@article {750, title = {Arabidopsis bioinformatics resources: The current state, challenges, and priorities for the future}, journal = {Plant Direct}, volume = {3}, year = {2019}, month = {01/2019}, url = {https://onlinelibrary.wiley.com/doi/full/10.1002/pld3.109}, author = {Colleen Doherty and Joanna Friesner and Brian Gregory and Ann Loraine and Molly Megraw and Nicholas Provart and R Keith Slotkin and Chris Town and Sarah M Assmann and Michael Axtell and Tanya Berardini and Sixue Chen and Malia Gehan and Eva Huala and Pankaj Jaiswal and Stephen Larson and Song Li and Sean May and Todd Michael and Chris Pires and Chris Topp and Justin Walley and Eve Wurtele} } @article {746, title = {The Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology}, journal = {Plant Physiology}, volume = {175}, year = {2017}, month = {12/2017}, pages = {1499-1509}, abstract = {
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.
}, url = {http://www.plantphysiol.org/content/175/4/1499}, author = {Joanna Friesner and Sarah M. Assmann and Ruth Bastow and Julia Bailey-Serres and Jim Beynon and Volker Brendel and C. Robin Buell and Alexander Bucksch and Wolfgang Busch and Taku Demura and Jose R. Dinneny and Colleen J. Doherty and Andrea L. Eveland and Pascal Falter-Braun and Malia A. Gehan and Michael Gonzales and Erich Grotewold and Rodrigo Gutierrez and Ute Kramer and Gabriel Krouk and Shisong Ma and R.J. Cody Markelz and Molly Megraw and Blake C. Meyers and James A.H. Murray and Nicholas J. Provart and Sue Rhee and Roger Smith and Edgar P. Spalding and Crispin Taylor and Tracy K. Teal and Keiko U. Torii and Chris Town and Matthew Vaughn and Richard Vierstra and Doreen Ware and Olivia Wilkins and Cranos Williams and Siobhan M. Brady} } @article {742, title = {Establishment of Expression in the SHORTROOT-SCARECROW Transcriptional Cascade through Opposing Activities of Both Activators and Repressors}, journal = {Dev Cell}, volume = {39}, year = {2016}, month = {12/2016}, pages = {585-596}, abstract = {

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.

}, keywords = {Arabidopsis Proteins/ genetics/ metabolism, Arabidopsis/ genetics/growth \& development/ metabolism, Computer Simulation, Gene Expression Regulation, Plant, Gene Regulatory Networks, Genes, Plant, Genes, Reporter, Genes, Synthetic, Models, Genetic, Plant Roots/cytology/metabolism, Plants, Genetically Modified, Promoter Regions, Genetic, Repressor Proteins/genetics/metabolism, Trans-Activators/genetics/metabolism, Transcription Factors/ genetics/ metabolism, Two-Hybrid System Techniques}, isbn = {1878-1551 (Electronic)1534-5807 (Linking)}, url = {https://doi.org/10.1016/j.devcel.2016.09.031}, author = {Sparks, E. E. and Drapek, C. and Gaudinier, A. and Li, S. and Ansariola, M. and Shen, N. and Hennacy, J. H. and Zhang, J. and Turco, G. and Petricka, J. J. and Foret, J. and Hartemink, A. J. and Gordan, R. and Megraw, M. and Brady, S. M. and Benfey, P. N.} } @article {319, title = {Paired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures.}, journal = {Plant Cell}, volume = {26}, year = {2014}, month = {2014 Jul}, pages = {2746-60}, abstract = {

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\&$\#$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 \"TSS tag clusters\" 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 \"TATA-less\" 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.

[Link to Additional Data and Supplementary Materials]

}, keywords = {Arabidopsis, Arabidopsis Proteins, Binding Sites, Cluster Analysis, DNA, Plant, Gene Expression Regulation, Plant, Genome, Plant, Models, Genetic, Nucleotide Motifs, Plant Roots, Promoter Regions, Genetic, RNA, Messenger, RNA, Plant, Sequence Analysis, DNA, Species Specificity, TATA Box, Transcription Factors, Transcription Initiation Site}, issn = {1532-298X}, doi = {10.1105/tpc.114.125617}, author = {Morton, Taj and Petricka, Jalean and Corcoran, David L and Li, Song and Winter, Cara M and Carda, Alexa and Benfey, Philip N and Ohler, Uwe and Megraw, Molly} } @article {321, title = {The protein expression landscape of the Arabidopsis root.}, journal = {Proc Natl Acad Sci U S A}, volume = {109}, year = {2012}, month = {2012 May 1}, pages = {6811-8}, abstract = {

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.

}, keywords = {Arabidopsis, Arabidopsis Proteins, Base Sequence, Chromatography, Liquid, DNA Primers, Gene Expression Profiling, Plant Roots, Plants, Genetically Modified, Protein Array Analysis, Protein Interaction Mapping, Proteome, Proteomics, RNA, Plant, Tandem Mass Spectrometry}, issn = {1091-6490}, doi = {10.1073/pnas.1202546109}, author = {Petricka, Jalean J and Schauer, Monica A and Megraw, Molly and Breakfield, Natalie W and Thompson, J Will and Georgiev, Stoyan and Soderblom, Erik J and Ohler, Uwe and Moseley, Martin Arthur and Grossniklaus, Ueli and Benfey, Philip N} } @article {322, title = {A stele-enriched gene regulatory network in the Arabidopsis root.}, journal = {Mol Syst Biol}, volume = {7}, year = {2011}, month = {2011 Jan 18}, pages = {459}, abstract = {

Tightly controlled gene expression is a hallmark of multicellular development and is accomplished by transcription factors (TFs) and microRNAs (miRNAs). Although many studies have focused on identifying downstream targets of these molecules, less is known about the factors that regulate their differential expression. We used data from high spatial resolution gene expression experiments and yeast one-hybrid (Y1H) and two-hybrid (Y2H) assays to delineate a subset of interactions occurring within a gene regulatory network (GRN) that determines tissue-specific TF and miRNA expression in plants. We find that upstream TFs are expressed in more diverse cell types than their targets and that promoters that are bound by a relatively large number of TFs correspond to key developmental regulators. The regulatory consequence of many TFs for their target was experimentally determined using genetic analysis. Remarkably, molecular phenotypes were identified for 65\% of the TFs, but morphological phenotypes were associated with only 16\%. This indicates that the GRN is robust, and that gene expression changes may be canalized or buffered.

}, keywords = {Arabidopsis, Arabidopsis Proteins, Gene Expression Profiling, Gene Regulatory Networks, MicroRNAs, Plant Roots, Reproducibility of Results, Systems Biology, Transcription Factors, Two-Hybrid System Techniques}, issn = {1744-4292}, doi = {10.1038/msb.2010.114}, author = {Brady, Siobhan M and Zhang, Lifang and Megraw, Molly and Martinez, Natalia J and Jiang, Eric and Yi, Charles S and Liu, Weilin and Zeng, Anna and Taylor-Teeples, Mallorie and Kim, Dahae and Ahnert, Sebastian and Ohler, Uwe and Ware, Doreen and Walhout, Albertha J M and Benfey, Philip N} } @article {328, title = {Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer.}, journal = {Proc Natl Acad Sci U S A}, volume = {105}, year = {2008}, month = {2008 May 13}, pages = {7004-9}, abstract = {

MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs that function as negative gene regulators. miRNA deregulation is involved in the initiation and progression of human cancer; however, the underlying mechanism and its contributions to genome-wide transcriptional changes in cancer are still largely unknown. We studied miRNA deregulation in human epithelial ovarian cancer by integrative genomic approach, including miRNA microarray (n = 106), array-based comparative genomic hybridization (n = 109), cDNA microarray (n = 76), and tissue array (n = 504). miRNA expression is markedly down-regulated in malignant transformation and tumor progression. Genomic copy number loss and epigenetic silencing, respectively, may account for the down-regulation of approximately 15\% and at least approximately 36\% of miRNAs in advanced ovarian tumors and miRNA down-regulation contributes to a genome-wide transcriptional deregulation. Last, eight miRNAs located in the chromosome 14 miRNA cluster (Dlk1-Gtl2 domain) were identified as potential tumor suppressor genes. Therefore, our results suggest that miRNAs may offer new biomarkers and therapeutic targets in epithelial ovarian cancer.

}, keywords = {DNA, Neoplasm, Down-Regulation, Epigenesis, Genetic, Epithelial Cells, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genome, Human, Humans, MicroRNAs, Neoplasm Staging, Ovarian Neoplasms, Ribonuclease III, RNA, Messenger, Survival Analysis}, issn = {1091-6490}, doi = {10.1073/pnas.0801615105}, author = {Zhang, Lin and Volinia, Stefano and Bonome, Tomas and Calin, George Adrian and Greshock, Joel and Yang, Nuo and Liu, Chang-Gong and Giannakakis, Antonis and Alexiou, Pangiotis and Hasegawa, Kosei and Johnstone, Cameron N and Megraw, Molly S and Adams, Sarah and Lassus, Heini and Huang, Jia and Kaur, Sippy and Liang, Shun and Sethupathy, Praveen and Leminen, Arto and Simossis, Victor A and Sandaltzopoulos, Raphael and Naomoto, Yoshio and Katsaros, Dionyssios and Gimotty, Phyllis A and DeMichele, Angela and Huang, Qihong and B{\"u}tzow, Ralf and Rustgi, Anil K and Weber, Barbara L and Birrer, Michael J and Hatzigeorgiou, Artemis G and Croce, Carlo M and Coukos, George} } @article {331, title = {MicroRNA promoter element discovery in Arabidopsis.}, journal = {RNA}, volume = {12}, year = {2006}, month = {2006 Sep}, pages = {1612-9}, abstract = {

In this study we present a method of identifying Arabidopsis miRNA promoter elements using known transcription factor binding motifs. We provide a comparative analysis of the representation of these elements in miRNA promoters, protein-coding gene promoters, and random genomic sequences. We report five transcription factor (TF) binding motifs that show evidence of overrepresentation in miRNA promoter regions relative to the promoter regions of protein-coding genes. This investigation is based on the analysis of 800-nucleotide regions upstream of 63 experimentally verified Transcription Start Sites (TSS) for miRNA primary transcripts in Arabidopsis. While the TATA-box binding motif was also previously reported by Xie and colleagues, the transcription factors AtMYC2, ARF, SORLREP3, and LFY are identified for the first time as overrepresented binding motifs in miRNA promoters.

}, keywords = {Arabidopsis, Base Sequence, Binding Sites, Databases, Genetic, Feedback, Physiological, Genes, Plant, MicroRNAs, Promoter Regions, Genetic, TATA Box, Transcription Factors, Transcription Initiation Site}, issn = {1355-8382}, doi = {10.1261/rna.130506}, author = {Megraw, Molly and Baev, Vesselin and Rusinov, Ventsislav and Jensen, Shane T and Kalantidis, Kriton and Hatzigeorgiou, Artemis G} } @article {332, title = {microRNAs exhibit high frequency genomic alterations in human cancer.}, journal = {Proc Natl Acad Sci U S A}, volume = {103}, year = {2006}, month = {2006 Jun 13}, pages = {9136-41}, abstract = {

MicroRNAs (miRNAs) are endogenous noncoding RNAs, which negatively regulate gene expression. To determine genomewide miRNA DNA copy number abnormalities in cancer, 283 known human miRNA genes were analyzed by high-resolution array-based comparative genomic hybridization in 227 human ovarian cancer, breast cancer, and melanoma specimens. A high proportion of genomic loci containing miRNA genes exhibited DNA copy number alterations in ovarian cancer (37.1\%), breast cancer (72.8\%), and melanoma (85.9\%), where copy number alterations observed in \>15\% tumors were considered significant for each miRNA gene. We identified 41 miRNA genes with gene copy number changes that were shared among the three cancer types (26 with gains and 15 with losses) as well as miRNA genes with copy number changes that were unique to each tumor type. Importantly, we show that miRNA copy changes correlate with miRNA expression. Finally, we identified high frequency copy number abnormalities of Dicer1, Argonaute2, and other miRNA-associated genes in breast and ovarian cancer as well as melanoma. These findings support the notion that copy number alterations of miRNAs and their regulatory genes are highly prevalent in cancer and may account partly for the frequent miRNA gene deregulation reported in several tumor types.

}, keywords = {Breast Neoplasms, Female, Gene Dosage, Gene Expression Profiling, Humans, MicroRNAs, Neoplasms, Nucleic Acid Hybridization, Oligonucleotide Array Sequence Analysis, Ovarian Neoplasms, Statistics as Topic}, issn = {0027-8424}, doi = {10.1073/pnas.0508889103}, author = {Zhang, Lin and Huang, Jia and Yang, Nuo and Greshock, Joel and Megraw, Molly S and Giannakakis, Antonis and Liang, Shun and Naylor, Tara L and Barchetti, Andrea and Ward, Michelle R and Yao, George and Medina, Angelica and O{\textquoteright}brien-Jenkins, Ann and Katsaros, Dionyssios and Hatzigeorgiou, Artemis and Gimotty, Phyllis A and Weber, Barbara L and Coukos, George} }