Land Explorer for QIAGEN® Ingenuity® Pathway Analysis (IPA®)

Connect the dots: Deriving
insights from thousands of‘omics datasets

Sample to InsightWith the rise of ‘omics technologies and large-scale consortia projects, biological systems are studied at an
unprecedented scale, generating enormous numbers of heterogeneous and often large datasets. These
high-throughput technologies enable ‘omics studies that evaluate thousands of genes and millions ofvariants. Approaches to combine many thousands of datasets spanning multiple ‘omics types provide a morecomprehensive understanding of complex genotype-phenotype associations than the analysis of a single
dataset alone. To make this easier to do, Land Explorer for QIAGEN IPA brings together the data for overhalf a million ‘omics samples into one intuitive interface.

It can be burdensome to compile, store,

a conceptual challenge but a practicalsearch, analyze and visualize all relevant

hurdle in the daily analysis of ‘omics data.

information from massive repositories ofEven with sufficient resources to download‘omics experiments for medical conditions,and store the data, it takes a combinationsuch as how gene regulation changesof biology expertise to properly annotate

by tissue, disease or treatment condition.

various features related to the samples

Mining data and the literature can identify

or subjects from which the data was col-some key links; still, data relevant tolected, bioinformatics skills for reanalyzing

research is often buried or hard to find,datasets to be consistent with one another,limiting the identification of meaningful

and further resources to publish them toconnections. Researchers often end up

researchers in a comprehensible interface.

‘cherry picking’ the studies they use forcomparisons, leading to results that may
be biased in favor or against a particularassociation.

“The quintessential inquiry in the genomics
era,” postulates Dr. Ben Darbro, M.D.,
Ph.D., Associate Professor and Director of
the Shivanand R. Patil Cytogenetics andYet, to explore these highly valuable but

Molecular Laboratory at the University

complex datasets constitutes not onlyof Iowa, “is, ‘Here’s my data, give me“I use IPA quite a bit because there’s nothing that has
more information. There’s nothing out there that has thiskind of knowledge base behind it.”Dr. Ben Darbro, M.D., Ph.D., Associate Professor and Director of the
Shivanand R. Patil Cytogenetics and Molecular Laboratory at the University of Iowa

2 	Connect the dots

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an answer’. That’s always been the

Ultimately, it is difficult and time-consum-question ever since we started to

ing to analyze, interpret and contextualize

develop ‘omics techniques. One gene, one

findings from ‘omics experiments, which

hypothesis, one system – that’s pretty easy to

slows down scientific discovery. Therefore,

interpret. But anytime you get into thethere is a need for robust and advanced‘omicsproteomics,

analysis strategies to harness the valuetranscriptomics, genomics—it’s really hardof these comprehensive high-throughput

to focus on just one thing in an analysis.

data, to make it easier to identify true

Not to mention, you wouldn’t want to do

associations and reduce the number ofthat, because you would be wasting all thefalse associations.

space—whether

rest of this information.”

Land Explorer for QIAGEN IPA:
A massive collection of ‘omics data at your fingertips
Land Explorer for QIAGEN Ingenuity

IPA to discover its tissue or cell expression

Pathway Analysis (IPA) is QIAGEN’s web-in other samples or datasets. For biologists

based platform that helps life sciencethat would like to draw on the vast number

researchers perform insightful data analysisof ‘omics data types, Land Explorer is aand interpretation. It enables the under-

great resource that also serves as a central

standing of experimental results within thelocation to visualize these data in various

context of various biological systems, andways, and works through web browsers,

empowers contextualizing findings fromallowing the exploration and visualiza-

QIAGEN IPA against more data than could

tion of large amounts of content in one

ever be analyzed by a single person.

place (Table 1), without having to installThis powerful tool dramatically accelerates

separate software platforms. Not only does

data interpretation, hypothesis generation

Land Explorer free up time and resources

and, ultimately, scientific publication.

to create impactful visualizations withLand Explorer for IPA enables you toaccess a vast amount of information to
identify gene signatures and explore ‘omics
data for individual genes and to visualize
expression correlation across multiple
genes. You can jump from a gene ofinterest identified in an analysis in QIAGEN

Connect the dots

public datasets, but it also helps you
develop and validate

research

hypotheses

andprovides evidence
that can be used
for publication.

10/2020	

“The number one thing thatstands out in QIAGEN IPA
versus any other solution is thatit’s intuitive and easy to use.“3

To provide comprehensive systems biology

datasets,

includingRNA-seq

analysis, Land Explorer facilitates thetranscript expression, RNA-seq geneinterrogation of findings in real time against

fusion expression, expression microarrays,

a library of over 500,000 samples acrossmethylation

tissues, treatments and diseases fromreverse phase protein lysate microarray

thousands of ‘omics experiments to identify(RPPA), somatic mutations, copy number

unique or common biological features.

alterations and curated metadata/survival

You can explore a wide variety of ‘omics

data (Table 2). Land Explorer lets you

microarrays,

gene/

miRNA-seq,

survey interactive plots of gene expression
Table 1. Data sources for Land Explorer*in 51 human tissues for gene-level andLand NameLand Type

Sample CountNo. Data Types

GTEx

BodyMap

17783

7

Blueprint

BodyMap

629

6

HPA

BodyMap373

8

expression across multiple parameters

HumanRNAi

Cell line

1298

1(Figure 1), including cell type, cell line,

CellLine_GSK

Cell line

1289

2

tissue, disease, treatment, sample type

CCLE

Cell line

1054

14

CellLine_Pfizer

Cell line399

2

and project or clinical metadata. Besides

HumanCRISPR

Cell line

342

1

CellLine_NCI

Cell line299

3

correlation views let you integrate different

LINCS

Cell line

1152101

data types for samples with, for example,

HumanDisease

Disease15899610

both gene expression data and copy

MouseDisease

Disease52422

9

RatDisease

Disease9667

8

number variation data. With this solution

TCGA

Oncology

22687

14

ICGC

Oncology

11805

10

expression, mutation status, copy number

METABRIC

Oncology4129

2

and other details from any gene of interest

TARGET

Oncology3942

12

using thousands of statistical comparisons

expO

Oncology

2159

1

BeatAML

Oncology

666

8from a consistent data analysis, processing,

CGCI

Oncology

110

6

OncoGEO

Oncology

63816

11

to make true apples-to-apples comparisonsindividual splice variants and visualize
patternsofdifferential

transcript

viewing a single data type in Land Explorer,for ‘omics data analysis, you can inspectcuration and statistical pipeline (Figure 2)

Hematology

Oncology

29492

12

between different studies. Such a featTumorMutations

Oncology

21508

4

is simply not possible using other data

ClinicalOutcome

Oncology

11405

1

Pediatrics

Oncology

11030

10

repositories.

MetastaticCancer

Oncology

4091

3

OncoMouse

Oncology

1733

8*As of September 21, 2020

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Table 2. Data types (and respective sample counts) available in Land Explorer• RNA-seq gene/transcript expression, exons, junctions, fusions, mutations (70.000+)• Expression microarrays (390.000+)• Methylation microarrays (26.000+)• miRNA-seq (16.000+)• Somatic Mutations, copy number alterations (121.000+)• Curated metadata/survival data

B

A

C

Figure 1. Overall sample distribution from Land Explorer. The Sample Explorer lets you browse data across all available lands based on collections or
individual lands for samples or tissues of interest and combine all oncology- and disease-focused studies in one simple interface. The Sample Explorer
lets you survey 548,000 ‘omics samples from OncoLand and DiseaseLand, including 15 consortium databases (TCGA, LINCS, GTEx, CCLE, ICGC,TARGET and more) containing 184,000 samples, 6000 independent projects, over 331,000 samples from individual publications, 33,000 samples in
specialty Lands and over 60,000 statistical comparisons.
(A) Tissue distribution in Land Explorer. The tissue with the highest number of samples is blood, with over 150,000 samples and is followed by thecentral nervous system, which has 75,000 samples. (B) Land Explorer has multiple different experimental dataset types for a given sample set, including
DNA-seq, RNA-seq, CNV, expression microarray and protein. (C) Tree block view of different sample types. The tree block view lets you filter down tosamples or tissues of interest, identify which Land the samples are present within, and then quickly go into that Land that has already been pre-filtered
to have the specific tissues that you would like to further investigate. Green: body map samples; turquoise: oncology-related samples; dark blue: noncancer related disease samples; purple: cell line samples; light blue: non-categorized (other) samples.

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5Case study: Identify genetic determinants of diseasecritical for tumor suppression.” Tools in

Dr. Darbro’s research program focuses on

the intersection between these two worlds,

the genetic determinants of disease andand inform one another. Dr. Darbro says,

is split into two research groups: germline“I think that’s probably the real strength

disorders and cancer. “I’m really inter-of looking at both germline disorders andested in that intersection [between germline

cancer at the same time—that some of thedisorders and cancer], and I think one

pathways and tools that you become quiteof the main areas of intersection is at acompetent with, or developed, for one

pathway level,” Dr. Darbro says. “Yougroup can also be used to study the otherthink about things like conditions that leadgroup.”

to autism spectrum disorder or tuberous

With this framework, Dr. Darbro’s teamsclerosis or other germline disorders, butalso performs gene discovery in neuro-

they’re impacting pathways that are also

developmental disorders like intellectual

IPA, such as Land Explorer, help reveal

Sample metadata

MetaData
Curation

Data
Selection• Multiple curators per project• Control vocabulary (CV)

Project metadata

Data
Analysis

Comparison/results

Comparison metadataLand
Database• Metadata QC• Stats modeling• Add new projects– driven by client requests

Raw Data

Processing• QC, project filtering• Download raw data and meta data

Sample signal• Post analysis QC• Project sources are publicly
available data
– GEO, SRA, ArrayExpress,
consortium repositories• Generate comparisons• Signal extraction• Normalization• Sample level QC

Figure 2. Land Data processing workflow. The ‘omics datasets in Land Explorer are curated and re-processed, adding extra value to each dataset. The
QIAGEN team invested over 42,000 person-hours in expert curation to curate the more than 500,000 samples contained in Land Explorer. Furthermore,
it has taken an estimated 835,000 CPU-hours (or 95.3 CPU-years) from SRA download to full analysis, to analyze the 83,500 RNA-seq samples
contained in Land Explorer. Finally, performing statistical analyses on the 325,000 samples used in comparisons converts to 9,750 person-hours ofstatistical analysis.

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disability, autism spectrum disorder andIn

epilepsy. “We’re able to take a list of genes

discoveringand start to try to see if there’s a relation-

genes are regulated

ship between them. One of the things wein

did after running an IPA analysis on genes

respond to gene

was use Land Explorer to try to figure out amutations,little bit more about some of the upstream

Darbro’s laboratory

regulators and the canonical pathways thatuses

we found to be enriched. We try to branch

QIAGEN IPA, suchout and say, ‘all right, well, what otheras Land Explorer

studies have seen this as well? What otherandstudies have these pathways or these

Match, to go one

upstream regulators enriched?’”step

To take the exploration of these genes andthe underlying biology to the next level,
Dr. Darbro’s research groups look at thefunctional relevance of these genes
and associated networks and identify
biomarkers. “Not only is there an aspectof exploring those genes and how they
directly interact with one another, but also
understanding what functional networks
they’re a part of. If we have expression

addition

disease

tools

to
how
and
Dr.
in

“Anytime you get into the‘omics space—whether
proteomics, transcriptomics,
genomics—it’s really hard
to focus on just one thing inan analysis. Not to mention,
you wouldn’t want to dothat, because you would be
wasting all the rest of thisinformation.”

Analysisfurther

toexplore drug treatments. “The other way
we use IPA—that I think is probably how
we’re going to continue to use it heavily—is in the drug selection area. There’s no
shortage of drugs out there. There areentire screening facilities that exist where
you send them some cells and they test athousand drugs to see what they do. But
using tools in IPA is really a much moretargeted way to find drug candidates.”

data and copy number data, we canuse IPA’s statistical framework to basicallysay that this particular copy number
variant is functional because all of thegenes in it change their expression in
the right direction. ”Dr. Darbro says these
CNVs are often found in both germline
diseases and cancer. “They’re the kind ofthings that make for decent biomarkers
because they’re probably actually involvedin the pathogenesis...as opposed to the
myriad of ‘bystander lesions’ that show up.”Connect the dots

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7

How Land Explorer for IPA can help advanceyour research
Explore differential expression inhuman disease studies
Land Explorer for QIAGEN IPA tremendously accelerates how you examine gene
expression in studies of human diseases
to identify gene signatures and discoverbiomarkers. For instance, the gene
HNF1A is a transcription factor highly
expressed in the liver and regulates theexpression of several liver-specific genes.
In Land Explorer, there are many different

comparisons where HNF1A is up- or
down-regulated. Filters enable you to
focus on specific human diseases, suchas liver diseases (Figure 3). This reveals
several studies that found significant
differential regulation of HNF1A, such as
those in hepatitis C, hepatitis B and acuteliver failure. You can further survey thesestudies in more detail, such as all the genes
differentially regulated in those studies.
This ability to start with a specific gene ofinterest, then focus on a specific tissue,

Figure 3. Comparison of the HNF1A gene expression by disease state. Statistical comparisons for differential expression of HNF1A in liver diseases
using a bubble plot. HNF1A was found to be down-regulated compared to the control samples in acute liver failure, but different studies found HNF1A
to be either up-regulated or down-regulated in hepatitis C, depending on the experimental conditions. X-axis represents differential gene expression of
HNF1A by Log2 fold change. Y-axis represents individual disease states. Bubble size represents the significance (p-value) from the differential expression(DESeq2 for RNA-seq and General Linear Model for microarray) of HNF1A. Larger bubbles represent greater significance. Selecting dot(s) in this view
will populate a Details table at the bottom of the view showing some key metadata from the comparison(s).

8 	Land Explorer for QIAGEN IPA

10/2020and then to include other significant genes,
allows you to expand gene signatures to awhole set of genes in minutes. These genes
can then be explored further in QIAGEN
IPA to understand the biological underpinnings of the studies.

Discover survival rates for cancer
gene mutations
Land Explorer for IPA enables the exploration of mutations for a particular gene, toget answers about how these mutations
affect the expression of other genes, what

Explore gene expression innormal and disease cell types
When you want to assess ‘omics data,such as gene expression, from different cell
types, major difficulties include finding theright datasets to analyze, and processingthese datasets to make them comparable.
This can be resource-intensive, both interms of funds and time, and becomes more
and more difficult with the number of datasets that you include, since each one must
be processed to make it comparable. Land
Explorer for QIAGEN IPA helps you examine gene expression in normal and disease
samples of specific cell types more efficiently. For example, you can explore gene
expression in immune cells in both normal
and tumor samples. Land Explorer allows
you to visualize differences in expression
across thousands of different samples with
RNA-seq data, and filter down to the tissues (for example, blood, esophagus andnerves) or by cell types (normal or tumor

diseases they may influence and how theymight affect patient survival. For example,
the tumor suppressor gene PTEN is found in
most tissues, and mutations of this gene are
involved in the development of many cancers and other diseases (Figure 4A). Land
Explorer incorporates several public datasets to facilitate ‘omics analysis and improve
the level of generated insights. By featuringa collection of highly curated ‘omics data,
Land Explorer streamlines the analysis process to quickly focus efforts toward candidates with clear biological relevance tohelp you identify and explore contextual
data in seconds. Using Land Explorer, you
can survey the mutation prevalence acrossall the cancers within The Cancer Genome
Atlas (TCGA), then subset samples froma particular cancer with a PTEN mutationfrom samples that do not. From here, Land
Explorer can generate the survival plots for
PTEN in samples that are wild-type versus
cancer of interest (Figure 4B).

samples of different immune cell types).

“One of the things we did after running an IPA analysis on genes was use Land
Explorer to try to figure out a little bit more about some of the upstream regulatorsand the canonical pathways that we found to be enriched. We try to branch out
and say, ‘what other studies have seen this as well?’”Land Explorer for QIAGEN IPA

10/2020	

9

A

B

Figure 4. (A) DNA-seq somatic mutation distribution for PTEN by tumor type. Somatic mutation distribution of PTEN in tumor samples from the Cancer
Genome Atlas (TCGA), including insertions, deletions and substitutions. PTEN is somatically mutated in over 60% of uterine corpus endometrial
carcinoma (UCEC) samples and over 30% of glioblastoma multiforme (GBM) samples. X-axis represents the percent of mutated samples. (B) Survival
plots for PTEN wild-type versus PTEN-mutation in cancer samples of interest. Survival and disease-free survival time of patients with or without specificgene mutations for PTEN were analyzed using Kaplan-Meier curves.

10 	Connect the dots

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Explore genes implicated in adiseaseExplorer for IPA let you quickly narrow

Upon identifying a list of genes from an

studies that are of most interest. Finally, you

analysis in QIAGEN IPA, you can learn
more about those genes individually in Land
Explorer. For example, Land Explorer can
aid in the study of the ACE2 gene, which is
required for the SARS-CoV-2 virus to enter
cells and is responsible for the COVID-19

down your focus to only the collection of
can look for co-expression or co-regulationof genes like ACE2 with other genes like
TMPRSS2—one of the essential host factors
for SARS-CoV-2 pathogenicity (Figure 5B).

A

disease. Land Explorer for IPA contains avast amount of valuable information, suchas gene expression in normal tissue anddisease tissue, or whether the gene is differentially over- or under-expressed acrossthousands of studies that have been curated
and processed by QIAGEN experts. With
just a few clicks, you can investigate wherea gene is up- or down-regulated in humandisease studies, and which statistical comparisons found differential regulation forB

ACE2. Land Explorer for IPA enables you
to drill down on the studies that are of mostinterest, with filters for metadata. For example, if you are studying ACE2 you may beinterested only in projects concerning infectious diseases (Figure 5A). Because the
QIAGEN curation teams carefully curate
samples using a controlled vocabulary foreach project, the metadata filters in Land
Figure 5. (A) Comparison of ACE2 gene expression by disease state. Statistical comparisons for differential expression of ACE2 in infectious diseases(viral) using a bubble plot. X-axis represents differential gene expression of ACE2 by Log2 fold change. Y-axis represents individual disease states.
Bubble size represents the significance (p-value) from the differential expression (DESeq2 for RNA-seq and General Linear Model for microarray) of
ACE2. Larger bubbles represent greater significance. Selecting dot(s) in this view will populate a Details table at the bottom of the view showing somekey metadata from the comparison(s). (B) Co-expression of ACE2 and TMPRSS2 in human tissues. The multi-gene correlation view shows a scatter viewcomparing the RNA-seq data for ACE2 and TMPRSS2. This scatter plot shows that ACE2 and TMPRSS2 are highly co-expressed in lung and brain
samples. Values plotted are Log2(FPKM + 0.1).

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11

Reveal the secrets of thousands of ‘omics
datasets to gain deeper biological insights

Dr. Darbro reiterates, “I think the number one thing thatLand Explorer for QIAGEN IPA is a powerful resourcethat it’s intuitive and easy to use. Number two is the sheer

for life science researchers who want to gain deeper
insights into their biological data by quickly and easily
leveraging the vast number of available ‘omics datasets. It
serves as a central location to visualize and compare thesedata in many different ways with just a few clicks. With acontinually growing catalog of over 500,000 curated‘omics datasets, each which are carefully re-processed,
you can quickly find answers to your most pressing
research questions. Explore valuable ‘omics datasets in a
simple and interactive way in order to easily understandgene expression data across tissues, discover how genes

stands out in QIAGEN IPA versus any other solution iscomprehensiveness. I use IPA quite a bit because there’s
nothing that has more information. There’s nothing out therethat has this kind of knowledge base behind it. The otherthing that IPA has is other tools. It’s great to have all theseanalysis tools just in one place. I don’t think there’s beena single project that I’ve worked on that we haven’t founda use for some of the tools from IPA like Land Explorer.”Dr. Darbro reflects, “I first became a QIAGEN IPA
customer at least 10 years ago, and every single time wemust make a decision whether to renew [the license], wehave.”

are regulated in diseases and in response to treatments
or mutations, and produce publication-quality visuals tocommunicate your findings.

Understand complex ‘omics data. Start your free trial of Land Explorer for QIAGEN IPA today.
Visit digitalinsights.qiagen.com/IPA

QIAGEN Ingenuity Pathway Analysis (QIAGEN IPA) is intended for molecular biology applications. This product is notintended for the diagnosis, prevention or treatment of a disease.
For up-to-date licensing information and product-specific disclaimers, see the respective Ingenuity product site. Further
information can be requested from ts-bioinformatics.qiagen.com or by contacting your local account manager.
Trademarks: QIAGEN®, Sample to Insight®, Ingenuity®, IPA® (QIAGEN Group). Registered names, trademarks, etc. used in this document, even when not specifically marked as such, are not
to be considered unprotected by law.
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