Biological Analysis and Interpretation in IPA ®
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Comprehensive pathway and network analysis of complex 'omics data
Biological Analysis and Interpretation in IPA®
October 2013Gene Chen 陳冠文 Senior Specialist of GGA & IPA Certified Analyst
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How can I analyze existing data …
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Search multiple Websites
Read multiple articles
Mine Internal Databases
Wrangle multiple Excel sheets
Spend time in the lab
How Researchers Ask Questions Now
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Agenda
• Introduction to Ingenuity Pathways Analysis (IPA)
• Introduction to Ingenuity Knowledge Base
• Questions Arise During Experimental Process :
– How Can IPA Help You?
• Data Analysis & Interpretation in IPA
– Case study for Cross Platform Integration of Metabolomics and
Transcriptomics from a Diabetic Mouse Model• Q&A
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• Ingenuity Systems is a pioneer and leading provider in capturing information, structuring information, building tools that turn information into knowledge
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IPAIPA is an All-in-one, web-based software applicationEnables researchers to model, analyze, and understand the complex biological and chemical systems at the core of life science research
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IPA Applications:
• Disease Mechanisms
• Target Identification and Variation
• Biomarker Discovery
• Drug Mechanism of Action
• Drug Mechanism of Toxicity
Experimental Platform Supported :
• Gene Expression: (mRNA, miRNA, microarray platform, Next-gen sequencing, qPCR)
• Proteomics• Genotyping • Metabolomics Identifiers
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2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 thru Sept. 2013
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Peer-Reviewed Research Articles Citing IPA
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Peer-Reviewed Publications Citing IPA
9483
Full bibliography at www.ingenuity.com
Expression, Proteomics, SNP, Copy Number, RNAi,
miRNA,
Oncology, Cardiovascular Disease, Neuroscience,
Metabolic Disease, Inflammation/Immunology
, Infectious Disease
Basic, Translational, Drug Discovery & Development
Research
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史丹佛大學醫學圖書館
麻省理工學院
癌症研究中心
哈佛醫學院
明尼蘇達大學
杜克大學
美國國家衛生研究院 加州大學舊金山分校
匹茲堡大學
使用 IPA之研究機構
波士頓大學 德國癌症研究中心
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嬌生藥廠
惠氏藥廠 默克化學
輝瑞藥廠
必治妥藥廠
默克雪蘭諾生物製藥
傑克森實驗室
美國安進
阿斯特捷利康公司
轉譯基因組學研究所
賽諾菲安萬特藥廠 葛蘭素史克藥廠
使用 IPA之企業
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Introduction to Ingenuity Knowledge Base
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Ingenuity Expert FindingsFrom full text, contextual detail, experimentally demonstrated
Original sentence from publication
Ingenuity Expert Findings
nNOS overexpression mice showed
reduced myocardial contractility.
Transgenic nNOS in myocardium from mouse
heart decreases the contractility of
myocardium in left ventricle from mouse
heart.
Francisella organisms
efficiently induce IL-1beta
processing and release.
Francisella tularensis subsp. novicida U112 increases (in a time-dependent manner)
release of human IL1B protein from human
monocytes.
► Contextual details: Manual curation process captures relevant details
► Experimentally demonstrated: Findings are from full text articles – includes tables and figures
► Structured: Supports computation and answering in-depth biological questions in the relevant context
► High quality: QC’d to ensure accuracy
► Timely information: Weekly updates so up to date information is captured
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Ingenuity ContentExpert Extraction: Full text from top journals• Coverage of top journals, plus review articles and
textbooks• Manually extracted by Ph.D. scientists
Import Annotations, Findings: • OMIM, GO, Entrez Gene• Tissue and Fluid Expression Location• Molecular Interactions (e.g. BIND, TarBase)
Internally curated knowledge:• Signaling & Metabolic Pathways• Drug/Target/Disease relationships• Toxicity Lists
All findings structured for computation and updated weekly
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Ingenuity® Supported Third Party Information•Synonyms, Protein Family, Domains
– GO, Entrez Gene, Pfam
•Tissue and Biofluid Expression & Location– GNF, Plasma Proteome
•Molecular Interactions– BIND, DIP, MIPS, IntAct, Biogrid, MINT, Cognia, etc.
•miRNA/mRNA target databases– TargetScan, TarBase, miRBase
•Gene to Disease Associations– OMIM, GWAS
•Exploratory Clinical Biomarkers
•Clinical Trial and drug information– ClinicalTrials.gov, Drugs@FDA, Mosby’s Drug
Consult,..etc
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▶Helps you to find highly relevant and contextual information. ex: direction of change
▶Makes information computationally accessible and available for queries. ex:• Query over any type of
connections (molecular, cellular, organism)
• Make leaps from one concept to another and ask “Is there a path that might lead from A to B?
▶Ensures we are all talking about the same concept– regardless of your preferred nomenclature (semantically consistent). : ex : IL-1 beta increases regulation of COX1:
Which COX1? cyclooxygenase or cytochrome c oxidase – both are enzymes
THE INGENUITY ONTOLOGY
The Ingenuity OntologyStructures, translates, and integrates information
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Explore the Ingenuity Knowledge Base• Extensive: Leverages knowledge in one
place- Largest scientific knowledge base of its kind
with modeled relationships between proteins, genes, complexes, cells, tissues, drugs, pathways and diseases
• Structured: Captures relevant details- Scientific statements are modeled into
Findings (often causal) using the Ingenuity Ontology
• Expert Review Process: Checked for accuracy- Findings go through extensive QC process
• Timely: Frequent updates and up-to-date knowledge- Findings are added weekly
THE INGENUITY KNOWLEDGE BASE
Ingenuity ExpertAssist Findings
• High coverage (abstracts)• Timely• High-quality
Ingenuity Expert Findings
• From the full text• Contextual details• Timely• High-quality
Ingenuity Supported Third Party Information
Ingenuity Expert Knowledge
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ExperimentalPlatforms
Expression Arrays Proteomics Traditional Assays
Apoptosis Angiogenesis
Metastasis
Molecules Fas Vegf
CellularProcesses
DiseaseProcesses
Disease phenotype, physiological response
Cellular phenotypes, pathways
Molecular modules
Molecular “fingerprint” – cancer vs. normal cells
The Challenge Integrate – Interpret – Gain Therapeutic Insight from Experimental Data
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Expression Arrays Proteomics Traditional Assays
Apoptosis Angiogenesis
Cancer
Molecules Fas VEGFA
CellularProcesses
DiseaseProcesses
Educate in vivo, in vitro assays
Search for genes implicated in disease
Identify related cellular processes, pathways
Generate hypothesis of molecular mechanismbevacizumab
The Challenge Rapid understanding and interpretation of experimental systems
ExperimentalPlatforms
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Questions Arise During Experimental Process :
How Can IPA Help You?
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IPA Allows Scientists to Explore Biological Findings• Browse and Search the comprehensive Ingenuity Knowledge Base
– Gene/Chemical Search – Functions Search– Pathway Search
• Build Pathways; Build Hypotheses– Use Build Tools to explore which molecules have molecular interactions with
molecule(s) of interest– Use the Overlay tools to layer additional functional, drug and biomarker information
• Analyze Data; Interpret Cause and Effect; Discover the Biology– Gain insights into the Biological Functions, Canonical Pathways and Molecular
Networks that involve dataset molecules– Predict Transcription Factors & Upstream Regulators involved in transcriptional
changes and connect Regulators into Mechanistic and Causal Networks– Explore the Causal Effects of network changes
• Filter Datasets– Biomarkers and Biofluid expression– microRNA Target Filter for miRNA-mRNA relationships
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Search for Genes, Chemicals, Diseases, Functions, or Pathways
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Build Pathways; Build HypothesesSearch and Explore Examples• Tell me about my gene of interest – Insulin / INS
– What canonical signalling pathways does it appear in?– What are the transcriptional regulators of this gene?– What Ligand-Dependent Nuclear Receptors are regulated by these Transcription Factors?
• What GPCRs are involved in diabetes?– How do they interconnect?– What other biological processes (functions) are these genes involved in?– What are the molecular connections that link these genes to cytokines involved in obesity?– What drugs target these genes?
• Tell me about rosiglitazone?– What clinical trials are running with rosiglitazone?– How does rosiglitazone treatment affect the gene expression of these diabetes GPCRs
and obesity cytokines?
• What are the upstream regulators of the gene expression changes induced by rosiglitazone treatment?
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Build and Grow Networks of Molecules
Grow Upstream from AKT1 to kinases and phosphatases
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View Canonical Pathways and Link Additional Molecules
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Cause and Effect Analytics• Upstream Regulator Analysis (including Transcription Factors)
– Predicts which Transcriptional Regulators and other upstream molecules are driving gene expression changes and predicts which are activated / inhibited to explain gene expression observed in a dataset
• Create Mechanistic and Causal Networks– Connect upstream regulators into networks to help understand the
regulatory control of the gene expression seen– Use the Ingenuity Knowledge Base of causal relationships to predict
regulators that can be causally linked to the dataset molecules for unprecedented understanding of biological regulation
• Downstream Effects Analysis– Predict increase or decreases in downstream biological processes
(functions) and disease using the direction of change in your gene expression data
• Molecule Activity Predictor– Visualize the predicted activity of causally connected molecules in Networks
and Pathways
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Upstream Regulators and Mechanistic Networks
Upstream Regulator
Dataset Molecules
Regulator
Upstream Regulator
Dataset Molecules
Additional Upstream Regulators
Algorithm chains interacting regulators together to create a “Mechanistic Network”
Mechanistic Network
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Upstream Regulator AnalysisIdentify important signaling molecules for a more complete regulatory picture
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• Quickly filter by molecule type• Filter by biological context• Generate regulators-targets network to identify key relationships
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Mechanistic NetworksHow might the upstream molecule drive the observed expression changes?
• Hypothesis generation and visualization– Each hypothesis generated indicates the molecules
predicted to be in the signaling cascade
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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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Compare Canonical Pathways across analyses as a heatmap
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• Find molecules causally relevant to a disease, phenotype, or function
• Filter by specific genetic evidence or species
• Explore association with other similar diseases or phenotypes/symptoms leveraging the depth of the Ingenuity Ontology and the Human Phenotype Ontology
BioProfiler*: Find, Filter and Explore
*Available for additional cost
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miRNA Target Filter
miRNA Data
Molecule Type
Pathways (Cancer/ Growth)
88 data points
13,690 targets
1,090 targets
333 targets
?32
targets
mRNA↑↓↓↑
39 targets
Use Pathway tools to build hypothesis for microRNA to mRNA target association
Filter Datasets for Biomarkers or miRNA Targets
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SummaryIPA is a powerful data analysis and reference tool used by thousands of scientists worldwide
• Browse and search the comprehensive Ingenuity Knowledge Base
• Build pathways• Build hypotheses• Analyze and filter data• Discover and interpret cause and effect• Build enterprise knowledge base and results repository
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IPA: Unique Tools for Biological Analysis and Interpretation
Gene View & Chem View Summaries
Human Isoform Views
Interaction Networks
Biological Functions
Canonical Pathways
Upstream Regulators/ Causal Networks
BioProfiler
Build & Overlay Tools
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Data Analysis & Interpretation in IPA
- Case study for Cross Platform Integration of
Metabolomics and Transcriptomics from a
Diabetic Mouse Model
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Background• T2DM is one of the most common diseases of the western world• 150 million afflicted worldwide.• Animal models can aid discovery of biomarkers and clinical
compounds.• However no animal model reflects all aspects of the human form of the
disease.• Omics analysis across model systems could provide supporting
evidence of the value of those animal models.• Metabolic manifestations of diabetes associated with insensitivity to
insulin include:– Uncontrolled lipogenesis– Hepatic glucose production– Mitochondrial dysfunction – Altered protein turnover
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Dataset used• Integration of Metabolomics and Transcriptomics Data to
Aid Biomarker Discovery in Type 2 Diabetes. Connor S et al. 2009
• Metabolites identified from Urine of dB/dB mice compared to dB/+ controls using a Non Targeted NMR based approach.
• Transcriptomic analysis performed on tissue from liver, adipose and muscle using Affymetrix arrays
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dB/dB mouse model• Lack functional Leptin receptor (LEPR-)• Leads to defective leptin-mediated signal transduction.• Results in:
– Chronic overeating– Obesity– Severe hyperinsulinaemia– Hyperglycaemia and dyslipidaemia
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Aim of case study• What diabetes aligned phenotypes are highlighted by the
IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress.
• Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model?
• Are there differences in transcript and metabolite levels relevant to gluconeogenesis (Hepatic Glucose Production)?
• Can we identify putative serum/tissue biomarkers relevant to a dB/dB model?
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Aim of case study• What diabetes aligned phenotypes are highlighted by the
IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress.
• Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model?
• Are there differences in transcript and metabolite levels relevant to gluconeogenesis (Hepatic Glucose Production)?
• Can we identify putative serum/tissue biomarkers relevant to a dB/dB model?
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Metabolite upload and mapping
Includes some phase-1 and phase-2 type transformed metabolites.68 out of 74 metabolites mapped.
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Summary of metabolic analysis
Networks built around the metabolites also include key protein regulators of relevant functions and pathways• T2DM and Insulin
receptor signalling.• Hyperglycemia,
hyperinsulinemia and quantity of lipid
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Network 1
Dysregulated metabolites and network associated proteins highlight:• Lipid metabolism• Carbohydrate metabolism• Branched Chain Amino
Acid metabolism
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Key regulators of diabetes: ADIPOR2
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Aim of case study• What diabetes aligned phenotypes are highlighted by the
IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress.
• Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model?
• Are there differences in transcript and metabolite levels relevant to glucose metabolism?
• Can we identify putative serum/tissue biomarkers relevant to a dB/dB model?
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Liver
Muscle
Network 1- metabolite and transcript data
Inclusion of Liver, Muscle and Adipose transcript data
Adipose
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Aim of case study• What diabetes aligned phenotypes are highlighted by the
IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress.
• Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model?
• Are there differences in transcript and metabolite levels relevant to glucose metabolism?
• Can we identify putative serum/tissue biomarkers relevant to a dB/dB model?
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Carbohydrate metabolism
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TCA Cycle
Upregulated Citrate Cycle feeding Pyruvate into Gluconeogenesis
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Gluconeogenesis
Liver Muscle Adipose
Gluconeogenesis upregulated at transcript level in Liver, but not Muscle or Adipose
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Aim of case study• What diabetes aligned phenotypes are highlighted by the
IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress.
• Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model?
• Are there differences in transcript and metabolite levels relevant to glucose metabolism?
• Can we identify putative serum/tissue biomarkers relevant to a dB/dB model?
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Pre-established Clinical Biomarkers
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Putative novel biomarkers
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Putative novel biomarkers• Hyperglycaemia
– Glucose– Creatinine
• Hyperinsulinaemia– Glucose
• Diabetes– Creatine
• All detectable in Serum/Urine and phenotypically tagged to Diabetes and/or co-morbidities
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Summary• What diabetes aligned phenotypes are highlighted by the
IPA Metabolite analysis e.g changes in lipid, glucose and protein metabolism, mitochondrial dysfunction and oxidative stress. – Molecular networks and functions from metabolite data align to a
range of carbohydrate, lipid and protein metabolism functions and pathways
• Can we integrate metabolite and transcript data into a concerted analysis of a dB/dB model?– Ready alignment of array data and metabolite data identifies key
metabolic pathways perturbed across gluconeogenesis, citrate cycle and branched chain amino acid metabolism
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Summary• Are there differences in transcript and metabolite levels
relevant to glucose metabolism?– Gluconeogenesis upregulated in liver c.f. adipose and muscle
tissue. – Citrate cycle and Valine, Leucine and Isoleucine degradation
support this hypothesis• Can we identify putative serum/tissue biomarkers relevant
to a dB/dB model? – Established diagnosis (INS) and efficacy (Glucose) biomarkers for
T2DM, obesity & dislipidaemia – Putative biomarkers in metabolite profile (glucose, creatine &
creatinine) for diabetes, hyperglycaemia and hyperinsulinaemia
Office: +886-2-2795-1777#1169
Fax: +886-2-2793-8009
My E-mail: Genechen@gga.asia
MSC Support: msc-support@gga.asia
Thank you
歡迎與我們聯絡
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Predicting upstream regulators of a dataset
↑ ↓↓ ↑ ↑ Differential Gene Expression (Uploaded Data)↑
Predicted activation state of TF/UR: 1 = Consistent with activation of UR -1 = Consistent with inhibition of UR
1 -11 1 1 1
+++-
Note that the actual z-score is weighted by the underlying findings, the relationship bias, and dataset bias
• z-score is a statistical measure of the match between expected relationship direction and observed gene expression
• z-score > 2 or < -2 is considered significant
Literature-based effect TF/UR has on downstream genes
Every possible TF & Upstream Regulator in theIngenuity Knowledge Base is analyzed
++
=(7-1)/√8 = 2.12 (=predicted activation)
↓
-
1
↑
1
+
UR
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Single- vs. Mechanistic- vs. Causal NetworksLeveraging the network to create more upstream regulators
Single Upstream Regulator
Dataset Molecules
Regulator
Causal Network ScoringCasual connection to disease, function, or gene of interest
Upstream Regulator
Dataset Molecule
s
Transcription Factors Mechanistic Network of
Upstream Regulators
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Peer-Reviewed Publications citing IPASorted by Research Area, Through September 2013
Oncology27%
Inflammation/Immunology12%
Neuroscience9%
Infectious Disease7%
Bioinformatics/General Methods5%
Toxicity/Safety Assessment5%
Reproductive Biology4%
Hematology4%
Genetics4%
Metabolic Disease3%
Cardiovascular Disease4%
Developmental Biology3%
Stem Cell Biology4%
Wound Healing/Injury3%
Eye Disease2%
Nutritional Sciences2%
Renal Disorders1%
Aging1%
Bone Development1%
Liver Disease1%
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Peer-Reviewed Publications Citing IPA Sorted by Experimental Platforms Through September 2013
Gene Expression Profiling72%
Proteomics Profiling13%
RNAi4%
miRNA6%
Genotyping4%
Methylation Profiling3%
ChIP-on-Chip1%
Metabolomics1%
Next-Gen Sequencing2%
Chromatin Immunoprecipitation1%
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