RNA Informatics討論專(zhuān)題
五個(gè)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簡(jiǎn)介
RNA本體聯(lián)盟(RNA Ontology Consortium,ROC)是用來(lái)搭建一個(gè)整合的概念架構-RNA本體( Ontology,RO)-用它來(lái)理解RNA在生物學(xué)上的功能,用它進(jìn)行RNA生物學(xué)、化學(xué)以及基因組學(xué)前沿研究。確切的目標就是創(chuàng )建一套有關(guān)RNA兼容的結構、具有動(dòng)態(tài)形式的控制字匯和分類(lèi)系統，這些都是以RNA序列、次級結構以及三維結構為基礎。它們的中心目標就是鑒定所有RNA的特征，相互作用，以及在一些數據庫和文獻中提及存儲的RNA基序（motif）,給予他們定義，用一種結構性的方式來(lái)進(jìn)行書(shū)面的定義。這些都是非常及時(shí)有用的關(guān)于RNA對積累和進(jìn)展的知識。因此構建RO的目標有以下幾點(diǎn)：
1. 整合所有的RNA序列和結構數據庫
2. 創(chuàng )建強大的軟件平臺
3. 強大科學(xué)家隊伍將多樣的信息數據和數據的積累轉化成生物學(xué)的應用型知識推動(dòng)RNA科學(xué)的進(jìn)步
為了達到這些目標就是關(guān)注于ROC之間RNA科學(xué)家的相互交流與合作，一起面對面的頻繁多討論，辯論以及解決一些概念性的問(wèn)題。因此一些重要學(xué)術(shù)交流會(huì )顯得十分的重要，這些會(huì )議能夠在RNA研究的不同層次和水平上創(chuàng )建研究的方向。ROC希望通過(guò)整個(gè)分散的信息數據資源，研發(fā)出整合的軟件以及合作交流工具來(lái)擴大以及增強bench科學(xué)家對試驗數據的闡釋,計算機及其基因組學(xué)者對基因組數據的闡釋。RCO通過(guò)會(huì )議及其網(wǎng)絡(luò )平臺一起緊密工作在一起，用Gene Ontology 和Sequence Ontology的資源來(lái)共同創(chuàng )建更為廣發(fā)的整合的Ontology來(lái)推動(dòng)RNA研究。
2.Gene Ontology簡(jiǎn)介
Gene Ontology通過(guò)提供一套結構、具有動(dòng)態(tài)形式的控制字匯和分類(lèi)系統來(lái)解釋真核生物的基因和蛋白質(zhì)在細胞內所扮演的角色。同時(shí)大部分基因在不同的真核生物中擁有共同的主要生物學(xué)功能，因此利用Gene Ontology可透過(guò)在某物種上所獲得的基因或蛋白質(zhì)的生物學(xué)知識來(lái)解釋在其他物種中所對應的基因或蛋白質(zhì)。Gene Ontology Consortium 有一個(gè)整合的分類(lèi)系統，一個(gè)基因或蛋白質(zhì)可通過(guò)分子功能（molecular function）、生物過(guò)程（biological process）、基因產(chǎn)物的細胞成分( cellular componet)三個(gè)層次得到注釋。Gene Ontology project是能將生物學(xué)統一起來(lái)的工具，是我們對基因及其產(chǎn)物進(jìn)行功能分類(lèi)時(shí)，應需參考的數據庫。同時(shí)還出現了利用GO的控制字匯對UniProt進(jìn)行注釋的數據庫Gene Ontology Annotation (GOA) database.
3.Ontologies 的優(yōu)勢
a.Ontologies主要目標:
b.展示和共享社團知識
c.展示數據庫信息
d.支持跨越多個(gè)數據庫智能查詢(xún)
e.enable reuse of domain knowledge
f.support automated reasoning and inference over domain knowledge.
4 RNA ontology 涉及的知識領(lǐng)域
作為RNA領(lǐng)域新興的概念,主要知識領(lǐng)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 次級結構以及Watson-Crick 堿基配對
3 RNA 3D 結構和基序: backbone conformations, base stacking, and tertiary interactions.
4 RNA同源序列的比對.
5 RNA比對與3D結構之間的關(guān)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 分子生物學(xué)(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 信息學(xué)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
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.
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
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簡(jiǎn)介:
在利用gene-finding 軟件預測基因編碼區的同時(shí)，就嘗試著(zhù)用生物信息學(xué)方法對ncRNA 進(jìn)行鑒定；但由于ncRNA缺少編碼蛋白質(zhì)的基因所具有的典型特征，如啟動(dòng)子和終止子、開(kāi)放閱讀框、特異的剪切位點(diǎn)、多聚腺苷酸化位點(diǎn)和CG 島等，且ncRNA 基因較小，用于gene-finding 軟件的基序(motif)變動(dòng)較大等，因此，到目前為止，還沒(méi)有高效且通用的ncRNA 基因的預測算法?，F在能成功對ncRNA預測的gene-finding編程軟件一般被設計成只能搜索單一種類(lèi)的ncRNA，如tRNAScan-SE 搜索tRNA、snoScan 搜索帶C/D盒的snoRNAs、SnoGps 搜索帶H/ACA 盒的snoRNAs、mirScan 搜索microRNA等等。一些基于基序聚類(lèi)的軟件，如RNAmotifs、Erpin以及Patsearch也用于對ncRNA 的搜索，但是這些軟件同搜索單一種類(lèi)的ncRNA軟件相比，靈敏度和特異性都較差。實(shí)際上，用實(shí)驗方法已證實(shí)的ncRNA 很少是用這類(lèi)軟件鑒定出來(lái)的。隨著(zhù)各種生物物種基因組計劃的實(shí)施，基因組的序列比較分析可用來(lái)檢測ncRNA和cis-regulatoryRNA 的二級結構，如用QRNA 已檢測出在大腸桿菌、釀酒酵母菌和激烈火球菌中的ncRNA，并在隨后的實(shí)驗中得到了證實(shí)。
舉例來(lái)說(shuō):
ncRNA Identification Methods Examples:
1. (Sequence homology methods)
在一些例子中,當兩個(gè)物種的進(jìn)化距離比較近,一個(gè)簡(jiǎn)單的序列相似性的比對,通過(guò)BLAST或者FASTA就足夠確認RNA基因.在比較緊密相關(guān)的RNA基因地時(shí)候這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針對512個(gè)進(jìn)化保守的ncRNA進(jìn)行干擾并進(jìn)行細胞分析,他們鑒定了一個(gè)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.(他們也用了siRNA方法,得到了與shRNA同樣效果)
1.參考文獻:A. T. Willingham et al., Science 309, 1570 (2005).
2.參考文獻:A. T. Willingham, Q. L. Deveraux, G. M. Hampton, P.Aza-Blanc, Oncogene 23, 8392 (2004).
推薦的ncRNA網(wǎng)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 在一級結構和次級結構的保守性讓很多科學(xué)家對miRNA分子進(jìn)化樹(shù)進(jìn)行研究。這方面的文獻很多，只需利用完整的數據庫，搜索關(guān)鍵詞miRNA ,evolution,Phylogenetic trees,您可以獲得很classic文獻。不同特點(diǎn)的miRNA需要具體的調整分析和研究方案！
以miRNA17為例簡(jiǎn)要說(shuō)明分析的途徑：
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 是一個(gè)整合的數據，被開(kāi)發(fā)用來(lái)存儲已知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，來(lái)自人，小鼠，大鼠以及狗的miRNA基因，可以從miRNAase獲得，試驗已經(jīng)證實(shí)的miRNA targets在文獻中可以獲得。
3.假定的miroRNA precursors可以通過(guò)RNAz來(lái)鑒定，RNAz是一個(gè)序列比較分析的工具
4.假定的miRNA基因的成熟miRNA可以通過(guò)mmiRNA來(lái)確認，mmiRNA使用機器智能學(xué)習的方法
5.miRanda是一個(gè)用來(lái)在四種哺乳動(dòng)物基因組中的基因的3' UTR區域的保守區預計miRNA靶點(diǎn)的工具
6.miRNA map也提供已知的miRNA的表達圖譜，跨物種的比較，基因的注釋以及與別的生物數據庫進(jìn)行交叉檢索
7.文本和圖片的網(wǎng)頁(yè)交互性界面在http://mirnamap.mbc.nctu.edu.tw/提供了方便的檢索功能
Non Coding RNA 專(zhuān)家及其網(wǎng)址
Reuven Agami
Division of Tumor Biology - The Netherlands Cancer Institute - Amsterdam (The
Netherlands)
r.agami@nki.nl
Webpage: http://research.nki.nl/agamilab/
Philippe Bastin
Trypanosome Cell Biology Unit - Parasitology Department - Pasteur Institute - Paris (France)
pbastin@pasteur.fr
Webpage:
http://www.mnhn.fr/museum/foffice/science/science/Enseignement/rubmastere/ssuniteensmaster/fiche1.xsp
David Baulcombe
The Sainsbury Laboratory - John Innes Centre - Norwich
david.baulcombe@sainsbury-laboratory.ac.uk
Webpage: www.sainsbury-laboratory.ac.uk
René Bernards
Division of Molecular Carcinogenesis - The Netherlands Cancer Institute - Amsterdam (The Netherlands)
r.bernards@nki.nl
Webpage: http://www.biomedicalgenetics.nl/Members/Bernards/bernards.html
Jürgen Brosius
Institute of Experimental Pathology / Molecular Neurobiology - University of Münster -
Münster (Germany)
RNA.world@uni-muenster.de
Webpage: http://zmbe2.uni-muenster.de/expath/frames.htm
Witold Filipowicz
Friedrich Miescher Institute for Biomedical Research - Basel (Switzerland)
Witold.Filipowicz@fmi.ch
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)
henzte@embl.de
Webpage: http://www.embl-heidelberg.de/ExternalInfo/hentze/
Ivo L. Hofacker
Theoretical Biochemistry Group - Institute for Theoretical Chemistry - University OF Vienna -
Vienna (Austria)
ivo@tbi.univie.ac.at
Webpage: http://www.tbi.univie.ac.at/~ivo/
Alexander Hüttenhofer
Division of Genomics & RNomics - Innsbruck Medical University - Innsbruck (Austria)
Alexander.Huettenhofer@i-med.ac.at
Webpage: http://genomics.i-med.ac.at/staff/a_huettenhofer.html
Craig P. Hunter
Department of Molecular and Cellular Biology – Harvard University - Cambridge (USA)
hunter@mcb.harvard.edu
Webpage: http://www.mcb.harvard.edu/hunter/
Elisa Izaurralde
Max Planck Institute for Developmental Biology - Tübingen (Germany)
Elisa.Izaurralde@tuebingen.MPG.de
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 “
macino@bce.uniroma1.it
Javier Martinez
Institute of Molecular Biotechnology of the Austrian Academy of Sciences - Vienna (Austria)
javier.martinez@imba.oeaw.ac.at
Wolfgang Nellen
Department of Genetics - University of Kassel - Kassel (Germany)
nellen@uni-kassel.de
Webpage: http://www.biologie.uni-kassel.de/genetics/
Mikiko C. Siomi
Institute for Genome Research - University of Tokushima - Tokushima (Japan)
E-mail: siomim@genome.tokushima-u.ac.jp
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)
stoffel@imsb.biol.ethz.ch
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)
ttuschl@mail.rockefeller.edu
Webpage: http://www.rockefeller.edu/labheads/tuschl/
Jörg Vogel
Max Planck Institute for Infection Biology - Berlin (Germany)
vogel@mpiib-berlin.mpg.de
Webpage: http://www.mpiib-berlin.mpg.de/research/RNABiology.htm
Mihaela Zavolan
Division of Bioinformatics - Biozentrum, University of Basel - Basel (Switzerland)
Mihaela.Zavolan@unibas.ch
Webpage: http://www.biozentrum.unibas.ch/zavolan/index.html
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