@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 {747, title = {IndeCut evaluates performance of network motif discovery algorithms}, journal = {Bioinformatics}, volume = {34}, year = {2018}, month = {05/2018}, pages = {1514-1521}, abstract = {
Motivation
Genomic networks represent a complex map of molecular interactions which are descriptive of the biological processes occurring in living cells. Identifying the small over-represented circuitry patterns in these networks helps generate hypotheses about the functional basis of such complex processes. Network motif discovery is a systematic way of achieving this goal. However, a reliable network motif discovery outcome requires generating random background networks which are the result of a uniform and independent graph sampling method. To date, there has been no method to numerically evaluate whether any network motif discovery algorithm performs as intended on realistically sized datasets\—thus it was not possible to assess the validity of resulting network motifs.
Results
In this work, we present IndeCut, the first method to date that characterizes network motif finding algorithm performance in terms of uniform sampling on realistically sized networks. We demonstrate that it is critical to use IndeCut prior to running any network motif finder for two reasons. First, IndeCut indicates the number of samples needed for a tool to produce an outcome that is both reproducible and accurate. Second, IndeCut allows users to choose the tool that generates samples in the most independent fashion for their network of interest among many available options.
}, url = {https://academic.oup.com/bioinformatics/article/34/9/1514/4721785}, author = {Mitra Ansariola and Molly Megraw and David Koslicki} } @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 {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 {327, title = {Frequency and fate of microRNA editing in human brain.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Sep}, pages = {5270-80}, abstract = {

Primary transcripts of certain microRNA (miRNA) genes (pri-miRNAs) are subject to RNA editing that converts adenosine to inosine (A--\>I RNA editing). However, the frequency of the pri-miRNA editing and the fate of edited pri-miRNAs remain largely to be determined. Examination of already known pri-miRNA editing sites indicated that adenosine residues of the UAG triplet sequence might be edited more frequently. In the present study, therefore, we conducted a large-scale survey of human pri-miRNAs containing the UAG triplet sequence. By direct sequencing of RT-PCR products corresponding to pri-miRNAs, we examined 209 pri-miRNAs and identified 43 UAG and also 43 non-UAG editing sites in 47 pri-miRNAs, which were highly edited in human brain. In vitro miRNA processing assay using recombinant Drosha-DGCR8 and Dicer-TRBP (the human immuno deficiency virus transactivating response RNA-binding protein) complexes revealed that a majority of pri-miRNA editing is likely to interfere with the miRNA processing steps. In addition, four new edited miRNAs with altered seed sequences were identified by targeted cloning and sequencing of the miRNAs that would be processed from edited pri-miRNAs. Our studies predict that approximately 16\% of human pri-miRNAs are subject to A--\>I editing and, thus, miRNA editing could have a large impact on the miRNA-mediated gene silencing.

}, keywords = {Adenosine, Adenosine Deaminase, Animals, Base Sequence, Brain, Humans, Inosine, Mice, MicroRNAs, Molecular Sequence Data, RNA Editing, RNA Precursors, RNA Processing, Post-Transcriptional, RNA-Binding Proteins}, issn = {1362-4962}, doi = {10.1093/nar/gkn479}, author = {Kawahara, Yukio and Megraw, Molly and Kreider, Edward and Iizasa, Hisashi and Valente, Louis and Hatzigeorgiou, Artemis G and Nishikura, Kazuko} } @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} }