Objectives

Broadly speaking, our lab is interested in the application of bioinformatics and machine learning towards understanding genome structure, transcriptional regulation, and post-transcriptional regulation.  More specifically, we are currently devoted to research in the following areas:

• Computational and statistical approaches for:
  
  • Gene identification
  • MicroRNA identification
  • MicroRNA target identification
  • Promoter and cis-element identification
  • Expressed Sequence Tag (EST) coding potential

Our lab is in active collaboration with several computational and experimental labs.

  Members

Principal Investigator: Artemis G. Hatzigeorgiou

PhD Candidates: Genomics and Computational Biology Graduate Group

• Molly Megraw, 2002--

• Computational analysis for the study of microRNA gene regulation.

• Praveen Sethupathy, 2003--

• Combined computational-experimental approach to improve mammalian microRNA target prediction for the purpose of further understanding the roles of microRNAs in various biological networks and disease processes.

  Software

DIANA-EST Analyzer           A tool for predicting coding regions in EST sequences

DIANA-TIS Analyzer            A tool for predicting coding starts in EST sequences

DIANA-MicroT Analyzer      A tool for the prediction of microRNA targets

  Databases and Web Resources

TarBase    A database of experimentally supported microRNA targets in 8 species

miRGen    A database for the study of animal microRNA genomic organization and function

  Selected Publications

• Y. Kawahara, B. Zinshteyn, P. Sethupathy, H. Iizasa, A.G. Hatzigeorgiou, and K. Nishikura (2007). Redirection of silencing targets by adenosine-to-inosine editing of microRNAs. Science, Accepted.
• M. Megraw, P. Sethupathy, B. Corda, and A.G. Hatzigeorgiou (2006). miRGen: A database for the study of animal microRNA genomic organization and function. Nucleic Acids Research, 35: D149-D155.
• P. Sethupathy, M. Megraw, and A.G. Hatzigeorgiou (2006). A guide through present computational approaches for the identification of mammalian microRNA targets. Nature Methods, 3: 881-886.   [Link to TargetCombo website]
• W. Kang, R. Mukerjee, J.J. Gartner, A.G. Hatzigeorgiou, R.M. Sandri-Goldin, and N.W. Fraser (2006). Characterization of a spliced exon product of herpes simplex type-1 latency-associated transcript in productively infected cells. Virology, 356: 106-114.
• M. Megraw, V. Baev, V. Rusinov, S. Jensen, K. Kalantidis, and A.G. Hatzigeorgiou (2006). MicroRNA Promoter Element Discovery in Arabidopsis. RNA, 12:1612-1619.   [Link to Supplementary Material]
• L. Zhang, J. Huang, N. Yang, J. Greshock, M.S. Megraw, A. Giannakakis, S. Liang, T.L. Naylor, A. Barchetti, M.R. Ward, G. Yao, A. Medina, A. O'brien-Jenkins, D. Katsaros, A. Hatzigeorgiou, P.A. Gimotty, B.L. Weber, and G. Coukos (2006). MicroRNAs exhibit high frequency genomics alterations in human cancers. Proc Natl Acad Sci USA, 103:9136-9141.
• A. Gupta, J.J. Gartner, P. Sethupathy, A.G. Hatzigeorgiou, and N.W. Fraser (2006). Anti-Apoptotic Function Of A MicroRNA Encoded By The Herpes Simplex Virus Latency Associated Transcript. Nature, 442:82-85.
• P. Sethupathy, B. Corda and A.G. Hatzigeorgiou (2006). TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA, 12: 192-197.
• P. Sethupathy, M. Megraw, M.I. Barrasa, and A.G. Hatzigeorgiou (2005). Computational Identification of Regulatory Factors Involved in MicroRNA Transcription. Lecture Notes in Computer Science, 3746: 457-468.
• H. Miller, , A.G. Hatzigeorgiou, ..., International Chicken Genome Sequencing Consortium (2004). Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature, 432: 7018.
• M. Yousef, A. Kouranov, Z. Mourelatos, and A.G. Hatzigeorgiou (2004). Prediction of Human microRNA Targets using Parallel Computing. WSEAS Transactions on Information Science and Applications, 1: 581-586.
• M. Kiriakidou, P.T. Nelson, A. Kouranov, P. Fitziev, C. Bouyioukos, Z. Mourelatos, and A.G. Hatzigeorgiou. (2004). A combined computational-experimental approach predicts human microRNA targets. Genes & Development, 18: 1165-78.
• M. Reczko and A.G. Hatzigeorgiou (2004). Prediction of the subcellular localization of eukaryotic proteins using sequence signals and composition. Proteomics, 4: 1591-1596.
• P.T. Nelson, A.G. Hatzigeorgiou, and Z. Mourelatos (2003). miRNP:miRNA association in polyribosomes, in a human neuronal cell line. RNA, 10: 387-94.
• V. B. Bajic, S. Tang, H. Han, V. Brusic and A. G. Hatzigeorgiou (2002). Artificial Neural Networks based systems for recognition of genomic signals and regions. Informatica, 26: 389-400.
• A.G. Hatzigeorgiou (2002). Translation initiation site prediction in human cDNAs with high accuracy. Bioinformatics, 17: 913-919.
• A.G. Hatzigeorgiou, P. Vizief, and M Reczko (2001). DIANA-EST: A statistical analysis. Bioinformatics, 17:913-919.

  Contact

The DNA Intelligent Analysis Laboratory (DIANA lab) is located in the Penn Center for Bioinformatics at the University of Pennsylvania

  Penn Center for Bioinformatics (PCBI)
  1407 Blockley Hall
  423 Guardian Drive
  Philadelphia, PA 19104-6021
  Office Tel: (215) 898-3258
  Lab Tel:    (215) 573-3729
  Fax No.     (215) 573-3111 
  
  Molly Megraw:			megraw (at) mail.med.upenn.edu
  Praveen Sethupathy: 		praveens (at) mail.med.upenn.edu
  Artemis G. Hatzigeorgiou: 	artemis (at) pcbi.upenn.edu
  

  Support

The lab is supported by a career award grant from the National Science Foundation (NSF).

  Photos

The DIANA lab enjoys visiting Vancouver B.C. for the Keystone Conference in January 2006.