RNA Informatics Topics RNA bioinformatics RNA...

RNA Informatics讨论专题

五个目的:
(1) Molecular biologists who need to interpret RNA sequence and probing data to produce plausible 3D models for functional RNAs they study;

(2) biologists seeking to catalogue and understand the diversity of life and the inter-relationships of living things;

(3) biochemists and nano-technologists seeking to understand the mechanisms of the most ancient “molecular machines” – RNA-containing supermolecular structures such as the ribosome and splicesosome;

(4) genomicists seeking to discover non-coding RNAs in genomes; and

(5) academic, government, and industry scientists who research and develop RNA pharmaceuticals or drugs that target RNA

 

1. RNA Ontology Consortium简介

RNA本体联盟(RNA Ontology Consortium,ROC)是用来搭建一个整合的概念架构-RNA本体( Ontology,RO)-用它来理解RNA在生物学上的功能,用它进行RNA生物学、化学以及基因组学前沿研究。确切的目标就是创建一套有关RNA兼容的结构、具有动态形式的控制字汇和分类系统，这些都是以RNA序列、次级结构以及三维结构为基础。它们的中心目标就是鉴定所有RNA的特征，相互作用，以及在一些数据库和文献中提及存储的RNA基序（motif）,给予他们定义，用一种结构性的方式来进行书面的定义。这些都是非常及时有用的关于RNA对积累和进展的知识。因此构建RO的目标有以下几点：

1.  整合所有的RNA序列和结构数据库

2.  创建强大的软件平台

3.  强大科学家队伍将多样的信息数据和数据的积累转化成生物学的应用型知识推动RNA科学的进步

为了达到这些目标就是关注于ROC之间RNA科学家的相互交流与合作，一起面对面的频繁多讨论，辩论以及解决一些概念性的问题。因此一些重要学术交流会显得十分的重要，这些会议能够在RNA研究的不同层次和水平上创建研究的方向。ROC希望通过整个分散的信息数据资源，研发出整合的软件以及合作交流工具来扩大以及增强bench科学家对试验数据的阐释,计算机及其基因组学者对基因组数据的阐释。RCO通过会议及其网络平台一起紧密工作在一起，用Gene Ontology 和Sequence Ontology的资源来共同创建更为广发的整合的Ontology来推动RNA研究。

2.Gene Ontology简介
Gene Ontology通过提供一套结构、具有动态形式的控制字汇和分类系统来解释真核生物的基因和蛋白质在细胞内所扮演的角色。同时大部分基因在不同的真核生物中拥有共同的主要生物学功能，因此利用Gene Ontology可透过在某物种上所获得的基因或蛋白质的生物学知识来解释在其他物种中所对应的基因或蛋白质。Gene Ontology Consortium 有一个整合的分类系统，一个基因或蛋白质可通过分子功能（molecular function）、生物过程（biological process）、基因产物的细胞成分( cellular componet)三个层次得到注释。Gene Ontology project是能将生物学统一起来的工具，是我们对基因及其产物进行功能分类时，应需参考的数据库。同时还出现了利用GO的控制字汇对UniProt进行注释的数据库Gene Ontology Annotation (GOA) database.

3.Ontologies 的优势
a.Ontologies主要目标:
b.展示和共享社团知识
c.展示数据库信息
d.支持跨越多个数据库智能查询
e.enable reuse of domain knowledge
f.support automated reasoning and inference over domain knowledge.

4 RNA ontology 涉及的知识领域
作为RNA领域新兴的概念,主要知识领域如下:
1 RNA 序列信息(1D): coding and noncoding, and their identification in genomes (to be incorporated within the Sequence Ontology).
2 RNA 次级结构以及Watson-Crick 碱基配对
3 RNA 3D 结构和基序: backbone conformations, base stacking, and tertiary interactions.
4 RNA同源序列的比对.
5 RNA比对与3D结构之间的关系.
6 RNA–RNA, RNA–protein, and RNA–ligand (metabolite,drug, metal and other ion, and water) interactions.
7 RNA conformational changes and dynamics of functional significance.
8 RNA 分子生物学(RNA加工,成熟以及剪接等等).
9 Biochemical and biophysical experimental data relating to RNA structure and structure–function relationships.
10 RNA as regulator of biological networks and pathways.

RNA Bioinformatics-RNA 信息学工具

1. Functional_RNAs
a. Non-Coding RNA database
http://biobases.ibch.poznan.pl/ncRNA/
Non-translatable RNA transcripts that appear to work at the RNA level.

b. Rfam
http://www.sanger.ac.uk/Software/Rfam/
Database of structure-annotated multiple sequence alignments, covariance models and family annotation for a number of non-coding RNA families

c. SCOR
http://scor.berkeley.edu/
The Structural Classification of RNA (SCOR) is a database designed to provide a comprehensive perspective and understanding of RNA motif structure, function, tertiary interactions and their relationships

d. tRNAscan-SE
http://www.genetics.wustl.edu/eddy/tRNAscan-SE/
tRNAscan-SE allows you to search for tRNA genes in genomic sequence. (site hosted by Eddy Lab at WashU)

2. RNA_General_Links_and_Information
a. NDB
http://ndbserver.rutgers.edu/
NDB (Nucleic Acid Database) is a repository of three-dimensional structural information about nucleic acids.

b. RNA folding Servers
http://kinefold.u-strasbg.fr/rna.html
List of RNA folding servers and related web sites maintained by Herve Isambert.

c. RNA Informatics Links
http://www-lbit.iro.umontreal.ca/RNA_Links/RNA.shtml
An exhaustive list of RNA links; from the experts in the Major lab.

d. RNAbase
http://www.rnabase.org/
RNAbase is a searchable and annotated database of all publicly available RNA structures.

e. The RNA World
http://www.imb-jena.de/RNA.html
An RNA resource hub.

f. The Zuker Group
http://www.bioinfo.rpi.edu/applications/mfold/
Algorithms, thermodynamics and databases for RNA secondary structure.

RNA_Motif_Search_and_Comparison
a. Riboswitch finder
http://riboswitch.bioapps.biozentrum.uni-wuerzburg.de/server.html
RNA motif search program that identifies RNA motifs called riboswitches which are metabolic binding domains in mRNA that regulate gene expression. The program was originally designed around a set of riboswitches found in Bacillus subtilis.

b. RNABOB
http://www.genetics.wustl.edu/eddy/software/#rnabob
Fast RNA motif/pattern searcher; from the authors: If you re looking for an RNA motif that fits a hard consensus pattern -- a la PROSITE patterns, but with base-pairing -- you might check out RNABOB; not a Web-tool; based on RNAMOT.

c. RNAMOT
http://www.esil.univ-mrs.fr/~dgaut/download/
RNA motif search program; not a Web-tool.

d. Transterm UTR Motif Search
http://guinevere.otago.ac.nz/transterm.html
Transterm is an interactive database providing access to RNA sequences and their associated motifs. The RNA sequences are derived from all gene sequence data in Genbank, including complete genomes, divided into putative 5' and 3'UTRs, initiation and term

e. http://www.ambion.com/techlib/
Company web site with very good technical resources including an excellent links page, summaries of recent papers on RNA-related topics, and free access to review articles and web features on RNA-related research topics.

3.RNA_Sequence_Retrieval
http://www.ncbi.nlm.nih.gov/
http://www.ebi.ac.uk/embl/index.html

1. BLAST
Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches. BLAST can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families.

2. EBI Tools
EBI Tools is a project that aims to provide programmatic access to the various databases and retrieval and analysis services that the European Bioinformatics Institute (EBI) provides through Simple Object Access Protocol (SOAP) and other related web service technologies.

3. EMBOSS
Diverse suite of tools for sequence analysis; many programs analagous to GCG; context-sensitive help for each tool.

4. Entrez
NCBI information retrieval system, including GenBank, MMDB (structures), genomes, population sets, OMIM, taxonomy and PubMed.

5. FeatureExtract
The FeatureExtract server extracts sequence and feature annotations, such as intron/exon structure, from GenBank entries and other GenBank format files.

6. GeneLynx
A portal to the human genome. Query by text or BLAST, to access heaps of info from primary and secondary databases of genomic resources, transcripts, protein sequences, function, associated diseases, homologs, ests.

7. PubCrawler
It goes to the library. You go to the pub; receive email alerts for current contents of PubMed and GenBank; e.g. use accession number of htg record as query to receive sequence updates (as the version number changes).

8. Ribosomal Database Project
Highly curated database of aligned and annotated rRNA sequences with accompanying phylogenies; data available for download.

9. SeqHound
Seqhound is a sequence retrieval system that provides access to biological sequence, structure and functional annotation data. Seqhound can be accessed via the web interface, through the remote API, or by installing locally.

10. WU BLAST
Washington University Basic Local Alignment Search Tool

4.RNA_Structure_Predicition_and_Visualization
a. CARNAC
http://bioinfo.lifl.fr/carnac
Server which predicts conserved secondary structure elements of homologous RNAs. The input of a set of RNA sequences are not required to be previously aligned.

b. DEQOR
http://cluster-1.mpi-cbg.de/Deqor/deqor.html
Tool which aids in the design and quality control of small interfering RNAs (siRNAs) for RNA interference (RNAi) and gene silencing. It evaluates the inhibitory potency of potential siRNA sequences as well as identifying gene regions that have a high sil

c. ERPIN
http://tagc.univ-mrs.fr/erpin/
ERPIN (Easy RNA Profile IdentificatioN) takes as input an RNA sequence alignment and secondary structure annotation and will identify a wide variety of known RNA motifs (such as tRNAs, 5S rRNAs, SRP RNA, C/D box snoRNAs, hammerhead motifs, miRNAs and others

d. wustl
http://cic.cs.wustl.edu/RNA/
Server which provides iterated loop matching and maximum weighted matching algorithms for pseudoknot containing RNA secondary structure prediction. Algorithms can apply thermodynamic and comparative information, and thus can be used for either aligned

e. Kinefold
http://kinefold.u-strasbg.fr/
Kinefold calculates (and animates) the folding kinetics of RNA sequences including pseudoknots.

f. Mfold
http://www.bioinfo.rpi.edu/applications/mfold/old/rna/
Predict RNA secondary structure from sequence; does not predict pseudoknots

g.MolMovDB
http://molmovdb.org/
The Database of Macromolecular Movements (MolMovD contains a collection of animated protein and RNA structures to assist in the exploration of macromolecular flexibility. Software for structure analysis is also available.

h. MOLPROBITY
http://kinemage.biochem.duke.edu/molprobity/main.php?use_king=1
MOLPROBITY is a structure analysis and validation program that can calculate and display steric, H-bonding, and van der Waals interactions for known structures of proteins, nucleic acids, and complexes.

i. PKNOTS
http://www.genetics.wustl.edu/eddy/software/#pk
Predict pseudoknot structures in RNA sequence; source code only.

j. RDfolder
http://rna.cbi.pku.edu.cn:1977/rna/index.php
A RNA secondary structure prediction program which implements two methods, one based on random stacking and the other based on helical region distributions.

f. RNAfold
http://www.tbi.univie.ac.at/cgi-bin/RNAfold.cgi
Predict RNA secondary structure from sequence; note sequence length limit.

g. RNAsoft
http://www.rnasoft.ca/
Software for RNA/DNA secondary structure prediction and design

h. Sfold
http://sfold.wadsworth.org
Server with three tools for the rational design of small interfering RNAs (Sirna), antisense oligonucleotides (Soligo), and trans-cleaving ribozymes (Sribo). A fourth tool, Srna, returns output including general folding features.

i. siDirect
http://design.RNAi.jp/
Server for computing small interfering RNA (siRNA) sequences which are best suited for mammalian RNA interference (RNAi). The site accepts a sequence as input and returns a list of siRNA candidates.
j. siRNA Selection Server
http://jura.wi.mit.edu/bioc/siRNA
Server aiding the design of short interfering RNAs (siRNAs) by providing information on stability, SNPs and specificity of the a potential siRNA.

k. siRNAdb
http://sirna.cgb.ki.se/
This resource includes siSearch, AOSearch, and a siRNAdb which provides a platform for mining an siRNA database, and searching for non-specific matches to your siRNA (small interfering RNAs).

l. siRNAdb
http://smi-web.stanford.edu/projects/helix/sstructview/
RNA secondary structure viewer applet; must be integrated into web page to be implemented; can link to multiple computational backends.

M.TROD
http://www.cellbio.unige.ch/RNAi.html
T7 RNAi Oligo Designer (TROD) aids in the design of DNA oligonucleotides for short interfering RNA (siRNA) synthesis with T7 RNA polymerase.It takes an input of a cDNA sequence and outputs a list of DNA oligos for ordering.

N.Vienna RNA Package
http://www.tbi.univie.ac.at/~ivo/RNA/
Comprises a C codelibrary and several stand-alone programs for the prediction and comparison of RNA secondary structures.

5. RNA: Three-Dimensional 3-D Structures
a. Ribosome Images (Wadsworth Center Microscope 3D Database)
http://www.wadsworth.org/spider_3d/home_page.html

b. RNase P 3D models
http://jwbrown.mbio.ncsu.edu/RNaseP/RNA/threeD/threeD.html
RNase P 3D models

c. SCOR: Structural Classification of RNA
http://scor.lbl.gov/
SCOR: Structural Classification of RNA

d. The Nucleic Acid Database (ND
http://ndbserver.rutgers.edu/NDB/

6.UTR bioinformatics
a.UTR Blast
http://www.ba.itb.cnr.it/BIG/Blast/BlastUTR.html
UTRBlast is an online tool which can blast your untranslated region UTR and compare its similarity to other UTR regions.

b.UTR Home
http://www.ba.itb.cnr.it/BIG/UTRHome/
UTR Home. A collection of UTR resources and online tools.

c. UTRdb
http://www.ba.itb.cnr.it/srs7bin/cgi-bin/wgetz?-page top
UTRdb. A database of UTR sequences. Find your UTR RNA or DNA sequence of interest.

d. UTRScan UTR Scan
http://www.ba.itb.cnr.it/BIG/UTRScan/
UTRScan UTR Scan.The program UTRscan looks for UTR functional elements by searching through user submitted sequence data for the patterns defined in the UTRsite collection.

e. UTRSite
http://www2.ba.itb.cnr.it/UTRSite/
UTRSite is a collection of functional sequence patterns located in 5' or 3' UTR sequences

 

 

ncRNA简介:
在利用gene-finding 软件预测基因编码区的同时，就尝试着用生物信息学方法对ncRNA 进行鉴定；但由于ncRNA缺少编码蛋白质的基因所具有的典型特征，如启动子和终止子、开放阅读框、特异的剪切位点、多聚腺苷酸化位点和CG 岛等，且ncRNA 基因较小，用于gene-finding 软件的基序(motif)变动较大等，因此，到目前为止，还没有高效且通用的ncRNA 基因的预测算法。现在能成功对ncRNA预测的gene-finding编程软件一般被设计成只能搜索单一种类的ncRNA，如tRNAScan-SE 搜索tRNA、snoScan 搜索带C/D盒的snoRNAs、SnoGps 搜索带H/ACA 盒的snoRNAs、mirScan 搜索microRNA等等。一些基于基序聚类的软件，如RNAmotifs、Erpin以及Patsearch也用于对ncRNA 的搜索，但是这些软件同搜索单一种类的ncRNA软件相比，灵敏度和特异性都较差。实际上，用实验方法已证实的ncRNA 很少是用这类软件鉴定出来的。随着各种生物物种基因组计划的实施，基因组的序列比较分析可用来检测ncRNA和cis-regulatoryRNA 的二级结构，如用QRNA 已检测出在大肠杆菌、酿酒酵母菌和激烈火球菌中的ncRNA，并在随后的实验中得到了证实。
举例来说:

ncRNA Identification Methods Examples:
1.   (Sequence homology methods)
在一些例子中,当两个物种的进化距离比较近,一个简单的序列相似性的比对,通过BLAST或者FASTA就足够确认RNA基因.在比较紧密相关的RNA基因地时候这些同源性的搜索是第一步
2.   (Pattern matching and covariance models)
For the identification of P/MRP RNA as well as IRE we used a combination of pattern searches and secondary structure profile searches with cmsearch of the Infernal package. Nuclear P RNA and MRP RNA sequences are poorly conserved in sequence. However,three conserved regions are shared; CR-I, CR-IV and CR-V. For nuclear P RNA there are also conserved elements in the domain 2 to take into account; CR-II and CR-III. Therefore, for the identification of P and MRP RNA we used a pattern based on consensus features including the CR-I, CR-IV and CR-V motifs as well as base-pairing rules consistent with the helix P2.When a P or MRP RNA gene was not found using these patterns new searches were carried out where mismatches were allowed. After the pattern matching procedure, sequences fitting the secondary structure template were further analyzed with Rfam covariance models. Highscoring candidates were further analyzed for characteristics typical for P/MRP RNA secondary structure; base pairing between the CR-I and CR-V motifs, presence of CR-IV as well as the helices P1, P2 and P3. Also IREs were identified using a combination of pattern matching and covariance models.To identify as many potential IREs as possible we primarily searched available mRNA sequences. In case there was no available mRNA, genomic sequences was searched for regions homologous to available proteins/mRNAs. Whenever an IRE candidate was found in a genomic sequence it was checked for reasonable proximity to the protein/mRNA match.Candidate sequences were checked for conserved primary sequence motifs and the ability to fold into a secondary structure typical for the iron responsive element

3. Profile HMMs of highly conserved regions in P and MRP RNA
For prediction of P and MRP RNAs we also used profile HMMs created from CR-I and CR-V multiple alignments. We further analyzed all genomic sequences that contained the CR-I and CR-V motifs and where the distance between the two motifs is less than 3000 bases. Advantages of this method are that large genomes may be searched quickly (100 Mbases in a few minutes) and in a highly specific manner identifies the P and MRP RNA genes.Candidates identified in the search based on HMM profiles were further analyzed to check
that other conserved features of the RNA were present

4.Identification of protein homologues
An efficient method for protein identification is PSI-BLAST (Position Specific Iterative BLAST). PSI-BLAST can repeatedly search the target databases, using a multiple alignment of high scoring sequences found in each search round to generate a new more sensitive scoring matrix able to find distantly related sequences that are sometimes missed in a BLAST search. Multiple PSI-BLAST searches with different query sequences were carried out in order to identify as many homologues as possible belonging to a certain protein family.The NCBI Genbank protein set was used as the primary source, but additional proteins were identified from individual genome projects or identified from TBLASTN searches of genome sequences. Whenever relevant, these novel sequences were included in the set of sequences used as database in the PSI-BLAST search.We also used profile HMMs at the Pfam database for Pop1, Pop3 (Rpp38), Pop5, Rpp14,Rpp20, Rpp25, Rpp40, Rpr2 (Rpp21) to identify homologues. In cases where available Pfam models were not sufficient or present, new models were created from multiple alignments and used with the HMMER package to find additional homologues.
To identify homologues to previously known proteins whose mRNAs are known to contain IREs we mainly used BLAST to search the NCBI Genbank set of proteins. Some gene sequences that were not in Genbank were identified by Genewise [160] Genewise uses a combination of comparative analysis (aligns proteins to genomic sequences) together with statistical signals to predict genes. For classification of proteins we also made use of phylogenetic analysis, including methods of parsimony, maximum likelihood and neighbour-joining..

5.ncRNA prediction using de novo methods
As opposed to the methods that detect new members of already known ncRNA families described previously (IRE and MRP/P RNA identification), we have also used two de novo methods, QRNA and RNAz , to computationally screen the S.cerevisae genome for ncRNAs.

QRNA makes a prediction of ncRNA based on pairwise alignments . It compares the score of three distinct models of sequence evolution to decide which one describes best thegiven alignment: a pair SCFG is used to model the evolution of secondary structure, a pair hidden Markov model (HMM) describes the evolution of protein coding sequence, and a different pair HMM implements the independent model of a sequence with an evolutionary random pattern not consistent with either a secondary structure or protein coding sequence.QRNA is currently limited to pairwise alignments, and rather slow for ncRNA gene prediction at a genomic scale. A program similar to QRNA, which tests for complementary mutations in three-sequence multiple alignments, is ddbRNA . It searches for common stems in the multiple alignments in a greedy fashion. The assessment of the significance of the conserved structure is based on shuffled alignments.

The program RNAz makes a prediction of ncRNA based on multiple sequence alignments . It uses two independent criteria for classification: a z-score measuring thermodynamic stability of individual sequences, and a structure conservation index obtained by comparing folding energies of the individual sequences with the predicted consensus folding. The two criteria are then combined to detect conserved and stable RNA secondary structures with high sensitivity and specificity. Yet another application suitable for multiple alignments is MSARI . The approach uses information from a larger set of sequence-aligned orthologs to detect significant ncRNA secondary structures. Primary sequence alignments are often inaccurate. In MSARI, one part of the method tries to correct errors in multiple alignments through energy minimisation calculations

 

T. Willingham在2005年用shRNA的arrayed library针对512个进化保守的ncRNA进行干扰并进行细胞分析,他们鉴定了一个ncRNA repressor of the nuclear factor of activated T cells (NFAT), whichinteracts with multiple proteins including members of the importin-betasuperfamily and likely functions as a specific regulator of NFAT nuclear trafficking.(他们也用了siRNA方法,得到了与shRNA同样效果)
1.参考文献:A. T. Willingham et al., Science 309, 1570 (2005).

2.参考文献:A. T. Willingham, Q. L. Deveraux, G. M. Hampton, P.Aza-Blanc, Oncogene 23, 8392 (2004).

推荐的ncRNA网址
http://www.ncrna.org/
http://research.imb.uq.edu.au/rnadb/
http://noncode.bioinfo.org.cn/index.htm
http://biobases.ibch.poznan.pl/ncRNA/

 

 

miRNA 在一级结构和次级结构的保守性让很多科学家对miRNA分子进化树进行研究。这方面的文献很多，只需利用完整的数据库，搜索关键词miRNA ,evolution,Phylogenetic trees,您可以获得很classic文献。不同特点的miRNA需要具体的调整分析和研究方案！
以miRNA17为例简要说明分析的途径：
1.The publicly available genome databaseswere searched using blastn against all pre-miRNAs of the mir17 family . Conversely, the entire MicroRNA Registry, was compared against the genomic sequences near the putative family members.

2.Exact locations of homologs of known miRNAs were identified using clustalw alignments and subsequent prediction of the secondary structure using Vienna RNA Package , in particular the programs RNAfold,RNAalifold, RNALfold, and alidot, in order to verify the hairpin structure of the precursor.

3.Phylogenetic trees were reconstructed both with Maximum Parsimony and Neighbor-joining using the phylip package with standard parameters. The phylogeny of the entire clusters was computed using a concatenation of the alignments of the individual paralogous microRNAs according to their order in the cluster, and treating microRNAs that are not present in a particular cluster as missing data. This ensures that distances are measured based on nucleic acid substitution frequencies, not based on changes of cluster organization. In order to identify distant sequence similarities between pre-miRNAs from different paralog groups we compute a similarity score based on the significance of the alignment score.

This method produces robust similarity scores in regimes where reliable global alignments cannot be obtained.
The duplication history of the mir17 family was reconstructed by hand based on the following assumptions: Edit operations are
a.duplications of individual microRNAs within a linked cluster,
b.the deletion of a microRNA,and
c.the duplication of an entire cluster.
In other words, we explicitly exlude the possibility of recombination between paralog clusters within an organism and copying of individual microRNAs from one cluster to another.The available data do not contain any evidence that such processes might play a role.

 

第二部分Mapping miRNA genes

1.miRNA Map 是一个整合的数据，被开发用来存储已知miRNA 基因，假定的miRNA基因，已知的miRNA targets和假定的miRNA target.(Hsu et al 2006).
2.已知的miRNA基因，来自人，小鼠，大鼠以及狗的miRNA基因，可以从miRNAase获得，试验已经证实的miRNA targets在文献中可以获得。
3.假定的miroRNA precursors可以通过RNAz来鉴定，RNAz是一个序列比较分析的工具
4.假定的miRNA基因的成熟miRNA可以通过mmiRNA来确认，mmiRNA使用机器智能学习的方法
5.miRanda是一个用来在四种哺乳动物基因组中的基因的3' UTR区域的保守区预计miRNA靶点的工具
6.miRNA map也提供已知的miRNA的表达图谱，跨物种的比较，基因的注释以及与别的生物数据库进行交叉检索
7.文本和图片的网页交互性界面在http://mirnamap.mbc.nctu.edu.tw/提供了方便的检索功能

 

Non Coding RNA 专家及其网址

Reuven Agami
Division of Tumor Biology - The Netherlands Cancer Institute - Amsterdam (The
Netherlands)
[e-mail]
Webpage: http://research.nki.nl/agamilab/

Philippe Bastin
Trypanosome Cell Biology Unit - Parasitology Department - Pasteur Institute - Paris (France)
[e-mail]
Webpage:
http://www.mnhn.fr/museum/foffice/science/science/Enseignement/rubmastere/ssuniteensmaster/fiche1.xsp

David Baulcombe
The Sainsbury Laboratory - John Innes Centre - Norwich
[e-mail]
Webpage: www.sainsbury-laboratory.ac.uk

René Bernards
Division of Molecular Carcinogenesis - The Netherlands Cancer Institute - Amsterdam (The Netherlands)
[e-mail]
Webpage: http://www.biomedicalgenetics.nl/Members/Bernards/bernards.html

Jürgen Brosius
Institute of Experimental Pathology / Molecular Neurobiology - University of Münster -
Münster (Germany)
[e-mail]
Webpage: http://zmbe2.uni-muenster.de/expath/frames.htm

Witold Filipowicz
Friedrich Miescher Institute for Biomedical Research - Basel (Switzerland)
[e-mail]
Webpage:
http://www.fmi.ch/html/research/research_groups/epigenetics/Witold_Filipowicz/Witold_Filipowicz.html

Matthias W. Hentze
European Molecular Biology Laboratory - University Hospital Heidelberg- Heidelberg
(Germany)
[e-mail]
Webpage: http://www.embl-heidelberg.de/ExternalInfo/hentze/

Ivo L. Hofacker
Theoretical Biochemistry Group - Institute for Theoretical Chemistry - University OF Vienna -
Vienna (Austria)
[e-mail]
Webpage: http://www.tbi.univie.ac.at/~ivo/

Alexander Hüttenhofer
Division of Genomics & RNomics - Innsbruck Medical University - Innsbruck (Austria)
[e-mail]
Webpage: http://genomics.i-med.ac.at/staff/a_huettenhofer.html

Craig P. Hunter
Department of Molecular and Cellular Biology – Harvard University - Cambridge (USA)
[e-mail]
Webpage: http://www.mcb.harvard.edu/hunter/

Elisa Izaurralde
Max Planck Institute for Developmental Biology - Tübingen (Germany)
[e-mail]
Webpage: http://www.eb.tuebingen.mpg.de/departments/2-biochemistry/staff/elisa-izaurralde

Giuseppe Macino
Dipartimento di Biotecnologie Cellulari ed Ematologia - Sezione di Genetica Molecolare -Università di Roma “La Sapienza” - Rome (Italy)
[e-mail]

Javier Martinez
Institute of Molecular Biotechnology of the Austrian Academy of Sciences - Vienna (Austria)
[e-mail]
Wolfgang Nellen
Department of Genetics - University of Kassel - Kassel (Germany)

[e-mail]
Webpage: http://www.biologie.uni-kassel.de/genetics/

Mikiko C. Siomi
Institute for Genome Research - University of Tokushima - Tokushima (Japan)
E-mail: [e-mail]
Webpage: http://www.genome.tokushima-u.ac.jp/dgfa/index.html

Markus Stoffel
Institute of Molecular Systems Biology - Swiss Federal Institute of Technology Zürich - Zürich
(Switzerland)
[e-mail]
Webpage: http://www.imsb.ethz.ch/researchgroup/stmarku

Thomas Tuschl
Laboratory of RNA Molecular Biology - Howard Hughes Medical Institute - The Rockefeller
University - New York (USA)
[e-mail]
Webpage: http://www.rockefeller.edu/labheads/tuschl/

Jörg Vogel
Max Planck Institute for Infection Biology - Berlin (Germany)
[e-mail]
Webpage: http://www.mpiib-berlin.mpg.de/research/RNABiology.htm

Mihaela Zavolan
Division of Bioinformatics - Biozentrum, University of Basel - Basel (Switzerland)
[e-mail]
Webpage: http://www.biozentrum.unibas.ch/zavolan/index.html