ingenuity pathway analysis(ipa) · uniprot/ swiss-prot accession ucsc (hg18) kegg codelink...

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Copyright© 2019 GGA Corp. All rights reserved.

IPA資料庫使用介紹：如何查找資訊、繪製路徑及上傳dataset

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Clair Tsai 蔡宜庭

創源生技業務主任

Ingenuity Pathway Analysis(IPA)

Basic Training Course

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About GGA

CEO: Christopher Tsai, Ph.D. 蔡政憲博士

Established: Nov. 2008

Main Product & Service Areas:

Genetic Testing & Molecular Diagnosis

Scientific Informatics & Bio IT

IPO Date: September 17, 2012

Stock Ticker: 4160 (Taiwan OTC)

GGA is part of the BIONET Group

(訊聯生物科技)

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Molecular Science Center in GGA

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生物資訊

奈米與材料科學

企業品質管理系統

電子化實驗室管理

生命科學

化學資訊

分子視算中心MSC

世界一流資訊產品

軟體與資料庫

安裝與調校合規電腦

系統確效(CSV)

在地化服務使用者教育訓練

* FIRST *

* ONLY *

02-2795-1777#3012

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Copyright and Disclaimer

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Copyright © 2019 GGA Corp. All rights reserved.

• This presentation and/or any related documents contains statements regarding our plans or

expectations for future features, enhancements or functionalities of current or future products

(collectively "Enhancements"). Our plans or expectations are subject to change at any time at our

discretion. Accordingly, GGA Corp. is making no representation, undertaking no commitment or legal

obligation to create, develop or license any product or Enhancements.

• The presentation, documents or any related statements are not intended to, nor shall, create any legal

obligation upon GGA Corp., and shall not be relied upon in purchasing any product. Any such

obligation shall only result from a written agreement executed by both parties.

• In addition, information disclosed in this presentation and related documents, whether oral or written,

is confidential or proprietary information of GGA Corp.. It shall be used only for the purpose of

furthering our business relationship, and shall not be disclosed to third parties.

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QIAGEN Bioinformatics Solution

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Ingenuity Variant Analysis

QIAGEN® Clinical Insight

Interpret (QCI-I)

Ingenuity Pathway

Analysis

HGMD®

Biomedical

Genomics

Workbench

&

Biomedical

Genomics Server

Solution

QIAGEN® Clinical

Insight Analyze

(QCI-A)

QIAseq FX

Library Kits

QIAseq 1-step

Amplicon KIt

QIAseq Ultralow

Input Kit

REPLI-g® Single

Cell Kits

QIAseqTargeted

RNA Panels

QIAseq DNAseq

Panels v3

QIAamp DNA kits

PAXgene RNA/DNA kits

GeneRead DNA FFPE Kit

QIAamp Circulating NA Kit

exoRNeasy Kits

RNeasy Kits

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QIAGEN BioX Product Portfolio

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By NGS Data analysis workflow

1st Analysis 2nd Analysis 3rd Analysis

From NGS sequencers

Generate NGS reads sequences

*.fastq

Reads processingQC check, Adapter TrimmingMapping (Resequencing)Variant callRNA-seq analysisEpigenetics analysisMetagenomics analysis

Annotation interpretation

Variant analysis

Pathway analysis

QIAGEN Clinical Insight - Analyze (QCI-A / QCI-AU) QCI-Interpret (QCI-I)

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Why are we using IPA?

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What is the difference?

pathwayYour

dataset

Disease

function

Relationship

With other

Observation

What are the

relationship

between each

molecules?

What do they

relate to each

other?

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What can Qiagen bioinformatics products do for you?

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QIAGEN solution: NGS 數據串接分析處理

定量並找出顯著表現的基因

生物路徑分析 (IPA)

定序資料之序列比對

找尋上游調控因子

產生可能因子假說

資料與描述型資料匯入

(CLC)

辨識定序資料內之變異資訊 (IVA)

變異位點註解以及找出潛在性生物意義變異點

CLC序列分析頁面：視覺化基因表現圖譜與變異圖譜，並能整合在同一個瀏覽頁面內

CLC製圖：多功能分析

2D 階層式分群PCA與火山圖文氏圖

IVA變異位點分析：區分變異點以及其生物、路

徑至表型關係

IPA：了解上下游基因表現對生物途徑的影響

IPA：分辨轉錄異構物與生物意義

標準化&

統計分析

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IPA

IPA application:

• Biomarker finding

• Toxicity Functions

• Diseases regulation

IPA supported platform :

• Gene expression: qPCR analysisMicroarray RNA-Seq (NGS)microRNAmRNA

• ProteomicsPhosphoProteomicsNew

• metabolomics

IPA orthologous :Arabidopsis thaliana

Bos taurus (bovine)

Caenorhabditis elegans

Gallus gallus (chicken)

Pan troglodytes (chimpanzee)

Danio rerio (zebrafish)

Canis lupus familiaris (canine)

Drosophila melanogaster

Macaca mulatta (Rhesus Monkey)

Saccharomyces cerevisiae

Schizosaccharomyces pombe

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Supported Identifiers for Data Upload

Vendor

IDsGene Protein Transcript microRNA SNP Chemical

AffymetrixEntrez Gene

(LocusLink)GenPept Ensembl

miRBase

(mature)

Affy SNP

IDs

CAS

Registry

Number

Agilent GenBank

International

Protein

Index (IPI)

RefSeqmiRBase

(stemloop)dbSNP HMDB

Life Tech

(ABI)

Symbol-

human

(HUGO/

HGNC, EG)

UniProt/

Swiss-Prot

Accession

UCSC

(hg18)KEGG

CodelinkSymbol-

mouse (EG)

UCSC

(hg19)

PubChem

CID

IlluminaSymbol- rat

(EG)

Ingenuity GI Number

UniGene

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Get more complete mapping during dataset upload!

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Peer-reviewed publications citing Ingenuity apps

>17,000 publications that used IPA -- and growing!

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Multi-Omics Analysis using IPA

Integrate and compare genomics, transcriptomics, proteomics and metabolomics data to see the big picture on your focus research

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Multi Experiment

Phosphorylation

Protein

Expression

mRNA

Expression

miRNA

Expression

+

+

+

IPA

analysis

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用IPA來發掘實驗資料中各類型關係

gene/protein/metabolite/drug

gene/protein/metabolite/drug

Biomarker

pathway Toxicity

Function

Disease

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Capturing molecular information from the literature

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Canonical Pathway

Drug

Upstream TF

Biomarker

DiseaseFunction

Downstream gene

Gather this type of information for nearly every gene. Inferences can be made from the resulting networks.

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How IPA content is different: context and direction of effect

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Third Party Database

Synonyms, Protein Family, Domains

– GO, Entrez Gene, Pfam

Tissue and Biofluid Expression & Location

– GNF, Plasma Proteome

Molecular Interactions

– BIND, DIP, MIPS, IntAct, BiogridNew, MINT, Cognia, etc.

miRNA/mRNA target databases

– TarBase, TargetScan, miRecords

Gene to Disease Associations

– OMIM, GWAS databases

Metabolomics

– HumanCycNew

Clinical Trial information

– ClinicalTrials.gov

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Gene View

Summaries

Human & Mouse

Isoform Views

BioProfiler

Diseases & Bio

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Build & Overlay

and Interactions

Gene View

Summaries

Unique Tools for Biological Analysis and Interpretation

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Gene View

Summaries

Human & Mouse

Isoform Views

BioProfiler

Diseases & Bio

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Build & Overlay

and Interactions

Human & Mouse

Isoform Views


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Gene View

Summaries

Human & Mouse

Isoform Views

BioProfiler

Diseases & Bio

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Build & Overlay

and Interactions

BioProfiler


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Gene View

Summaries

Human & Mouse

Isoform Views

BioProfiler

Diseases & Bio

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Build & Overlay

and Interactions

Diseases & Bio

Functions


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Gene View

Summaries

Human & Mouse

Isoform Views

BioProfiler

Diseases & Bio

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Build & Overlay

and Interactions

Canonical

Pathways


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Gene View

Summaries

Human & Mouse

Isoform Views

BioProfiler

Diseases & Bio

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Build & Overlay

and Interactions

Upstream Regulator

Analysis


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Gene View

Summaries

Human & Mouse

Isoform Views

BioProfiler

Diseases & Bio

Functions

Canonical

Pathways

Upstream Regulator

Analysis

Build & Overlay

and Interactions

Build & Overlay

and Interactions


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IsoProfiler

Multi-omics

Overlay

Comparison

Analysis

Phosphorylation

Analysis

Analysis Matchw/ OmicSoft Lands


IsoProfiler

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IsoProfiler

Multi-omics

Overlay

Comparison

Analysis

Phosphorylation

Analysis



Multi-omics

Overlay

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IsoProfiler

Multi-omics

Overlay

Comparison

Analysis

Phosphorylation

Analysis



Phosphorylation

Analysis

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IsoProfiler

Multi-omics

Overlay

Comparison

Analysis

Phosphorylation

Analysis



Comparison

Analysis

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IsoProfiler

Multi-omics

Overlay

Comparison

Analysis

Phosphorylation

Analysis



Analysis Match*

w/ OmicSoft Lands

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Entry Points in IPA

Biological Questions

Search Experiment Data Custom Pathway

Expression arrays

Protein arrays

Mass spec

2D Gel electrophoresis Networks

Core

IPA-Tox

IPA-Biomarker

IPA-Metabolomics

Bio/Tox Functions

Diseases/Disorders

Canonical Pathways

Upstream regulators

Mechanistic/Casual Network

Interaction Network

Communicate & Collaborate

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Unprecedented discovery, together

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TCGA

GEO

Array Express

SRA

Array Suite

Curation, Processing, & QA

Journal articles

OMIM

Clinical Trials

Etc.

COSMIC

MGD

Etc.

Curation & QA

Datasets integrated into

OmicSoft LandsCurated

Findings

OncoLand

DiseaseLand

49,000+

Expression

comparison

datasets• Biological analyses of

each dataset

• Compare your analysis

to all OmicSoft analyses

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Analysis Match

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截至今日，本次更新增加了19,000組分析完成的數據至Analysis Match分析功能中，您可以在Analysis Match分析模組當中，比較您的實驗資料與外部資料庫如TCGA、LINCS等，在不同的癌症類型，疾病類型與實驗組中分析其相似及相異關係度。

並於七月中旬，會自LINCS（NIH Library of Integrated Network-Based Cellular Signatures）再添加大約28,000個分析組，總數將超過49,000個(相較於上季僅有8100+個分析組)

Your own

Dataset

Analysis

from your

dataset

Donor

Datasets

Analysis from

donor datasets

Compare

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Analysis Match

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截至今日，本次更新增加了19,000組分析完成的數據至Analysis Match分析功能中，您可以在Analysis Match分析模組當中，比較您的實驗資料與外部資料庫如TCGA、LINCS等，在不同的癌症類型，疾病類型與實驗組中分析其相似及相異關係度。

並於七月中旬，會自LINCS（NIH Library of Integrated Network-Based Cellular Signatures）再添加大約28,000個分析組，總數將超過49,000個(相較於上季僅有8100+個分析組)

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Training

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Login ID: [email protected]

Password: tsai1234

Dataset: https://goo.gl/mx2nuj

https://reurl.cc/b5bZ6

https://goo.gl/mx2nuj

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Searching

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•Genes and chemicals

•Diseases and Functions

•Pathways and tox lists • Advanced search: Limiting results to a molecule type, family

or subcellular location

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Scales for Gene/Chemical, Disease, Pathway

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Gene

Chemical

Set of Genes and Chemicals

associated with Disease/Function

(without relationship information)



(with relationship information)

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Gene View

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• Explore sample-level human tissue expression through OmicSoft Land Explorer

Now you can examine detailed expression patterns across human tissues directly

from IPA’s Isoform Views. IPA now offers access to a lite version of OmicSoft Land

Explorer. With this new feature, you can provide interactive plots of gene

expression in 51 different human tissues from the GTEx project, for both gene level

and individual splice variants. You can filter the view for a particular tissue, or filter

on metadata, such as tissue donor age or gender. You can also download the

detailed sample-level expression data for the gene.

https://gtexportal.org/home/

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Gene View

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• View of human isoform-level expression in human tissue samples for FABP4

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Key Terminology

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• Finding:6.6 million

– A single piece of evidence from a literature source or database in the Ingenuity Knowledge Base

– Includes context of the fact such as experiment type, species, tissue/cell location, etc.

• Canonical Pathway (Signaling and Metabolic)

– Are generated prior to data input, based on the literature

– Do NOT change upon data input

– Do have directionality

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Key Terminology

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Networks:

Generated de novo based upon input data

Do NOT have directionality

Canonical Pathways (Signaling and Metabolic):

Are pre-built and generated prior to data input, based on the literature

Do have directionality (proceed “from A to Z”)

My Pathways and Path Designer Pathways:

Custom built pathways manually created based on user input

Relationship Type:

An interaction between two molecules in IPA (seen as a line)

Direct (physical contact) and Indirect (do NOT require physical contact)

Node

Single molecule in Network such as Gene, Chemicals, Disease, and Pathway

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Scales for Gene/Chemical, Disease, Pathway

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Gene

Chemical



(without relationship information)



(with relationship information)

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Build and Grow Networks of Molecules

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Grow Upstream from AKT1 to

kinases and phosphatases

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Grow

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• Using one molecule of interest as a starting point, the Grow feature allows you to find (and add) other molecules of interest to a pathway. You can also grow to functions and diseases.

B

A

C

starting point

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How Path Explorer Works

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• Calculates the “Shortest Path” between 2 molecules or 2 sets of molecules

• If 2 molecules/sets don’t have specific connections in IPA, Path Explorer will find how many and which molecules can be added to this pathway to create the shortest path

– Shortest Path (n)

– Shortest Path + 1 (n+1)

A n =1

n+1

BA

B

n =0

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Connect

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• Connect allows you to see specific molecular interactions between nodes or groups of nodes.

BA

C

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Overlay

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• Overlay tools allows you to compare and contrast different kinds of information within networks

BA

C

A

A

A

Expression Level

Disease

Drug target

Biomarker

C

C

Molecular Activity Prediction

Canonical Pathway

Lists

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Path Designer

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• Use Path Designer to transform your networks and pathways in IPA into publication quality pathway graphics rich with color, customized text and fonts, biological icons, organelles, and custom backdrops.

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準備IPA分析用的Dataset

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ReplicatesAverage必須有一欄放入ID Other observations

(Comparison)

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準備IPA分析用的Dataset

重複性實驗的數值平均、p-vlaue或fold-change等統計計算，要先在IPA分析之前完成。

將實驗資料用 Excel 表格檔案儲存，檔案裡面只能有一個Sheet存在。

Excel Sheet當中必須要有一欄是列出分子的ID (如Gene Symbol, Refseq number,

Uniprot number, HMDB等常用命名皆支援)

每個Excel Sheet 最多可以放入 20個 observations (即20個實驗變因的資料欄的意思)

每個Observation可以有3個不同的表現值種類 (ex. p-Value，fold-change等)

表格欄位最上方只能有一個Head row (首行)

資料上傳到IPA後，可以在cut-off 值欄位進行設定，讓使用者決定門檻來決定表現顯著

有差異的生物分子。意味著原始實驗資料中有些分子的數值不夠顯著，可以用cut-off值

作為門檻排除於分析運算中。那些通過cut-off值的分子們在IPA中稱之為Analysis-

Ready Molecules。

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Get more complete mapping during dataset upload

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IPA can now improve your success of mapping identifiers in your datasets by evaluating

more than one column of gene or chemical IDs.

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Live Demo

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Statistical Analyses Used in IPA

z-score

• Predicts Activation or Inhibition

• Correlation between what is known (IPA Knowledge Base) and

your expression data

p-value of overlap

• Null hypothesis: No overlap between molecules from dataset and

disease/function/upstream regulator/pathway.

• Calculate using the right-tailed Fisher’s Exact Test.

• Significant p-value ≤ 0.05

Note: Benjamini-Hochberg correction for multiple testing can be

implemented in some cases

p-value of overlap

z-score

1-1 1 1 0 11 1 1

TR

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Network types in IPA

Upstream Analysis

Dataset Molecules

Upstream

Regulator

Dataset Molecules

Other upstream

regulators

Mechanistic Network of Upstream Regulators

Regulator Effect Network

Any

Diseases / functions

Interaction Network

Dataset Molecules

Function Analysis

Diseases / functions

Dataset Molecules

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IPA 分析結果

Summary: 將顯示前五名各頁籤之分析結果

Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway

Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules，以及根據研究文獻預測它們是否是被啟動或是被抑制。

Disease&Function : 了解實驗結果在各分析疾病調控上之結果

Networks : 呈現實驗資料中的分子間的網路關係。並且可以利用Build Tool與Overlay Tool進行延伸與知識的拓展，以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。

Regulator effect:將上下游之調控路徑整合

Analysis Match:比對外部實驗組資訊與實驗之調控相似相異性

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Interpret Downstream Biological Functions

Identify over-represented biological functions and predict how those

functions are increased or decreased in the experiment

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Downstream Effects Analysis介紹

Click to show bar chart

每個矩形可以經由點擊進入下一層分區: Mid-

level functional category (level 2) 與Specific functions (level 3)

方塊代表受實驗影響的生物功能與疾病，顏色可以用[Color by]指定是z-score, -log (p-value), 或是 # of genes上色。如果是用z-score上色的話，藍色區塊是預測被減低的功能，橘色則是此功能會增加。是根據實驗資料做出的演算。

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IPA 分析結果








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Canonical Pathway Analysis

點擊特定Canonical Pathway名稱上方的Bar條，下方視窗會出現dataset 中有參與組成該 pathway的分子 ID

點擊“Open Pathway” 則可以展開那個Canonical Pathway，實驗資料中的分子會用顏色提示。

click

Canonical Pathways結果標籤:受影響的Signaling Pathway與Metabolic Pathway 依照顯著性用條狀圖排列

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IPA 分析結果








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TR effect on downstream genes (Literature)

N = 8 genes

Upstream Analysis Activation z–score

Statistical measure of correlation between the transcription regulator (TR) and resulting gene expression

Actual z-score can be weighted by relationship types, relationship bias, data bias

z-score > 2 or < -2 is considered significant

1-1 1 1 0 11 1 1

Differential gene expression(Uploaded Data)

TR

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IPA 分析結果








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Concept of “Regulator Effects” - Spring 2014Hypotheses for how activated or inhibited upstream regulators cause downstream effects on biology

Upstream Regulators

A

Molecules in the dataset

Disease or

Function

Downstream Effects Analysis

Algorithm

First iteration

Simplest Regulator Effects result

A

Disease or

Function

Displays a relationship between the

regulator and disease/function if it exists

Causally consistent networks score higher

The algorithm runs iteratively to merge additional regulators with diseases and functions

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Concept of “Regulator Effects”

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IPA 分析結果








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How Networks Are Generated

1. Focus molecules are “seeds”

2. Focus molecules with the most interactions to other focus molecules are then connected together to form a network

3. Non-focus molecules from the dataset are then added

4. Molecules from the Ingenuity’s Knowledge Base are added

5. Resulting Networks are scored and then sorted based on the score

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Live Demo

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歡迎與我們聯絡Office: +886-2-2795-1777#3012

Fax: +886-2-2793-8009 EXT 1022

My E-mail: [email protected]

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ingenuity pathway analysis(ipa) · uniprot/ swiss-prot accession ucsc (hg18) kegg codelink...

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