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云之南

专业背景：计算机科学研究方向与兴趣: JavaEE-Web软件开发, 生物信息学, 数据挖掘与机器学习, 智能信息系统目前工作: 基因组, 转录组, NGS高通量数据分析, 生物数据挖掘, 植物系统发育和比较进化基因组学

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RNA 生物信息讨论专题

2009-11-23 20:15:46| 分类：生物信息学 | 标签： |举报 |字号大中小订阅

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RNA 生物信息讨论专题
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 diersity of life and the inter-relationships of liing 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 discoer non-coding RNAs in genomes; and
(5) academic, goernment, and industry scientists who research and deelop RNA pharmaceuticals or drugs that target RNA.

第一部分:
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 oer 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.

5.RNA数据库的整合
见图

（缩略图，点击图片链接看原图）

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 leel.

b. Rfam
http://www.sanger.ac.uk/Software/Rfam/
Database of structure-annotated multiple sequence alignments, coariance 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 proide a comprehensie perspectie 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://ndbserer.rutgers.edu/
NDB (Nucleic Acid Database) is a repository of three-dimensional structural information about nucleic acids.

b. RNA folding Serers
http://kinefold.u-strasbg.fr/rna.html
List of RNA folding serers and related web sites maintained by Here Isambert.

c. RNA Informatics Links
http://www-lbit.iro.umontreal.ca/RNA_Links/RNA.shtml
An exhaustie 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 aailable 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/serer.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.uni-mrs.fr/~dgaut/download/
RNA motif search program; not a Web-tool.

d. Transterm UTR Motif Search
http://guineere.otago.ac.nz/transterm.html
Transterm is an interactie database proiding access to RNA sequences and their associated motifs. The RNA sequences are deried from all gene sequence data in Genbank, including complete genomes, diided into putatie 5' and 3'UTRs, initiation and term

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

3.RNA_Sequence_Retrieal
http://www.ncbi.nlm.nih.go/
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 eolutionary relationships between sequences as well as help identify members of gene families.

2. EBI Tools
EBI Tools is a project that aims to proide programmatic access to the arious databases and retrieal and analysis serices that the European Bioinformatics Institute (EBI) proides through Simple Object Access Protocol (SOAP) and other related web serice technologies.

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

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

5. FeatureExtract
The FeatureExtract serer 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; receie email alerts for current contents of PubMed and GenBank; e.g. use accession number of htg record as query to receie sequence updates (as the ersion number changes).

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

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

10. WU BLAST
Washington Uniersity Basic Local Alignment Search Tool

4.RNA_Structure_Predicition_and_isualization
a. CARNAC
http://bioinfo.lifl.fr/carnac
Serer which predicts consered secondary structure elements of homologous RNAs. The input of a set of RNA sequences are not required to be preiously 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 ealuates the inhibitory potency of potential siRNA sequences as well as identifying gene regions that hae a high sil

c. ERPIN
http://tagc.uni-mrs.fr/erpin/
ERPIN (Easy RNA Profile IdentificatioN) takes as input an RNA sequence alignment and secondary structure annotation and will identify a wide ariety 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/
Serer which proides iterated loop matching and maximum weighted matching algorithms for pseudoknot containing RNA secondary structure prediction. Algorithms can apply thermodynamic and comparatie 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.MolMoDB
http://molmodb.org/
The Database of Macromolecular Moements (MolMoD contains a collection of animated protein and RNA structures to assist in the exploration of macromolecular flexibility. Software for structure analysis is also aailable.

h. MOLPROBITY
http://kinemage.biochem.duke.edu/molprobity/main.php?use_king=1
MOLPROBITY is a structure analysis and alidation program that can calculate and display steric, H-bonding, and an 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.uniie.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
Serer with three tools for the rational design of small interfering RNAs (Sirna), antisense oligonucleotides (Soligo), and trans-cleaing ribozymes (Sribo). A fourth tool, Srna, returns output including general folding features.

i. siDirect
http://design.RNAi.jp/
Serer 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 Serer
http://jura.wi.mit.edu/bioc/siRNA
Serer aiding the design of short interfering RNAs (siRNAs) by proiding 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 proides 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/sstructiew/
RNA secondary structure iewer 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.ienna RNA Package
http://www.tbi.uniie.ac.at/~io/RNA/
Comprises a C codelibrary and seeral 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.go/
SCOR: Structural Classification of RNA

d. The Nucleic Acid Database (ND
http://ndbserer.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.

吴佰霖 wrote:
目前miRAN和ncRNA都是一个热点，大家对一个序列怎么分析为ncRNA有什么好的流程吗

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 coariance 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 consered in sequence. Howeer,three consered regions are shared; CR-I, CR-I and CR-. For nuclear P RNA there are also consered 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-I and CR- 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 coariance models. Highscoring candidates were further analyzed for characteristics typical for P/MRP RNA secondary structure; base pairing between the CR-I and CR- motifs, presence of CR-I as well as the helices P1, P2 and P3. Also IREs were identified using a combination of pattern matching and coariance models.To identify as many potential IREs as possible we primarily searched aailable mRNA sequences. In case there was no aailable mRNA, genomic sequences was searched for regions homologous to aailable proteins/mRNAs. Wheneer 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 consered primary sequence motifs and the ability to fold into a secondary structure typical for the iron responsie element

3. Profile HMMs of highly consered regions in P and MRP RNA
For prediction of P and MRP RNAs we also used profile HMMs created from CR-I and CR- multiple alignments. We further analyzed all genomic sequences that contained the CR-I and CR- motifs and where the distance between the two motifs is less than 3000 bases. Adantages 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 consered features of the RNA were present

4.Identification of protein homologues
An efficient method for protein identification is PSI-BLAST (Position Specific Iteratie 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 sensitie 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 indiidual genome projects or identified from TBLASTN searches of genome sequences. Wheneer releant, these noel 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 aailable 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 preiously 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 comparatie 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 noo methods
As opposed to the methods that detect new members of already known ncRNA families described preiously (IRE and MRP/P RNA identification), we hae also used two de noo methods, QRNA and RNAz , to computationally screen the S.cereisae genome for ncRNAs.

QRNA makes a prediction of ncRNA based on pairwise alignments . It compares the score of three distinct models of sequence eolution to decide which one describes best thegien alignment: a pair SCFG is used to model the eolution of secondary structure, a pair hidden Marko model (HMM) describes the eolution of protein coding sequence, and a different pair HMM implements the independent model of a sequence with an eolutionary 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 consered 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 indiidual sequences, and a structure conseration index obtained by comparing folding energies of the indiidual sequences with the predicted consensus folding. The two criteria are then combined to detect consered and stable RNA secondary structures with high sensitiity 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.

谢谢了！！！！！！！！！！！！！！！！！！！！！！！！！

xiaohe0377 wrote:
楼主，你好！
我们都知道RNAi已经成为当今生物学界研究基因功能等首选的工具！！但是我们可否使用该技术来研究ncRNA的功能，特别是一些长链ncRNA，他们的结构和功能非常复杂，可以说迄今，很少有ncRNA的作用机制和功能被人们完全揭示的。
比如，我们打算使用siRNA抑制在癌症中特异性高表达的ncRNA（如H19等），可否会发挥抑癌作用。
谢谢！！

A. 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 actiated 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. Deeraux, 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/

redkindszhusl wrote:
基因沉默战友，你的帖子太好了，给你投上一票。我想请教一下，不同物种同类非编码RNA序列中AU和GC含量的差异与物种进化间有没有联系，能不能指点一下！（我是这方面的新手，最好能提供点文献）谢谢在先！

我给您推荐几篇文献，关于Eolutionary Patterns of Non-Coding RNAs您可以点击下载

Eolutionary Patterns of Non-Coding RNAs.part1.rar (263.67k)

identified in AT-rich hyperthermophiles.part2.rar (104.35k)
RNA base compostion.rar (162.04k)
utionary Discrimination of Mammalian Consered.pdf (197.26k)

ncRNA 研究的tilling array design

（缩略图，点击图片链接看原图）

学习中。。。。。。。。。

redkindszhusl wrote:
多谢沉默基因战友，你提供的文献很有用，特别是两篇有关GC含量方面的。不知道可否对miRNA进行进化树的构建或密码子方面的探索?再次感谢！！

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 ,eolution,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 aailable genome databaseswere searched using blastn against all pre-miRNAs of the mir17 family . Conersely, the entire MicroRNA Registry, was compared against the genomic sequences near the putatie family members.

2.Exact locations of homologs of known miRNAs were identified using clustalw alignments and subsequent prediction of the secondary structure using ienna RNA Package , in particular the programs RNAfold,RNAalifold, RNALfold, and alidot, in order to erify 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 indiidual 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 indiidual 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 indiidual microRNAs from one cluster to another.The aailable data do not contain any eidence that such processes might play a role.

多谢基因沉默高手指点，太感谢了，后面遇到问题再请教！

在RNAi中mRNA断裂的补充说明：

RT-PCR对siRNA 沉默效果的评价
虽然通过siRNA处理，整个基因的mRNA都受到影响，围绕 siRNA周围的区域是受沉默影响最大的区域。就时效性研究来看，编码区是siRNA效应的最稳定区，非翻译区相对稳定性较弱。这与断裂位点和非翻译区之间的距离有关。因此引物最好选择在编码区内的任何位点，为了最大的灵敏度推荐使用围绕着siRNA位点进行设计。为了比较不同的siRNA针对不同的基因的相对沉默效果，最好在选择引物的时候用对于每个目的基因而言选择与siRNA靶点距离一致的引物更加有可比性！

还没开工，对沉默兄敬仰！

请问，siRNA的设计，是由生物公司设计好还是实验者本人设计好呢，那些在网站上应用设计软件设计的siRNA的可靠性如何？另外，向您请教BLAST进行序列比对的方法，尤其是结果分析方面。我是新手，刚刚接触RNAi，还请多多指导。万分感谢！

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

gndtyhj wrote:
请问，siRNA的设计，是由生物公司设计好还是实验者本人设计好呢，那些在网站上应用设计软件设计的siRNA的可靠性如何？另外，向您请教BLAST进行序列比对的方法，尤其是结果分析方面。我是新手，刚刚接触RNAi，还请多多指导。万分感谢！

请您搜索相关的园子里的RNAi的帖子，也可以去我的groups里面查询
http://groups.google.com/group/siRNA
设计方案和比对的原则里面都有！

收益非浅!

确实收获不少!

Eolutionary rates ary among rRNA structural elements
Nucleic Acids Research, 2007, ol. 35, No. 10 3339-3354
S. Smit, J. Widmann and R. Knight*
Department of Chemistry and Biochemistry, Uniersity of Colorado, Boulder, CO 80309

Understanding patterns of rRNA eolution is critical for a number of fields, including structure prediction and phylogeny. The standard model of RNA eolution is that compensatory mutations in stems make up the bulk of the changes between homologous sequences, while unpaired regions are relatiely homogeneous. We show that considerable heterogeneity exists in the relatie rates of eolution of different secondary structure categories (stems, loops, bulges, etc.) within the rRNA, and that in eukaryotes, loops actually eole much faster than stems. Both rates of eolution and abundance of different structural categories ary with distance from functionally important parts of the ribosome such as the tRNA path and the peptidyl transferase center. For example, fast-eoling residues are mainly found at the surface; stems are enriched at the subunit interface, and junctions near the peptidyl transferase center. Howeer, different secondary structure categories eole at different rates een when these effects are accounted for. The results demonstrate that relatie rates and patterns of eolution are lineage specific, suggesting that phylogenetically and structurally specific models will improe eolutionary and structural predictions.

download link:
http://nar.oxfordjournals.org/cgi/reprint/35/10/3339?ck=nck

RNAmmer: consistent and rapid annotation of ribosomal RNA genes
Nucleic Acids Research, 2007, ol. 35, No. 9 3100-3108
Karin Lagesen1,2,*, Peter Hallin3, Einar Andreas R?dland1,2,4,5, Hans-Henrik St?rfeldt3, Torbj?rn Rognes1,2,4 and Daid W. Ussery1,2,3

The publication of a complete genome sequence is usually accompanied by annotations of its genes. In contrast to protein coding genes, genes for ribosomal RNA (rRNA) are often poorly or inconsistently annotated. This makes comparatie studies based on rRNA genes difficult. We hae therefore created computational predictors for the major rRNA species from all kingdoms of life and compiled them into a program called RNAmmer. The program uses hidden Marko models trained on data from the 5S ribosomal RNA database and the European ribosomal RNA database project. A pre-screening step makes the method fast with little loss of sensitiity, enabling the analysis of a complete bacterial genome in less than a minute. Results from running RNAmmer on a large set of genomes indicate that the location of rRNAs can be predicted with a ery high leel of accuracy. Noel, unannotated rRNAs are also predicted in many genomes. The software as well as the genome analysis results are aailable at the CBS web serer.

http://nar.oxfordjournals.org/cgi/reprint/gkm1601

Microarray analysis of newly synthesized RNA in cells and animals
M. Kenzelmann*??, S. Maertens?, M. Hergenhahn§?, S. Kueffer?, A. Hotz-Wagenblatt , L. Li**, S. Wang?, C. Ittrich??,T. Lemberger*, R. Arribas??, S. Jonnakuty , M. C. Hollstein§, W. Schmid*, N. Gretz**, H. J. Gro¨ ne?, and G. Schu¨ tz*
6164–6169 PNAS April 10, 2007 ol. 104 no. 15

Current methods to analyze gene expression measure steady-state leels of mRNA. To specifically analyze mRNA transcription, we hae deeloped a technique that can be applied in io in intact cells and animals. Our method makes use of the cellular pyrimidine salage pathway and is based on affinity-chromatographic isolation of thiolated mRNA. When combined with data on mRNA steady-state leels, this method is able to assess the relatie contributions of mRNA synthesis and degradation/stabilization. It oercomes limitations associated with currently aailable methods such as mechanistic interention that disrupts cellular physiology, or the inability to apply the techniques in io. Our method was first tested in serum response of cultured fibroblast cells and then
applied to the study of renal ischemia reperfusion injury, demonstrating its applicability for whole organs in io. Combined with data on mRNA steady-state leels, this method proided a detailed analysis of regulatory mechanisms of mRNA expression and the relatie contributions of RNA synthesis and turnoer within distinct pathways, and identification of genes expressed at low abundance at the transcriptional leel.

http://www.pnas.org/cgi/reprint/104/15/6164

Specificity, duplex degradation and subcellular localization of antagomirs
Nucleic Acids Research, 2007, ol. 35, No. 9 2885–2892
Jan Kru¨ tzfeldt1,y, Satoru Kuwajima1, Rai Braich2, Kallanthottathil G. Rajee2,
John Pena3, Thomas Tuschl3, Muthiah Manoharan2 and Markus Stoffel1,

MicroRNAs (miRNAs) are an abundant class of 20–23-nt long regulators of gene expression. The study of miRNA function in mice and potential therapeutic approaches largely depend on modified oligonucleotides. We recently demonstrated silencing miRNA function in mice using chemically modified and cholesterol-conjugated RNAs termed ‘antagomirs’. Here, we further characterize the properties and function of antagomirs in mice. We demonstrate that antagomirs harbor optimized phosphorothioate modifications, require 419-nt length for highest efficiency and can discriminate between single nucleotide mismatches of the targeted miRNA. Degradation of different chemically protected miRNA/antagomir duplexes in mouse liers and localization of antagomirs in a cytosolic compartment that is distinct from processing (P)-bodies indicates a degradation mechanism independent of the RNA interference (RNAi) pathway. Finally, we show that antagomirs, although incapable of silencing miRNAs in the central nerous system (CNS) when injected systemically, efficiently target miRNAs when injected locally into the mouse cortex. Our data further alidate the effectieness of antagomirs in io and should facilitate future studies to silence miRNAs for functional analysis and in clinically releant settings.

http://www.pubmedcentral.nih.go/articlerender.fcgi?artid=1888827

zhangyanlhow wrote:
我准备做RNA干扰的裸鼠实验，已经构建了质粒载体并瞬时转染了细胞

后接种了裸鼠（曾经想用G418筛选稳定株的但没有成功),现在我们老师又让

我用质粒shRNA直接给长瘤子的裸鼠瘤内注射呢，还让我再合成个siRNA片段

也瘤内注射一下，我查了文献还能腹腔、尾静脉注射，我不知道是构建质粒

载体好还是用siRNA片段用于裸鼠好，有没有必要siRNA和shRNA都做呢？还是

做一种就行？直接转染细胞接种裸鼠、瘤内注射、腹腔注射、尾静脉注射到

底哪种方法好呢?

用siRNA片段还是用shRNA质粒，这个要根据具体情况而定，两种方法都做是一种可行的方法，如果您经费足够。具体使用何种方法进行给药，这个具体情况具体分析，给您一个参考如下：

（缩略图，点击图片链接看原图）

非常感谢基因沉默的解答，综合考虑了一下，shRNA比siRNA稳定一些，我现在打算只用shRNA质粒然后用不同的方法注射比较一下效果如何算了，我是怕shRNA和siRNA两个都做意义不大，而且挺费钱的，再和我们老板商量一下吧，以后遇到问题还得麻烦你了，再次感谢

bired wrote:
还没开工，对沉默兄敬仰！

Non-coding RNAs: Small Inhibitory-RNA ( siRNA; RNAi; microRNA; miRNA)
http://www.ncbi.nlm.nih.go/Class/NAWBIS/Modules/RNA/rna13a.html

Animations of Inhibitory RNA Action:
Nature Reiews - A high quality moie describing inhibitory RNA eents and mechanisms.
http://www.nature.com/focus/rnai/animations/animation/animation.htm

Nature Reiews Genetics - A flash animation [Nature Reiews Genetics 2; 110-119 (2001) "Post-Transcriptional Gene Silencing by Double-Stranded RNA." Figure 1.]
http://www.nature.com/nrg/journal/2/n2/animation/nrg0201_110a_swf_MEDIA1.html

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