clarivate analytics systems biology solutions

Clarivate Analytics Systems Biology Solutions

Prepared for AstraZeneca

Carlos Granja, PhD

Solution Scientist

January 2020

1© 2019 Clarivate Analytics

2

Metacore™ Your GPS in Pathway Analysis

INPUT METABASEMETACORE

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> 2,350,000 molecular interactions

> 920,000 protein-protein interactions

Manually curated by experts

(>50 PhD, MD)

• Understand your OMIC’s data in the

context of validated biological pathways.

• Generate and confirm hypotheses for

novel biomarkers, targets, mechanisms of

action.

3,701 journal titles

1,574 Pathway Maps

> 1,100 prebuilt networks

© 2019 Clarivate Analytics

https://portal.genego.com/cgi/data_manager.cgi

~2,300,000 interactions in total

• Molecular Interactions module is a comprehensive database that provides information on 2,315,807 physical and functional interactions between proteins, RNAs and compounds.

• For an interaction to be useful a researcher needs to understand three things:

– what is affecting what (direction)

– how one molecule affects the other (mechanism)

– and what happens as a result (effect)

• MetaBase is the only database on the market to possess all three of these for all interactions. Without all three of these the ability to interpret results but also utilize the content computationally is very limited.

• All interactions are scientifically confirmed and cover 40 different mechanisms including transcription regulation, miRNA action, drug-target interactions, metabolism and more.

• This comprehensive records allow:

– Discovering of novel targets – identification of genes which are over-connected with disease genes

– Repurposing of drugs – identify possible use of existing drugs for other indications by identifying causal role of a target for other diseases using network analysis;

– Access content essential for such areas of bioinformatic analysis as network analysis, causal gene identification for genomics, transcriptomics, proteomics and metabolomics.

Molecular Interactions

~1,600 pathway maps

• Pathway Maps module is a comprehensive database that is ideal for visualization, interpretation and analysis of molecular processes in biological cells. This content is effectively a detailed map of which pairs of molecules interact with each other in various biological chain reactions (known as pathways)

• Interactions on maps are always derived from literature references with the majority of interactions also being confirmed with references specific for larger pathway level

• The modules contains a total of 1669 pathway maps. This content is ideal for pathway analysis of genomics, transcriptomics, proteomics discovery:

– Intuitive visual biological reference about major cellular processes;

– Discovery of a novel target – pathway analysis allows the identification of key molecules that regulate major cellular processes;

– Repurposing of drugs – identify possible use of existing drugs for other indications by identifying causal role of a target for other diseases;

– Biomarker discovery – identification of experimentally identified genes to be involved in specific processes;

– Access content essential for such areas of bioinformatics analysis as pathway analysis especially topological impact analysis.

Pathway maps

270,000 gene variants

• Disease Biomarkers module is a comprehensive database that provides information on early discovery genomic aberrations (including gene variants, mRNA and protein abundance and activity changes) and endogenous compounds covering ~3,600 therapeutic indications.

• Database records are based on ~142,000 molecular biology disease discovery articles. These early stage records allow:

– Discovery of a novel target – a gene associated with a condition could be a potential target for a novel drug;

– Repurposing of drugs – identify possible use of existing drugs for other indications by identifying causal role of a target for other diseases;

– Testing and development of novel biomarker panels;

– Access content essential for such areas of bioinformatics analysis as gene-phenotype network analysis, disease similarity analysis, increase value of pathway analysis.

Disease biomarkers

• Toxicity Biomarkers module is a comprehensive database that provides information on animal toxicity studies (mouse, rat) focused on biomarkers (mRNA, protein or compound) of organ pathology detection in response to toxic or protective agent administration.

• A subset of database records contain toxic responses related with gene variants (mostly knock-out experiments)

• Database contains ~ 507,000 toxicity biomarker records that:

• Give insight into 17 major organ-specific toxic pathology responses;

• Help to validate pre-clinical toxicity assays results;

• Might be built in custom toxicity databases and QSAR models;

• Test and development of biomarker panels that indicate organ specific tox effects of drug candidates.

• Support decision making on lead optimization projects through toxic and protective agents data analysis.

Toxicity biomarkers

Key Pathway Advisor



Powerful analytics

Key Pathway Advisor

Use Key Hubs to fill gaps on pathways

Up-regulated genes

Differentially expressed genes

(DEGs) Down-regulated genes

Predict Key Hubs on molecular network

which drive differential expression

Identify Key Pathways affected by differential

expression and aberrant signaling

SPIA aims to identify perturbed pathways in a given condition by combining enrichment of perturbed genes in the pathway with the actual amount of perturbation, leading to the most promising candidate pathways and thus candidate genes.

SPIA captures two different probabilities for each pathway to calculate Impact P-value:1. The enrichment of differentially expressed genes within the pathway (classic p-value); and2. The level of perturbation within the pathway as measured by propagating expression changes through

the pathway (perturbation probability). It includes individual molecule perturbation levels as calculated by this formula:


Signaling Pathway Impact Analysis (SPIA)

Key Pathway Advisor

Molecule perturbation Gene expressionAccumulation of incoming interactions

Interaction effect (positive/negative)

Identify perturbed pathways in a given condition by combining enrichment of perturbed genes in the pathway with the actual amount of perturbation, leading to the most promising candidate pathways and thus candidate genes.

Both gene expression and interactions (direction and effect) are taken into account.


Signalling Pathway Impact analysis (SPIA)

Key Pathway Advisor

A

B C D

F

Molecule perturbation of gene A

1. Gene expression of A is +22. Add incoming effect from B

B perturbation (3) as it is3. Add incoming effect from C

C perturbation (-1.5) as it is4. Add incoming effect from D

D perturbation (2) is divided by 3

+3 -1.5 +2

+2.8

The more agreement with upstream events, the higher the molecule perturbation.

Cell growth

+2.8

Cell adhesionCell motility

Identify perturbed pathways in a given condition by combining enrichment of perturbed genes in the pathway with the actual amount of perturbation, leading to the most promising candidate pathways and thus candidate genes.

Both gene expression and interactions (direction and effect) are taken into account.


Signalling Pathway Impact analysis (SPIA)

Key Pathway Advisor

A

B C D

F

Molecule perturbation of gene A

1. Gene expression of A is +22. Add incoming effect from B

B perturbation (3) as it is3. Add incoming effect from C

C perturbation (-1.5) as it is4. Add incoming effect from D

D perturbation (2) is divided by 3

+3 -1.5 +2

+2.8

+2.8

𝑀𝑃𝐴 = 2 + +13

1+ +1

−1.5

1+ −1

2

3= 2.8

B C D


Calculating molecular perturbation score

Key Pathway Advisor

-3.148 -3.148

Molecule perturbation Gene expressionAccumulation of incoming interactions


𝑀𝑃𝐸𝑅𝐾2 = 0+ +1−3.148

2+ +1 (

−3.148

2)

𝑀𝑃𝐸𝑅𝐾2 = −3.148

MEK2 MEK1-3.148


Calculating molecular perturbation score

Key Pathway Advisor

-3.148 -3.148

Molecule perturbation Gene expressionAccumulation of incoming interactions*


𝑀𝑃𝑇𝑢𝑏𝑒𝑟𝑖𝑛 = 0+ −1 (−3.148

2)

𝑀𝑃𝑇𝑢𝑏𝑒𝑟𝑖𝑛 = 1.574

ERK2

*Transcription regulation interactions are not accounted because that may cause discordance with input data leading to incorr ect interpretation

-3.1481.574

Thank you

Carlos Granja, PhD

[email protected]

+44 (0) 75987 24245

clarivate.com/cortellis


http://www.clarivate.com/cortellis

clarivate analytics systems biology solutions

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