Events

Workshop: From proteins to noncoding RNAs and their intimacy

2012-10-24: University of Copenhagen, at 11:00, Auditorium A2-82.01, Thorvaldsensvej 40, Frederiksberg Campus. Speakers, Des Higgins, Christian W. Zwieb, Yiota Poirazi, and Cedric Notredame.

Registration is not necessary but if you would like to join the lunch we kindly ask you to send one email; per person with the subject line: "workshop lunch" to contact@rth.dk at the latest October 22nd at noon.

Program

11:00 Welcome
Jan Gorodkin
11:00 - 11:40 Clustal Omega: possibly the last multiple sequence alignment program you will ever need
Des Higgins
11:40 - 12:20 The ncRNA-protein connection
Christian W. Zwieb
12:20 - 13:10 Lunch
Sandwiches; soft drinks; tee/coffee; signing up is needed to participate in the lunch.
13:10 - 13:50 Computational tools for the identification of miRNA genes, mature miRNAs and their targets
Yiota Poirazi
13:50 - 14:30 Using Evolution to find Long Non Coding RNAs
Cedric Notredame

Clustal Omega: possibly the last multiple sequence alignment program you will ever need
Des Higgins, Conway Institute, University College Dublin
http://www.bioinf.ucd.ie

Most Multiple Sequence Alignments (MSA) are made using a range of related heuristics that involve clustering the sequences and building an alignment that follows the clusters. These methods have served us well for the past 20 years but are now starting to creak. I will describe a new program called Clustal Omega which can make alignments of any number of sequences. It gives good quality alignments in reasonable times and has extensive features for adding new sequences to or for exploiting information in existing alignments. 

The ncRNA-protein connection
Christian W. Zwieb, Department of Biochemistry, University of Texas Health Science Center at San Antonio
http://rnp.uthscsa.edu/rnp/zwieb/ZwiebFac.html

Recent years have seen a large number of noncoding (nc) RNAs take center stage in our understanding of fundamental cellular processes. It is well known but not always appreciated that many ncRNAs associate with proteins either transiently or as integral components of ribonucleoproteins (RNPs). This seminar explores the ncRNA-protein relationship in two RNPs. (1) Human signal recognition particle (SRP), responsible for the co-translational targeting of secretory proteins to biological membranes. And (2) bacterial transfer-messenger (tm) RNP, designed to rescue ribosomes that are stalled on non-stop mRNA.

Computational tools for the identification of miRNA genes, mature miRNAs and their targets
Yiota Poirazi, Computational Biology Lab,  Institute of Molecular Biology and Biotechnology, F.O.R.T.H
http://www.imbb.forth.gr/personal_page/poirazi.html

I will discuss recent work in our lab regarding the development of methods and tools for the identification of novel miRNA genes, the respective miRNA:miRNA* duplexes, the mature miRNA molecules and their potential targets. Most of the work is done using mammalian genomes (human and mouse) but recent work regarding the prediction of miRNA:miRNA* duplexes extends to other species. The methods I will discuss include Hidden Markov Models, Bayesian classifiers and Support Vector Machines. I will also present some of the wet lab experiments done in collaboration with other labs in order to confirm some of our model predictions, and the potential consequences of finding novel miRNAs in cancer associated genomic regions.

Using Evolution to find Long Non Coding RNAs
Cedric Notredame, Centro de Regulacio Genomica, Barcelona
http://www.tcoffee.org/homepage.html

The recent report of 10,000 loci coding for long non coding RNAs in the human genome is certainly one of the most striking recent reports of the ENCODE consortium. In this seminar, I will show how one can use sequence comparison techniques to explore this complex new source of data and use evolutionary profiling as a classification tool. I will introduce various methods dealing with RNA that include RNA database search methods, RNA evolutionary profiling and structure based comparison. I will also present some of the strategies we are using in the lab to combine homology based evidence with weak but trustworthy RNASeq transcriptomic data.