NDLI: Machine learning classification of archaea and bacteria identifies novel predictive genomic features

Content Provider	Springer Nature : BioMed Central
Author	Bobbo, Tania Biscarini, Filippo Yaddehige, Sachithra K. Alberghini, Leonardo Rigoni, Davide Bianchi, Nicoletta Taccioli, Cristian
Abstract	Archaea and Bacteria are distinct domains of life that are adapted to a variety of ecological niches. Several genome-based methods have been developed for their accurate classification, yet many aspects of the specific genomic features that determine these differences are not fully understood. In this study, we used publicly available whole-genome sequences from bacteria ( $N=2546$ ) and archaea ( $N=109$ ). From these, a set of genomic features (nucleotide frequencies and proportions, coding sequences (CDS), non-coding, ribosomal and transfer RNA genes (ncRNA, rRNA, tRNA), Chargaff’s, topological entropy and Shannon’s entropy scores) was extracted and used as input data to develop machine learning models for the classification of archaea and bacteria. The classification accuracy ranged from 0.993 (Random Forest) to 0.998 (Neural Networks). Over the four models, only 11 examples were misclassified, especially those belonging to the minority class (Archaea). From variable importance, tRNA topological and Shannon’s entropy, nucleotide frequencies in tRNA, rRNA and ncRNA, CDS, tRNA and rRNA Chargaff’s scores have emerged as the top discriminating factors. In particular, tRNA entropy (both topological and Shannon’s) was the most important genomic feature for classification, pointing at the complex interactions between the genetic code, tRNAs and the translational machinery. tRNA, rRNA and ncRNA genes emerged as the key genomic elements that underpin the classification of archaea and bacteria. In particular, higher nucleotide diversity was found in tRNA from bacteria compared to archaea. The analysis of the few classification errors reflects the complex phylogenetic relationships between bacteria, archaea and eukaryotes.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-024-10832-y.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/s12864-024-10832-y
Journal	BMC Genomics
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2024-10-14
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Genomics Archaea Bacteria Machine learning
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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