@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 {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 {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} }