@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 {751, title = {PlantSimLab-a modeling and simulation web tool for plant biologists}, journal = {BMC Bioinformatics}, volume = {20}, year = {2019}, month = {12/2019}, pages = {1-11}, url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3094-9}, author = {S Ha and E Dimitrova and Stefan Hoops and D Altarawy and M Ansariola and D Deb and J Glazebrook and R Hillmer and H Shahin and F Katagiri and J McDowell and M Megraw and J Setubal and BM Tyler and Reinhard Laubenbacher} } @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 {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 {323, title = {Editing of Epstein-Barr virus-encoded BART6 microRNAs controls their dicer targeting and consequently affects viral latency.}, journal = {J Biol Chem}, volume = {285}, year = {2010}, month = {2010 Oct 22}, pages = {33358-70}, abstract = {

Certain primary transcripts of miRNA (pri-microRNAs) undergo RNA editing that converts adenosine to inosine. The Epstein-Barr virus (EBV) genome encodes multiple microRNA genes of its own. Here we report that primary transcripts of ebv-miR-BART6 (pri-miR-BART6) are edited in latently EBV-infected cells. Editing of wild-type pri-miR-BART6 RNAs dramatically reduced loading of miR-BART6-5p RNAs onto the microRNA-induced silencing complex. Editing of a mutation-containing pri-miR-BART6 found in Daudi Burkitt lymphoma and nasopharyngeal carcinoma C666-1 cell lines suppressed processing of miR-BART6 RNAs. Most importantly, miR-BART6-5p RNAs silence Dicer through multiple target sites located in the 3\&$\#$39;-UTR of Dicer mRNA. The significance of miR-BART6 was further investigated in cells in various stages of latency. We found that miR-BART6-5p RNAs suppress the EBNA2 viral oncogene required for transition from immunologically less responsive type I and type II latency to the more immunoreactive type III latency as well as Zta and Rta viral proteins essential for lytic replication, revealing the regulatory function of miR-BART6 in EBV infection and latency. Mutation and A-to-I editing appear to be adaptive mechanisms that antagonize miR-BART6 activities.

}, keywords = {Cell Line, Tumor, Epstein-Barr Virus Infections, Epstein-Barr Virus Nuclear Antigens, Gene Silencing, Herpesvirus 4, Human, Humans, Immediate-Early Proteins, MicroRNAs, Ribonuclease III, RNA Editing, RNA, Viral, Trans-Activators, Viral Proteins, Virus Latency}, issn = {1083-351X}, doi = {10.1074/jbc.M110.138362}, author = {Iizasa, Hisashi and Wulff, Bjorn-Erik and Alla, Nageswara R and Maragkakis, Manolis and Megraw, Molly and Hatzigeorgiou, Artemis and Iwakiri, Dai and Takada, Kenzo and Wiedmer, Andreas and Showe, Louise and Lieberman, Paul and Nishikura, Kazuko} } @article {324, title = {miRGen 2.0: a database of microRNA genomic information and regulation.}, journal = {Nucleic Acids Res}, volume = {38}, year = {2010}, month = {2010 Jan}, pages = {D137-41}, abstract = {

MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3\&$\#$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/.

}, keywords = {3{\textquoteright} Untranslated Regions, Algorithms, Animals, Cell Line, Tumor, Computational Biology, Databases, Genetic, Databases, Nucleic Acid, Humans, Information Storage and Retrieval, Internet, Mice, MicroRNAs, Polymorphism, Single Nucleotide, Software, Transcription Factors}, issn = {1362-4962}, doi = {10.1093/nar/gkp888}, author = {Alexiou, Panagiotis and Vergoulis, Thanasis and Gleditzsch, Martin and Prekas, George and Dalamagas, Theodore and Megraw, Molly and Grosse, Ivo and Sellis, Timos and Hatzigeorgiou, Artemis G} } @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 {329, title = {miRGen: a database for the study of animal microRNA genomic organization and function.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jan}, pages = {D149-55}, abstract = {

miRGen is an integrated database of (i) positional relationships between animal miRNAs and genomic annotation sets and (ii) animal miRNA targets according to combinations of widely used target prediction programs. A major goal of the database is the study of the relationship between miRNA genomic organization and miRNA function. This is made possible by three integrated and user friendly interfaces. The Genomics interface allows the user to explore where whole-genome collections of miRNAs are located with respect to UCSC genome browser annotation sets such as Known Genes, Refseq Genes, Genscan predicted genes, CpG islands and pseudogenes. These miRNAs are connected through the Targets interface to their experimentally supported target genes from TarBase, as well as computationally predicted target genes from optimized intersections and unions of several widely used mammalian target prediction programs. Finally, the Clusters interface provides predicted miRNA clusters at any given inter-miRNA distance and provides specific functional information on the targets of miRNAs within each cluster. All of these unique features of miRGen are designed to facilitate investigations into miRNA genomic organization, co-transcription and targeting. miRGen can be freely accessed at http://www.diana.pcbi.upenn.edu/miRGen.

}, keywords = {Animals, Data Interpretation, Statistical, Databases, Nucleic Acid, Genomics, Humans, Internet, Mice, MicroRNAs, Rats, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkl904}, author = {Megraw, Molly and Sethupathy, Praveen and Corda, Benoit and Hatzigeorgiou, Artemis G} } @article {330, title = {A guide through present computational approaches for the identification of mammalian microRNA targets.}, journal = {Nat Methods}, volume = {3}, year = {2006}, month = {2006 Nov}, pages = {881-6}, abstract = {

Computational microRNA (miRNA) target prediction is a field in flux. Here we present a guide through five widely used mammalian target prediction programs. We include an analysis of the performance of these individual programs and of various combinations of these programs. For this analysis we compiled several benchmark data sets of experimentally supported miRNA-target gene interactions. Based on the results, we provide a discussion on the status of target prediction and also suggest a stepwise approach toward predicting and selecting miRNA targets for experimental testing.

}, keywords = {3{\textquoteright} Untranslated Regions, 5{\textquoteright} Untranslated Regions, Animals, Computational Biology, Gene Targeting, Humans, MicroRNAs, Predictive Value of Tests, RNA, Messenger, Sensitivity and Specificity, Software}, issn = {1548-7091}, doi = {10.1038/nmeth954}, author = {Sethupathy, Praveen and Megraw, Molly and Hatzigeorgiou, Artemis G} }