Sat, 26 Jul 2008 06:31:01 BST CiteULike: heliopais's Huang http://www.citeulike.org/user/heliopais/author/Huang CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/heliopais/article/2937476 <i>Gene, Vol. 418, No. 1-2. (15 July 2008), pp. 34-40.</i><br /><br />MicroRNAs (miRNAs) represent a family of small noncoding RNAs with important regulatory roles in diverse biological processes ranging from cell differentiation to organism development. In chickens, the full set of miRNAs and the expression patterns of miRNAs during development are still poorly understood when compared to the other vertebrates. In this study, we identified 29 novel miRNAs and 140 potential miRNA loci in the chicken genome by combining the experimental and computational analyses. Detailed expression patterns of 49 miRNAs were first characterized by Northern blotting and indicated the cooperativity of the miRNA expression with their function in embryogenesis and organogenesis. Twenty-seven miRNA clusters were systematically evaluated in the chicken genome and diverse expression patterns for closely linked miRNAs were observed. Our results significantly expand the set of known miRNAs in the chicken and provide the basis for understanding the structural and functional evolution of miRNA genes in vertebrates. Identification of novel chicken microRNAs and analysis of their genomic organization Peng Shao Hui Zhou Zhen-Dong Xiao Jie-Hua He Mian-Bo Huang Yue-Qin Chen Liang-Hu Qu doi:10.1016/j.gene.2008.04.004 Gene, Vol. 418, No. 1-2. (15 July 2008), pp. 34-40. 2008-06-27T14:35:40-00:00 2008 Gene 418 1-2 34 40 microrna microrna_gene_finding http://www.citeulike.org/user/heliopais/article/2770890 <i>Molecular and cellular biology, Vol. 27, No. 19. (October 2007), pp. 6889-6902.</i><br /><br />Gfi1 transcriptionally governs hematopoiesis, and its mutations produce neutropenia. In an effort to identify Gfi1-interacting proteins and also to generate new candidate genes causing neutropenia, we performed a yeast two-hybrid screen with Gfi1. Among other Gfi1-interacting proteins, we identified a previously uncharacterized member of the PR domain-containing family of tumor suppressors, PRDM5. PRDM5 has 16 zinc fingers, and we show that it acts as a sequence-specific, DNA binding transcription factor that targets hematopoiesis-associated protein-coding and microRNA genes, including many that are also targets of Gfi1. PRDM5 epigenetically regulates transcription similarly to Gfi1: it recruits the histone methyltransferase G9a and class I histone deacetylases to its target gene promoters and demonstrates repressor activity on synthetic reporters; on endogenous target genes, however, it functions as an activator, in addition to a repressor. Interestingly, genes that PRDM5 activates, as opposed to those it represses, are also targets of Gfi1, suggesting a competitive mechanism through which two repressors could cooperate in order to become transcriptional activators. In neutropenic patients, we identified PRDM5 protein sequence variants perturbing transcriptional function, suggesting a potentially important role in hematopoiesis. Epigenetic regulation of protein-coding and microRNA genes by the Gfi1-interacting tumor suppressor PRDM5. Z Duan RE Person HH Lee S Huang J Donadieu R Badolato HL Grimes T Papayannopoulou MS Horwitz doi:10.1128/MCB.00762-07 Molecular and cellular biology, Vol. 27, No. 19. (October 2007), pp. 6889-6902. 2008-05-08T11:37:33-00:00 2007 Molecular and cellular biology 0270-7306 27 19 6889 6902 genetic_regulation pr_domain http://www.citeulike.org/user/heliopais/article/2706260 <i>BMC Genomics, Vol. 9 (22 April 2008), 185.</i> Deep Sequencing of Chicken microRNAs Joan Burnside Ming Ouyang Amy Anderson Erin Bernberg Cheng Lu Blake Meyers Pamela Green Milos Markis Grace Isaacs Emily Huang Robin Morgan doi:10.1186/1471-2164-9-185 BMC Genomics, Vol. 9 (22 April 2008), 185. 2008-04-23T07:16:18-00:00 2008 BMC Genomics 1471-2164 9 185 chicken_mirnas deep_sequencing microrna_gene_finding mirna http://www.citeulike.org/user/heliopais/article/1625362 <i>Genome Biology, Vol. 8 (04 September 2007), R183.</i> DAVID Gene Functional Classification Tool: A novel biological module-centric algorithm to functionally analyze large gene list Da Huang Brad Sherman Qina Tan Jack Collins Gregory Alvord Jean Roayaei Robert Stephens Michael Baseler Clifford Lane Richard Lempicki doi:10.1186/gb-2007-8-9-r183 Genome Biology, Vol. 8 (04 September 2007), R183. 2007-09-05T21:41:52-00:00 2007 Genome Biology 1465-6906 8 R183 functional_genomics http://www.citeulike.org/user/heliopais/article/2018176 <i>Nat Meth, Vol. 4, No. 12. (December 2007), pp. 1045-1049.</i> Using expression profiling data to identify human microRNA targets Jim Huang Tomas Babak Timothy Corson Gordon Chua Sofia Khan Brenda Gallie Timothy Hughes Benjamin Blencowe Brendan Frey Quaid Morris doi:10.1038/nmeth1130 Nat Meth, Vol. 4, No. 12. (December 2007), pp. 1045-1049. 2007-11-29T18:47:18-00:00 2007 Nat Meth 4 12 1045 1049 Nature Publishing Group genmir microrna http://www.citeulike.org/user/heliopais/article/1991370 <i>Nucl. Acids Res. (19 November 2007), gkm1012.</i><br /><br />MicroRNAs (miRNAs) are small non-coding RNA molecules that can negatively regulate gene expression and thus control numerous cellular mechanisms. This work develops a resource, miRNAMap 2.0, for collecting experimentally verified microRNAs and experimentally verified miRNA target genes in human, mouse, rat and other metazoan genomes. Three computational tools, miRanda, RNAhybrid and TargetScan, were employed to identify miRNA targets in 3'-UTR of genes as well as the known miRNA targets. Various criteria for filtering the putative miRNA targets are applied to reduce the false positive prediction rate of miRNA target sites. Additionally, miRNA expression profiles can provide valuable clues on the characteristics of miRNAs, including tissue specificity and differential expression in cancer/normal cell. Therefore, quantitative polymerase chain reaction experiments were performed to monitor the expression profiles of 224 human miRNAs in 18 major normal tissues in human. The negative correlation between the miRNA expression profile and the expression profiles of its target genes typically helps to elucidate the regulatory functions of the miRNA. The interface is also redesigned and enhanced. The miRNAMap 2.0 is now available at http://miRNAMap.mbc.nctu.edu.tw/. 10.1093/nar/gkm1012 miRNAMap 2.0: genomic maps of microRNAs in metazoan genomes Sheng-Da Hsu Chia-Huei Chu Ann-Ping Tsou Shu-Jen Chen Hua-Chien Chen Paul Hsu Yung-Hao Wong Yi-Hsuan Chen Gian-Hung Chen Hsien-Da Huang doi:10.1093/nar/gkm1012 Nucl. Acids Res. (19 November 2007), gkm1012. 2007-11-27T07:52:23-00:00 2007 Nucl. Acids Res. gkm1012 database microrna http://www.citeulike.org/user/heliopais/article/1550916 <i>Journal of Computational Biology, Vol. 14, No. 5. (June 2007), pp. 550-563.</i><br /><br />MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, an open question is how to efficiently obtain functional miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms. In this paper, we propose a novel Bayesian model and learning algorithm, GenMiR++ (Generative model for miRNA regulation), that accounts for patterns of gene expression using miRNA expression data and a set of candidate miRNA targets. A set of high-confidence functional miRNA targets are then obtained from the data using a Bayesian learning algorithm. Our model scores 467 high-confidence miRNA targets out of 1,770 targets obtained from TargetScanS in mouse at a false detection rate of 2.5%: several confirmed miRNA targets appear in our high-confidence set, such as the interactions between miR-92 and the signal transduction gene MAP2K4, as well as the relationship between miR-16 and BCL2, an anti-apoptotic gene which has been implicated in chronic lymphocytic leukemia. We present results on the robustness of our model showing that our learning algorithm is not sensitive to various perturbations of the data. Our high-confidence targets represent a significant increase in the number of miRNA targets and represent a starting point for a global understanding of gene regulation. Bayesian Inference of MicroRNA Targets from Sequence and Expression Data. Jim Huang Quaid Morris Brendan Frey doi:10.1089/cmb.2007.R002 Journal of Computational Biology, Vol. 14, No. 5. (June 2007), pp. 550-563. 2007-08-10T06:54:52-00:00 2007 Journal of Computational Biology 1066-5277 14 5 550 563 genmir microrna microrna_target_prediction http://www.citeulike.org/user/heliopais/article/2464047 <i>Pacific Symposium in Biocomputing (24 September 2007), pp. 52-63.</i><br /><br />We present a new model and learning algorithm, GenMiR3, which takes into account mRNA sequence features in addition to paired mRNA and miRNA expression profiles when scoring candidate miRNA-mRNA interactions. We evaluate three candidate sequence features for predicting miRNA targets by assessing the expression support for the predictions of each feature and the consistency of Gene Ontology Biological Process annotation of their target sets. We consider as sequence features the total energy of hybridization between the microRNA and target, conservation of the target site and the context score which is a composite of five individual sequence features. We demonstrate that only the total energy of hybridization is predictive of paired miRNA and mRNA expression data and Gene Ontology enrichment but this feature adds little to the total accuracy of GenMiR3 predictions using for expression features alone. Comparing sequence and expression for predicting microRNA targets using GenMiR3. JC Huang BJ Frey QD Morris Pacific Symposium in Biocomputing (24 September 2007), pp. 52-63. 2008-03-04T12:13:24-00:00 2007 Pacific Symposium in Biocomputing 1793-5091 52 63 genmir microrna http://www.citeulike.org/user/heliopais/article/1165273 <i>FEBS Letters, Vol. 581, No. 6. (20 March 2007), pp. 1081-1086.</i><br /><br />MicroRNAs (miRNAs) are a class of non-coding RNA that post-transcriptionally regulates the expression of target genes by binding to mRNAs. As one form of alternative splicing, intron retention has influence upon mRNA modification and protein encoding. The effect of miRNA on mRNA containing retained intron within 3' UTR, however, has not been systematically elucidated. Here, we examined a total of 2864 human genes which contain at least one retained intron from the MAASE and ASD databases and found 387 genes having contained retained introns within 3' UTR. The effect of retained introns upon miRNA targets was explored with three web-based programs for miRNA prediction including miRanda, TargetScanS and PicTar. The results showed that retained introns can increase putative miRNA targets in human mRNA. Retained introns have higher chances than other regions of 3' UTR in involving the site of miRNAs targets of most genes which contain putative miRNA targets within it. Furthermore, some transcripts contain miRNA targets solely because of the retained introns in 3' UTR. In addition, we examined those `Ignored' retained introns by miRanda software and the results indicated that miRNAs may contain many more putative targets. Retained introns increase putative microRNA targets within 3' UTRs of human mRNA Sheng Tan Jiaming Guo Qianli Huang Xueping Chen Jesse Li-Ling Qingwei Li Fei Ma doi:10.1016/j.febslet.2007.02.009 FEBS Letters, Vol. 581, No. 6. (20 March 2007), pp. 1081-1086. 2007-03-15T10:25:37-00:00 2007 FEBS Letters 581 6 1081 1086 Elsevier microrna http://www.citeulike.org/user/heliopais/article/1730440 <i>Bioinformatics, Vol. 20, No. 18. (12 December 2004), pp. 3691-3693.</i><br /><br />SUMMARY: Inferring genetic network architecture from time series data generated from high-throughput experimental technologies, such as cDNA microarray, can help us to understand the system behavior of living organisms. We have developed an interactive tool, GeneNetwork, which provides four reverse engineering models and three data interpolation approaches to infer relationships between genes. GeneNetwork enables a user to readily reconstruct genetic networks based on microarray data without having intimate knowledge of the mathematical models. A simple graphical user interface enables rapid, intuitive mapping and analysis of the reconstructed network allowing biologists to explore gene relationships at the system level. AVAILABILITY: Download from http://genenetwork.sbl.bc.sinica.edu.tw/. SUPPLEMENTARY INFORMATION: Supplement documentation of algorithms for the four approaches is downloadable at the above location. GeneNetwork: an interactive tool for reconstruction of genetic networks using microarray data. CC Wu HC Huang HF Juan ST Chen Bioinformatics, Vol. 20, No. 18. (12 December 2004), pp. 3691-3693. 2007-10-05T09:28:25-00:00 2004 Bioinformatics 1367-4803 20 18 3691 3693 genetic_regulatory_network