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Bi Y, Li F, Guo X, Wang Z, Pan T, Guo Y, Webb GI, Yao J, Jia C, Song J. Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations. Brief Bioinform 2022; 23:bbac467. [PMID: 36341591 PMCID: PMC10148739 DOI: 10.1093/bib/bbac467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/09/2022] [Accepted: 09/29/2022] [Indexed: 11/09/2022] Open
Abstract
Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.
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Affiliation(s)
- Yue Bi
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Fuyi Li
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Zhikang Wang
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Tong Pan
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria 3004, Australia
| | - Geoffrey I Webb
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | | | - Cangzhi Jia
- School of Science, Dalian Maritime University, Dalian 116026, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
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Cadenelli N, Jun SW, Polo J, Wright A, Carrera D, Arvind. Enabling Genomics Pipelines in Commodity Personal Computers With Flash Storage. Front Genet 2021; 12:615958. [PMID: 33995473 PMCID: PMC8116887 DOI: 10.3389/fgene.2021.615958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 03/19/2021] [Indexed: 11/13/2022] Open
Abstract
Analysis of a patient's genomics data is the first step toward precision medicine. Such analyses are performed on expensive enterprise-class server machines because input data sets are large, and the intermediate data structures are even larger (TB-size) and require random accesses. We present a general method to perform a specific genomics problem, mutation detection, on a cheap commodity personal computer (PC) with a small amount of DRAM. We construct and access large histograms of k-mers efficiently on external storage (SSDs) and apply our technique to a state-of-the-art reference-free genomics algorithm, SMUFIN, to create SMUFIN-F. We show that on two PCs, SMUFIN-F can achieve the same throughput at only one third (36%) the hardware cost and half (45%) the energy compared to SMUFIN on an enterprise-class server. To the best of our knowledge, SMUFIN-F is the first reference-free system that can detect somatic mutations on commodity PCs for whole human genomes. We believe our technique should apply to other k-mer or n-gram-based algorithms.
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Affiliation(s)
| | - Sang-Woo Jun
- Computer Science Department, University of California, Irvine, Irvine, CA, United States
| | - Jordà Polo
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Andrew Wright
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States
| | - David Carrera
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Arvind
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States
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Jiang P, Hu Y, Wang Y, Zhang J, Zhu Q, Bai L, Tong Q, Li T, Zhao L. Efficient Mining of Variants From Trios for Ventricular Septal Defect Association Study. Front Genet 2019; 10:670. [PMID: 31440271 PMCID: PMC6694746 DOI: 10.3389/fgene.2019.00670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Accepted: 06/27/2019] [Indexed: 11/28/2022] Open
Abstract
Ventricular septal defect (VSD) is a fatal congenital heart disease showing severe consequence in affected infants. Early diagnosis plays an important role, particularly through genetic variants. Existing panel-based approaches of variants mining suffer from shortage of large panels, costly sequencing, and missing rare variants. Although a trio-based method alleviates these limitations to some extent, it is agnostic to novel mutations and computational intensive. Considering these limitations, we are studying a novel variants mining algorithm from trio-based sequencing data and apply it on a VSD trio to identify associated mutations. Our approach starts with irrelevant k-mer filtering from sequences of a trio via a newly conceived coupled Bloom Filter, then corrects sequencing errors by using a statistical approach and extends kept k-mers into long sequences. These extended sequences are used as input for variants needed. Later, the obtained variants are comprehensively analyzed against existing databases to mine VSD-related mutations. Experiments show that our trio-based algorithm narrows down candidate coding genes and lncRNAs by about 10- and 5-folds comparing with single sequence-based approaches, respectively. Meanwhile, our algorithm is 10 times faster and 2 magnitudes memory-frugal compared with existing state-of-the-art approach. By applying our approach to a VSD trio, we fish out an unreported gene—CD80, a combination of two genes—MYBPC3 and TRDN and a lncRNA—NONHSAT096266.2, which are highly likely to be VSD-related.
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Affiliation(s)
- Peng Jiang
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yaofei Hu
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yiqi Wang
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Jin Zhang
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Qinghong Zhu
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Lin Bai
- School of Computing and Electronic Information, Guangxi University, Nanning, China
| | - Qiang Tong
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Tao Li
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Liang Zhao
- Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.,School of Computing and Electronic Information, Guangxi University, Nanning, China
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