TY - JOUR T1 - Paired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures. JF - Plant Cell Y1 - 2014 A1 - Morton, Taj A1 - Petricka, Jalean A1 - Corcoran, David L A1 - Li, Song A1 - Winter, Cara M A1 - Carda, Alexa A1 - Benfey, Philip N A1 - Ohler, Uwe A1 - Megraw, Molly KW - Arabidopsis KW - Arabidopsis Proteins KW - Binding Sites KW - Cluster Analysis KW - DNA, Plant KW - Gene Expression Regulation, Plant KW - Genome, Plant KW - Models, Genetic KW - Nucleotide Motifs KW - Plant Roots KW - Promoter Regions, Genetic KW - RNA, Messenger KW - RNA, Plant KW - Sequence Analysis, DNA KW - Species Specificity KW - TATA Box KW - Transcription Factors KW - Transcription Initiation Site AB -

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' 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]

VL - 26 IS - 7 ER - TY - JOUR T1 - Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer. JF - Proc Natl Acad Sci U S A Y1 - 2008 A1 - Zhang, Lin A1 - Volinia, Stefano A1 - Bonome, Tomas A1 - Calin, George Adrian A1 - Greshock, Joel A1 - Yang, Nuo A1 - Liu, Chang-Gong A1 - Giannakakis, Antonis A1 - Alexiou, Pangiotis A1 - Hasegawa, Kosei A1 - Johnstone, Cameron N A1 - Megraw, Molly S A1 - Adams, Sarah A1 - Lassus, Heini A1 - Huang, Jia A1 - Kaur, Sippy A1 - Liang, Shun A1 - Sethupathy, Praveen A1 - Leminen, Arto A1 - Simossis, Victor A A1 - Sandaltzopoulos, Raphael A1 - Naomoto, Yoshio A1 - Katsaros, Dionyssios A1 - Gimotty, Phyllis A A1 - DeMichele, Angela A1 - Huang, Qihong A1 - Bützow, Ralf A1 - Rustgi, Anil K A1 - Weber, Barbara L A1 - Birrer, Michael J A1 - Hatzigeorgiou, Artemis G A1 - Croce, Carlo M A1 - Coukos, George KW - DNA, Neoplasm KW - Down-Regulation KW - Epigenesis, Genetic KW - Epithelial Cells KW - Female KW - Gene Expression Profiling KW - Gene Expression Regulation, Neoplastic KW - Genome, Human KW - Humans KW - MicroRNAs KW - Neoplasm Staging KW - Ovarian Neoplasms KW - Ribonuclease III KW - RNA, Messenger KW - Survival Analysis AB -

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.

VL - 105 IS - 19 ER - TY - JOUR T1 - A guide through present computational approaches for the identification of mammalian microRNA targets. JF - Nat Methods Y1 - 2006 A1 - Sethupathy, Praveen A1 - Megraw, Molly A1 - Hatzigeorgiou, Artemis G KW - 3' Untranslated Regions KW - 5' Untranslated Regions KW - Animals KW - Computational Biology KW - Gene Targeting KW - Humans KW - MicroRNAs KW - Predictive Value of Tests KW - RNA, Messenger KW - Sensitivity and Specificity KW - Software AB -

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.

VL - 3 IS - 11 ER -