">Grossniklaus, Ueli</style></author><author><style face="normal" font="default" size="100%">Benfey, Philip N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The protein expression landscape of the Arabidopsis root.</style></title><secondary-title><style face="normal" font="default" size="100%">Proc Natl Acad Sci U S A</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Proc. Natl. Acad. Sci. U.S.A.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Arabidopsis Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromatography, Liquid</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Primers</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Roots</style></keyword><keyword><style  face="normal" font="default" size="100%">Plants, Genetically Modified</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Array Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteome</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteomics</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Tandem Mass Spectrometry</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 May 1</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">6811-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Because proteins are the major functional components of cells, knowledge of their cellular localization is crucial to gaining an understanding of the biology of multicellular organisms. We have generated a protein expression map of the Arabidopsis root providing the identity and cell type-specific localization of nearly 2,000 proteins. Grouping proteins into functional categories revealed unique cellular functions and identified cell type-specific biomarkers. Cellular colocalization provided support for numerous protein-protein interactions. With a binary comparison, we found that RNA and protein expression profiles are weakly correlated. We then performed peak integration at cell type-specific resolution and found an improved correlation with transcriptome data using continuous values. We performed GeLC-MS/MS (in-gel tryptic digestion followed by liquid chromatography-tandem mass spectrometry) proteomic experiments on mutants with ectopic and no root hairs, providing complementary proteomic data. Finally, among our root hair-specific proteins we identified two unique regulators of root hair development.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">18</style></issue></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%">Alexiou, Panagiotis</style></author><author><style face="normal" font="default" size="100%">Vergoulis, Thanasis</style></author><author><style face="normal" font="default" size="100%">Gleditzsch, Martin</style></author><author><style face="normal" font="default" size="100%">Prekas, George</style></author><author><style face="normal" font="default" size="100%">Dalamagas, Theodore</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Grosse, Ivo</style></author><author><style face="normal" font="default" size="100%">Sellis, Timos</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">miRGen 2.0: a database of microRNA genomic information and regulation.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3' Untranslated Regions</style></keyword><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Nucleic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Information Storage and Retrieval</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">D137-41</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3&amp;#39;UTR region of mRNAs. MicroRNAs are produced from longer transcripts which can code for more than one mature miRNAs. miRGen 2.0 is a database that aims to provide comprehensive information about the position of human and mouse microRNA coding transcripts and their regulation by transcription factors, including a unique compilation of both predicted and experimentally supported data. Expression profiles of microRNAs in several tissues and cell lines, single nucleotide polymorphism locations, microRNA target prediction on protein coding genes and mapping of miRNA targets of co-regulated miRNAs on biological pathways are also integrated into the database and user interface. The miRGen database will be continuously maintained and freely available at http://www.microrna.gr/mirgen/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Database issue</style></issue></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%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Pereira, Fernando</style></author><author><style face="normal" font="default" size="100%">Jensen, Shane T</style></author><author><style face="normal" font="default" size="100%">Ohler, Uwe</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A transcription factor affinity-based code for mammalian transcription initiation.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Res.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Base Composition</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Polymerase II</style></keyword><keyword><style  face="normal" font="default" size="100%">TATA Box</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Initiation Site</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription, Genetic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">644-56</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The recent arrival of large-scale cap analysis of gene expression (CAGE) data sets in mammals provides a wealth of quantitative information on coding and noncoding RNA polymerase II transcription start sites (TSS). Genome-wide CAGE studies reveal that a large fraction of TSS exhibit peaks where the vast majority of associated tags map to a particular location ( approximately 45%), whereas other active regions contain a broader distribution of initiation events. The presence of a strong single peak suggests that transcription at these locations may be mediated by position-specific sequence features. We therefore propose a new model for single-peaked TSS based solely on known transcription factors (TFs) and their respective regions of positional enrichment. This probabilistic model leads to near-perfect classification results in cross-validation (auROC = 0.98), and performance in genomic scans demonstrates that TSS prediction with both high accuracy and spatial resolution is achievable for a specific but large subgroup of mammalian promoters. The interpretable model structure suggests a DNA code in which canonical sequence features such as TATA-box, Initiator, and GC content do play a significant role, but many additional TFs show distinct spatial biases with respect to TSS location and are important contributors to the accurate prediction of single-peak transcription initiation sites. The model structure also reveals that CAGE tag clusters distal from annotated gene starts have distinct characteristics compared to those close to gene 5&amp;#39;-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70% of mammalian microRNAs based on currently available data.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;http://megraw-dev.cgrb.oregonstate.edu/node/715&quot;&gt;[Links to Tools and Supplementary Materials]&lt;/a&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record></records></xml>