Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation.
There is a growing public concern about the lack of reproducibility of experimental data published in peer-reviewed scientific literature. Herein, we review the most recent alerts regarding experimental data quality and discuss initiatives taken thus far to address this problem, especially in the area of chemical genomics. Going beyond just acknowledging the issue, we propose a chemical and biological data curation workflow that relies on existing cheminformatics approaches to flag, and when appropriate, correct possibly erroneous entries in large chemogenomics data sets. We posit that the adherence to the best practices for data curation is important for both experimental scientists who generate primary data and deposit them in chemical genomics databases and computational researchers who rely on these data for model development.
Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh, North Carolina 27695, United States.
Denis
D
Fourches
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Chapel Hill, North Carolina 27599, United States.
Eugene
E
Muratov
Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Chapel Hill, North Carolina 27599, United States.
Alexander
A
Tropsha
D032921:Consensus; D023281:Genomics; D011786:Quality Control; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000592:standards*