E. Schlagberger – Scientific Information Services f. t. Biomedical-Section of the Max-Planck Society,
April 2016
IPA – Ingenuity Pathway Analysis from QIAGEN (USA)
Date: last updated August 2016
For the interpretation of ´Omics (Proteomics & Genomics) data
(IPA is updated on a weekly basis and has four releases per year.)
The “IPA-Core Analysis” quickly explores (in circa 7 minutes) relationships, mechanism,
functions, and pathways which are relevant for a dataset. The regulator analysis surfaces
molecules as Bio-Profilers identify molecules which are causally necessary to a disease or
phenotype. Observations of upstream- and downstream effects of biological processes in
IPA, support to create new scientific analyses of genes.
Special graphical features and evaluated literature:
The Ingenuity Knowledge Base database contains around 5.5 million findings
(context-based results, spring 2016) e.g. for specifies, diseases, mutation
types and relationships.
Findings from 3600 journals (abstracts) had been reviewed and included;
more than 300 known full text journals were curated manually, including
tables and figures. Publications were included from 1954 till today.
“Core Analysis”: In a first step Identifiers (like genes, proteins and RNA
sequences) are generated as genes. Furthermore each gene is supplemented
with a description, location, family and related drugs.
In a second step IPA determines the p-value(s), (means probability-value) of
the most known canonical pathways, up- and downstream regulators (down=
arrow in red, up= arrow in green) and diseases/biological functions.
Graphic representation:
Features & tools of the pathways visualize direct (designed as line) or indirect
(designed as broken line) interactions or the increase or decrease of
biological functions with different colors and create an interactive gene view.
Species-specific identifiers for:
- Mammalia: Human, Mouse and Rat.
- New: Dog, Zebrafish, Arabidopsis, Nematode, Fruitfly.
Choice of reference sets of the “IPA-Core Analysis”:
- Ingenuity Knowledge Base (Endogenous Chemicals only)
- Ingenuity Knowledge Base (Genes only)
- (User Dataset)
- Affymetrix
- Agilent
- Code Link
- Illumina
- Life Technologies (Applied Biosystems).
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E. Schlagberger – Scientific Information Services f. t. Biomedical-Section of the Max-Planck Society,
April 2016
Other Analysis options to Core Analysis:
- Tox Analysis
- Metabolomics Analysis
- Biomarker Filter
- Filter Dataset
- microRNA Target Filter
- IsoProfiler Beta
Formats for data-analysis:
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-
A dataset can be quickly uploaded as excel-file or text-file.
Data can be explored tab delimited as text-format or as excel-file.
A summary sheet of the analysis can be explored as pdf.
The generated images can be produced in jpg-format which is available for print in
300/600 dpi as for publications or for presentations in 96 dpi; in case of publication
please cite IPA!
General upload raw dataset:
-
-
Only one column may be designated as the ID column. A gene/protein ID is a unique
public or vendor identifier that represents a gene or protein. IPA takes items found in
the Gene/Protein ID column and attempts to map them to genes that exist in the
Ingenuity Pathways Knowledge Base.
Observation 1 to (…) is matched to the one and same ID; so that each observation
must have the same number, type and order of expression value columns.
A maximum of 20 observations in a single file may be uploaded into IPA.
Only one header raw is allowed (except for metadata rows).
“Networks” can only be composed in case of scores.
Markup languages for uploading the Users imported pathway workflows:
-
SBML (all versions and levels)
BioPax (all versions and levels)
SIF
XGMML
PSI-MI
The IPA-user can import his own created pathways. Furthermore the user can also modify
his pathways by adding molecules or generating new pathways’ interactions. The graphic
representation tool allows it to visualize these connections.
With the “compare”-Function the user can compare results of different analysis and
visualize as list intersections.
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E. Schlagberger – Scientific Information Services f. t. Biomedical-Section of the Max-Planck Society,
April 2016
An Expression Value is a numerical value that indicates the degree of activity or importance
of each gene. The types of values and their expected range are listed below:
Expression Value Types
Ratio1
Fold Change2
Log Ratio
p-value
False Discovery Rate, qvalue
Intensity
Other (normalized
around zero)
Expected Values
(0,+INF)
(-INF, -1) and (1, +INF)
(-INF, +INF)
(0, 1)
(0, 100)
(0, +INF)
(-INF, +INF)
Further Functions:
Under “My Projects”
-
“Libraries” with canonical pathways e.g. for comparison.
A “Share”-function allows the scientist to work with other IPA participants.
Overview supported identifiers for data upload:
Source: Qiagen_IPA_training_Max-Planck_Society.
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E. Schlagberger – Scientific Information Services f. t. Biomedical-Section of the Max-Planck Society,
April 2016
Further information & help, please go to:
http://www.ingenuity.com/products/ipa
http://www.ingenuity.com/science/platform/content-sources
http://ingenuity.force.com/ipa/IPATutorials?id=kA250000000TNA2CAO
http://ingenuity.force.com/ipa/articles/Feature_Description/Legend (keys as symbols for features).
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