CYPminer: an automated cytochrome P450 identification, classification, and data analysis tool for genome data sets across kingdoms

Journal Article · Wed Apr 29 00:00:00 EDT 2020 · BMC Bioinformatics

DOI:https://doi.org/10.1186/s12859-020-3473-2· OSTI ID:1629970

Kweon, Ohgew ^[1]; Kim, Seong-Jae ^[2]; Kim, Jae Hyun ^[3]; Nho, Seong Won ^[2]; Bae, Dongryeoul ^[2]; Chon, Jungwhan ^[2]; Hart, Mark ^[2]; Baek, Dong-Heon ^[4]; Kim, Young-Chang ^[5]; Wang, Wenjun ^[6]; Kim, Sung-Kwan ^[6]; Sutherland, John B. ^[2]; Cerniglia, Carl E. ^[2]

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1. U.S. Food and Drug Administration (FDA), Jefferson, AR (United States). National Center for Toxicological Research (NCTR). Division of Microbiology; DOE/OSTI
2. U.S. Food and Drug Administration (FDA), Jefferson, AR (United States). National Center for Toxicological Research (NCTR). Division of Microbiology
3. U.S. Food and Drug Administration (FDA), Jefferson, AR (United States). National Center for Toxicological Research (NCTR)
4. Dankook Univ., Yongin (Korea, Republic of). School of Dentistry. Dept. of Oral Microbiology and Immunology
5. Chungbuk National Univ., Cheongju (Korea, Republic of). Dept. of Microbiology
6. Univ. of Arkansas, Little Rock, AR (United States). Dept. of Business Information Systems

Background: Cytochrome P450 monooxygenases (termed CYPs or P450s) are hemoproteins ubiquitously found across all kingdoms, playing a central role in intracellular metabolism, especially in metabolism of drugs and xenobiotics. The explosive growth of genome sequencing brings a new set of challenges and issues for researchers, such as a systematic investigation of CYPs across all kingdoms in terms of identification, classification, and pan-CYPome analyses. Such investigation requires an automated tool that can handle an enormous amount of sequencing data in a timely manner. Results: CYPminer was developed in the Python language to facilitate rapid, comprehensive analysis of CYPs from genomes of all kingdoms. CYPminer consists of two procedures i) to generate the Genome-CYP Matrix (GCM) that lists all occurrences of CYPs across the genomes, and ii) to perform analyses and visualization of the GCM, including pan-CYPomes (pan- and core-CYPome), CYP co-occurrence networks, CYP clouds, and genome clustering data. The performance of CYPminer was evaluated with three datasets from fungal and bacterial genome sequences. Conclusions: CYPminer completes CYP analyses for large-scale genomes from all kingdoms, which allows systematic genome annotation and comparative insights for CYPs. CYPminer also can be extended and adapted easily for broader usage.

• Research Organization: Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
• Grant/Contract Number: SC0014664
• OSTI ID: 1629970
• Journal Information: BMC Bioinformatics, Journal Name: BMC Bioinformatics Journal Issue: 1 Vol. 21; ISSN 1471-2105
• Publisher: BioMed Central
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
97 MATHEMATICS AND COMPUTING
CYP co-occurrence network
CYP classification
CYP identification
Cytochrome P450
Data analysis
Pan-CYPome
Python
Software