生物学反应及信号通路(Signaling Pathway)数据库Reactome - 生物信息 - 生物秀
标题: 生物学反应及信号通路(Signaling Pathway)数据库Reactome
摘要: [生物学反应及信号通路(Signaling Pathway)数据库Reactome]http:
www gramene org pathway ricecyc htmlhttp:
plantreactome oicr on ca ReactomeGWT entrypoint htmlhttp:
arabidopsisreactome org download index htmlhttp:
en wikipedia org wiki Reactom…… [关键词:数据库 生物学 信号通路 复合体 基因 蛋白质 元件]……
http://www.gramene.org/pathway/ricecyc.htmlhttp://plantreactome.oicr.on.ca/ReactomeGWT/entrypoint.htmlhttp://arabidopsisreactome.org/download/index.htmlhttp://en.wikipedia.org/wiki/ReactomeWebsite
http://www.reactome.org
Download URL
http://www.reactome.org/download/index.html
反应组学（Reactome,
http://www.reactome.org）是一个汇集了由专家撰写，经同行评阅的有关人体内各项反应及学路径的文章的数据库，该数据库相当于
一个有效的数据资源以及电子图书。该库目前发布了共计2975个人类蛋白、2907项学反应以及4455个引用文献。该数据库为人们提供了一个全新的
从整体水平上对生物学途径进行研究的工具，同时，它也是一个改良的搜索及数据挖掘工具，可以简化与生物学途径相关的数据搜索与研究。此外，对用户提供的高
通量数据组进行分析，也变得更为简单。目前，由于直系同源预测方法的改进，反应组学数据库也开始收录其它模式生物(model organism)的数据了，现在通过与其它数据库合作和人
工注释方式，已经收录了包括拟南芥(Arabidopsis thaliana)（Arabidopsis）、水稻(Rice)（Oryza sativa）、果蝇（Drosophila）及原鸡（Gallus gallus）等22种模式物种的反应组学数据。反应组学的数据库内容和相关软件都是开源共享，免费使用的。
1 更广泛的覆盖人类生物学反应途径
最新版本的反应组学数据（版本26，于2008年
9月公布）覆盖了UniProt数据库中20,000个经人工注释过的人类蛋白质中大约12.5%的蛋白质，这一数量在过去三年中增长了2.7倍。反应组
学数据库中对46个主要人类生物学研究领域，如细胞凋亡(Apoptosis)、HIV、流感病毒生活周期、复制、转录、生理止血机制、碳水化合物代谢途径等进行了注释，
并对与OMIM疾病表型相关的1005个蛋白质的正常功能进行了记录（http://www.ncbi.nlm.nih.gov/omim/）。
2 更完善的工具、软件及数据模型
2.1 修订后的直系同源预测方法
用第二代OrthoMCL聚类处理方法（clustering procedure，
http://reactome.org/electronic_inference.html）对OrthoMCL
DB（http://www.orthomcl.org/cgi-bin/OrthoMclWeb.cgi）中高质量的基因组数据进行处理，可以发现22
个在进化上与人类有分歧物种的直系同源基因。新方法只将每个基因最长的转录片段作为研究对象，并采用基因为基础的方法代替蛋白质为基础的方法，编制用于
OrthoMCL的Ensembl 标识符及在反应组学中应用的UniProt的序列号。这些改进提高了在不影响准确率的情况下使用电子注释时的成功率。
2.2 经改善的对大规模数据组进行分析、数据挖掘以及建立模型的工具
2.2.1 Skypainter
目前的Skypainter版本可以帮助用户直观地、更有效地看到通过大规模试验鉴定出的基因之间的功能关系（图1A及B）。
2.2.2 Biomat
新开发投入使用的Reactome BioMart
工具（www.biomart.org）进一步简化了研究人员进行数据挖掘、交叉数据库分析以及大规模基因功能分析等工作。用户可以在不同的数据组（如生
物学反应途径、生物学反应、参加反应的复合体等）之间进行选择，设定关键字，对要查找的数据特性进行限制，以便进行数据过滤，缩小查找范围。例如，对
database进行搜索，用户便可以找到所有有某种蛋白质的复合体（图1C）参与的信号通路(Signaling Pathway)。而且，用户还可以将Reactome数据组中的“复合体”
信息与UniProt proteome相连，从而找到这些复合体所有组份的序列。
2.2.3 Reactome
数据模型中的改变。
研究人员尽可能地将数据模型最小化，以利于数据管理和使用。新增功能之一是有关一个“黑匣子（black
box）”反应类别，可以帮助用户找到那些特征定义不完全的生物学元件；另外一个新的功能是“另一细胞元件（entityOnOtherCell）”，则
可以对发生在两个细胞内的生物学元件及存在的复合物进行描述。
3 未来的发展方向
Reactome项目的长
期目标是，为用户提供一个直观表现发生在生物体内的反应及反应途径的图表。为了实现这一目标，Reactome已经研发出了第二代整体水平反应途径可视化
工具：通过更强大的“导航”功能，包括扩大缩小画面、滚动鼠标移动页面，对生物学过程进行高亮标注等，这个版本的工具可以为用户展示参与反应的分子之间更
加细致的关系（图1D）。
这种对参加反应的分子以及反应过程进行“鸟瞰”式展示的方式，使得用户可以简便地获知每个参加反应的生物分子的细节信息。使用网页上改进后具备自动完成功能的搜索功能可以直接搜索反应名称、信号通路(Signaling Pathway)名称、个体名称、标识符和GO分子功能名称等。
Reactome项目正在
开发兼具强大导航功能和可视化信号通路(Signaling Pathway)搜索功能的用户使用界面。为了达到这个目的，他们最近开发了SBGN工具
（http://www.sbgn.org），使用SBGN就可以做出图1E那样的图。目前，他们在http:
//brie8.cshl.edu:8080/ReactomeTools/AuthorTool/authorToolLaunch.html网站上发
布了该工具的测试版供大家测试。
原文检索：Nucleic Acids Research, 2009, Vol. 37, Database issue D619–D622Reactome is a free online database of biological pathways.[1][2] There are several Reactomes that concentrate on specific organisms, the largest of these is focused on human biology,
the following description concentrates on the human Reactome. It is
authored by expert biologists, in collaboration with Reactome editorial
staff who are all PhD level biologists. Content is cross-referenced to
many bioinformatics databases. The rationale behind Reactome is to
visually represent biological pathways in full mechanistic detail, while
making the source data available in a computationally accessible
The website can be used to browse pathways and submit data to a suite
of data analysis tools. The underlying data is fully downloadable in a
number of standard formats including pdf, SBML and Biopax. Pathway
diagrams use a Systems Biology Graphical Notation (SBGN)-based style.
The core unit of the Reactome data model is the reaction. Entities
(nucleic acids, proteins, complexes and small molecules) participating
in reactions form a network of biological interactions and are grouped
into pathways. Examples of biological pathways in Reactome include
signaling, innate and acquired immune function, transcriptional
regulation, translation, apoptosis and classical intermediary
metabolism.
The pathways represented in Reactome are species-specific, with each
pathway step supported by literature citations that contain an
experimental verification of the process represented. If no experimental
verification using human reagents exists, pathways may contain steps
manually inferred from non-human experimental details, but only if an
expert biologist, named as Author of the pathway, and a second
biologist, names as reviewer, agree that this is a valid inference to
make. The human pathways are used to computationally generate by an
orthology-based process derived pathways in other organisms.
1 Database Organization2 Database Content3 Tools4 See also5 References6 External links
6.1 Related resources
Database Organization
In Reactome, human biological processes are annotated by breaking
them down into series of molecular events. Like classical chemistry
reactions each Reactome event has input physical entities (substrates)
which interact, possibly facilitated by enzymes or other molecular
catalysts, to generate output physical entities (products).
Reactions include the classical chemical interconversions of
intermediary metabolism, binding events, complex formation, transport
events that direct molecules between cellular compartments, and events
such as the activation of a protein by cleavage of one or more of its
peptide bonds. Inpidual events can be grouped together into pathways.
Physical entities can be small molecules like glucose or ATP, or
large molecules like , RNA, and proteins, encoded directly or
indirectly in the human genome. Physical entities are cross-referenced
to relevant external databases, such as UniProt for proteins and ChEBI
for small molecules. Localization of molecules to subcellular
compartments is a key feature of the regulation of human biological
processes, so molecules in the Reactome database are associated with
specific locations. Thus in Reactome instances of the same chemical
entity in different locations (e.g., extracellular glucose and cytosolic
glucose) are treated as distinct chemical entities.
The Gene Ontology
controlled vocabularies are used to describe the subcellular locations
of molecules and reactions, molecular functions, and the larger
biological processes that a specific reaction is part of.
Database Content
The database contains curated annotations that cover a perse set of topics in molecular and cellular biology. Details of current and future annotation projects can be found in the calendar of annotation projects.
cell cyclemetabolismsignalingtransportcell motilityimmune functionhost-virus interactionneural function
There are tools on the website for viewing an interactive pathway
diagram, performing pathway mapping and pathway over-representation
analysis and for overlaying expression data onto Reactome pathways. The
pathway mapping and over-representation tools take a single column of
protein/compound identifiers, Uniprot and ChEBI accessions are preferred
but the interface will accept and interpret many other identifiers or
symbols. Mixed identifiers can be used. Over-representation results are
presented as a list of statistically over-represented pathways (note
that the default view only shows the major pathway topics, which may not
be the most significant, click on the Open All button to see the
subpathways).
Expression data is submitted in a multi-column format, the first
column identifying the protein, additional columns are expected to be
numeric expression values, they can in fact be any numeric value, e.g.
differential expression, quantitiative proteomics, GWAS scores. The
expression data is represented as colouring of the corresponding
proteins in pathway diagrams, using the colours of the visible spectrum
so "hot" red colours represent high values. If multiple columns of
numeric data are submitted the overlay tool can display them as separate
"experiments", e.g. timepoints or a disease progression.
The database can be browsed and searched as an on-line textbook. An on-line users" guide is available. Users can also download the current data set or inpidual pathways and reactions in a variety of formats including PDF, BioPAX, and SBML.
KEGG (The Kyoto Encyclopedia of Genes and Genomes)BioCyc database collectionBRENDA (The BRaunschweig ENzyme DAtabase)WikiPathwaysComparative Toxicogenomics Database
References
Jump up ^ Croft,
D.; O"Kelly, G.; Wu, G.; Haw, R.; Gillespie, M.; Matthews, L.; Caudy,
M.; Garapati, P.; Gopinath, G.; Jassal, B.; Jupe, S.; Kalatskaya, I.;
Mahajan, S.; May, B.; Ndegwa, N.; Schmidt, E.; Shamovsky, V.; Yung, C.;
Birney, E.; Hermjakob, H.; d"Eustachio, P.; Stein, L. (2010). "Reactome: A database of reactions, pathways and biological processes". Nucleic Acids Research 39 (Database issue): D691–D697. doi:10.1093/nar/gkq1018. PMC3013646. PMID.editJump up ^ Joshi-Tope,
G.; Gillespie, M.; Vastrik, I.; d"Eustachio, P.; Schmidt, E.; De Bono,
B.; Jassal, B.; Gopinath, G.; Wu, G.; Matthews, L.; Lewis, S.; Birney,
E.; Stein, L. (2004). "Reactome: A knowledgebase of biological pathways". Nucleic Acids Research 33 (Database issue): D428–D432. doi:10.1093/nar/gki072. PMC540026. PMID.edit
External links
Reactome homepageReactome article in MetaBaseReactome on TwitterReactome Quick Tour on EBI Train OnLine
Related resources
Other molecular pathway databases
HumanCycGeneNetworkPanther PathwaysWikiPathwaysPathway Commons
相关热词：
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Pathway Maps
KEGG PATHWAY is a collection of manually drawn
representing our knowledge on the molecular interaction, reaction and relation networks for:
KEGG PATHWAY is a reference database for .
Pathway Identifiers
Each pathway map is identified by the combination of 2-4 letter prefix code and 5 digit number (see ). The prefix has the following meaning:
mapmanually drawn reference pathway
koreference pathway highlighting KOs
ecreference metabolic pathway highlighting EC numbers
rnreference metabolic pathway highlighting reactions
&org&organism-specific pathway generated by converting KOs to gene
identifiers
and the numbers starting with the following:
011global map (lines linked to KOs)
012overview map (lines linked to KOs)
010chemical structure map (no KO expansion)
07drug structure map (no KO expansion)
otherregular map (boxes linked to KOs)
are used for different types of maps.
1. Metabolism
1.0 Global and overview maps
1.1 Carbohydrate metabolism
1.2 Energy metabolism
1.3 Lipid metabolism
1.4 Nucleotide metabolism
1.5 Amino acid metabolism
1.6 Metabolism of other amino acids
1.7 Glycan biosynthesis and metabolism
1.8 Metabolism of cofactors and vitamins
1.9 Metabolism of terpenoids and polyketides
1.10 Biosynthesis of other secondary metabolites
00333 New!
1.11 Xenobiotics biodegradation and metabolism
1.12 Chemical structure transformation maps
2. Genetic Information Processing
2.1 Transcription
2.2 Translation
2.3 Folding, sorting and degradation
2.4 Replication and repair
3. Environmental Information Processing
3.1 Membrane transport
3.2 Signal transduction
3.3 Signaling molecules and interaction
4. Cellular Processes
4.1 Transport and catabolism
4.2 Cell growth and death
04217 New!
04218 New!
4.3 Cellular community - eukaryotes
4.4 Cellular community - prokaryotes
4.5 Cell motility
5. Organismal Systems
5.1 Immune system
5.2 Endocrine system
04926 New!
5.3 Circulatory system
5.4 Digestive system
5.5 Excretory system
5.6 Nervous system
5.7 Sensory system
5.8 Development
5.10 Environmental adaptation
6. Human Diseases
6.1 Cancers: Overview
6.2 Cancers: Specific types
05225 New!
6.3 Immune diseases
6.4 Neurodegenerative diseases
6.5 Substance dependence
6.6 Cardiovascular diseases
6.7 Endocrine and metabolic diseases
6.8 Infectious diseases: Bacterial
6.9 Infectious diseases: Viral
05167 New!
05165 New!
6.10 Infectious diseases: Parasitic
6.11 Drug resistance: Antimicrobial
6.12 Drug resistance: Antineoplastic
7. Drug Development
7.1 Chronology: Antiinfectives
7.2 Chronology: Antineoplastics
7.3 Chronology: Nervous system agents
7.4 Chronology: Other drugs
7.5 Target-based classification: G protein-coupled receptors
7.6 Target-based classification: Nuclear receptors
7.7 Target-based classification: Ion channels
7.8 Target-based classification: Transporters
7.9 Target-based classification: Enzymes
7.10 Structure-based classification
7.11 Skeleton-based classification
Pathway Mapping
KEGG PATHWAY mapping is the process to map molecular datasets, especially large-scale datasets in genomics, transcriptomics, proteomics, and metabolomics, to the KEGG pathway maps for biological interpretaion of higher-level systemic functions.
- basic pathway mapping tool
- advanced pathway mapping tool
- selected pathway map coloring tool
Last updated: September 29, 2017}