• INGENUITY Pathway Analysis

INGENUITY Pathway Analysis

2018 夏季版
售前试用
INGENUITY Pathway Analysis - IPA 是一款一体化的云端数据分析平台,可实现miRNA、SNP微阵列及代谢组学、蛋白质组学和RNA-seq等实验数据的分析和生物学解释,帮助研究者快速分析和理解实验数据。IPA的引用文献已超过62000篇。欢迎广大生物学相关工作者加入QQ群:475878928,进行IPA的技术交流。

IPA 所基于的后台是一个高度结构化的 INGENUITY Knowledge Base 数据库。该数据库历经近20年的海量知识数据投入;拥有 200 名 phD 阅读文献;对 700 多种权威杂志进行全文阅读,3000 种杂志进行摘要阅读,目前,该数据库存储了 510 万条以上的生物实验发现,130 万种互作信息和 3 万种以上分子信息;此外,还构建了完整的功能疾病分类数据库,绘制了 800 种信号通路、代谢通路等;并与公共数据库进行全面合作,保持每周更新一次。INGENUITY Knowledge Base 为众多以发现和创造为己任的科研工作者们,提供了及时、准确的生物信息学数据支撑。

IPA 的作用并不局限于通路分析,也可对基因表达、microRNA、SNP 微阵列等数据;代谢物组学和蛋白质组学的实验数据;以及一些小规模实验数据进行分析,还可以搜索到有关基因、蛋白质、化学品和药物的信息,并能为您创建一个系统的实验交互模型。该软件已在世界顶尖制药公司和科研机构中广泛使用,至今引用该软件发表的高水平科研文献已超过 14000 篇。


INGENUITY® Knowledge Base

  • 来自数千种杂志的生物学信息,内容涵盖蛋白质、基因、SNP、miRNA、复合物、细胞、组织、药物、通路和疾病信息等;

  • 200 名 PhD 每天阅读 300 种权威杂志全文和 3000 种杂志摘要;

  • 超过 500 万条生物学信息、130 万种互作信息、3 万种以上分子信息、800 种信号通路、代谢通路,经过多轮人工质控以及每周更新;

  • 每三个月对分析工具进行算法改进和功能更新,紧贴研究最前沿。


应用

IPA 帮助您揭示以下列领域数据背后的真相:

  • 转录组学: IPA 几乎可以解决所有转录组相关的生物学问题;

  • 生物标记物发现:从实验数据集中识别最相关、最可能的候选生物标志物;

  • miRNA 研究:结合筛选工具和 miRNA-mRNA 数据发现 miRNA 的潜在调控机制;

  • 毒物基因组学: 提供化合物毒性及安全评估信息,提供全面的药理作用、药物代谢途径和毒性作用机制信息;

  • 代谢组学:提供关键的调控信息,帮助理解代谢组数据中从细胞形态学到代谢作用机制的信息;

  • 药物再定位 : 通过药物对不同疾病组织的刺激结果的表达谱分析,发现已知药物的新应用领域;

  • 蛋白质组学:深入了解蛋白表达机制和相关生物学过程;

  • 因果网络分析:找到特定病理学状态下激活的基因,研究其成为候选治疗靶标的可行性。


IPA基础版 - 主要功能

1. 搜索功能,可搜索基因、化合物、通路、疾病或生物学功能、药物等的最新研究进展,辅助文献调研。

2. 分子网络构建,可探索分子同疾病或生物学功能之间的关系,构建分子互作网络(致病机制网络),并可美化网络图,可直接用于发表。

3. 分子活性预测,基于已有的分子调控相关的文献信息,对兴趣分子的上下游分子的活性进行预测。

4. 找寻分子网络的疾病关联,将分子网络中的分子同已有的疾病或功能进行映射,判定该分子网络主要同哪些疾病或生物学功能相关。

5. 分子网络中的分子自动按照亚细胞定位进行分布,后期作图可添加细胞结构,按照分子在行使功能时的位置,对分子进行排布。

6. 为分子网络添加表达信息,将分子的表达信息映射到您所构建的关键分子网络中/兴趣网络中,解释分子网络的调控机制。

7. Core分析,也是最重要最常用的一个功能,可实现RNA-seq、microarray、miRNA、蛋白质组学、基因组学、SNP或代谢组学数据的数据分析。分析结果涵盖:经典通路分析、上游调控因子分析(含机制网络分析)、下游调控效应分析、调控网络分析、自定义网络分析、自定义分子列表分析、Networks绘制。此外也可实现多组学数据的联合分析,如对miRNA数据和mRNA数据进行联合分析。

8. 多组比较的平行分析,可对处理后不同时间或不同剂量处理实验分别得到的Core分析结果,进行平行比较分析,给出通路、上游因子或下游效应的激活或抑制趋势。

9. Biomarker注释,快速找到您数据中的Biomarker分子,并注释上类型和文献证据。

IPA 高级分析模块

1. 因果网络分析,是对上游调控分析的一个扩展,可通过加入间接调控靶标分子的上游调控因子,对新的调控机制进行挖掘。用户可以快速查看与某一个特定疾病或表型相关的调控网络,然后权衡最感兴趣的和最相关的因果网络,并作为假设来解释观测到的生物学现象。

2. BioProfiler工具,过滤/鉴定致病分子。BioProfiler 就是一个数据库内容的搜索工具,可生成疾病、表型和生物学过程等的相关分子谱,列出所有与关键词相关的基因和化合物及其详细信息。这个工具的好处在于可生成直观和全面的输出结果,有助于用户基于研究问题去寻找、过滤和排序基因和化合物。这样用户就可以:聚焦于兴趣分子、寻找致病基因、过滤出符合特定遗传类型或源自特定物种的证据、探索相似疾病或表型的关联。

3. IsoProfiler,找寻RNA-Seq数据中的关键isoforms,在Isoform水平,发现表达异常的基因。

4. Relationship 导出,可用于进一步分析,如在Cytoscape中进行显示。

5. My findings工具,加入与您研究相关的文献,从而实现更多个性化分析。

IPA AM 分析模块

自动挖掘与您的IPA Core Analysis结果相似(或相反)的其它生物学数据,可辅助验证您的数据分析结果,或为共同的生物学机制提供更多视野。可将您的分析结果同其它已经建立好的分析结果进行匹配,或者是同来自公共数据库的上千个人和小鼠的表达谱数据分析进行匹配。

IPA中Analysis Match分析来自于超过6000多个高质量的人和小鼠的疾病和肿瘤数据(对SRA、GEO、Array Express、TCGA等数据的再加工)。这些数据集来自QIAGEN最新收购的公司OmicSoft,是DiseaseLand(遗传病基因组数据库) 和 OncoLand(基因组数据库)两个数据库的比较,代表了多种多样的比较,如disease和normal、treatment vs non-treatment等等。


公众号文章

联合分析肿瘤基因组和转录组数据,探索乳腺癌亚型2017-11-29点击查看
IPA:microRNA和mRNA数据联合分析2017-12-1
点击查看
IPA:探索中药的抗病机制2018-1-31
点击查看
IPA 助力癌症免疫疗法效果的异质性研究2018-3-28点击查看
IPA:使用二代测序和生物信息学方法鉴定参与成骨细胞老化的基因2018-6-6点击查看
IPA 功能模块集锦2018-7-7
点击查看
IPA 案例 - 神经损伤修复研究2018-7-31点击查看
IPA 在人类多型性胶质母细胞瘤研究中的应用2018-8-16
点击查看
IPA 在代谢组学数据分析中的应用2018-8-29点击查看

IPA中文使用教程

IPA 从头手动绘制通路图
点击阅读
IPA 数据准备、上传与分析
点击阅读
IPA Core Analysis 结果解读点击阅读
IPA - miRNA表达谱数据简明分析流程
点击阅读
IPA 磷酸化蛋白质组学数据分析点击阅读

发表在Nature及其子刊上的IPA引用文献

Alculumbre, Solana G., et al. "Diversification of human plasmacytoid predendritic cells in response to a single stimulus." Nature immunology 19.1 (2018): 63.

Zhao, Di, et al. "Synthetic essentiality of chromatin remodelling factor CHD1 in PTEN-deficient cancer." Nature 542.7642 (2017): 484.

Youngblood, Ben, et al. "Effector CD8 T cells dedifferentiate into long-lived memory cells." Nature 552.7685 (2017): 404.

Satoh, Takashi, et al. "Identification of an atypical monocyte and committed progenitor involved in fibrosis." Nature 541.7635 (2017): 96.

Patel, Shashank J., et al. "Identification of essential genes for cancer immunotherapy." Nature 548.7669 (2017): 537.

Nelson, Christopher P., et al. "Association analyses based on false discovery rate implicate new loci for coronary artery disease." Nature genetics 49.9 (2017): 1385.

Naik, Shruti, et al. "Inflammatory memory sensitizes skin epithelial stem cells to tissue damage." Nature 550.7677 (2017): 475.

Medugorac, Ivica, et al. "Whole-genome analysis of introgressive hybridization and characterization of the bovine legacy of Mongolian yaks." Nature genetics 49.3 (2017): 470.

McGovern, Naomi, et al. "Human fetal dendritic cells promote prenatal T-cell immune suppression through arginase-2." Nature 546.7660 (2017): 662.


产品功能英文图文介绍

IPA has a broad set of features that allow you to quickly understand and visualize your data

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Comparison Analysis

Quickly visualize trends and similarities across analyses using heat maps for Canonical Pathway, Downstream Effects, Upstream regulators and Causal Network Analyses. Prioritize by score, hierarchical cluster or trend.


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Human and Mouse Isoform View

Understand the structure and function of both human and mouse isoforms (splice variants). For each gene, toggle between human or mouse RefSeq and Ensembl to visualize associated isoforms on the Isoform View.


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Regulator Effects

Provides insights into your data by integrating Upstream Regulator results with Downstream Effects results to create causal hypotheses that explain what may be occurring upstream to cause particular phenotypic or functional outcomes downstream.


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Causal Network Analysis

The new Causal Network Analysis provides a comprehensive approach to identifying upstream molecules that control the expression of the genes in your datasets. Expanding beyond "direct" or "single hop" relationships between the upstream regulator and the target molecules in the dataset, Causal Networks uncovers networks of regulators that connect to the dataset targets. Focus on the networks that are of highest relevance by scoring the resulting causal networks against molecules or diseases/functions of interest.


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Disease View

Provides details associated with the disease or biological function such as molecules associated with that disease or function, known drug targets, drugs known to target those molecules, and more.


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Interactive Disease and Functions Nodes

Interactive visual exploration of causality between molecules and disease, function, or phenotypes from a network or My Pathway.


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BioProfiler

Quickly profile a disease or phenotype by understanding its associated genes and compounds. Identify genes known to be causally relevant as potential targets or identify targets of toxicity, associated known drugs, biomarkers and pathways.


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Upstream Regulator Analysis

Predict upstream molecules, including microRNA and transcription factors, which may be causing the observed gene expression changes.


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Mechanistic Networks

Automatically generate plausible signaling cascades describing potential mechanism of action leading to observed gene expression changes.


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Downstream Effects Analysis

Identify whether significant downstream biological processes are increased or decreased based on gene expression results.


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Canonical Pathways

Pathway Analysis, Canonical Pathways, Overlapping Pathways, Pathway Import and scoring. Determine most significantly affected pathways.

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Network Analysis

Build and explore transcriptional networks, microRNA-mRNA target networks, phosphorylation cascades and Protein-Protein or Protein-DNA interaction networks. Identify regulatory events that lead from signaling events to transcriptional effects.  Understand toxicity responses by exploring connections between drugs or targets and related genes or chemicals.  edit and expand networks based on the molecular relationships most relevant to the project.

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MicroRNA Target Filter

Reduce the number of steps it takes to confidently, quickly, and easily identify mRNA targets by letting you examine microRNA-mRNA pairings, explore the related biological context, and filter based on relevant biological information as well as the expression information.  The microRNA Target Filter in IPA provides insights into the biological effects of microRNAs, using experimentally validated interactions from TarBase and miRecords, as well as predicted microRNA-mRNA interactions from TargetScan. Additionally, IPA includes a large number of microRNA-related findings from the peer-reviewed literature.


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Tox Lists and Tox Functions

IPA-Tox uses Toxicity Functions in combination with Toxicity Lists to link experimental data to clinical pathology endpoints, understand pharmacological response, and support mechanism of action and mechanism of toxicity hypothesis generation.

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Molecule Activity Predictor (MAP)

Interrogate sub-networks and Canonical Pathways and generate hypotheses by selecting a molecule of interest, indicating up or down regulation, and simulating directional consequences of downstream molecules and the inferred activity upstream in the network or pathway


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Isoform View

Quickly move beyond statistical analysis of high-throughput RNA-Seq data to understand the biological implications of your data.  Identify significantly regulated isoforms in your experiment and determine their potential impact using information about functional protein domains and isoform-specific literature


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Gene and ChemView

IPA's Search & Explore capabilities offer researchers access to the most current Findings available on genes, drugs, chemicals, protein families, normal cellular and disease processes, and signaling and metabolic pathways.

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Biomarker Filter

Rapidly identify the best biomarker candidates based on biological characteristics most relevant to the discovery study.


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Path Designer

Transform your networks and pathways in IPA into publication-quality pathway graphics rich with color, customized text and fonts, biological icons, organelles, and custom backgrounds.  Expand and explore pathways using the high quality content stored in IPA.




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