1
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Santucci K, Cheng Y, Xu SM, Janitz M. Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches. Brief Funct Genomics 2024:elae031. [PMID: 39158328 DOI: 10.1093/bfgp/elae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/29/2024] [Accepted: 07/31/2024] [Indexed: 08/20/2024] Open
Abstract
Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing. Recent improvements in the accuracy of long-read sequencing technologies have expanded the scope for novel splice isoform detection and have also enabled a far more accurate reconstruction of complex splicing patterns and transcriptomes. Additionally, the incorporation and advancements of machine learning and deep learning algorithms in bioinformatic software have significantly improved the reliability of long-read sequencing transcriptomic studies. However, there is a lack of consensus on what bioinformatic tools and pipelines produce the most precise and consistent results. Thus, this review aims to discuss and compare the performance of available methods for novel isoform discovery with long-read sequencing technologies, with 25 tools being presented. Furthermore, this review intends to demonstrate the need for developing standard analytical pipelines, tools, and transcript model conventions for novel isoform discovery and transcriptomic studies.
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Affiliation(s)
- Kristina Santucci
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Yuning Cheng
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Si-Mei Xu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Michael Janitz
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
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2
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Liu Y, Yang C, Li HD, Wang J. IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions. Bioinformatics 2023; 39:btad530. [PMID: 37647643 PMCID: PMC10491952 DOI: 10.1093/bioinformatics/btad530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/21/2023] [Accepted: 08/29/2023] [Indexed: 09/01/2023] Open
Abstract
MOTIVATION A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider the relevance of each feature to specific functions and ignore the noise caused by the irrelevant features. In this case, we hypothesize that constructing a feature selection framework to extract the function-relevant features might help improve the model accuracy in isoform function prediction. RESULTS In this article, we present a feature selection-based approach named IsoFrog to predict isoform functions. First, IsoFrog adopts a reversible jump Markov Chain Monte Carlo (RJMCMC)-based feature selection framework to assess the feature importance to gene functions. Second, a sequential feature selection procedure is applied to select a subset of function-relevant features. This strategy screens the relevant features for the specific function while eliminating irrelevant ones, improving the effectiveness of the input features. Then, the selected features are input into our proposed method modified domain-invariant partial least squares, which prioritizes the most likely positive isoform for each positive MIG and utilizes diPLS for isoform function prediction. Tested on three datasets, our method achieves superior performance over six state-of-the-art methods, and the RJMCMC-based feature selection framework outperforms three classic feature selection methods. We expect this proposed methodology will promote the identification of isoform functions and further inspire the development of new methods. AVAILABILITY AND IMPLEMENTATION IsoFrog is freely available at https://github.com/genemine/IsoFrog.
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Affiliation(s)
- Yiwei Liu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Changhuo Yang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Hong-Dong Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China
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3
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Zhang Y, Yang X, Van de Peer Y, Chen J, Marchal K, Shi T. Evolution of isoform-level gene expression patterns across tissues during lotus species divergence. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 112:830-846. [PMID: 36123806 PMCID: PMC7613771 DOI: 10.1111/tpj.15984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 09/09/2022] [Indexed: 05/03/2023]
Abstract
Both gene duplication and alternative splicing (AS) drive the functional diversity of gene products in plants, yet the relative contributions of the two key mechanisms to the evolution of gene function are largely unclear. Here, we studied AS in two closely related lotus plants, Nelumbo lutea and Nelumbo nucifera, and the outgroup Arabidopsis thaliana, for both single-copy and duplicated genes. We show that most splicing events evolved rapidly between orthologs and that the origin of lineage-specific splice variants or isoforms contributed to gene functional changes during species divergence within Nelumbo. Single-copy genes contain more isoforms, have more AS events conserved across species, and show more complex tissue-dependent expression patterns than their duplicated counterparts. This suggests that expression divergence through isoforms is a mechanism to extend the expression breadth of genes with low copy numbers. As compared to isoforms of local, small-scale duplicates, isoforms of whole-genome duplicates are less conserved and display a less conserved tissue bias, pointing towards their contribution to subfunctionalization. Through comparative analysis of isoform expression networks, we identified orthologous genes of which the expression of at least some of their isoforms displays a conserved tissue bias across species, indicating a strong selection pressure for maintaining a stable expression pattern of these isoforms. Overall, our study shows that both AS and gene duplication contributed to the diversity of gene function during the evolution of lotus.
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Affiliation(s)
- Yue Zhang
- CAS Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xingyu Yang
- Wuhan Institute of Landscape Architecture, Wuhan 430081, China
| | - Yves Van de Peer
- Department of Plant Biotechnology and Bioinformatics, Ghent University, and VIB Center for Plant Systems Biology, Ghent 9052, Belgium
- Centre for Microbial Ecology and Genomics, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria 0028, South Africa
- College of Horticulture, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China
| | - Jinming Chen
- CAS Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan 430074, China
- Corresponding author details: Jinming Chen: ; Kathleen Marchal: ; Tao Shi:
| | - Kathleen Marchal
- Department of Plant Biotechnology and Bioinformatics, Ghent University, and VIB Center for Plant Systems Biology, Ghent 9052, Belgium
- Department of Information Technology, IDLab, IMEC, Ghent University, Ghent 9052, Belgium
- Corresponding author details: Jinming Chen: ; Kathleen Marchal: ; Tao Shi:
| | - Tao Shi
- CAS Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan 430074, China
- Corresponding author details: Jinming Chen: ; Kathleen Marchal: ; Tao Shi:
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4
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Castaldi PJ, Abood A, Farber CR, Sheynkman GM. Bridging the splicing gap in human genetics with long-read RNA sequencing: finding the protein isoform drivers of disease. Hum Mol Genet 2022; 31:R123-R136. [PMID: 35960994 PMCID: PMC9585682 DOI: 10.1093/hmg/ddac196] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 02/04/2023] Open
Abstract
Aberrant splicing underlies many human diseases, including cancer, cardiovascular diseases and neurological disorders. Genome-wide mapping of splicing quantitative trait loci (sQTLs) has shown that genetic regulation of alternative splicing is widespread. However, identification of the corresponding isoform or protein products associated with disease-associated sQTLs is challenging with short-read RNA-seq, which cannot precisely characterize full-length transcript isoforms. Furthermore, contemporary sQTL interpretation often relies on reference transcript annotations, which are incomplete. Solutions to these issues may be found through integration of newly emerging long-read sequencing technologies. Long-read sequencing offers the capability to sequence full-length mRNA transcripts and, in some cases, to link sQTLs to transcript isoforms containing disease-relevant protein alterations. Here, we provide an overview of sQTL mapping approaches, the use of long-read sequencing to characterize sQTL effects on isoforms, the linkage of RNA isoforms to protein-level functions and comment on future directions in the field. Based on recent progress, long-read RNA sequencing promises to be part of the human disease genetics toolkit to discover and treat protein isoforms causing rare and complex diseases.
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Affiliation(s)
- Peter J Castaldi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of General Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Abdullah Abood
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Charles R Farber
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | - Gloria M Sheynkman
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22903, USA
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA 22903, USA
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5
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Qiu S, Yu G, Lu X, Domeniconi C, Guo M. Isoform function prediction by Gene Ontology embedding. Bioinformatics 2022; 38:4581-4588. [PMID: 35997558 DOI: 10.1093/bioinformatics/btac576] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/13/2022] [Accepted: 08/22/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION High-resolution annotation of gene functions is a central task in functional genomics. Multiple proteoforms translated from alternatively spliced isoforms from a single gene are actual function performers and greatly increase the functional diversity. The specific functions of different isoforms can decipher the molecular basis of various complex diseases at a finer granularity. Multi-instance learning (MIL)-based solutions have been developed to distribute gene(bag)-level Gene Ontology (GO) annotations to isoforms(instances), but they simply presume that a particular annotation of the gene is responsible by only one isoform, neglect the hierarchical structures and semantics of massive GO terms (labels), or can only handle dozens of terms. RESULTS We propose an efficacy approach IsofunGO to differentiate massive functions of isoforms by GO embedding. Particularly, IsofunGO first introduces an attributed hierarchical network to model massive GO terms, and a GO network embedding strategy to learn compact representations of GO terms and project GO annotations of genes into compressed ones, this strategy not only explores and preserves hierarchy between GO terms but also greatly reduces the prediction load. Next, it develops an attention-based MIL network to fuse genomics and transcriptomics data of isoforms and predict isoform functions by referring to compressed annotations. Extensive experiments on benchmark datasets demonstrate the efficacy of IsofunGO. Both the GO embedding and attention mechanism can boost the performance and interpretability. AVAILABILITYAND IMPLEMENTATION The code of IsofunGO is available at http://www.sdu-idea.cn/codes.php?name=IsofunGO. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sichao Qiu
- School of Software, Shandong University, Jinan, Shandong 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, Shandong 250101, China
| | - Guoxian Yu
- School of Software, Shandong University, Jinan, Shandong 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, Shandong 250101, China
| | - Xudong Lu
- School of Software, Shandong University, Jinan, Shandong 250101, China.,Joint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, Shandong 250101, China
| | | | - Maozu Guo
- College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
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6
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Yu G, Huang Q, Zhang X, Guo M, Wang J. Tissue Specificity Based Isoform Function Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3048-3059. [PMID: 34185647 DOI: 10.1109/tcbb.2021.3093167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Alternative splicing enables a gene spliced into different isoforms and hence protein variants. Identifying individual functions of these isoforms help deciphering the functional diversity of proteins. Although much efforts have been made for automatic gene function prediction, few efforts have been moved toward computational isoform function prediction, mainly due to the unavailable (or scanty) functional annotations of isoforms. Existing efforts directly combine multiple RNA-seq datasets without account of the important tissue specificity of alternative splicing. To bridge this gap, we introduce a novel approach called TS-Isofun to predict the functions of isoforms by integrating multiple functional association networks with respect to tissue specificity. TS-Isofun first constructs tissue-specific isoform functional association networks using multiple RNA-seq datasets from tissue-wise. Next, TS-Isofun assigns weights to these networks and models the tissue specificity by selectively integrating them with adaptive weights. It then introduces a joint matrix factorization-based data fusion model to leverage the integrated network, gene-level data and functional annotations of genes to infer the functions of isoforms. To achieve coherent weight assignment and isoform function prediction, TS-Isofun jointly optimizes the weights of individual networks and the isoform function prediction in a unified objective function. Experimental results show that TS-Isofun significantly outperforms state-of-the-art methods and the account of tissue specificity contributes to more accurate isoform function prediction.
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7
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Hao DC, Chen H, Xiao PG, Jiang T. A Global Analysis of Alternative Splicing of Dichocarpum Medicinal Plants, Ranunculales. Curr Genomics 2022; 23:207-216. [PMID: 36777007 PMCID: PMC9878827 DOI: 10.2174/1389202923666220527112929] [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: 03/28/2022] [Revised: 04/19/2022] [Accepted: 04/26/2022] [Indexed: 11/22/2022] Open
Abstract
Background: The multiple isoforms are often generated from a single gene via Alternative Splicing (AS) in plants, and the functional diversity of the plant genome is significantly increased. Despite well-studied gene functions, the specific functions of isoforms are little known, therefore, the accurate prediction of isoform functions is exceedingly wanted. Methods: Here we perform the first global analysis of AS of Dichocarpum, a medicinal genus of Ranunculales, by utilizing full-length transcriptome datasets of five Chinese endemic Dichocarpum taxa. Multiple software were used to identify AS events, the gene function was annotated based on seven databases, and the protein-coding sequence of each AS isoform was translated into an amino acid sequence. The self-developed software DIFFUSE was used to predict the functions of AS isoforms. Results: Among 8,485 genes with AS events, the genes with two isoforms were the most (6,038), followed by those with three isoforms and four isoforms. Retained intron (RI, 551) was predominant among 1,037 AS events, and alternative 3' splice sites and alternative 5' splice sites were second. The software DIFFUSE was effective in predicting functions of Dichocarpum isoforms, which have not been unearthed. When compared with the sequence alignment-based database annotations, DIFFUSE performed better in differentiating isoform functions. The DIFFUSE predictions on the terms GO:0003677 (DNA binding) and GO: 0010333 (terpene synthase activity) agreed with the biological features of transcript isoforms. Conclusion: Numerous AS events were for the first time identified from full-length transcriptome datasets of five Dichocarpum taxa, and functions of AS isoforms were successfully predicted by the self-developed software DIFFUSE. The global analysis of Dichocarpum AS events and predicting isoform functions can help understand the metabolic regulations of medicinal taxa and their pharmaceutical explorations.
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Affiliation(s)
- Da-Cheng Hao
- Biotechnology Institute, School of Environment and Chemical Engineering, Dalian Jiaotong University, Dalian 116028, China;,Institute of Molecular Plant Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK;,Address correspondence to these authors at the School of Environment and Chemical Engineering, Dalian Jiaotong University, Dalian 116028, China; Tel: 0086-411-84572552; E-mail: ; and Department of Computer Science and Engineering, University of California, Riverside, CA, USA; Tel/Fax: 001-951-827-2991; E-mail:
| | - Hao Chen
- Department of Computer Science and Engineering, University of California, Riverside, CA, USA;,Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA;,These authors contributed equally to this work.
| | - Pei-Gen Xiao
- Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Beijing 100193, China
| | - Tao Jiang
- Department of Computer Science and Engineering, University of California, Riverside, CA, USA;,Bioinformatics Division, BNRIST/Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China,Address correspondence to these authors at the School of Environment and Chemical Engineering, Dalian Jiaotong University, Dalian 116028, China; Tel: 0086-411-84572552; E-mail: ; and Department of Computer Science and Engineering, University of California, Riverside, CA, USA; Tel/Fax: 001-951-827-2991; E-mail:
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8
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Wang J, Zhang L, Zeng A, Xia D, Yu J, Yu G. DeepIII: Predicting Isoform-Isoform Interactions by Deep Neural Networks and Data Fusion. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2177-2187. [PMID: 33764878 DOI: 10.1109/tcbb.2021.3068875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Alternative splicing enables a gene translating into different isoforms and into the corresponding proteoforms, which actually accomplish various biological functions of a living body. Isoform-isoform interactions (IIIs) provide a higher resolution interactome to explore the cellular processes and disease mechanisms than the canonically studied protein-protein interactions (PPIs), which are often recorded at the coarse gene level. The knowledge of IIIs is critical to map pathways, understand protein complexity and functional diversity, but the known IIIs are very scanty. In this paper, we propose a deep learning based method called DeepIII to systematically predict genome-wide IIIs by integrating diverse data sources, including RNA-seq datasets of different human tissues, exon array data, domain-domain interactions (DDIs) of proteins, nucleotide sequences and amino acid sequences. Particularly, DeepIII fuses these data to learn the representation of isoform pairs with a four-layer deep neural networks, and then performs binary classification on the learnt representation to achieve the prediction of IIIs. Experimental results show that DeepIII achieves a superior prediction performance to the state-of-the-art solutions and the III network constructed by DeepIII gives more accurate isoform function prediction. Case studies further confirm that DeepIII can differentiate the individual interaction partners of different isoforms spliced from the same gene. The code and datasets of DeepIII are available at http://mlda.swu.edu.cn/codes.php?name=DeepIII.
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9
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Yu G, Yang Y, Yan Y, Guo M, Zhang X, Wang J. DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2166-2176. [PMID: 33571094 DOI: 10.1109/tcbb.2021.3058801] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Alternative splicing produces different isoforms from the same gene locus, it is an important mechanism for regulating gene expression and proteome diversity. Although the prediction of gene(ncRNA)-disease associations has been extensively studied, few (or no) computational solutions have been proposed for the prediction of isoform-disease association (IDA) at a large scale, mainly due to the lack of disease annotations of isoforms. However, increasing evidences confirm the associations between diseases and isoforms, which can more precisely uncover the pathology of complex diseases. Therefore, it is highly desirable to predict IDAs. To bridge this gap, we propose a deep neural network based solution (DeepIDA) to fuse multi-type genomics and transcriptomics data to predict IDAs. Particularly, DeepIDA uses gene-isoform relations to dispatch gene-disease associations to isoforms. In addition, it utilizes two DNN sub-networks with different structures to capture nucleotide and expression features of isoforms, Gene Ontology data and miRNA target data, respectively. After that, these two sub-networks are merged in a dense layer to predict IDAs. The experimental results on public datasets show that DeepIDA can effectively predict IDAs with AUPRC (area under the precision-recall curve) of 0.9141, macro F-measure of 0.9155, G-mean of 0.9278 and balanced accuracy of 0.9303 across 732 diseases, which are much higher than those of competitive methods. Further study on sixteen isoform-disease association cases again corroborates the superiority of DeepIDA. The code of DeepIDA is available at http://mlda.swu.edu.cn/codes.php?name=DeepIDA.
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Yu G, Zhou G, Zhang X, Domeniconi C, Guo M. DMIL-IsoFun: predicting isoform function using deep multi-instance learning. Bioinformatics 2021; 37:4818-4825. [PMID: 34282449 DOI: 10.1093/bioinformatics/btab532] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 06/20/2021] [Accepted: 07/16/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Alternative splicing creates the considerable proteomic diversity and complexity on relatively limited genome. Proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions of this gene, which reflect the functional knowledge of genes at a finer granular level. Recently, some computational approaches have been proposed to differentiate isoform functions using sequence and expression data. However, their performance is far from being desirable, mainly due to the imbalance and lack of annotations at isoform-level, and the difficulty of modeling gene-isoform relations. RESULT We propose a deep multi-instance learning based framework (DMIL-IsoFun) to differentiate the functions of isoforms. DMIL-IsoFun firstly introduces a multi-instance learning convolution neural network trained with isoform sequences and gene-level annotations to extract the feature vectors and initialize the annotations of isoforms, and then uses a class-imbalance Graph Convolution Network to refine the annotations of individual isoforms based on the isoform co-expression network and extracted features. Extensive experimental results show that DMIL-IsoFun improves the Smin and Fmax of state-of-the-art solutions by at least 29.6% and 40.8%. The effectiveness of DMIL-IsoFun is further confirmed on a testbed of human multiple-isoform genes, and Maize isoforms related with photosynthesis. AVAILABILITY The code and data are available at http://www.sdu-idea.cn/codes.php?name=DMIL-Isofun. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Guoxian Yu
- School of Software, Shandong University, Jinan, 250101, China.,College of Computer and Information Sciences, Southwest University, Chongqing, 400715, China.,Computer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, SA
| | - Guangjie Zhou
- School of Software, Shandong University, Jinan, 250101, China.,College of Computer and Information Sciences, Southwest University, Chongqing, 400715, China
| | - Xiangliang Zhang
- Computer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, SA
| | - Carlotta Domeniconi
- Department of Computer Science, George Mason University, Fairfax, 22030, USA
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
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11
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Li HD, Yang C, Zhang Z, Yang M, Wu FX, Omenn GS, Wang J. IsoResolve: predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation. Bioinformatics 2021; 37:522-530. [PMID: 32966552 PMCID: PMC8088322 DOI: 10.1093/bioinformatics/btaa829] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 08/12/2020] [Accepted: 09/09/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION High resolution annotation of gene functions is a central goal in functional genomics. A single gene may produce multiple isoforms with different functions through alternative splicing. Conventional approaches, however, consider a gene as a single entity without differentiating these functionally different isoforms. Towards understanding gene functions at higher resolution, recent efforts have focused on predicting the functions of isoforms. However, the performance of existing methods is far from satisfactory mainly because of the lack of isoform-level functional annotation. RESULTS We present IsoResolve, a novel approach for isoform function prediction, which leverages the information from gene function prediction models with domain adaptation (DA). IsoResolve treats gene-level and isoform-level features as source and target domains, respectively. It uses DA to project the two domains into a latent variable space in such a way that the latent variables from the two domains have similar distribution, which enables the gene domain information to be leveraged for isoform function prediction. We systematically evaluated the performance of IsoResolve in predicting functions. Compared with five state-of-the-art methods, IsoResolve achieved significantly better performance. IsoResolve was further validated by case studies of genes with isoform-level functional annotation. AVAILABILITY AND IMPLEMENTATION IsoResolve is freely available at https://github.com/genemine/IsoResolve. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hong-Dong Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering
| | - Changhuo Yang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, Hunan 410083, China
| | - Mengyun Yang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N5A9, Canada
| | - Gilbert S Omenn
- Institute for Systems Biology, Seattle, WA 98101, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering
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12
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Yu G, Zeng J, Wang J, Zhang H, Zhang X, Guo M. Imbalance deep multi‐instance learning for predicting isoform–isoform interactions. INT J INTELL SYST 2021. [DOI: 10.1002/int.22402] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Guoxian Yu
- School of Software Shandong University Jinan China
- College of Computer and Information Science Southwest University Chongqing China
- Joint SDU‐NTU Centre for Artificial Intelligence Research Shandong University Jinan China
| | - Jie Zeng
- College of Computer and Information Science Southwest University Chongqing China
| | - Jun Wang
- College of Computer and Information Science Southwest University Chongqing China
- Joint SDU‐NTU Centre for Artificial Intelligence Research Shandong University Jinan China
| | - Hong Zhang
- College of Computer and Information Science Southwest University Chongqing China
| | - Xiangliang Zhang
- CEMSE King Abdullah University of Science and Technology Thuwal Saudi Arabia
| | - Maozu Guo
- School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China
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13
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Chen H, Shaw D, Bu D, Jiang T. FINER: enhancing the prediction of tissue-specific functions of isoforms by refining isoform interaction networks. NAR Genom Bioinform 2021; 3:lqab057. [PMID: 34169280 PMCID: PMC8219044 DOI: 10.1093/nargab/lqab057] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 05/18/2021] [Accepted: 06/03/2021] [Indexed: 12/24/2022] Open
Abstract
Annotating the functions of gene products is a mainstay in biology. A variety of databases have been established to record functional knowledge at the gene level. However, functional annotations at the isoform resolution are in great demand in many biological applications. Although critical information in biological processes such as protein-protein interactions (PPIs) is often used to study gene functions, it does not directly help differentiate the functions of isoforms, as the 'proteins' in the existing PPIs generally refer to 'genes'. On the other hand, the prediction of isoform functions and prediction of isoform-isoform interactions, though inherently intertwined, have so far been treated as independent computational problems in the literature. Here, we present FINER, a unified framework to jointly predict isoform functions and refine PPIs from the gene level to the isoform level, enabling both tasks to benefit from each other. Extensive computational experiments on human tissue-specific data demonstrate that FINER is able to gain at least 5.16% in AUC and 15.1% in AUPRC for functional prediction across multiple tissues by refining noisy PPIs, resulting in significant improvement over the state-of-the-art methods. Some in-depth analyses reveal consistency between FINER's predictions and the tissue specificity as well as subcellular localization of isoforms.
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Affiliation(s)
- Hao Chen
- Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA
| | - Dipan Shaw
- Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA
| | - Dongbo Bu
- Key Lab of Intelligent Information Process, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tao Jiang
- Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA
- Bioinformatics Division, BNRIST/Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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14
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Pozo F, Martinez-Gomez L, Walsh TA, Rodriguez JM, Di Domenico T, Abascal F, Vazquez J, Tress ML. Assessing the functional relevance of splice isoforms. NAR Genom Bioinform 2021; 3:lqab044. [PMID: 34046593 PMCID: PMC8140736 DOI: 10.1093/nargab/lqab044] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 04/22/2021] [Accepted: 05/17/2021] [Indexed: 12/20/2022] Open
Abstract
Alternative splicing of messenger RNA can generate an array of mature transcripts, but it is not clear how many go on to produce functionally relevant protein isoforms. There is only limited evidence for alternative proteins in proteomics analyses and data from population genetic variation studies indicate that most alternative exons are evolving neutrally. Determining which transcripts produce biologically important isoforms is key to understanding isoform function and to interpreting the real impact of somatic mutations and germline variations. Here we have developed a method, TRIFID, to classify the functional importance of splice isoforms. TRIFID was trained on isoforms detected in large-scale proteomics analyses and distinguishes these biologically important splice isoforms with high confidence. Isoforms predicted as functionally important by the algorithm had measurable cross species conservation and significantly fewer broken functional domains. Additionally, exons that code for these functionally important protein isoforms are under purifying selection, while exons from low scoring transcripts largely appear to be evolving neutrally. TRIFID has been developed for the human genome, but it could in principle be applied to other well-annotated species. We believe that this method will generate valuable insights into the cellular importance of alternative splicing.
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Affiliation(s)
- Fernando Pozo
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Laura Martinez-Gomez
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Thomas A Walsh
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - José Manuel Rodriguez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Tomas Di Domenico
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Federico Abascal
- Somatic Evolution Group, Wellcome Sanger Institute, Hinxton CB10 1SA, UK
| | - Jesús Vazquez
- Cardiovascular Proteomics Laboratory, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Michael L Tress
- Bioinformatics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
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15
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Huang Q, Wang J, Zhang X, Guo M, Yu G. IsoDA: Isoform-Disease Association Prediction by Multiomics Data Fusion. J Comput Biol 2021; 28:804-819. [PMID: 33826865 DOI: 10.1089/cmb.2020.0626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
A gene can be spliced into different isoforms by alternative splicing, which contributes to the functional diversity of protein species. Computational prediction of gene-disease associations (GDAs) has been studied for decades. However, the process of identifying the isoform-disease associations (IDAs) at a large scale is rarely explored, which can decipher the pathology at a more granular level. The main bottleneck is the lack of IDAs in current databases and the multilevel omics data fusion. To bridge this gap, we propose a computational approach called Isoform-Disease Association prediction by multiomics data fusion (IsoDA) to predict IDAs. Based on the relationship between a gene and its spliced isoforms, IsoDA first introduces a dispatch and aggregation term to dispatch gene-disease associations to individual isoforms, and reversely aggregate these dispatched associations to their hosting genes. At the same time, it fuses the genome, transcriptome, and proteome data by joint matrix factorization to improve the prediction of IDAs. Experimental results show that IsoDA significantly outperforms the related state-of-the-art methods at both the gene level and isoform level. A case study further shows that IsoDA credibly identifies three isoforms spliced from apolipoprotein E, which have individual associations with Alzheimer's disease, and two isoforms spliced from vascular endothelial growth factor A, which have different associations with coronary heart disease. The codes of IsoDA are available at http://mlda.swu.edu.cn/codes.php?name=IsoDA.
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Affiliation(s)
- Qiuyue Huang
- College of Computer and Information Science, Southwest University, Chongqing, China.,School of Software, Shandong University, Jinan, China
| | - Jun Wang
- School of Software, Shandong University, Jinan, China
| | - Xiangliang Zhang
- Department of Computer Science, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Maozu Guo
- Department of Computer Science, College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
| | - Guoxian Yu
- College of Computer and Information Science, Southwest University, Chongqing, China.,School of Software, Shandong University, Jinan, China.,Department of Computer Science, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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16
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Yao Z, Zhang J, Zou X. A general index for linear and nonlinear correlations for high dimensional genomic data. BMC Genomics 2020; 21:846. [PMID: 33256599 PMCID: PMC7706065 DOI: 10.1186/s12864-020-07246-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/18/2020] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND With the advance of high throughput sequencing, high-dimensional data are generated. Detecting dependence/correlation between these datasets is becoming one of most important issues in multi-dimensional data integration and co-expression network construction. RNA-sequencing data is widely used to construct gene regulatory networks. Such networks could be more accurate when methylation data, copy number aberration data and other types of data are introduced. Consequently, a general index for detecting relationships between high-dimensional data is indispensable. RESULTS We proposed a Kernel-Based RV-coefficient, named KBRV, for testing both linear and nonlinear correlation between two matrices by introducing kernel functions into RV2 (the modified RV-coefficient). Permutation test and other validation methods were used on simulated data to test the significance and rationality of KBRV. In order to demonstrate the advantages of KBRV in constructing gene regulatory networks, we applied this index on real datasets (ovarian cancer datasets and exon-level RNA-Seq data in human myeloid differentiation) to illustrate its superiority over vector correlation. CONCLUSIONS We concluded that KBRV is an efficient index for detecting both linear and nonlinear relationships in high dimensional data. The correlation method for high dimensional data has possible applications in the construction of gene regulatory network.
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Affiliation(s)
- Zhihao Yao
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 China
- Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072 China
| | - Jing Zhang
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 China
- Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072 China
| | - Xiufen Zou
- School of Mathematics and Statistics, Wuhan University, Wuhan, 430072 China
- Hubei Key Laboratory of Computational Science, Wuhan University, Wuhan, 430072 China
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17
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Yu G, Wang K, Domeniconi C, Guo M, Wang J. Isoform function prediction based on bi-random walks on a heterogeneous network. Bioinformatics 2020; 36:303-310. [PMID: 31250882 DOI: 10.1093/bioinformatics/btz535] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 06/21/2019] [Accepted: 06/26/2019] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION Alternative splicing contributes to the functional diversity of protein species and the proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions. Computationally predicting the functions of genes has been studied for decades. However, how to distinguish the functional annotations of isoforms, whose annotations are essential for understanding developmental abnormalities and cancers, is rarely explored. The main bottleneck is that functional annotations of isoforms are generally unavailable and functional genomic databases universally store the functional annotations at the gene level. RESULTS We propose IsoFun to accomplish Isoform Function prediction based on bi-random walks on a heterogeneous network. IsoFun firstly constructs an isoform functional association network based on the expression profiles of isoforms derived from multiple RNA-seq datasets. Next, IsoFun uses the available Gene Ontology annotations of genes, gene-gene interactions and the relations between genes and isoforms to construct a heterogeneous network. After this, IsoFun performs a tailored bi-random walk on the heterogeneous network to predict the association between GO terms and isoforms, thus accomplishing the prediction of GO annotations of isoforms. Experimental results show that IsoFun significantly outperforms the state-of-the-art algorithms and improves the area under the receiver-operating curve (AUROC) and the area under the precision-recall curve (AUPRC) by 17% and 44% at the gene-level, respectively. We further validated the performance of IsoFun on the genes ADAM15 and BCL2L1. IsoFun accurately differentiates the functions of respective isoforms of these two genes. AVAILABILITY AND IMPLEMENTATION The code of IsoFun is available at http://mlda.swu.edu.cn/codes.php? name=IsoFun. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Guoxian Yu
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Keyao Wang
- College of Computer and Information Science, Southwest University, Chongqing, China
| | - Carlotta Domeniconi
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
| | - Maozu Guo
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China.,Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing, China
| | - Jun Wang
- College of Computer and Information Science, Southwest University, Chongqing, China
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18
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Mishra SK, Muthye V, Kandoi G. Computational Methods for Predicting Functions at the mRNA Isoform Level. Int J Mol Sci 2020; 21:ijms21165686. [PMID: 32784445 PMCID: PMC7460821 DOI: 10.3390/ijms21165686] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/05/2020] [Accepted: 08/06/2020] [Indexed: 11/16/2022] Open
Abstract
Multiple mRNA isoforms of the same gene are produced via alternative splicing, a biological mechanism that regulates protein diversity while maintaining genome size. Alternatively spliced mRNA isoforms of the same gene may sometimes have very similar sequence, but they can have significantly diverse effects on cellular function and regulation. The products of alternative splicing have important and diverse functional roles, such as response to environmental stress, regulation of gene expression, human heritable, and plant diseases. The mRNA isoforms of the same gene can have dramatically different functions. Despite the functional importance of mRNA isoforms, very little has been done to annotate their functions. The recent years have however seen the development of several computational methods aimed at predicting mRNA isoform level biological functions. These methods use a wide array of proteo-genomic data to develop machine learning-based mRNA isoform function prediction tools. In this review, we discuss the computational methods developed for predicting the biological function at the individual mRNA isoform level.
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19
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Sulakhe D, D'Souza M, Wang S, Balasubramanian S, Athri P, Xie B, Canzar S, Agam G, Gilliam TC, Maltsev N. Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources. Brief Bioinform 2020; 20:1754-1768. [PMID: 29931155 DOI: 10.1093/bib/bby047] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 05/02/2018] [Indexed: 12/30/2022] Open
Abstract
In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.
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Affiliation(s)
- Dinanath Sulakhe
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, USA
| | - Mark D'Souza
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA
| | - Sheng Wang
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL, USA
| | - Sandhya Balasubramanian
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Genentech, Inc. 1 DNA Way, Mail Stop: 35-6J, South San Francisco, CA, USA
| | - Prashanth Athri
- Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, Kasavanahalli, Carmelaram P.O., Bengaluru, Karnataka, India
| | - Bingqing Xie
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA
| | - Stefan Canzar
- Toyota Technological Institute at Chicago, 6045 S. Kenwood Avenue, Chicago, IL, USA.,Gene Center, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Gady Agam
- Department of Computer Science, Illinois Institute of Technology, Chicago, IL, USA
| | - T Conrad Gilliam
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, USA
| | - Natalia Maltsev
- Department of Human Genetics, University of Chicago, 920 E. 58th Street, Chicago, IL, USA.,Computation Institute, University of Chicago, 5735 S. Ellis Avenue, Chicago, IL, USA
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20
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Shaw D, Chen H, Jiang T. DeepIsoFun: a deep domain adaptation approach to predict isoform functions. Bioinformatics 2020; 35:2535-2544. [PMID: 30535380 DOI: 10.1093/bioinformatics/bty1017] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 11/07/2018] [Accepted: 12/08/2018] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Isoforms are mRNAs produced from the same gene locus by alternative splicing and may have different functions. Although gene functions have been studied extensively, little is known about the specific functions of isoforms. Recently, some computational approaches based on multiple instance learning have been proposed to predict isoform functions from annotated gene functions and expression data, but their performance is far from being desirable primarily due to the lack of labeled training data. To improve the performance on this problem, we propose a novel deep learning method, DeepIsoFun, that combines multiple instance learning with domain adaptation. The latter technique helps to transfer the knowledge of gene functions to the prediction of isoform functions and provides additional labeled training data. Our model is trained on a deep neural network architecture so that it can adapt to different expression distributions associated with different gene ontology terms. RESULTS We evaluated the performance of DeepIsoFun on three expression datasets of human and mouse collected from SRA studies at different times. On each dataset, DeepIsoFun performed significantly better than the existing methods. In terms of area under the receiver operating characteristics curve, our method acquired at least 26% improvement and in terms of area under the precision-recall curve, it acquired at least 10% improvement over the state-of-the-art methods. In addition, we also study the divergence of the functions predicted by our method for isoforms from the same gene and the overall correlation between expression similarity and the similarity of predicted functions. AVAILABILITY AND IMPLEMENTATION https://github.com/dls03/DeepIsoFun/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dipan Shaw
- Department of Computer Science and Engineering, University of California, Riverside, CA, USA
| | - Hao Chen
- Department of Computer Science and Engineering, University of California, Riverside, CA, USA
| | - Tao Jiang
- Department of Computer Science and Engineering, University of California, Riverside, CA, USA.,Bioinformatics Division, BNRIST/Department of Computer Science and Technology, Tsinghua University, Beijing, China
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21
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Wang D, Zou X, Fai Au K. A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data. Methods 2020; 189:54-64. [PMID: 32534132 DOI: 10.1016/j.ymeth.2020.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 05/22/2020] [Accepted: 06/06/2020] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION Alternative splicing makes significant contributions to functional diversity of transcripts and proteins. Many alternatively spliced gene isoforms have been shown to perform specific biological functions under different contexts. In addition to gene-level expression, the advances of high-throughput sequencing offer a chance to estimate isoform-specific exon expression with a high resolution, which is informative for studying splice variants with network analysis. RESULTS In this study, we propose a novel network-based analysis framework to predict isoform-specific functions from exon-level RNA-Seq data. In particular, based on exon-level expression data, we firstly propose a unified framework, referred to as Iso-Net, to integrate two new mathematical methods (named MINet and RVNet) that infer co-expression networks at different data scenarios. We demonstrate the superior prediction accuracy of Iso-Net over the existing methods for most simulation data, especially in two extreme cases: sample size is very small and exon numbers of two isoforms are quite different. Furthermore, by defining relevant quantitative measures (e.g., Jaccard correlation coefficient) and combining differential co-expression network analysis and GO functional enrichment analysis, a co-expression network analysis framework is developed to predict functions of isoforms and further, to discover their distinct functions within the same gene. We apply Iso-Net to study gene isoforms for several important transcription factors in human myeloid differentiation with the exon-level RNA-Seq data from three different cell lines. AVAILABILITY AND IMPLEMENTATION Iso-Net is open source and freely available from https://github.com/Dingjie-Wang/Iso-Net.
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Affiliation(s)
- Dingjie Wang
- Department of Biomedical Informatics, The Ohio State University, OH 43210, USA; School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China; Computational Science Hubei Key Laboratory, Wuhan University, Wuhan 430072, China
| | - Xiufen Zou
- School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China; Computational Science Hubei Key Laboratory, Wuhan University, Wuhan 430072, China.
| | - Kin Fai Au
- Department of Biomedical Informatics, The Ohio State University, OH 43210, USA.
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22
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ISOGO: Functional annotation of protein-coding splice variants. Sci Rep 2020; 10:1069. [PMID: 31974522 PMCID: PMC6978412 DOI: 10.1038/s41598-020-57974-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 01/07/2020] [Indexed: 12/25/2022] Open
Abstract
The advent of RNA-seq technologies has switched the paradigm of genetic analysis from a genome to a transcriptome-based perspective. Alternative splicing generates functional diversity in genes, but the precise functions of many individual isoforms are yet to be elucidated. Gene Ontology was developed to annotate gene products according to their biological processes, molecular functions and cellular components. Despite a single gene may have several gene products, most annotations are not isoform-specific and do not distinguish the functions of the different proteins originated from a single gene. Several approaches have tried to automatically annotate ontologies at the isoform level, but this has shown to be a daunting task. We have developed ISOGO (ISOform + GO function imputation), a novel algorithm to predict the function of coding isoforms based on their protein domains and their correlation of expression along 11,373 cancer patients. Combining these two sources of information outperforms previous approaches: it provides an area under precision-recall curve (AUPRC) five times larger than previous attempts and the median AUROC of assigned functions to genes is 0.82. We tested ISOGO predictions on some genes with isoform-specific functions (BRCA1, MADD,VAMP7 and ITSN1) and they were coherent with the literature. Besides, we examined whether the main isoform of each gene -as predicted by APPRIS- was the most likely to have the annotated gene functions and it occurs in 99.4% of the genes. We also evaluated the predictions for isoform-specific functions provided by the CAFA3 challenge and results were also convincing. To make these results available to the scientific community, we have deployed a web application to consult ISOGO predictions (https://biotecnun.unav.es/app/isogo). Initial data, website link, isoform-specific GO function predictions and R code is available at https://gitlab.com/icassol/isogo.
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23
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Isoform-Disease Association Prediction by Data Fusion. BIOINFORMATICS RESEARCH AND APPLICATIONS 2020. [DOI: 10.1007/978-3-030-57821-3_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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24
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Abstract
Alternative Splicing produces multiple mRNA isoforms of genes which have important diverse roles such as regulation of gene expression, human heritable diseases, and response to environmental stresses. However, little has been done to assign functions at the mRNA isoform level. Functional networks, where the interactions are quantified by their probability of being involved in the same biological process are typically generated at the gene level. We use a diverse array of tissue-specific RNA-seq datasets and sequence information to train random forest models that predict the functional networks. Since there is no mRNA isoform-level gold standard, we use single isoform genes co-annotated to Gene Ontology biological process annotations, Kyoto Encyclopedia of Genes and Genomes pathways, BioCyc pathways and protein-protein interactions as functionally related (positive pair). To generate the non-functional pairs (negative pair), we use the Gene Ontology annotations tagged with "NOT" qualifier. We describe 17 Tissue-spEcific mrNa iSoform functIOnal Networks (TENSION) following a leave-one-tissue-out strategy in addition to an organism level reference functional network for mouse. We validate our predictions by comparing its performance with previous methods, randomized positive and negative class labels, updated Gene Ontology annotations, and by literature evidence. We demonstrate the ability of our networks to reveal tissue-specific functional differences of the isoforms of the same genes. All scripts and data from TENSION are available at: https://doi.org/10.25380/iastate.c.4275191 .
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Affiliation(s)
- Gaurav Kandoi
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA
| | - Julie A Dickerson
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA, USA.
- Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA.
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25
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Chen H, Shaw D, Zeng J, Bu D, Jiang T. DIFFUSE: predicting isoform functions from sequences and expression profiles via deep learning. Bioinformatics 2019; 35:i284-i294. [PMID: 31510699 PMCID: PMC6612874 DOI: 10.1093/bioinformatics/btz367] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
MOTIVATION Alternative splicing generates multiple isoforms from a single gene, greatly increasing the functional diversity of a genome. Although gene functions have been well studied, little is known about the specific functions of isoforms, making accurate prediction of isoform functions highly desirable. However, the existing approaches to predicting isoform functions are far from satisfactory due to at least two reasons: (i) unlike genes, isoform-level functional annotations are scarce. (ii) The information of isoform functions is concealed in various types of data including isoform sequences, co-expression relationship among isoforms, etc. RESULTS In this study, we present a novel approach, DIFFUSE (Deep learning-based prediction of IsoForm FUnctions from Sequences and Expression), to predict isoform functions. To integrate various types of data, our approach adopts a hybrid framework by first using a deep neural network (DNN) to predict the functions of isoforms from their genomic sequences and then refining the prediction using a conditional random field (CRF) based on co-expression relationship. To overcome the lack of isoform-level ground truth labels, we further propose an iterative semi-supervised learning algorithm to train both the DNN and CRF together. Our extensive computational experiments demonstrate that DIFFUSE could effectively predict the functions of isoforms and genes. It achieves an average area under the receiver operating characteristics curve of 0.840 and area under the precision-recall curve of 0.581 over 4184 GO functional categories, which are significantly higher than the state-of-the-art methods. We further validate the prediction results by analyzing the correlation between functional similarity, sequence similarity, expression similarity and structural similarity, as well as the consistency between the predicted functions and some well-studied functional features of isoform sequences. AVAILABILITY AND IMPLEMENTATION https://github.com/haochenucr/DIFFUSE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hao Chen
- Department of Compute Science and Engineering, University of California, Riverside, CA, USA
| | - Dipan Shaw
- Department of Compute Science and Engineering, University of California, Riverside, CA, USA
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Dongbo Bu
- Key Lab of Intelligent Information Process, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Tao Jiang
- Department of Compute Science and Engineering, University of California, Riverside, CA, USA
- Bioinformatics Division, BNRIST/Department of Computer Science and Technology, Tsinghua University, Beijing, China
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26
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Yang Z, Liu Z, Meng L, Ma S. Identification of key pathways regulated by a set of competitive long non-coding RNAs in oral squamous cell carcinoma. J Int Med Res 2019; 47:1758-1765. [PMID: 30862271 PMCID: PMC6460590 DOI: 10.1177/0300060519827190] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Objective The aim of this study was to identify important pathways regulated by a set of long non-coding RNAs (lncRNAs) in oral squamous cell carcinoma (OSCC). Methods A lncRNA-mediated competitive endogenous RNA network (LMCN) was constructed using information on microRNA (miRNA)–mRNA interactions and lncRNA–miRNA intersections from the E-GEOD-37991 transcription profiling data in the ArrayExpress database. A random walk with restart ranking algorithm was then applied to evaluate the influences of protein-coding genes regulated by competitive lncRNAs. Pathway enrichment scores were calculated based on the propagation scores of protein-coding genes. Finally, permutation tests were used to estimate the significance of the pathways. Results We obtained lncRNA–mRNA interactions based on miRNAs common to both miRNA–mRNA interactions and lncRNA–miRNA intersections, and used interactions with a z-score > 0.7 to construct a LMCN. Ten lncRNAs were identified as source nodes in the LMCN, and nine pathways with enrichment scores >0.8, including ‘Cell cycle’, ‘Endocytosis’, and ‘Pathways in cancer’, were significantly enriched by these source nodes. Conclusions Nine significant pathways regulated by a set of competitive lncRNAs were identified in OSCC, which may play important roles in the development of OSCC via the cell cycle and endocytosis.
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Affiliation(s)
- Zhifeng Yang
- Department of Stomatology, The Second People's Hospital of Liaocheng, Shandong Province, P. R. China
| | - Zili Liu
- Department of Stomatology, The Second People's Hospital of Liaocheng, Shandong Province, P. R. China
| | - Lingqiu Meng
- Department of Stomatology, The Second People's Hospital of Liaocheng, Shandong Province, P. R. China
| | - Shuyan Ma
- Department of Stomatology, The Second People's Hospital of Liaocheng, Shandong Province, P. R. China
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Saha A, Kim Y, Gewirtz ADH, Jo B, Gao C, McDowell IC, Engelhardt BE, Battle A. Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Res 2017; 27:1843-1858. [PMID: 29021288 PMCID: PMC5668942 DOI: 10.1101/gr.216721.116] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Accepted: 08/22/2017] [Indexed: 11/24/2022]
Abstract
Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.
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LncSubpathway: a novel approach for identifying dysfunctional subpathways associated with risk lncRNAs by integrating lncRNA and mRNA expression profiles and pathway topologies. Oncotarget 2017; 8:15453-15469. [PMID: 28152521 PMCID: PMC5362499 DOI: 10.18632/oncotarget.14973] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Accepted: 01/10/2017] [Indexed: 02/01/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) play important roles in various biological processes, including the development of many diseases. Pathway analysis is a valuable aid for understanding the cellular functions of these transcripts. We have developed and characterized LncSubpathway, a novel method that integrates lncRNA and protein coding gene (PCG) expression with interactome data to identify disease risk subpathways that functionally associated with risk lncRNAs. LncSubpathway identifies the most relevance regions which are related with risk lncRNA set and implicated with study conditions through simultaneously considering the dysregulation extent of lncRNAs, PCGs and their correlations. Simulation studies demonstrated that the sensitivity and false positive rates of LncSubpathway were within acceptable ranges, and that LncSubpathway could accurately identify dysregulated regions that related with disease risk lncRNAs within pathways. When LncSubpathway was applied to colorectal carcinoma and breast cancer subtype datasets, it identified cancer type- and breast cancer subtype-related meaningful subpathways. Further, analysis of its robustness and reproducibility indicated that LncSubpathway was a reliable means of identifying subpathways that functionally associated with lncRNAs. LncSubpathway is freely available at http://www.bio-bigdata.com/lncSubpathway/.
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LncRNAs2Pathways: Identifying the pathways influenced by a set of lncRNAs of interest based on a global network propagation method. Sci Rep 2017; 7:46566. [PMID: 28425476 PMCID: PMC5397852 DOI: 10.1038/srep46566] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 03/22/2017] [Indexed: 02/06/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) have been demonstrated to play essential roles in diverse cellular processes and biological functions. Exploring the functions associated with lncRNAs may help provide insight into their underlying biological mechanisms. The current methods primarily focus on investigating the functions of individual lncRNAs; however, essential biological functions may be affected by the combinatorial effects of multiple lncRNAs. Here, we have developed a novel computational method, LncRNAs2Pathways, to identify the functional pathways influenced by the combinatorial effects of a set of lncRNAs of interest based on a global network propagation algorithm. A new Kolmogorov–Smirnov-like statistical measure weighted by the network propagation score, which considers the expression correlation among lncRNAs and coding genes, was used to evaluate the biological pathways influenced by the lncRNAs of interest. We have described the LncRNAs2Pathways methodology and illustrated its effectiveness by analyzing three lncRNA sets associated with glioma, prostate and pancreatic cancers. We further analyzed the reproducibility and robustness and compared our results with those of two other methods. Based on these analyses, we showed that LncRNAs2Pathways can effectively identify the functional pathways associated with lncRNA sets. Finally, we implemented this method as a freely available R-based tool.
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Chaitankar V, Karakülah G, Ratnapriya R, Giuste FO, Brooks MJ, Swaroop A. Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research. Prog Retin Eye Res 2016; 55:1-31. [PMID: 27297499 DOI: 10.1016/j.preteyeres.2016.06.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 06/06/2016] [Accepted: 06/08/2016] [Indexed: 02/08/2023]
Abstract
The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.
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Affiliation(s)
- Vijender Chaitankar
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Gökhan Karakülah
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Rinki Ratnapriya
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Felipe O Giuste
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Matthew J Brooks
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Anand Swaroop
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA.
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Panwar B, Menon R, Eksi R, Li HD, Omenn GS, Guan Y. Genome-Wide Functional Annotation of Human Protein-Coding Splice Variants Using Multiple Instance Learning. J Proteome Res 2016; 15:1747-53. [PMID: 27142340 DOI: 10.1021/acs.jproteome.5b00883] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The vast majority of human multiexon genes undergo alternative splicing and produce a variety of splice variant transcripts and proteins, which can perform different functions. These protein-coding splice variants (PCSVs) greatly increase the functional diversity of proteins. Most functional annotation algorithms have been developed at the gene level; the lack of isoform-level gold standards is an important intellectual limitation for currently available machine learning algorithms. The accumulation of a large amount of RNA-seq data in the public domain greatly increases our ability to examine the functional annotation of genes at isoform level. In the present study, we used a multiple instance learning (MIL)-based approach for predicting the function of PCSVs. We used transcript-level expression values and gene-level functional associations from the Gene Ontology database. A support vector machine (SVM)-based 5-fold cross-validation technique was applied. Comparatively, genes with multiple PCSVs performed better than single PCSV genes, and performance also improved when more examples were available to train the models. We demonstrated our predictions using literature evidence of ADAM15, LMNA/C, and DMXL2 genes. All predictions have been implemented in a web resource called "IsoFunc", which is freely available for the global scientific community through http://guanlab.ccmb.med.umich.edu/isofunc .
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Affiliation(s)
- Bharat Panwar
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Rajasree Menon
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Ridvan Eksi
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Hong-Dong Li
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
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Abstract
The laboratory mouse is the primary mammalian species used for studying alternative splicing events. Recent studies have generated computational models to predict functions for splice isoforms in the mouse. However, the functional relationship network, describing the probability of splice isoforms participating in the same biological process or pathway, has not yet been studied in the mouse. Here we describe a rich genome-wide resource of mouse networks at the isoform level, which was generated using a unique framework that was originally developed to infer isoform functions. This network was built through integrating heterogeneous genomic and protein data, including RNA-seq, exon array, protein docking and pseudo-amino acid composition. Through simulation and cross-validation studies, we demonstrated the accuracy of the algorithm in predicting isoform-level functional relationships. We showed that this network enables the users to reveal functional differences of the isoforms of the same gene, as illustrated by literature evidence with Anxa6 (annexin a6) as an example. We expect this work will become a useful resource for the mouse genetics community to understand gene functions. The network is publicly available at: http://guanlab.ccmb.med.umich.edu/isoformnetwork.
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Li HD, Omenn GS, Guan Y. A proteogenomic approach to understand splice isoform functions through sequence and expression-based computational modeling. Brief Bioinform 2016; 17:1024-1031. [PMID: 26740460 DOI: 10.1093/bib/bbv109] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 11/03/2015] [Indexed: 01/23/2023] Open
Abstract
The products of multi-exon genes are a mixture of alternatively spliced isoforms, from which the translated proteins can have similar, different or even opposing functions. It is therefore essential to differentiate and annotate functions for individual isoforms. Computational approaches provide an efficient complement to expensive and time-consuming experimental studies. The input data of these methods range from DNA sequence, to RNA selection pressure, to expressed sequence tags, to full-length complementary DNA, to exon array, to RNA-seq expression, to proteomic data. Notably, RNA-seq technology generates quantitative profiling of transcript expression at the genome scale, with an unprecedented amount of expression data available for developing isoform function prediction methods. Integrative analysis of these data at different molecular levels enables a proteogenomic approach to systematically interrogate isoform functions. Here, we briefly review the state-of-the-art methods according to their input data sources, discuss their advantages and limitations and point out potential ways to improve prediction accuracies.
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34
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Zhang W, Chang JW, Lin L, Minn K, Wu B, Chien J, Yong J, Zheng H, Kuang R. Network-Based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis. PLoS Comput Biol 2015; 11:e1004465. [PMID: 26699225 PMCID: PMC4689380 DOI: 10.1371/journal.pcbi.1004465] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Accepted: 07/11/2015] [Indexed: 11/18/2022] Open
Abstract
High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-Seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification. Net-RSTQ toolbox is available at http://compbio.cs.umn.edu/Net-RSTQ/.
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Affiliation(s)
- Wei Zhang
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Jae-Woong Chang
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Lilong Lin
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Peoples Republic of China
| | - Kay Minn
- Department of Cancer Biology, University of Kansas Medical Center, Kansas City, Kansas, United States of America
| | - Baolin Wu
- Division of Biostatistics, School of Public Health, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Jeremy Chien
- Department of Cancer Biology, University of Kansas Medical Center, Kansas City, Kansas, United States of America
| | - Jeongsik Yong
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Hui Zheng
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Peoples Republic of China
| | - Rui Kuang
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
- * E-mail:
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35
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Li JR, Sun CH, Li W, Chao RF, Huang CC, Zhou XJ, Liu CC. Cancer RNA-Seq Nexus: a database of phenotype-specific transcriptome profiling in cancer cells. Nucleic Acids Res 2015; 44:D944-51. [PMID: 26602695 PMCID: PMC4702907 DOI: 10.1093/nar/gkv1282] [Citation(s) in RCA: 89] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2015] [Accepted: 11/04/2015] [Indexed: 02/04/2023] Open
Abstract
The genome-wide transcriptome profiling of cancerous and normal tissue samples can provide insights into the molecular mechanisms of cancer initiation and progression. RNA Sequencing (RNA-Seq) is a revolutionary tool that has been used extensively in cancer research. However, no existing RNA-Seq database provides all of the following features: (i) large-scale and comprehensive data archives and analyses, including coding-transcript profiling, long non-coding RNA (lncRNA) profiling and coexpression networks; (ii) phenotype-oriented data organization and searching and (iii) the visualization of expression profiles, differential expression and regulatory networks. We have constructed the first public database that meets these criteria, the Cancer RNA-Seq Nexus (CRN, http://syslab4.nchu.edu.tw/CRN). CRN has a user-friendly web interface designed to facilitate cancer research and personalized medicine. It is an open resource for intuitive data exploration, providing coding-transcript/lncRNA expression profiles to support researchers generating new hypotheses in cancer research and personalized medicine.
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Affiliation(s)
- Jian-Rong Li
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan PhD Program in Medical Biotechnology National Chung Hsing University, Taichung 402, Taiwan
| | - Chuan-Hu Sun
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan
| | - Wenyuan Li
- Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Rou-Fang Chao
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan
| | - Chieh-Chen Huang
- Department of Life Sciences, National Chung Hsing University, Taichung 402, Taiwan
| | - Xianghong Jasmine Zhou
- Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Chun-Chi Liu
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 402, Taiwan PhD Program in Medical Biotechnology National Chung Hsing University, Taichung 402, Taiwan
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Sinha A, Nagarajaram HA. Nodes occupying central positions in human tissue specific PPI networks are enriched with many splice variants. Proteomics 2014; 14:2242-8. [PMID: 25092398 DOI: 10.1002/pmic.201400249] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 07/04/2014] [Accepted: 08/01/2014] [Indexed: 12/22/2022]
Abstract
The functional repertoire of genes in the eukaryotic organisms is enhanced by the phenomenon of alternative splicing. Hence, a node in a tissue specific protein-protein interaction (TS PPIN) network can be thought of as an ensemble of various spliced protein products of the corresponding gene expressed in that tissue. Here we demonstrate that the nodes that occupy topologically central positions characterized by high degree, betweenness, closeness, and eigenvector centrality values in TS PPINs of Homo sapiens are associated with high number of splice variants. We also show that the high "centrality" of these genes/nodes could in part be explained by the presence of a large number of promiscuous domains.
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Affiliation(s)
- Anupam Sinha
- Laboratory of Computational Biology, Centre for DNA Fingerprinting & Diagnostics (CDFD), Hyderabad, Telangana, India
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Li HD, Menon R, Omenn GS, Guan Y. The emerging era of genomic data integration for analyzing splice isoform function. Trends Genet 2014; 30:340-7. [PMID: 24951248 DOI: 10.1016/j.tig.2014.05.005] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Revised: 05/21/2014] [Accepted: 05/23/2014] [Indexed: 01/17/2023]
Abstract
The vast majority of multi-exon genes in humans undergo alternative splicing, which greatly increases the functional diversity of protein species. Predicting functions at the isoform level is essential to further our understanding of developmental abnormalities and cancers, which frequently exhibit aberrant splicing and dysregulation of isoform expression. However, determination of isoform function is very difficult, and efforts to predict isoform function have been limited in the functional genomics field. Deep sequencing of RNA now provides an unprecedented amount of expression data at the transcript level. We describe here emerging computational approaches that integrate such large-scale whole-transcriptome sequencing (RNA-seq) data for predicting the functions of alternatively spliced isoforms, and we discuss their applications in developmental and cancer biology. We outline future directions for isoform function prediction, emphasizing the need for heterogeneous genomic data integration and tissue-specific, dynamic isoform-level network modeling, which will allow the field to realize its full potential.
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Affiliation(s)
- Hong-Dong Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Rajasree Menon
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, MI, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, MI, USA; Department of Electrical Engineering and Computer Science, Ann Arbor, MI, USA.
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38
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Li W, Dai C, Kang S, Zhou XJ. Integrative analysis of many RNA-seq datasets to study alternative splicing. Methods 2014; 67:313-24. [PMID: 24583115 DOI: 10.1016/j.ymeth.2014.02.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Revised: 02/17/2014] [Accepted: 02/19/2014] [Indexed: 12/19/2022] Open
Abstract
Alternative splicing is an important gene regulatory mechanism that dramatically increases the complexity of the proteome. However, how alternative splicing is regulated and how transcription and splicing are coordinated are still poorly understood, and functions of transcript isoforms have been studied only in a few limited cases. Nowadays, RNA-seq technology provides an exceptional opportunity to study alternative splicing on genome-wide scales and in an unbiased manner. With the rapid accumulation of data in public repositories, new challenges arise from the urgent need to effectively integrate many different RNA-seq datasets for study alterative splicing. This paper discusses a set of advanced computational methods that can integrate and analyze many RNA-seq datasets to systematically identify splicing modules, unravel the coupling of transcription and splicing, and predict the functions of splicing isoforms on a genome-wide scale.
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Affiliation(s)
- Wenyuan Li
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Chao Dai
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Shuli Kang
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Xianghong Jasmine Zhou
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.
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