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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

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Quality Issues with Public Domain Chemogenomics Data.

Tuomo Kalliokoski^1,2, Christian Kramer³, Anna Vulpetti⁴

¹Computer-Aided Drug Design, Novartis Institutes for Biomedical Research, Postfach, 4002 Basel, Switzerland. kalliokoski@lead-discovery.de.

Molecular Informatics

|August 3, 2016

Summary

This summary is machine-generated.

Chemogenomics relies on the similarity principle, but data quality is crucial. This review highlights common issues in public biological data impacting chemogenomic analysis accuracy.

Keywords:

Chemogenomics Data accuracy Databases Experimental uncertainty

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Area of Science:

Chemogenomics
Bioinformatics
Data Science

Background:

Chemogenomics leverages the similarity principle: similar ligands bind similar targets.
Chemogenomic analysis demands extensive data, robust computational algorithms, and powerful computing resources.
Data for chemogenomics can be sourced from public repositories or generated internally, particularly in the pharmaceutical sector.

Purpose of the Study:

To review common data quality issues in public biological databases relevant to chemogenomics.
To identify sources of error in chemogenomic data, including experimental and annotation problems.
To emphasize the critical role of data quality in ensuring accurate chemogenomic analysis.

Main Methods:

Review of common issues in public domain biological data for chemogenomics.
Analysis of data quality challenges impacting chemogenomic model accuracy.
Discussion of data extraction and annotation problems.

Main Results:

Publicly available chemogenomics data has increased, enabling larger-scale analyses.
Significant data quality issues exist in public biological databases.
Errors can stem from experimental design, interpretation, data extraction, and annotation.

Conclusions:

Data quality is a paramount concern for reliable chemogenomic analysis.
Issues identified in public databases are not isolated and can affect various data sources.
Addressing data quality challenges is essential for advancing chemogenomics research.