1
|
Nagpal S, Singh R, Taneja B, Mande SS. MarkerML – Marker feature identification in metagenomic datasets using interpretable machine learning. J Mol Biol 2022; 434:167589. [DOI: 10.1016/j.jmb.2022.167589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 12/29/2022]
|
2
|
Bisht V, Acharjee A, Gkoutos GV. NFnetFu: A novel workflow for microbiome data fusion. Comput Biol Med 2021; 135:104556. [PMID: 34216888 PMCID: PMC8404037 DOI: 10.1016/j.compbiomed.2021.104556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 06/04/2021] [Accepted: 06/04/2021] [Indexed: 12/18/2022]
Abstract
Microbiome data analysis and its interpretation into meaningful biological insights remain very challenging for numerous reasons, perhaps most prominently, due to the need to account for multiple factors, including collinearity, sparsity (excessive zeros) and effect size, that the complex experimental workflow and subsequent downstream data analysis require. Moreover, a meaningful microbiome data analysis necessitates the development of interpretable models that incorporate inferences across available data as well as background biomedical knowledge. We developed a multimodal framework that considers sparsity (excessive zeros), lower effect size, intrinsically microbial correlations, i.e., collinearity, as well as background biomedical knowledge in the form of a cluster-infused enriched network architecture. Finally, our framework also provides a candidate taxa/Operational Taxonomic Unit (OTU) that can be targeted for future validation experiments. We have developed a tool, the term NFnetFU (Neuro Fuzzy network Fusion), that encompasses our framework and have made it freely available at https://github.com/VartikaBisht6197/NFnetFu.
Collapse
Affiliation(s)
- Vartika Bisht
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, UK
| | - Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, UK; Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, B15 2TT, UK; NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, B15 2WB, UK; MRC Health Data Research UK HDR, UK.
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, UK; Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, B15 2TT, UK; NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, B15 2WB, UK; MRC Health Data Research UK HDR, UK; NIHR Experimental Cancer Medicine Centre, B15 2TT, Birmingham, UK; NIHR Biomedical Research Centre, University Hospital Birmingham, Birmingham, B15 2TT, UK
| |
Collapse
|
3
|
Wu H, Wu FT, Zhou QH, Zhao DP. Comparative Analysis of Gut Microbiota in Captive and Wild Oriental White Storks: Implications for Conservation Biology. Front Microbiol 2021; 12:649466. [PMID: 33841373 PMCID: PMC8027120 DOI: 10.3389/fmicb.2021.649466] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/03/2021] [Indexed: 11/13/2022] Open
Abstract
The oriental white stork (Ciconia boyciana) is considered an endangered species based on the International Union for Conservation of Nature (IUCN) Red List. This study presents the first evidence on comparative analysis of gut microbial diversity of C. boyciana from various breeding conditions. To determine the species composition and community structure of the gut microbiota, 24 fecal samples from Tianjin Zoo and Tianjin Qilihai Wetland were characterized by sequencing 16S rRNA gene amplicons using the Illumina MiSeq platform. Firmicutes was found to be the predominant phylum. Analysis of community structure revealed significant differences in the species diversity and richness between the populations of the two breeding conditions. The greatest α-diversity was found in wild C. boyciana, while artificial breeding storks from Tianjin Zoo had the least α-diversity. Principal coordinates analysis showed that the microbial communities were different between the two studied groups. In conclusion, this study reveals the species composition and structure of the gut microbiota of oriental white storks under two breeding conditions, and our findings could contribute to the integrative conservation of this endangered bird.
Collapse
Affiliation(s)
- Hong Wu
- Tianjin Key Laboratory of Conservation and Utilization of Animal Diversity, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection, Ministry of Education, Guangxi Key Laboratory of Rare and Endangered Animal Ecology, Guangxi Normal University, Guilin, China
| | - Fang-Ting Wu
- Tianjin Key Laboratory of Conservation and Utilization of Animal Diversity, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Qi-Hai Zhou
- Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection, Ministry of Education, Guangxi Key Laboratory of Rare and Endangered Animal Ecology, Guangxi Normal University, Guilin, China
| | - Da-Peng Zhao
- Tianjin Key Laboratory of Conservation and Utilization of Animal Diversity, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection, Ministry of Education, Guangxi Key Laboratory of Rare and Endangered Animal Ecology, Guangxi Normal University, Guilin, China
| |
Collapse
|
4
|
Borroni D, Romano V, Kaye SB, Somerville T, Napoli L, Fasolo A, Gallon P, Ponzin D, Esposito A, Ferrari S. Metagenomics in ophthalmology: current findings and future prospectives. BMJ Open Ophthalmol 2019; 4:e000248. [PMID: 31276030 PMCID: PMC6557081 DOI: 10.1136/bmjophth-2018-000248] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 02/04/2019] [Accepted: 02/19/2019] [Indexed: 01/14/2023] Open
Abstract
Less than 1% of all microorganisms of the available environmental microbiota can be cultured with the currently available techniques. Metagenomics is a new methodology of high-throughput DNA sequencing, able to provide taxonomic and functional profiles of microbial communities without the necessity to culture microbes in the laboratory. Metagenomics opens to a ‘hypothesis-free’ approach, giving important details for future research and treatment of ocular diseases in ophthalmology, such as ocular infection and ocular surface diseases.
Collapse
Affiliation(s)
- Davide Borroni
- St Paul's Eye Unit, Department of Corneal and External Eye Diseases, Royal Liverpool University Hospital, Liverpool, United Kingdom.,Department of Doctoral Studies, Riga Stradins University, Riga, Latvia.,Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom.,Fondazione Banca Degli Occhi Del Veneto Onlus, Zelarino, Venezia, Italy
| | - Vito Romano
- St Paul's Eye Unit, Department of Corneal and External Eye Diseases, Royal Liverpool University Hospital, Liverpool, United Kingdom.,Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
| | - Stephen B Kaye
- St Paul's Eye Unit, Department of Corneal and External Eye Diseases, Royal Liverpool University Hospital, Liverpool, United Kingdom.,Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
| | - Tobi Somerville
- St Paul's Eye Unit, Department of Corneal and External Eye Diseases, Royal Liverpool University Hospital, Liverpool, United Kingdom.,Department of Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
| | - Luca Napoli
- Dipartimento di Specialità Medico-Chirurgiche, Scienze Radiologiche e Sanita Pubblica, Universita degli Studi di Brescia, Brescia, Italy
| | - Adriano Fasolo
- Fondazione Banca Degli Occhi Del Veneto Onlus, Zelarino, Venezia, Italy
| | - Paola Gallon
- Fondazione Banca Degli Occhi Del Veneto Onlus, Zelarino, Venezia, Italy
| | - Diego Ponzin
- Fondazione Banca Degli Occhi Del Veneto Onlus, Zelarino, Venezia, Italy
| | - Alfonso Esposito
- Centre for Integrative Biology (CIBIO), Trento University, Trento, Italy
| | - Stefano Ferrari
- Fondazione Banca Degli Occhi Del Veneto Onlus, Zelarino, Venezia, Italy
| |
Collapse
|
5
|
Alshawaqfeh M, Bashaireh A, Serpedin E, Suchodolski J. Reliable Biomarker discovery from Metagenomic data via RegLRSD algorithm. BMC Bioinformatics 2017; 18:328. [PMID: 28693478 PMCID: PMC5504766 DOI: 10.1186/s12859-017-1738-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 06/22/2017] [Indexed: 12/13/2022] Open
Abstract
Background Biomarker detection presents itself as a major means of translating biological data into clinical applications. Due to the recent advances in high throughput sequencing technologies, an increased number of metagenomics studies have suggested the dysbiosis in microbial communities as potential biomarker for certain diseases. The reproducibility of the results drawn from metagenomic data is crucial for clinical applications and to prevent incorrect biological conclusions. The variability in the sample size and the subjects participating in the experiments induce diversity, which may drastically change the outcome of biomarker detection algorithms. Therefore, a robust biomarker detection algorithm that ensures the consistency of the results irrespective of the natural diversity present in the samples is needed. Results Toward this end, this paper proposes a novel Regularized Low Rank-Sparse Decomposition (RegLRSD) algorithm. RegLRSD models the bacterial abundance data as a superposition between a sparse matrix and a low-rank matrix, which account for the differentially and non-differentially abundant microbes, respectively. Hence, the biomarker detection problem is cast as a matrix decomposition problem. In order to yield more consistent and solid biological conclusions, RegLRSD incorporates the prior knowledge that the irrelevant microbes do not exhibit significant variation between samples belonging to different phenotypes. Moreover, an efficient algorithm to extract the sparse matrix is proposed. Comprehensive comparisons of RegLRSD with the state-of-the-art algorithms on three realistic datasets are presented. The obtained results demonstrate that RegLRSD consistently outperforms the other algorithms in terms of reproducibility performance and provides a marker list with high classification accuracy. Conclusions The proposed RegLRSD algorithm for biomarker detection provides high reproducibility and classification accuracy performance regardless of the dataset complexity and the number of selected biomarkers. This renders RegLRSD as a reliable and powerful tool for identifying potential metagenomic biomarkers.
Collapse
Affiliation(s)
- Mustafa Alshawaqfeh
- Bioinformatics and Genomic Signal Processing Lab, ECEN Dept., Texas A&M University, College Station, 77843-3128, TX, USA
| | - Ahmad Bashaireh
- Bioinformatics and Genomic Signal Processing Lab, ECEN Dept., Texas A&M University, College Station, 77843-3128, TX, USA
| | - Erchin Serpedin
- Bioinformatics and Genomic Signal Processing Lab, ECEN Dept., Texas A&M University, College Station, 77843-3128, TX, USA.
| | - Jan Suchodolski
- College of Veterinary Medicine and Biomedical Sciences, Gastrointestinal Laboratory, Texas A&M University, College Station, 77843-3128, TX, USA
| |
Collapse
|