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Sun Y, Li H, Zheng L, Li J, Hong Y, Liang P, Kwok LY, Zuo Y, Zhang W, Zhang H. iProbiotics: a machine learning platform for rapid identification of probiotic properties from whole-genome primary sequences. Brief Bioinform 2021; 23:6444315. [PMID: 34849572 DOI: 10.1093/bib/bbab477] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/28/2021] [Accepted: 10/15/2021] [Indexed: 12/13/2022] Open
Abstract
Lactic acid bacteria consortia are commonly present in food, and some of these bacteria possess probiotic properties. However, discovery and experimental validation of probiotics require extensive time and effort. Therefore, it is of great interest to develop effective screening methods for identifying probiotics. Advances in sequencing technology have generated massive genomic data, enabling us to create a machine learning-based platform for such purpose in this work. This study first selected a comprehensive probiotics genome dataset from the probiotic database (PROBIO) and literature surveys. Then, k-mer (from 2 to 8) compositional analysis was performed, revealing diverse oligonucleotide composition in strain genomes and apparently more probiotic (P-) features in probiotic genomes than non-probiotic genomes. To reduce noise and improve computational efficiency, 87 376 k-mers were refined by an incremental feature selection (IFS) method, and the model achieved the maximum accuracy level at 184 core features, with a high prediction accuracy (97.77%) and area under the curve (98.00%). Functional genomic analysis using annotations from gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Rapid Annotation using Subsystem Technology (RAST) databases, as well as analysis of genes associated with host gastrointestinal survival/settlement, carbohydrate utilization, drug resistance and virulence factors, revealed that the distribution of P-features was biased toward genes/pathways related to probiotic function. Our results suggest that the role of probiotics is not determined by a single gene, but by a combination of k-mer genomic components, providing new insights into the identification and underlying mechanisms of probiotics. This work created a novel and free online bioinformatic tool, iProbiotics, which would facilitate rapid screening for probiotics.
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Affiliation(s)
- Yu Sun
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University, Hohhot 010070, China
| | - Haicheng Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University, Hohhot 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University, Hohhot 010070, China
| | - Jinzhao Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yan Hong
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University, Hohhot 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University, Hohhot 010070, China
| | - Lai-Yu Kwok
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of life sciences, Inner Mongolia University, Hohhot 010070, China
| | - Wenyi Zhang
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Heping Zhang
- Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot 010018, China
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Khan MA, Rubab S, Kashif A, Sharif MI, Muhammad N, Shah JH, Zhang YD, Satapathy SC. Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2019.11.014] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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Amin J, Sharif M, Yasmin M, Saba T, Anjum MA, Fernandes SL. A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning. J Med Syst 2019; 43:326. [DOI: 10.1007/s10916-019-1453-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 09/04/2019] [Indexed: 12/20/2022]
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