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Menor-Flores M, Vega-Rodríguez MA. A protein-protein interaction network aligner study in the multi-objective domain. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108188. [PMID: 38657382 DOI: 10.1016/j.cmpb.2024.108188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 04/14/2024] [Accepted: 04/17/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND AND OBJECTIVE The protein-protein interaction (PPI) network alignment has proven to be an efficient technique in the diagnosis and prevention of certain diseases. However, the difficulty in maximizing, at the same time, the two qualities that measure the goodness of alignments (topological and biological quality) has led aligners to produce very different alignments. Thus making a comparative study among alignments of such different qualities a big challenge. Multi-objective optimization is a computer method, which is very powerful in this kind of contexts because both conflicting qualities are considered together. Analysing the alignments of each PPI network aligner with multi-objective methodologies allows you to visualize a bigger picture of the alignments and their qualities, obtaining very interesting conclusions. This paper proposes a comprehensive PPI network aligner study in the multi-objective domain. METHODS Alignments from each aligner and all aligners together were studied and compared to each other via Pareto dominance methodologies. The best alignments produced by each aligner and all aligners together for five different alignment scenarios were displayed in Pareto front graphs. Later, the aligners were ranked according to the topological, biological, and combined quality of their alignments. Finally, the aligners were also ranked based on their average runtimes. RESULTS Regarding aligners constructing the best overall alignments, we found that SAlign, BEAMS, SANA, and HubAlign are the best options. Additionally, the alignments of best topological quality are produced by: SANA, SAlign, and HubAlign aligners. On the contrary, the aligners returning the alignments of best biological quality are: BEAMS, TAME, and WAVE. However, if there are time constraints, it is recommended to select SAlign to obtain high topological quality alignments and PISwap or SAlign aligners for high biological quality alignments. CONCLUSIONS The use of the SANA aligner is recommended for obtaining the best alignments of topological quality, BEAMS for alignments of the best biological quality, and SAlign for alignments of the best combined topological and biological quality. Simultaneously, SANA and BEAMS have above-average runtimes. Therefore, it is suggested, if necessary due to time restrictions, to choose other, faster aligners like SAlign or PISwap whose alignments are also of high quality.
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Affiliation(s)
- Manuel Menor-Flores
- Escuela Politécnica, Universidad de Extremadura,(1) Campus Universitario s/n, 10003 Cáceres, Spain.
| | - Miguel A Vega-Rodríguez
- Escuela Politécnica, Universidad de Extremadura,(1) Campus Universitario s/n, 10003 Cáceres, Spain.
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2
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Ayub U, Naveed H. GSLAlign: community detection and local PPI network alignment. J Biomol Struct Dyn 2024:1-9. [PMID: 38214492 DOI: 10.1080/07391102.2024.2301757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 12/29/2023] [Indexed: 01/13/2024]
Abstract
High throughput protein-protein interaction (PPI) profiling and computational techniques have resulted in generating a large amount of PPI network data. The study of PPI networks helps in understanding the biological processes of the proteins. The comparative study of the PPI networks helps in identifying the conserved interactions across the species. This article presents a novel local PPI network aligner 'GSLAlign' that consists of two stages. It first detects the communities from the PPI networks by applying the GraphSAGE algorithm using gene expression data. In the second stage, the detected communities are aligned using a community aligner that is based on protein sequence similarity. The community detection algorithm produces more separable and biologically accurate communities as compared to previous community detection algorithms. Moreover, the proposed community alignment algorithm achieves 3-8% better results in terms of semantic similarity as compared to previous local aligners. The average connectivity and coverage of the proposed algorithm are also better than the existing aligners.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Umair Ayub
- Department of Computer Science, Bahria University, Lahore, Pakistan
| | - Hammad Naveed
- National University of Computer and Emerging Sciences, Lahore, Pakistan and Computational Biology Research Lab, National University of Computer and Emerging Sciences, Lahore, Pakistan
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3
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Rodríguez-López M, Bordin N, Lees J, Scholes H, Hassan S, Saintain Q, Kamrad S, Orengo C, Bähler J. Broad functional profiling of fission yeast proteins using phenomics and machine learning. eLife 2023; 12:RP88229. [PMID: 37787768 PMCID: PMC10547477 DOI: 10.7554/elife.88229] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023] Open
Abstract
Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of 'priority unstudied' proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through 'guilt by association' with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.
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Affiliation(s)
- María Rodríguez-López
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
| | - Nicola Bordin
- University College London, Institute of Structural and Molecular BiologyLondonUnited Kingdom
| | - Jon Lees
- University College London, Institute of Structural and Molecular BiologyLondonUnited Kingdom
- University of BristolBristolUnited Kingdom
| | - Harry Scholes
- University College London, Institute of Structural and Molecular BiologyLondonUnited Kingdom
| | - Shaimaa Hassan
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
- Helwan University, Faculty of PharmacyCairoEgypt
| | - Quentin Saintain
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
| | - Stephan Kamrad
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
| | - Christine Orengo
- University College London, Institute of Structural and Molecular BiologyLondonUnited Kingdom
| | - Jürg Bähler
- University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentLondonUnited Kingdom
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Pan Y, Li R, Li W, Lv L, Guan J, Zhou S. HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:976-990. [PMID: 37730114 PMCID: PMC10928439 DOI: 10.1016/j.gpb.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 09/22/2023]
Abstract
A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteins that contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.
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Affiliation(s)
- Yuliang Pan
- Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
| | - Ruiyi Li
- Translational Medical Center for Stem Cell Therapy, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China
| | - Wengen Li
- Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
| | - Liuzhenghao Lv
- Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
| | - Jihong Guan
- Department of Computer Science and Technology, College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
| | - Shuigeng Zhou
- Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China.
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Fiannaca A, La Rosa M, La Paglia L, Gaglio S, Urso A. GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data. Brief Bioinform 2023; 24:bbad332. [PMID: 37756593 PMCID: PMC10530315 DOI: 10.1093/bib/bbad332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/17/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq data is identifying the cell types within tissue to estimate the quantitative composition of cell populations. Due to the massive amount of available scRNA-seq data, automatic classification approaches for cell typing, based on the most recent deep learning technology, are needed. Here, we present the gene ontology-driven wide and deep learning (GOWDL) model for classifying cell types in several tissues. GOWDL implements a hybrid architecture that considers the functional annotations found in Gene Ontology and the marker genes typical of specific cell types. We performed cross-validation and independent external testing, comparing our algorithm with 12 other state-of-the-art predictors. Classification scores demonstrated that GOWDL reached the best results over five different tissues, except for recall, where we got about 92% versus 97% of the best tool. Finally, we presented a case study on classifying immune cell populations in breast cancer using a hierarchical approach based on GOWDL.
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Affiliation(s)
- Antonino Fiannaca
- ICAR-CNR, National Research Council of Italy, Via Ugo La Malfa 153, 90146, Palermo, Italy
| | - Massimo La Rosa
- ICAR-CNR, National Research Council of Italy, Via Ugo La Malfa 153, 90146, Palermo, Italy
| | - Laura La Paglia
- ICAR-CNR, National Research Council of Italy, Via Ugo La Malfa 153, 90146, Palermo, Italy
| | - Salvatore Gaglio
- ICAR-CNR, National Research Council of Italy, Via Ugo La Malfa 153, 90146, Palermo, Italy
- Dipartimento di Ingegneria, Università degli studi di Palermo, Viale Delle Scienze, ed. 6, 90128, Palermo, Italy
| | - Alfonso Urso
- ICAR-CNR, National Research Council of Italy, Via Ugo La Malfa 153, 90146, Palermo, Italy
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Jablonski KP, Beerenwinkel N. Coherent pathway enrichment estimation by modeling inter-pathway dependencies using regularized regression. Bioinformatics 2023; 39:btad522. [PMID: 37610338 PMCID: PMC10471899 DOI: 10.1093/bioinformatics/btad522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 07/04/2023] [Accepted: 08/22/2023] [Indexed: 08/24/2023] Open
Abstract
MOTIVATION Gene set enrichment methods are a common tool to improve the interpretability of gene lists as obtained, for example, from differential gene expression analyses. They are based on computing whether dysregulated genes are located in certain biological pathways more often than expected by chance. Gene set enrichment tools rely on pre-existing pathway databases such as KEGG, Reactome, or the Gene Ontology. These databases are increasing in size and in the number of redundancies between pathways, which complicates the statistical enrichment computation. RESULTS We address this problem and develop a novel gene set enrichment method, called pareg, which is based on a regularized generalized linear model and directly incorporates dependencies between gene sets related to certain biological functions, for example, due to shared genes, in the enrichment computation. We show that pareg is more robust to noise than competing methods. Additionally, we demonstrate the ability of our method to recover known pathways as well as to suggest novel treatment targets in an exploratory analysis using breast cancer samples from TCGA. AVAILABILITY AND IMPLEMENTATION pareg is freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/pareg.html) as well as on https://github.com/cbg-ethz/pareg. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.
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Affiliation(s)
- Kim Philipp Jablonski
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel 4058, Switzerland
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel 4058, Switzerland
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Prebensen C, Lefol Y, Myhre PL, Lüders T, Jonassen C, Blomfeldt A, Omland T, Nilsen H, Berdal JE. Longitudinal whole blood transcriptomic analysis characterizes neutrophil activation and interferon signaling in moderate and severe COVID-19. Sci Rep 2023; 13:10368. [PMID: 37365222 PMCID: PMC10293211 DOI: 10.1038/s41598-023-37606-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/24/2023] [Indexed: 06/28/2023] Open
Abstract
A maladaptive inflammatory response has been implicated in the pathogenesis of severe COVID-19. This study aimed to characterize the temporal dynamics of this response and investigate whether severe disease is associated with distinct gene expression patterns. We performed microarray analysis of serial whole blood RNA samples from 17 patients with severe COVID-19, 15 patients with moderate disease and 11 healthy controls. All study subjects were unvaccinated. We assessed whole blood gene expression patterns by differential gene expression analysis, gene set enrichment, two clustering methods and estimated relative leukocyte abundance using CIBERSORT. Neutrophils, platelets, cytokine signaling, and the coagulation system were activated in COVID-19, and this broad immune activation was more pronounced in severe vs. moderate disease. We observed two different trajectories of neutrophil-associated genes, indicating the emergence of a more immature neutrophil phenotype over time. Interferon-associated genes were strongly enriched in early COVID-19 before falling markedly, with modest severity-associated differences in trajectory. In conclusion, COVID-19 necessitating hospitalization is associated with a broad inflammatory response, which is more pronounced in severe disease. Our data suggest a progressively more immature circulating neutrophil phenotype over time. Interferon signaling is enriched in COVID-19 but does not seem to drive severe disease.
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Affiliation(s)
- Christian Prebensen
- Department of Infectious Diseases, Oslo University Hospital, Kirkeveien 166, 0450, Oslo, Norway.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Yohan Lefol
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Microbiology, University of Oslo, Oslo, Norway
| | - Peder L Myhre
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Cardiology, Akershus University Hospital, Lørenskog, Norway
| | - Torben Lüders
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Molecular Biology, Akershus University Hospital, Lørenskog, Norway
| | | | - Anita Blomfeldt
- Department of Microbiology and Infection Control, Akershus University Hospital, Lørenskog, Norway
| | - Torbjørn Omland
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Cardiology, Akershus University Hospital, Lørenskog, Norway
| | - Hilde Nilsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Microbiology, University of Oslo, Oslo, Norway
| | - Jan-Erik Berdal
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Infectious Diseases, Akershus University Hospital, Lørenskog, Norway
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8
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Ruperti F, Papadopoulos N, Musser JM, Mirdita M, Steinegger M, Arendt D. Cross-phyla protein annotation by structural prediction and alignment. Genome Biol 2023; 24:113. [PMID: 37173746 PMCID: PMC10176882 DOI: 10.1186/s13059-023-02942-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Protein annotation is a major goal in molecular biology, yet experimentally determined knowledge is typically limited to a few model organisms. In non-model species, the sequence-based prediction of gene orthology can be used to infer protein identity; however, this approach loses predictive power at longer evolutionary distances. Here we propose a workflow for protein annotation using structural similarity, exploiting the fact that similar protein structures often reflect homology and are more conserved than protein sequences. RESULTS We propose a workflow of openly available tools for the functional annotation of proteins via structural similarity (MorF: MorphologFinder) and use it to annotate the complete proteome of a sponge. Sponges are highly relevant for inferring the early history of animals, yet their proteomes remain sparsely annotated. MorF accurately predicts the functions of proteins with known homology in [Formula: see text] cases and annotates an additional [Formula: see text] of the proteome beyond standard sequence-based methods. We uncover new functions for sponge cell types, including extensive FGF, TGF, and Ephrin signaling in sponge epithelia, and redox metabolism and control in myopeptidocytes. Notably, we also annotate genes specific to the enigmatic sponge mesocytes, proposing they function to digest cell walls. CONCLUSIONS Our work demonstrates that structural similarity is a powerful approach that complements and extends sequence similarity searches to identify homologous proteins over long evolutionary distances. We anticipate this will be a powerful approach that boosts discovery in numerous -omics datasets, especially for non-model organisms.
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Affiliation(s)
- Fabian Ruperti
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Faculty of Biosciences, Collaboration for joint Ph.D. degree between EMBL and Heidelberg University, Heidelberg, Germany
| | - Nikolaos Papadopoulos
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Department for Evolutionary Biology, University of Vienna, Vienna, Austria
| | - Jacob M Musser
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Milot Mirdita
- School of Biological Sciences, Seoul National University, Seoul, South Korea
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Detlev Arendt
- Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
- Centre for Organismal Studies, University of Heidelberg, Heidelberg, Germany.
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9
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Kartheeswaran KP, Rayan AXA, Varrieth GT. Enhanced disease-disease association with information enriched disease representation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:8892-8932. [PMID: 37161227 DOI: 10.3934/mbe.2023391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
OBJECTIVE Quantification of disease-disease association (DDA) enables the understanding of disease relationships for discovering disease progression and finding comorbidity. For effective DDA strength calculation, there is a need to address the main challenge of integration of various biomedical aspects of DDA is to obtain an information rich disease representation. MATERIALS AND METHODS An enhanced and integrated DDA framework is developed that integrates enriched literature-based with concept-based DDA representation. The literature component of the proposed framework uses PubMed abstracts and consists of improved neural network model that classifies DDAs for an enhanced literature-based DDA representation. Similarly, an ontology-based joint multi-source association embedding model is proposed in the ontology component using Disease Ontology (DO), UMLS, claims insurance, clinical notes etc. Results and Discussion: The obtained information rich disease representation is evaluated on different aspects of DDA datasets such as Gene, Variant, Gene Ontology (GO) and a human rated benchmark dataset. The DDA scores calculated using the proposed method achieved a high correlation mainly in gene-based dataset. The quantified scores also shown better correlation of 0.821, when evaluated on human rated 213 disease pairs. In addition, the generated disease representation is proved to have substantial effect on correlation of DDA scores for different categories of disease pairs. CONCLUSION The enhanced context and semantic DDA framework provides an enriched disease representation, resulting in high correlated results with different DDA datasets. We have also presented the biological interpretation of disease pairs. The developed framework can also be used for deriving the strength of other biomedical associations.
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Zheng Y, Young ND, Song J, Chang BC, Gasser RB. An informatic workflow for the enhanced annotation of excretory/secretory proteins of Haemonchus contortus. Comput Struct Biotechnol J 2023; 21:2696-2704. [PMID: 37143762 PMCID: PMC10151223 DOI: 10.1016/j.csbj.2023.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/16/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Major advances in genomic and associated technologies have demanded reliable bioinformatic tools and workflows for the annotation of genes and their products via comparative analyses using well-curated reference data sets, accessible in public repositories. However, the accurate in silico annotation of molecules (proteins) encoded in organisms (e.g., multicellular parasites) which are evolutionarily distant from those for which these extensive reference data sets are available, including invertebrate model organisms (e.g., Caenorhabditis elegans - free-living nematode, and Drosophila melanogaster - the vinegar fly) and vertebrate species (e.g., Homo sapiens and Mus musculus), remains a major challenge. Here, we constructed an informatic workflow for the enhanced annotation of biologically-important, excretory/secretory (ES) proteins ("secretome") encoded in the genome of a parasitic roundworm, called Haemonchus contortus (commonly known as the barber's pole worm). We critically evaluated the performance of five distinct methods, refined some of them, and then combined the use of all five methods to comprehensively annotate ES proteins, according to gene ontology, biological pathways and/or metabolic (enzymatic) processes. Then, using optimised parameter settings, we applied this workflow to comprehensively annotate 2591 of all 3353 proteins (77.3%) in the secretome of H. contortus. This result is a substantial improvement (10-25%) over previous annotations using individual, "off-the-shelf" algorithms and default settings, indicating the ready applicability of the present, refined workflow to gene/protein sequence data sets from a wide range of organisms in the Tree-of-Life.
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de la Fuente L, Del Pozo-Valero M, Perea-Romero I, Blanco-Kelly F, Fernández-Caballero L, Cortón M, Ayuso C, Mínguez P. Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks. Int J Mol Sci 2023; 24:ijms24021661. [PMID: 36675175 PMCID: PMC9864172 DOI: 10.3390/ijms24021661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
Screening for pathogenic variants in the diagnosis of rare genetic diseases can now be performed on all genes thanks to the application of whole exome and genome sequencing (WES, WGS). Yet the repertoire of gene-disease associations is not complete. Several computer-based algorithms and databases integrate distinct gene-gene functional networks to accelerate the discovery of gene-disease associations. We hypothesize that the ability of every type of information to extract relevant insights is disease-dependent. We compiled 33 functional networks classified into 13 knowledge categories (KCs) and observed large variability in their ability to recover genes associated with 91 genetic diseases, as measured using efficiency and exclusivity. We developed GLOWgenes, a network-based algorithm that applies random walk with restart to evaluate KCs' ability to recover genes from a given list associated with a phenotype and modulates the prediction of new candidates accordingly. Comparison with other integration strategies and tools shows that our disease-aware approach can boost the discovery of new gene-disease associations, especially for the less obvious ones. KC contribution also varies if obtained using recently discovered genes. Applied to 15 unsolved WES, GLOWgenes proposed three new genes to be involved in the phenotypes of patients with syndromic inherited retinal dystrophies.
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Affiliation(s)
- Lorena de la Fuente
- Department of Genetics, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain
- Bioinformatics Unit, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
| | - Marta Del Pozo-Valero
- Department of Genetics, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain
| | - Irene Perea-Romero
- Department of Genetics, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain
| | - Fiona Blanco-Kelly
- Department of Genetics, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain
| | - Lidia Fernández-Caballero
- Department of Genetics, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain
| | - Marta Cortón
- Department of Genetics, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain
| | - Carmen Ayuso
- Department of Genetics, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain
| | - Pablo Mínguez
- Department of Genetics, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain
- Bioinformatics Unit, Health Research Institute–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28049 Madrid, Spain
- Correspondence:
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Wang H, Zheng H, Chen DZ. TANGO: A GO-Term Embedding Based Method for Protein Semantic Similarity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:694-706. [PMID: 35030084 DOI: 10.1109/tcbb.2022.3143480] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We aim to quantitatively predict protein semantic similarities (PSS), which is vital to making biological discoveries. Previously, researchers commonly exploited Gene Ontology (GO) graphs (containing standardized hierarchically-organized GO terms for annotating distinct protein attributes) to learn GO term embeddings (vector representations) for quantifying protein attribute similarities and aggregate these embeddings to form protein embeddings for similarity measurement. However, two key properties of GO terms and annotated proteins are not yet well-explored by these learning-based methods: (1) taxonomy relations between GO terms; (2) GO terms' different contributions in describing protein semantics. In this paper, we propose TANGO, a new framework composed of a TAxoNomy-aware embedding module and an aggreGatiOn module. Our Embedding Module encodes taxonomic information into GO term embeddings by incorporating GO term topological distances in the GO graph hierarchy. Hence, distances between GO term embeddings can be used to more accurately measure shared meanings between correlated protein attributes. Our Aggregation Module automatically determines the contributions of GO terms when merging into the target protein embeddings, by mining GO term concept dependency relations in the GO graph and correlations in protein annotations. We conduct extensive experiments on several public datasets. On two PSS metrics, our new method significantly outperforms known methods by a large margin.
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13
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Sosa CC, Clavijo-Buriticá DC, García-Merchán VH, López-Rozo N, Riccio-Rengifo C, Diaz MV, Londoño DA, Quimbaya MA. GOCompare: An R package to compare functional enrichment analysis between two species. Genomics 2023; 115:110528. [PMID: 36462728 DOI: 10.1016/j.ygeno.2022.110528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/11/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022]
Abstract
Functional enrichment analysis is a cornerstone in bioinformatics as it makes possible to identify functional information by using a gene list as source. Different tools are available to compare gene ontology (GO) terms, based on a directed acyclic graph structure or content-based algorithms which are time-consuming and require a priori information of GO terms. Nevertheless, quantitative procedures to compare GO terms among gene lists and species are not available. Here we present a computational procedure, implemented in R, to infer functional information derived from comparative strategies. GOCompare provides a framework for functional comparative genomics starting from comparable lists from GO terms. The program uses functional enrichment analysis (FEA) results and implement graph theory to identify statistically relevant GO terms for both, GO categories and analyzed species. Thus, GOCompare allows finding new functional information complementing current FEA approaches and extending their use to a comparative perspective. To test our approach GO terms were obtained for a list of aluminum tolerance-associated genes in Oryza sativa subsp. japonica and their orthologues in Arabidopsis thaliana. GOCompare was able to detect functional similarities for reactive oxygen species and ion binding capabilities which are common in plants as molecular mechanisms to tolerate aluminum toxicity. Consequently, the R package exhibited a good performance when implemented in complex datasets, allowing to establish hypothesis that might explain a biological process from a functional perspective, and narrowing down the possible landscapes to design wet lab experiments.
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Affiliation(s)
- Chrystian C Sosa
- Department of Natural Sciences and Mathematics, Pontificia Universidad Javeriana, Cali, Cali, Colombia; Evolution, Ecology and Conservation Research Group EECO, Biology Program, Faculty of Basic Sciences and Technologies, Universidad del Quindío, Armenia, Colombia
| | | | - Victor Hugo García-Merchán
- Evolution, Ecology and Conservation Research Group EECO, Biology Program, Faculty of Basic Sciences and Technologies, Universidad del Quindío, Armenia, Colombia
| | - Nicolas López-Rozo
- Department of Natural Sciences and Mathematics, Pontificia Universidad Javeriana, Cali, Cali, Colombia
| | - Camila Riccio-Rengifo
- Department of Natural Sciences and Mathematics, Pontificia Universidad Javeriana, Cali, Cali, Colombia
| | | | - David Arango Londoño
- Department of Natural Sciences and Mathematics, Pontificia Universidad Javeriana, Cali, Cali, Colombia
| | - Mauricio Alberto Quimbaya
- Department of Natural Sciences and Mathematics, Pontificia Universidad Javeriana, Cali, Cali, Colombia.
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14
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Ismail E, Gad W, Hashem M. HEC-ASD: a hybrid ensemble-based classification model for predicting autism spectrum disorder disease genes. BMC Bioinformatics 2022; 23:554. [PMID: 36544099 PMCID: PMC9768984 DOI: 10.1186/s12859-022-05099-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Autism spectrum disorder (ASD) is the most prevalent disease today. The causes of its infection may be attributed to genetic causes by 80% and environmental causes by 20%. In spite of this, the majority of the current research is concerned with environmental causes, and the least proportion with the genetic causes of the disease. Autism is a complex disease, which makes it difficult to identify the genes that cause the disease. METHODS Hybrid ensemble-based classification (HEC-ASD) model for predicting ASD genes using gradient boosting machines is proposed. The proposed model utilizes gene ontology (GO) to construct a gene functional similarity matrix using hybrid gene similarity (HGS) method. HGS measures the semantic similarity between genes effectively. It combines the graph-based method, such as Wang method with the number of directed children's nodes of gene term from GO. Moreover, an ensemble gradient boosting classifier is adapted to enhance the prediction of genes forming a robust classification model. RESULTS The proposed model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database. The experimental results are promising as they improve the classification performance for predicting ASD genes. The results are compared with other approaches that used gene regulatory network (GRN), protein to protein interaction network (PPI), or GO. The HEC-ASD model reaches the highest prediction accuracy of 0.88% using ensemble learning classifiers. CONCLUSION The proposed model demonstrates that ensemble learning technique using gradient boosting is effective in predicting autism spectrum disorder genes. Moreover, the HEC-ASD model utilized GO rather than using PPI network and GRN.
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Affiliation(s)
- Eman Ismail
- grid.7269.a0000 0004 0621 1570Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Walaa Gad
- grid.7269.a0000 0004 0621 1570Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
| | - Mohamed Hashem
- grid.7269.a0000 0004 0621 1570Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
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15
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Teulière J, Bernard C, Corel E, Lapointe FJ, Martens J, Lopez P, Bapteste E. Network analyses unveil ageing-associated pathways evolutionarily conserved from fungi to animals. GeroScience 2022; 45:1059-1080. [PMID: 36508078 PMCID: PMC9886728 DOI: 10.1007/s11357-022-00704-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022] Open
Abstract
The genetic roots of the diverse paces and shapes of ageing and of the large variations in longevity observed across the tree of life are poorly understood. Indeed, pathways associated with ageing/longevity are incompletely known, both in terms of their constitutive genes/proteins and of their molecular interactions. Moreover, there is limited overlap between the genes constituting these pathways across mammals. Yet, dedicated comparative analyses might still unravel evolutionarily conserved, important pathways associated with longevity or ageing. Here, we used an original strategy with a double evolutionary and systemic focus to analyse protein interactions associated with ageing or longevity during the evolution of five species of Opisthokonta. We ranked these proteins and interactions based on their evolutionary conservation and centrality in past and present protein-protein interaction (PPI) networks, providing a big systemic picture of the evolution of ageing and longevity pathways that identified which pathways emerged in which Opisthokonta lineages, were conserved, and/or central. We confirmed that longevity/ageing-associated proteins (LAPs), be they pro- or anti-longevity, are highly central in extant PPI, consistently with the antagonistic pleiotropy theory of ageing, and identified key antagonistic regulators of ageing/longevity, 52 of which with homologues in humans. While some highly central LAPs were evolutionarily conserved for over a billion years, we report a clear transition in the functionally important components of ageing/longevity within bilaterians. We also predicted 487 novel evolutionarily conserved LAPs in humans, 54% of which are more central than mTOR, and 138 of which are druggable, defining new potential targets for anti-ageing treatments in humans.
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Affiliation(s)
- Jérôme Teulière
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Sorbonne Université, CNRS, Museum National d’Histoire Naturelle, EPHE, Université Des Antilles, Paris, France
| | - Charles Bernard
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Sorbonne Université, CNRS, Museum National d’Histoire Naturelle, EPHE, Université Des Antilles, Paris, France
| | - Eduardo Corel
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Sorbonne Université, CNRS, Museum National d’Histoire Naturelle, EPHE, Université Des Antilles, Paris, France
| | - François-Joseph Lapointe
- grid.14848.310000 0001 2292 3357Département de Sciences Biologiques, Complexe Des Sciences, Université de Montréal, Montréal, QC Canada
| | - Johannes Martens
- Sciences, Normes, Démocratie (SND), Sorbonne Université, CNRS, 75005 Paris, France
| | - Philippe Lopez
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Sorbonne Université, CNRS, Museum National d’Histoire Naturelle, EPHE, Université Des Antilles, Paris, France
| | - Eric Bapteste
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Sorbonne Université, CNRS, Museum National d'Histoire Naturelle, EPHE, Université Des Antilles, Paris, France.
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16
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Sarker B, Khare N, Devignes MD, Aridhi S. Improving automatic GO annotation with semantic similarity. BMC Bioinformatics 2022; 23:433. [PMID: 36510133 PMCID: PMC9743508 DOI: 10.1186/s12859-022-04958-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Automatic functional annotation of proteins is an open research problem in bioinformatics. The growing number of protein entries in public databases, for example in UniProtKB, poses challenges in manual functional annotation. Manual annotation requires expert human curators to search and read related research articles, interpret the results, and assign the annotations to the proteins. Thus, it is a time-consuming and expensive process. Therefore, designing computational tools to perform automatic annotation leveraging the high quality manual annotations that already exist in UniProtKB/SwissProt is an important research problem RESULTS: In this paper, we extend and adapt the GrAPFI (graph-based automatic protein function inference) (Sarker et al. in BMC Bioinform 21, 2020; Sarker et al., in: Proceedings of 7th international conference on complex networks and their applications, Cambridge, 2018) method for automatic annotation of proteins with gene ontology (GO) terms renaming it as GrAPFI-GO. The original GrAPFI method uses label propagation in a similarity graph where proteins are linked through the domains, families, and superfamilies that they share. Here, we also explore various types of similarity measures based on common neighbors in the graph. Moreover, GO terms are arranged in a hierarchical manner according to semantic parent-child relations. Therefore, we propose an efficient pruning and post-processing technique that integrates both semantic similarity and hierarchical relations between the GO terms. We produce experimental results comparing the GrAPFI-GO method with and without considering common neighbors similarity. We also test the performance of GrAPFI-GO and other annotation tools for GO annotation on a benchmark of proteins with and without the proposed pruning and post-processing procedure. CONCLUSION Our results show that the proposed semantic hierarchical post-processing potentially improves the performance of GrAPFI-GO and of other annotation tools as well. Thus, GrAPFI-GO exposes an original efficient and reusable procedure, to exploit the semantic relations among the GO terms in order to improve the automatic annotation of protein functions.
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Affiliation(s)
- Bishnu Sarker
- grid.29172.3f0000 0001 2194 6418CNRS, Inria, LORIA, University of Lorraine, 54000 Nancy, France ,grid.443078.c0000 0004 0371 4228Khulna University of Engineering and Technology, Khulna, Bangladesh ,grid.259870.10000 0001 0286 752XSchool of Applied Computational Sciences, Meharry Medical College, Nashville, TN USA
| | - Navya Khare
- grid.29172.3f0000 0001 2194 6418CNRS, Inria, LORIA, University of Lorraine, 54000 Nancy, France ,grid.419361.80000 0004 1759 7632International Institute of Information Technology, Hyderabad, India
| | | | - Sabeur Aridhi
- grid.29172.3f0000 0001 2194 6418CNRS, Inria, LORIA, University of Lorraine, 54000 Nancy, France
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17
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Ayub U, Naveed H. BioAlign: An Accurate Global PPI Network Alignment Algorithm. Evol Bioinform Online 2022; 18:11769343221110658. [PMID: 35898232 PMCID: PMC9309777 DOI: 10.1177/11769343221110658] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 06/02/2022] [Indexed: 11/15/2022] Open
Abstract
Motivation The advancement of high-throughput PPI profiling techniques results in generating a large amount of PPI data. The alignment of the PPI networks uncovers the relationship between the species that can help understand the biological systems. The comparative study reveals the conserved biological interactions of the proteins across the species. It can also help study the biological pathways and signal networks of the cells. Although several network alignment algorithms are developed to study and compare the PPI data, the development of the aligner that aligns the PPI networks with high biological similarity and coverage is still challenging. Results This paper presents a novel global network alignment algorithm, BioAlign, that incorporates a significant amount of biological information. Existing studies use global sequence and/or 3D-structure similarity to align the PPI networks. In contrast, BioAlign uses the local sequence similarity, predicted secondary structure motifs, and remote homology in addition to global sequence and 3D-structure similarity. The extra sources of biological information help BioAlign to align the proteins with high biological similarity. BioAlign produces significantly better results in terms of AFS and Coverage (6-32 and 7-34 with respect to MF and BP, respectively) than the existing algorithms. BioAlign aligns a much larger number of proteins that have high biological similarities as compared to the existing aligners. BioAlign helps in studying the functionally similar protein pairs across the species.
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Affiliation(s)
- Umair Ayub
- FAST School of Computing, National
University of Computer and Emerging Sciences, Lahore, Pakistan
- Computational Biology Research Lab,
Department of Computing, National University of Computer and Emerging Sciences,
Islamabad, Pakistan
| | - Hammad Naveed
- FAST School of Computing, National
University of Computer and Emerging Sciences, Lahore, Pakistan
- Computational Biology Research Lab,
Department of Computing, National University of Computer and Emerging Sciences,
Islamabad, Pakistan
- Hammad Naveed, Computational Biology
Research Lab, Department of Computing, National University of Computer and
Emerging Sciences, 852 Milaad Street, Block B, Faisal Town, Lahore, Pakistan.
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18
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Mazurier M, Drouaud J, Bahrman N, Rau A, Lejeune-Hénaut I, Delbreil B, Legrand S. Integrated sRNA-seq and RNA-seq Analyses Reveal a microRNA Regulation Network Involved in Cold Response in Pisum sativum L. Genes (Basel) 2022; 13:1119. [PMID: 35885902 PMCID: PMC9322779 DOI: 10.3390/genes13071119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/14/2022] [Accepted: 06/20/2022] [Indexed: 12/04/2022] Open
Abstract
(1) Background: Cold stress affects growth and development in plants and is a major environmental factor that decreases productivity. Over the past two decades, the advent of next generation sequencing (NGS) technologies has opened new opportunities to understand the molecular bases of stress resistance by enabling the detection of weakly expressed transcripts and the identification of regulatory RNAs of gene expression, including microRNAs (miRNAs). (2) Methods: In this study, we performed time series sRNA and mRNA sequencing experiments on two pea (Pisum sativum L., Ps) lines, Champagne frost-tolerant and Térèse frost-sensitive, during a low temperature treatment versus a control condition. (3) Results: An integrative analysis led to the identification of 136 miRNAs and a regulation network composed of 39 miRNA/mRNA target pairs with discordant expression patterns. (4) Conclusions: Our findings indicate that the cold response in pea involves 11 miRNA families as well as their target genes related to antioxidative and multi-stress defense mechanisms and cell wall biosynthesis.
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Affiliation(s)
- Mélanie Mazurier
- BioEcoAgro Joint Research Unit, Université de Lille, INRAE, Université de Liège, Université de Picardie Jules Verne, 59000 Lille, France; (M.M.); (N.B.); (B.D.)
| | - Jan Drouaud
- BioEcoAgro Joint Research Unit, INRAE, Université de Lille, Université de Liège, Université de Picardie Jules Verne, 80200 Estrées-Mons, France; (J.D.); (A.R.); (I.L.-H.)
| | - Nasser Bahrman
- BioEcoAgro Joint Research Unit, Université de Lille, INRAE, Université de Liège, Université de Picardie Jules Verne, 59000 Lille, France; (M.M.); (N.B.); (B.D.)
- BioEcoAgro Joint Research Unit, INRAE, Université de Lille, Université de Liège, Université de Picardie Jules Verne, 80200 Estrées-Mons, France; (J.D.); (A.R.); (I.L.-H.)
| | - Andrea Rau
- BioEcoAgro Joint Research Unit, INRAE, Université de Lille, Université de Liège, Université de Picardie Jules Verne, 80200 Estrées-Mons, France; (J.D.); (A.R.); (I.L.-H.)
- Université Paris-Saclay, AgroParisTech, INRAE, GABI, 78350 Jouy-en-Josas, France
| | - Isabelle Lejeune-Hénaut
- BioEcoAgro Joint Research Unit, INRAE, Université de Lille, Université de Liège, Université de Picardie Jules Verne, 80200 Estrées-Mons, France; (J.D.); (A.R.); (I.L.-H.)
| | - Bruno Delbreil
- BioEcoAgro Joint Research Unit, Université de Lille, INRAE, Université de Liège, Université de Picardie Jules Verne, 59000 Lille, France; (M.M.); (N.B.); (B.D.)
| | - Sylvain Legrand
- Univ. Lille, CNRS, UMR 8198—Evo-Eco-Paleo, 59000 Lille, France
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19
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Han Y, Klinger K, Rajpal DK, Zhu C, Teeple E. Empowering the discovery of novel target-disease associations via machine learning approaches in the open targets platform. BMC Bioinformatics 2022; 23:232. [PMID: 35710324 PMCID: PMC9202116 DOI: 10.1186/s12859-022-04753-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 05/26/2022] [Indexed: 11/10/2022] Open
Abstract
Background The Open Targets (OT) Platform integrates a wide range of data sources on target-disease associations to facilitate identification of potential therapeutic drug targets to treat human diseases. However, due to the complexity that targets are usually functionally pleiotropic and efficacious for multiple indications, challenges in identifying novel target to indication associations remain. Specifically, persistent need exists for new methods for integration of novel target-disease association evidence and biological knowledge bases via advanced computational methods. These offer promise for increasing power for identification of the most promising target-disease pairs for therapeutic development. Here we introduce a novel approach by integrating additional target-disease features with machine learning models to further uncover druggable disease to target indications. Results We derived novel target-disease associations as supplemental features to OT platform-based associations using three data sources: (1) target tissue specificity from GTEx expression profiles; (2) target semantic similarities based on gene ontology; and (3) functional interactions among targets by embedding them from protein–protein interaction (PPI) networks. Machine learning models were applied to evaluate feature importance and performance benchmarks for predicting targets with known drug indications. The evaluation results show the newly integrated features demonstrate higher importance than current features in OT. In addition, these also show superior performance over association benchmarks and may support discovery of novel therapeutic indications for highly pursued targets. Conclusion Our newly generated features can be used to represent additional underlying biological relatedness among targets and diseases to further empower improved performance for predicting novel indications for drug targets through advanced machine learning models. The proposed methodology enables a powerful new approach for systematic evaluation of drug targets with novel indications. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04753-4.
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Affiliation(s)
- Yingnan Han
- Translational Sciences, Sanofi US, Framingham, MA, 01701, USA
| | | | - Deepak K Rajpal
- Translational Sciences, Sanofi US, Framingham, MA, 01701, USA
| | - Cheng Zhu
- Translational Sciences, Sanofi US, Framingham, MA, 01701, USA.
| | - Erin Teeple
- Translational Sciences, Sanofi US, Framingham, MA, 01701, USA.
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20
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Gu Z, Hübschmann D. Simplify enrichment: A bioconductor package for clustering and visualizing functional enrichment results. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00073-0. [PMID: 35680096 PMCID: PMC10373083 DOI: 10.1016/j.gpb.2022.04.008] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/07/2022] [Accepted: 05/08/2022] [Indexed: 10/18/2022]
Abstract
Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates the biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters. We propose a new method named binary cut for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that binary cut could efficiently cluster functional terms into groups where terms showed consistent similarities within groups and were mutually exclusive between groups. We compared binary cut clustering on the similarity matrices obtained from different similarity measures and found that semantic similarity worked well with binary cut, while similarity matrices based on gene overlap showed less consistent patterns. We implemented the binary cut algorithm in the R package simplifyEnrichment, which additionally provides functionalities for visualizing, summarizing, and comparing the clustering. The simplifyEnrichment package and the documentation are available at https://bioconductor.org/packages/simplifyEnrichment/.
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Affiliation(s)
- Zuguang Gu
- Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg 69120, Germany.
| | - Daniel Hübschmann
- Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg 69120, Germany; Heidelberg Institute of Stem cell Technology and Experimental Medicine (HI-STEM), Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg 69120, Germany; Department of Pediatric Immunology, Hematology and Oncology, University Hospital Heidelberg, Heidelberg 69120, Germany.
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21
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Pu L, Singha M, Ramanujam J, Brylinski M. CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling. Oncotarget 2022; 13:695-706. [PMID: 35601606 PMCID: PMC9119687 DOI: 10.18632/oncotarget.28234] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/03/2022] [Indexed: 11/25/2022] Open
Abstract
Development of novel anti-cancer treatments requires not only a comprehensive knowledge of cancer processes and drug mechanisms of action, but also the ability to accurately predict the response of various cancer cell lines to therapeutics. Numerous computational methods have been developed to address this issue, including algorithms employing supervised machine learning. Nonetheless, high prediction accuracies reported for many of these techniques may result from a significant overlap among training, validation, and testing sets, making existing predictors inapplicable to new data. To address these issues, we developed CancerOmicsNet, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, CancerOmicsNet integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. The performance of CancerOmicsNet, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches. CancerOmicsNet generalizes well to unseen data, i.e., it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. CancerOmicsNet is freely available to the academic community at https://github.com/pulimeng/CancerOmicsNet.
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Affiliation(s)
- Limeng Pu
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA.,These authors contributed equally to this work
| | - Manali Singha
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.,These authors contributed equally to this work
| | - Jagannathan Ramanujam
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA.,Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Michal Brylinski
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA.,Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
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22
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Devkota K, Schmidt H, Werenski M, Murphy JM, Erden M, Arsenescu V, Cowen LJ. GLIDER: Function Prediction from GLIDE-based Neigborhoods. Bioinformatics 2022; 38:3395-3406. [PMID: 35575379 PMCID: PMC9237677 DOI: 10.1093/bioinformatics/btac322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 02/10/2022] [Accepted: 05/10/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Protein function prediction, based on the patterns of connection in a Protein-Protein Interaction (or Association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly successful set of recent approaches use random walk-based low dimensional embeddings, that tend to place functionally similar proteins into coherent spatial regions. However, these approaches lose valuable local graph structure from the network when considering only the embedding. We introduce GLIDER, a method that replaces a protein-protein interaction or association network with a new graph-based similarity network. GLIDER is based on a variant of our previous GLIDE method, which was designed to predict missing links in Protein-Protein Association networks, capturing implicit local and global (i.e. embedding-based) graph properties. RESULTS GLIDER outperforms competing methods on the task of predicting GO functional labels in cross-validation on a heterogeneous collection of four Human Protein-Protein Association networks derived from the 2016 DREAM Disease Module Identification Challenge, and also on three different protein-protein association networks built from the STRING database. We show that this is due to the strong functional enrichment that is present in the local GLIDER neighborhood in multiple different types of protein-protein association networks. Furthermore, we introduce the GLIDER graph neighborhood as a way for biologists to visualize the local neighborhood of a disease gene. As an application, we look at the local GLIDER neighborhoods of a set of known Parkinson's Disease GWAS genes, rediscover many genes which have known involvement in Parkinson's disease pathways, plus suggest some new genes to study. AVAILABILITY All code is publicly available and can be accessed here: https://github.com/kap-devkota/GLIDER. SUPPLEMENTARY INFORMATION is available at Bioinformatics online.
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Affiliation(s)
- Kapil Devkota
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA
| | - Henri Schmidt
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA
| | - Matt Werenski
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA
| | - James M Murphy
- Department of Mathematics, Tufts University, Medford, MA, 02155, USA
| | - Mert Erden
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA
| | - Victor Arsenescu
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA
| | - Lenore J Cowen
- Department of Computer Science, Tufts University, Medford, MA, 02155, USA
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23
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Lu Y, Feng Z, Zhang S, Wang Y. Annotating regulatory elements by heterogeneous network embedding. Bioinformatics 2022; 38:2899-2911. [PMID: 35561169 PMCID: PMC9326849 DOI: 10.1093/bioinformatics/btac185] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/05/2022] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Regulatory elements (REs), such as enhancers and promoters, are known as regulatory sequences functional in a heterogeneous regulatory network to control gene expression by recruiting transcription regulators and carrying genetic variants in a context specific way. Annotating those REs relies on costly and labor-intensive next-generation sequencing and RNA-guided editing technologies in many cellular contexts. RESULTS We propose a systematic Gene Ontology Annotation method for Regulatory Elements (RE-GOA) by leveraging the powerful word embedding in natural language processing. We first assemble a heterogeneous network by integrating context specific regulations, protein-protein interactions and gene ontology (GO) terms. Then we perform network embedding and associate regulatory elements with GO terms by assessing their similarity in a low dimensional vector space. With three applications, we show that RE-GOA outperforms existing methods in annotating TFs' binding sites from ChIP-seq data, in functional enrichment analysis of differentially accessible peaks from ATAC-seq data, and in revealing genetic correlation among phenotypes from their GWAS summary statistics data. AVAILABILITY AND IMPLEMENTATION The source code and the systematic RE annotation for human and mouse are available at https://github.com/AMSSwanglab/RE-GOA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yurun Lu
- CEMS, NCMIS, HCMS, MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Zhanying Feng
- CEMS, NCMIS, HCMS, MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Songmao Zhang
- CEMS, NCMIS, HCMS, MADIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Yong Wang
- To whom correspondence should be addressed. or
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24
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An integrated network representation of multiple cancer-specific data for graph-based machine learning. NPJ Syst Biol Appl 2022; 8:14. [PMID: 35487924 PMCID: PMC9054771 DOI: 10.1038/s41540-022-00226-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 04/04/2022] [Indexed: 12/20/2022] Open
Abstract
Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. In order to construct compact, yet information-rich cancer-specific networks, we developed a novel graph reduction algorithm. Driven by not only the topological information, but also the biological knowledge, the graph reduction increases the feature-only entropy while preserving the valuable graph-feature information. Subsequent comparative benchmarking simulations employing a tissue level cross-validation protocol demonstrate that the accuracy of a graph-based predictor of the drug efficacy is 0.68, which is notably higher than those measured for more traditional, matrix-based techniques on the same data. Overall, the non-Euclidean representation of the cancer-specific data improves the performance of machine learning to predict the response of cancer to pharmacotherapy. The generated data are freely available to the academic community at https://osf.io/dzx7b/.
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25
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Kamran AB, Naveed H. GOntoSim: a semantic similarity measure based on LCA and common descendants. Sci Rep 2022; 12:3818. [PMID: 35264663 PMCID: PMC8907294 DOI: 10.1038/s41598-022-07624-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 02/14/2022] [Indexed: 11/20/2022] Open
Abstract
The Gene Ontology (GO) is a controlled vocabulary that captures the semantics or context of an entity based on its functional role. Biomedical entities are frequently compared to each other to find similarities to help in data annotation and knowledge transfer. In this study, we propose GOntoSim, a novel method to determine the functional similarity between genes. GOntoSim quantifies the similarity between pairs of GO terms, by taking the graph structure and the information content of nodes into consideration. Our measure quantifies the similarity between the ancestors of the GO terms accurately. It also takes into account the common children of the GO terms. GOntoSim is evaluated using the entire Enzyme Dataset containing 10,890 proteins and 97,544 GO annotations. The enzymes are clustered and compared with the Gold Standard EC numbers. At level 1 of the EC Numbers for Molecular Function, GOntoSim achieves a purity score of 0.75 as compared to 0.47 and 0.51 GOGO and Wang. GOntoSim can handle the noisy IEA annotations. We achieve a purity score of 0.94 in contrast to 0.48 for both GOGO and Wang at level 1 of the EC Numbers with IEA annotations. GOntoSim can be freely accessed at (http://www.cbrlab.org/GOntoSim.html).
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Affiliation(s)
- Amna Binte Kamran
- Computational Biology Research Lab, Department of Computer Science, National University of Computer & Emerging Sciences (NUCES-FAST), Islamabad, 44800, Pakistan
| | - Hammad Naveed
- Computational Biology Research Lab, Department of Computer Science, National University of Computer & Emerging Sciences (NUCES-FAST), Islamabad, 44800, Pakistan.
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26
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Zhao C, Liu T, Wang Z. Functional Similarities of Protein-Coding Genes in Topologically Associating Domains and Spatially-Proximate Genomic Regions. Genes (Basel) 2022; 13:genes13030480. [PMID: 35328034 PMCID: PMC8951421 DOI: 10.3390/genes13030480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 02/26/2022] [Accepted: 03/05/2022] [Indexed: 02/01/2023] Open
Abstract
Topologically associating domains (TADs) are the structural and functional units of the genome. However, the functions of protein-coding genes existing in the same or different TADs have not been fully investigated. We compared the functional similarities of protein-coding genes existing in the same TAD and between different TADs, and also in the same gap region (the region between two consecutive TADs) and between different gap regions. We found that the protein-coding genes from the same TAD or gap region are more likely to share similar protein functions, and this trend is more obvious with TADs than the gap regions. We further created two types of gene–gene spatial interaction networks: the first type is based on Hi-C contacts, whereas the second type is based on both Hi-C contacts and the relationship of being in the same TAD. A graph auto-encoder was applied to learn the network topology, reconstruct the two types of networks, and predict the functions of the central genes/nodes based on the functions of the neighboring genes/nodes. It was found that better performance was achieved with the second type of network. Furthermore, we detected long-range spatially-interactive regions based on Hi-C contacts and calculated the functional similarities of the gene pairs from these regions.
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27
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Mallick K, Mallik S, Bandyopadhyay S, Chakraborty S. A Novel Graph Topology-Based GO-Similarity Measure for Signature Detection From Multi-Omics Data and its Application to Other Problems. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:773-785. [PMID: 32866101 DOI: 10.1109/tcbb.2020.3020537] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Large scale multi-omics data analysis and signature prediction have been a topic of interest in the last two decades. While various traditional clustering/correlation-based methods have been proposed, but the overall prediction is not always satisfactory. To solve these challenges, in this article, we propose a new approach by leveraging the Gene Ontology (GO)similarity combined with multiomics data. In this article, a new GO similarity measure, ModSchlicker, is proposed and the effectiveness of the proposed measure along with other standardized measures are reviewed while using various graph topology-based Information Content (IC)values of GO-term. The proposed measure is deployed to PPI prediction. Furthermore, by involving GO similarity, we propose a new framework for stronger disease-based gene signature detection from the multi-omics data. For the first objective, we predict interaction from various benchmark PPI datasets of Yeast and Human species. For the latter, the gene expression and methylation profiles are used to identify Differentially Expressed and Methylated (DEM)genes. Thereafter, the GO similarity score along with a statistical method are used to determine the potential gene signature. Interestingly, the proposed method produces a better performance ( 0.9 avg. accuracy and 0.95 AUC)as compared to the other existing related methods during the classification of the participating features (genes)of the signature. Moreover, the proposed method is highly useful in other prediction/classification problems for any kind of large scale omics data.
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28
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Edera AA, Milone DH, Stegmayer G. Anc2vec: embedding gene ontology terms by preserving ancestors relationships. Brief Bioinform 2022; 23:6523148. [PMID: 35136916 DOI: 10.1093/bib/bbac003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 12/13/2021] [Accepted: 01/04/2022] [Indexed: 12/11/2022] Open
Abstract
The gene ontology (GO) provides a hierarchical structure with a controlled vocabulary composed of terms describing functions and localization of gene products. Recent works propose vector representations, also known as embeddings, of GO terms that capture meaningful information about them. Significant performance improvements have been observed when these representations are used on diverse downstream tasks, such as the measurement of semantic similarity between GO terms and functional similarity between proteins. Despite the success shown by these approaches, existing embeddings of GO terms still fail to capture crucial structural features of the GO. Here, we present anc2vec, a novel protocol based on neural networks for constructing vector representations of GO terms by preserving three important ontological features: its ontological uniqueness, ancestors hierarchy and sub-ontology membership. The advantages of using anc2vec are demonstrated by systematic experiments on diverse tasks: visualization, sub-ontology prediction, inference of structurally related terms, retrieval of terms from aggregated embeddings, and prediction of protein-protein interactions. In these tasks, experimental results show that the performance of anc2vec representations is better than those of recent approaches. This demonstrates that higher performances on diverse tasks can be achieved by embeddings when the structure of the GO is better represented. Full source code and data are available at https://github.com/sinc-lab/anc2vec.
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Affiliation(s)
- Alejandro A Edera
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL, CONICET, Ciudad Universitaria UNL, 3000, Santa Fe, Argentina
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29
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Zhao C, Liu T, Wang Z. PANDA2: protein function prediction using graph neural networks. NAR Genom Bioinform 2022; 4:lqac004. [PMID: 35118378 PMCID: PMC8808544 DOI: 10.1093/nargab/lqac004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 11/20/2021] [Accepted: 01/05/2022] [Indexed: 12/13/2022] Open
Abstract
High-throughput sequencing technologies have generated massive protein sequences, but the annotations of protein sequences highly rely on the low-throughput and expensive biological experiments. Therefore, accurate and fast computational alternatives are needed to infer functional knowledge from protein sequences. The gene ontology (GO) directed acyclic graph (DAG) contains the hierarchical relationships between GO terms but is hard to be integrated into machine learning algorithms for functional predictions. We developed a deep learning system named PANDA2 to predict protein functions, which used the cutting-edge graph neural network to model the topology of the GO DAG and integrated the features generated by transformer protein language models. Compared with the top 10 methods in CAFA3, PANDA2 ranked first in cellular component ontology (CCO), tied first in biological process ontology (BPO) but had a higher coverage rate, and second in molecular function ontology (MFO). Compared with other recently-developed cutting-edge predictors DeepGOPlus, GOLabeler, and DeepText2GO, and benchmarked on another independent dataset, PANDA2 ranked first in CCO, first in BPO, and second in MFO. PANDA2 can be freely accessed from http://dna.cs.miami.edu/PANDA2/.
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Affiliation(s)
- Chenguang Zhao
- Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL 33124, USA
| | - Tong Liu
- Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL 33124, USA
| | - Zheng Wang
- Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL 33124, USA
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30
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An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:7937573. [PMID: 34795792 PMCID: PMC8594978 DOI: 10.1155/2021/7937573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 10/11/2021] [Indexed: 01/03/2023]
Abstract
Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this problem, we propose a parallelized semantic similarity measurement method based on Hadoop MapReduce for big text data. At first, we preprocess and extract the semantic features from documents. Then, we calculate the document semantic similarity based on ontology network structure under MapReduce framework. Finally, based on the generated semantic document similarity, document clusters are generated via clustering algorithms. To validate the effectiveness, we use two kinds of open datasets. The experimental results show that the traditional methods can hardly work for more than ten thousand biomedical documents. The proposed method keeps efficient and accurate for big dataset and is of high parallelism and scalability.
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31
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Chen Y, Nan S, Tian Q, Cai H, Duan H, Lu X. Automatic RadLex coding of Chinese structured radiology reports based on text similarity ensemble. BMC Med Inform Decis Mak 2021; 21:247. [PMID: 34789213 PMCID: PMC8596919 DOI: 10.1186/s12911-021-01604-9] [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: 08/04/2021] [Accepted: 08/05/2021] [Indexed: 12/02/2022] Open
Abstract
Background Standardized coding of plays an important role in radiology reports’ secondary use such as data analytics, data-driven decision support, and personalized medicine. RadLex, a standard radiological lexicon, can reduce subjective variability and improve clarity in radiology reports. RadLex coding of radiology reports is widely used in many countries, but translation and localization of RadLex in China are far from being established. Although automatic RadLex coding is a common way for non-standard radiology reports, the high-accuracy cross-language RadLex coding is hardly achieved due to the limitation of up-to-date auto-translation and text similarity algorithms and still requires further research. Methods We present an effective approach that combines a hybrid translation and a Multilayer Perceptron weighting text similarity ensemble algorithm for automatic RadLex coding of Chinese structured radiology reports.
Firstly, a hybrid way to integrate Google neural machine translation and dictionary translation helps to optimize the translation of Chinese radiology phrases to English. The dictionary is made up of 21,863 Chinese–English radiological term pairs extracted from several free medical dictionaries. Secondly, four typical text similarity algorithms are introduced, which are Levenshtein distance, Jaccard similarity coefficient, Word2vec Continuous bag-of-words model, and WordNet Wup similarity algorithms. Lastly, the Multilayer Perceptron model has been used to synthesize the contextual, lexical, character and syntactical information of four text similarity algorithms to promote precision, in which four similarity scores of two terms are taken as input and the output presents whether the two terms are synonyms. Results The results show the effectiveness of the approach with an F1-score of 90.15%, a precision of 91.78% and a recall of 88.59%. The hybrid translation algorithm has no negative effect on the final coding, F1-score has increased by 21.44% and 8.12% compared with the GNMT algorithm and dictionary translation. Compared with the single similarity, the result of the MLP weighting similarity algorithm is satisfactory that has a 4.48% increase compared with the best single similarity algorithm, WordNet Wup. Conclusions The paper proposed an innovative automatic cross-language RadLex coding approach to solve the standardization of Chinese structured radiology reports, that can be taken as a reference to automatic cross-language coding.
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Affiliation(s)
- Yani Chen
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, 310027, China.,Key Laboratory for Biomedical Engineering, Ministry of Education, Hangzhou, China
| | - Shan Nan
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Qi Tian
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, 310027, China.,Key Laboratory for Biomedical Engineering, Ministry of Education, Hangzhou, China
| | - Hailing Cai
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, 310027, China
| | - Huilong Duan
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, 310027, China.,Key Laboratory for Biomedical Engineering, Ministry of Education, Hangzhou, China
| | - Xudong Lu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, 310027, China. .,Key Laboratory for Biomedical Engineering, Ministry of Education, Hangzhou, China. .,School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
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32
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Sledzieski S, Singh R, Cowen L, Berger B. D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions. Cell Syst 2021; 12:969-982.e6. [PMID: 34536380 PMCID: PMC8586911 DOI: 10.1016/j.cels.2021.08.010] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 08/01/2021] [Accepted: 08/19/2021] [Indexed: 11/29/2022]
Abstract
We combine advances in neural language modeling and structurally motivated design to develop D-SCRIPT, an interpretable and generalizable deep-learning model, which predicts interaction between two proteins using only their sequence and maintains high accuracy with limited training data and across species. We show that a D-SCRIPT model trained on 38,345 human PPIs enables significantly improved functional characterization of fly proteins compared with the state-of-the-art approach. Evaluating the same D-SCRIPT model on protein complexes with known 3D structure, we find that the inter-protein contact map output by D-SCRIPT has significant overlap with the ground truth. We apply D-SCRIPT to screen for PPIs in cow (Bos taurus) at a genome-wide scale and focusing on rumen physiology, identify functional gene modules related to metabolism and immune response. The predicted interactions can then be leveraged for function prediction at scale, addressing the genome-to-phenome challenge, especially in species where little data are available.
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Affiliation(s)
- Samuel Sledzieski
- Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Rohit Singh
- Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Lenore Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, USA.
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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33
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Arsenescu V, Devkota K, Erden M, Shpilker P, Werenski M, Cowen LJ. MUNDO: protein function prediction embedded in a multispecies world. BIOINFORMATICS ADVANCES 2021; 2:vbab025. [PMID: 36699351 PMCID: PMC9710620 DOI: 10.1093/bioadv/vbab025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 09/11/2021] [Accepted: 09/23/2021] [Indexed: 01/28/2023]
Abstract
Motivation Leveraging cross-species information in protein function prediction can add significant power to network-based protein function prediction methods, because so much functional information is conserved across at least close scales of evolution. We introduce MUNDO, a new cross-species co-embedding method that combines a single-network embedding method with a co-embedding method to predict functional annotations in a target species, leveraging also functional annotations in a model species network. Results Across a wide range of parameter choices, MUNDO performs best at predicting annotations in the mouse network, when trained on mouse and human protein-protein interaction (PPI) networks, in the human network, when trained on human and mouse PPIs, and in Baker's yeast, when trained on Fission and Baker's yeast, as compared to competitor methods. MUNDO also outperforms all the cross-species methods when predicting in Fission yeast when trained on Fission and Baker's yeast; however, in this single case, discarding the information from the other species and using annotations from the Fission yeast network alone usually performs best. Availability and implementation All code is available and can be accessed here: github.com/v0rtex20k/MUNDO. Supplementary information Supplementary data are available at Bioinformatics Advances online. Additional experimental results are on our github site.
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Affiliation(s)
- Victor Arsenescu
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Kapil Devkota
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Mert Erden
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Polina Shpilker
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Matthew Werenski
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Lenore J Cowen
- Department of Computer Science, Tufts University, Medford, MA 02155, USA,To whom correspondence should be addressed.
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Kim J, Kim D, Sohn KA. HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball. Bioinformatics 2021; 37:2971-2980. [PMID: 33760022 DOI: 10.1093/bioinformatics/btab193] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 03/14/2021] [Accepted: 03/23/2021] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Knowledge manipulation of Gene Ontology (GO) and Gene Ontology Annotation (GOA) can be done primarily by using vector representation of GO terms and genes. Previous studies have represented GO terms and genes or gene products in Euclidean space to measure their semantic similarity using an embedding method such as the Word2Vec-based method to represent entities as numeric vectors. However, this method has the limitation that embedding large graph-structured data in the Euclidean space cannot prevent a loss of information of latent hierarchies, thus precluding the semantics of GO and GOA from being captured optimally. On the other hand, hyperbolic spaces such as the Poincaré balls are more suitable for modeling hierarchies, as they have a geometric property in which the distance increases exponentially as it nears the boundary because of negative curvature. RESULTS In this article, we propose hierarchical representations of GO and genes (HiG2Vec) by applying Poincaré embedding specialized in the representation of hierarchy through a two-step procedure: GO embedding and gene embedding. Through experiments, we show that our model represents the hierarchical structure better than other approaches and predicts the interaction of genes or gene products similar to or better than previous studies. The results indicate that HiG2Vec is superior to other methods in capturing the GO and gene semantics and in data utilization as well. It can be robustly applied to manipulate various biological knowledge. AVAILABILITYAND IMPLEMENTATION https://github.com/JaesikKim/HiG2Vec. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jaesik Kim
- Department of Computer Engineering, Ajou University, Suwon 16499, South Korea.,Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kyung-Ah Sohn
- Department of Computer Engineering, Ajou University, Suwon 16499, South Korea.,Department of Artificial Intelligence, Ajou University, Suwon 16499, South Korea
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35
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Kulmanov M, Smaili FZ, Gao X, Hoehndorf R. Semantic similarity and machine learning with ontologies. Brief Bioinform 2021; 22:bbaa199. [PMID: 33049044 PMCID: PMC8293838 DOI: 10.1093/bib/bbaa199] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.
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Affiliation(s)
| | | | - Xin Gao
- Computational Bioscience Research Center and lead of the Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology
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36
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Urzúa-Traslaviña CG, Leeuwenburgh VC, Bhattacharya A, Loipfinger S, van Vugt MATM, de Vries EGE, Fehrmann RSN. Improving gene function predictions using independent transcriptional components. Nat Commun 2021; 12:1464. [PMID: 33674610 PMCID: PMC7935959 DOI: 10.1038/s41467-021-21671-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 02/05/2021] [Indexed: 02/07/2023] Open
Abstract
The interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.
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Affiliation(s)
- Carlos G Urzúa-Traslaviña
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Vincent C Leeuwenburgh
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,The Stratingh Institute for Chemistry, University of Groningen, Groningen, The Netherlands
| | - Arkajyoti Bhattacharya
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Stefan Loipfinger
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marcel A T M van Vugt
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Elisabeth G E de Vries
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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Kitani T, Maddipatla SC, Madupuri R, Greco C, Hartmann J, Baraniuk JN, Vasudevan S. In Search of Newer Targets for Inflammatory Bowel Disease: A Systems and a Network Medicine Approach. NETWORK AND SYSTEMS MEDICINE 2021. [DOI: 10.1089/nsm.2020.0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Takashi Kitani
- Department of Neurology, Molecular and Cellular Biology, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Sushma C. Maddipatla
- Department of Biochemistry, Molecular and Cellular Biology, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Ramya Madupuri
- Department of Biochemistry, Molecular and Cellular Biology, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Christopher Greco
- Department of Biochemistry, Molecular and Cellular Biology, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Jonathan Hartmann
- Dahlgren Memorial Library, Graduate Health and Life Sciences Research Library, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - James N. Baraniuk
- Division of Rheumatology, Immunology and Allergy, Department of Medicine, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Sona Vasudevan
- Department of Biochemistry, Molecular and Cellular Biology, Georgetown University Medical Center, Washington, District of Columbia, USA
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Smejda M, Kądziołka D, Radczuk N, Krutyhołowa R, Chramiec-Głąbik A, Kędracka-Krok S, Jankowska U, Biela A, Glatt S. Same but different - Molecular comparison of human KTI12 and PSTK. BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR CELL RESEARCH 2021; 1868:118945. [PMID: 33417976 DOI: 10.1016/j.bbamcr.2020.118945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/01/2020] [Accepted: 12/23/2020] [Indexed: 11/18/2022]
Abstract
Kti12 and PSTK are closely related and highly similar proteins implicated in different aspects of tRNA metabolism. Kti12 has been identified as an essential regulatory factor of the Elongator complex, involved in the modification of uridine bases in eukaryotic tRNAs. PSTK phosphorylates the tRNASec-bound amino acid serine, which is required to synthesize selenocysteine. Kti12 and PSTK have previously been studied independently in various organisms, but only appear simultaneously in some animalia, including humans. As Kti12- and PSTK-related pathways are clinically relevant, it is of prime importance to understand their biological functions and mutual relationship in humans. Here, we use different tRNA substrates to directly compare the enzymatic activities of purified human KTI12 and human PSTK proteins. Our complementary Co-IP and BioID2 approaches in human cells confirm that Elongator is the main interaction partner of KTI12 but additionally indicate potential links to proteins involved in vesicular transport, RNA metabolism and deubiquitination. Moreover, we identify and validate a yet uncharacterized interaction between PSTK and γ-taxilin. Foremost, we demonstrate that human KTI12 and PSTK do not share interactors or influence their respective biological functions. Our data provide a comprehensive analysis of the regulatory networks controlling the activity of the human Elongator complex and selenocysteine biosynthesis.
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Affiliation(s)
- Marta Smejda
- Malopolska Centre of Biotechnology (MCB), Jagiellonian University, Krakow, Poland; Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland
| | - Dominika Kądziołka
- Malopolska Centre of Biotechnology (MCB), Jagiellonian University, Krakow, Poland
| | - Natalia Radczuk
- Malopolska Centre of Biotechnology (MCB), Jagiellonian University, Krakow, Poland
| | - Rościsław Krutyhołowa
- Malopolska Centre of Biotechnology (MCB), Jagiellonian University, Krakow, Poland; Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland
| | | | - Sylwia Kędracka-Krok
- Malopolska Centre of Biotechnology (MCB), Jagiellonian University, Krakow, Poland; Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland
| | - Urszula Jankowska
- Malopolska Centre of Biotechnology (MCB), Jagiellonian University, Krakow, Poland
| | - Anna Biela
- Malopolska Centre of Biotechnology (MCB), Jagiellonian University, Krakow, Poland.
| | - Sebastian Glatt
- Malopolska Centre of Biotechnology (MCB), Jagiellonian University, Krakow, Poland.
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Karimi M, Zhu S, Cao Y, Shen Y. De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks. J Chem Inf Model 2020; 60:5667-5681. [PMID: 32945673 PMCID: PMC7775287 DOI: 10.1021/acs.jcim.0c00593] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal underlying sequence-structure relationships and design protein sequences for an arbitrary, potentially novel, structural fold? In response to the question, we have developed novel deep generative models, namely, semisupervised gcWGAN (guided, conditional, Wasserstein Generative Adversarial Networks). To overcome training difficulties and improve design qualities, we build our models on conditional Wasserstein GAN (WGAN) that uses Wasserstein distance in the loss function. Our major contributions include (1) constructing a low-dimensional and generalizable representation of the fold space for the conditional input, (2) developing an ultrafast sequence-to-fold predictor (or oracle) and incorporating its feedback into WGAN as a loss to guide model training, and (3) exploiting sequence data with and without paired structures to enable a semisupervised training strategy. Assessed by the oracle over 100 novel folds not in the training set, gcWGAN generates more successful designs and covers 3.5 times more target folds compared to a competing data-driven method (cVAE). Assessed by sequence- and structure-based predictors, gcWGAN designs are physically and biologically sound. Assessed by a structure predictor over representative novel folds, including one not even part of basis folds, gcWGAN designs have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE. The ultrafast data-driven model is further shown to boost the success of a principle-driven de novo method (RosettaDesign), through generating design seeds and tailoring design space. In conclusion, gcWGAN explores uncharted sequence space to design proteins by learning generalizable principles from current sequence-structure data. Data, source codes, and trained models are available at https://github.com/Shen-Lab/gcWGAN.
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Affiliation(s)
- Mostafa Karimi
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
- TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas 77840, United States
| | - Shaowen Zhu
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
- TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas 77840, United States
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40
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Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2. PLoS One 2020; 15:e0236304. [PMID: 33284827 PMCID: PMC7721128 DOI: 10.1371/journal.pone.0236304] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 11/24/2020] [Indexed: 11/19/2022] Open
Abstract
Motivation Beside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship. Results We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks, and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.
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González-Gomariz J, Serrano G, Tilve-Álvarez CM, Corrales FJ, Guruceaga E, Segura V. UPEFinder: A Bioinformatic Tool for the Study of Uncharacterized Proteins Based on Gene Expression Correlation and the PageRank Algorithm. J Proteome Res 2020; 19:4795-4807. [PMID: 33155801 DOI: 10.1021/acs.jproteome.0c00364] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The Human Proteome Project (HPP) is leading the international effort to characterize the human proteome. Although the main goal of this project was first focused on the detection of missing proteins, a new challenge arose from the need to assign biological functions to the uncharacterized human proteins and describe their implications in human diseases. Not only the proteins with experimental evidence (uPE1 proteins) but also the uncharacterized missing proteins (uMPs) were the objects of study in this challenge, neXt-CP50. In this work, we developed a new bioinformatic approach to infer biological annotations for the uPE1 proteins and uMPs based on a "guilt-by-association" analysis using public RNA-Seq data sets. We used the correlation of these proteins with the well-characterized PE1 proteins to construct a network. In this way, we applied the PageRank algorithm to this network to identify the most relevant nodes, which were the biological annotations of the uncharacterized proteins. All of the generated information was stored in a database. In addition, we implemented the web application UPEFinder (https://upefinder.proteored.org) to facilitate the access to this new resource. This information is especially relevant for the researchers of the HPP who are interested in the generation and validation of new hypotheses about the functions of these proteins. Both the database and the web application are publicly available (https://github.com/ubioinformat/UPEfinder).
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Affiliation(s)
| | - Guillermo Serrano
- Bioinformatics Platform, CIMA University of Navarra, Pamplona E-31008, Spain
| | - Carlos M Tilve-Álvarez
- Fundación Profesor Nóvoa-Santos, Instituto de Investigación Biomédica da Coruña, Coruña E-15006, Spain
| | - Fernando J Corrales
- Proteomics Unit, National Center for Biotechnology, CSIC, Madrid E-28049, Spain
| | - Elizabeth Guruceaga
- IdiSNA, Navarra Institute for Health Research, Pamplona E-31008, Spain.,Bioinformatics Platform, CIMA University of Navarra, Pamplona E-31008, Spain
| | - Victor Segura
- Tracasa Instrumental, Sarriguren E-31621, Spain.,Sección de Ingeniería del Dato, Dirección General de Telecomunicaciones y Digitalización, Gobierno de Navarra, Sarriguren E-31621, Spain
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42
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Ayub U, Haider I, Naveed H. SAlign-a structure aware method for global PPI network alignment. BMC Bioinformatics 2020; 21:500. [PMID: 33148180 PMCID: PMC7640460 DOI: 10.1186/s12859-020-03827-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 10/20/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND High throughput experiments have generated a significantly large amount of protein interaction data, which is being used to study protein networks. Studying complete protein networks can reveal more insight about healthy/disease states than studying proteins in isolation. Similarly, a comparative study of protein-protein interaction (PPI) networks of different species reveals important insights which may help in disease analysis and drug design. The study of PPI network alignment can also helps in understanding the different biological systems of different species. It can also be used in transfer of knowledge across different species. Different aligners have been introduced in the last decade but developing an accurate and scalable global alignment algorithm that can ensures the biological significance alignment is still challenging. RESULTS This paper presents a novel global pairwise network alignment algorithm, SAlign, which uses topological and biological information in the alignment process. The proposed algorithm incorporates sequence and structural information for computing biological scores, whereas previous algorithms only use sequence information. The alignment based on the proposed technique shows that the combined effect of structure and sequence results in significantly better pairwise alignments. We have compared SAlign with state-of-art algorithms on the basis of semantic similarity of alignment and the number of aligned nodes on multiple PPI network pairs. The results of SAlign on the network pairs which have high percentage of proteins with available structure are 3-63% semantically better than all existing techniques. Furthermore, it also aligns 5-14% more nodes of these network pairs as compared to existing aligners. The results of SAlign on other PPI network pairs are comparable or better than all existing techniques. We also introduce [Formula: see text], a Monte Carlo based alignment algorithm, that produces multiple network alignments with similar semantic similarity. This helps the user to pick biologically meaningful alignments. CONCLUSION The proposed algorithm has the ability to find the alignments that are more biologically significant/relevant as compared to the alignments of existing aligners. Furthermore, the proposed method is able to generate alternate alignments that help in studying different genes/proteins of the specie.
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Affiliation(s)
- Umair Ayub
- Department of Computing, National University of Computer and Emerging Sciences, Islamabad, 40100, Pakistan.,Computational Biology Research Lab, Islamabad, 40100, Pakistan
| | - Imran Haider
- Department of Computing, National University of Computer and Emerging Sciences, Islamabad, 40100, Pakistan.,Computational Biology Research Lab, Islamabad, 40100, Pakistan
| | - Hammad Naveed
- Department of Computing, National University of Computer and Emerging Sciences, Islamabad, 40100, Pakistan. .,Computational Biology Research Lab, Islamabad, 40100, Pakistan.
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Carroll LM, Cheng RA, Kovac J. No Assembly Required: Using BTyper3 to Assess the Congruency of a Proposed Taxonomic Framework for the Bacillus cereus Group With Historical Typing Methods. Front Microbiol 2020; 11:580691. [PMID: 33072050 PMCID: PMC7536271 DOI: 10.3389/fmicb.2020.580691] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 08/25/2020] [Indexed: 12/19/2022] Open
Abstract
The Bacillus cereus group, also known as B. cereus sensu lato (s.l.), is a species complex comprising numerous closely related lineages, which vary in their ability to cause illness in humans and animals. The classification of B. cereus s.l. isolates into species-level taxonomic units is essential for facilitating communication between and among microbiologists, clinicians, public health officials, and industry professionals, but is not always straightforward. A recently proposed genomospecies-subspecies-biovar taxonomic framework aims to provide a standardized nomenclature for this species complex but relies heavily on whole-genome sequencing (WGS). It thus is unclear whether popular, low-cost typing methods (e.g., single- and multi-locus sequence typing) remain congruent with the proposed taxonomy. Here, we characterize 2,231 B. cereus s.l. genomes using a combination of in silico (i) average-nucleotide identity (ANI)-based genomospecies assignment, (ii) ANI-based subspecies assignment, (iii) seven-gene multi-locus sequence typing (MLST), (iv) single-locus panC group assignment, (v) rpoB allelic typing, and (vi) virulence factor detection. We show that sequence types (STs) assigned using MLST can be used for genomospecies assignment, and we provide a comprehensive list of ST/genomospecies associations. For panC group assignment, we show that an adjusted, eight-group framework is largely, albeit not perfectly, congruent with the proposed eight-genomospecies taxonomy, as panC alone may not distinguish (i) B. luti from Group II B. mosaicus and (ii) B. paramycoides from Group VI B. mycoides. We additionally provide a list of loci that capture the topology of the whole-genome B. cereus s.l. phylogeny that may be used in future sequence typing efforts. For researchers with access to WGS, MLST, and/or panC data, we showcase how our recently released software, BTyper3 (https://github.com/lmc297/BTyper3), can be used to assign B. cereus s.l. isolates to taxonomic units within this proposed framework with little-to-no user intervention or domain-specific knowledge of B. cereus s.l. taxonomy. We additionally outline a novel method for assigning B. cereus s.l. genomes to pseudo-gene flow units within proposed genomospecies. The results presented here highlight the backward-compatibility and accessibility of the recently proposed genomospecies-subspecies-biovar taxonomic framework and illustrate that WGS is not a necessity for microbiologists who want to use the proposed nomenclature effectively.
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Affiliation(s)
- Laura M. Carroll
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Rachel A. Cheng
- Department of Food Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
| | - Jasna Kovac
- Department of Food Science, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States
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Ayllon-Benitez A, Bourqui R, Thébault P, Mougin F. GSAn: an alternative to enrichment analysis for annotating gene sets. NAR Genom Bioinform 2020; 2:lqaa017. [PMID: 33575577 PMCID: PMC7671311 DOI: 10.1093/nargab/lqaa017] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 02/12/2020] [Accepted: 02/25/2020] [Indexed: 12/27/2022] Open
Abstract
The revolution in new sequencing technologies is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze data that are grouped according to a phenotype of interest, methods based on statistical enrichment became a standard in biology. However, these methods synthesize the biological information by a priori selecting the over-represented terms and may suffer from focusing on the most studied genes that represent a limited coverage of annotated genes within a gene set. Semantic similarity measures have shown great results within the pairwise gene comparison by making advantage of the underlying structure of the Gene Ontology. We developed GSAn, a novel gene set annotation method that uses semantic similarity measures to synthesize a priori Gene Ontology annotation terms. The originality of our approach is to identify the best compromise between the number of retained annotation terms that has to be drastically reduced and the number of related genes that has to be as large as possible. Moreover, GSAn offers interactive visualization facilities dedicated to the multi-scale analysis of gene set annotations. Compared to enrichment analysis tools, GSAn has shown excellent results in terms of maximizing the gene coverage while minimizing the number of terms.
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Affiliation(s)
- Aaron Ayllon-Benitez
- University of Bordeaux, Inserm UMR 1219, Bordeaux Population Health Research Center, team ERIAS, Bordeaux 33000, France
- University of Bordeaux, CNRS UMR 5800, LaBRI, Bordeaux 33400, France
| | - Romain Bourqui
- University of Bordeaux, CNRS UMR 5800, LaBRI, Bordeaux 33400, France
| | - Patricia Thébault
- University of Bordeaux, CNRS UMR 5800, LaBRI, Bordeaux 33400, France
| | - Fleur Mougin
- University of Bordeaux, Inserm UMR 1219, Bordeaux Population Health Research Center, team ERIAS, Bordeaux 33000, France
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Li S, Wan C, Zheng R, Fan J, Dong X, Meyer CA, Liu XS. Cistrome-GO: a web server for functional enrichment analysis of transcription factor ChIP-seq peaks. Nucleic Acids Res 2020; 47:W206-W211. [PMID: 31053864 PMCID: PMC6602521 DOI: 10.1093/nar/gkz332] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/19/2019] [Accepted: 05/02/2019] [Indexed: 01/12/2023] Open
Abstract
Characterizing the ontologies of genes directly regulated by a transcription factor (TF), can help to elucidate the TF’s biological role. Previously, we developed a widely used method, BETA, to integrate TF ChIP-seq peaks with differential gene expression (DGE) data to infer direct target genes. Here, we provide Cistrome-GO, a website implementation of this method with enhanced features to conduct ontology analyses of gene regulation by TFs in human and mouse. Cistrome-GO has two working modes: solo mode for ChIP-seq peak analysis; and ensemble mode, which integrates ChIP-seq peaks with DGE data. Cistrome-GO is freely available at http://go.cistrome.org/.
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Affiliation(s)
- Shaojuan Li
- Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Changxin Wan
- Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Rongbin Zheng
- Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Jingyu Fan
- Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Xin Dong
- Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Clifford A Meyer
- Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - X Shirley Liu
- Shanghai Key Laboratory of Tuberculosis, Clinical Translational Research Center, Shanghai Pulmonary Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.,Department of Data Sciences, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
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Singha M, Pu L, Shawky A, Busch K, Wu H, Ramanujam J, Brylinski M. GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth.. [DOI: 10.1101/2020.05.20.107458] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
AbstractGenomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to treatment with drugs. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. High prediction accuracies reported in the literature likely result from significant overlaps among training, validation, and testing sets, making many predictors inapplicable to new data. To address these issues, we developed GraphGR, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, GraphGR integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and genedisease associations, into a unified graph structure. In order to construct diverse and information-rich cancer-specific networks, we devised a novel graph reduction protocol based on not only the topological information, but also the biological knowledge. The performance of GraphGR, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches on the same data. Finally, several new predictions are validated against the biomedical literature demonstrating that GraphGR generalizes well to unseen data, i.e. it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. GraphGR is freely available to the academic community at https://github.com/pulimeng/GraphGR.
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47
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Gupta SK, Srivastava M, Osmanoglu Ö, Dandekar T. Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs. Sci Rep 2020; 10:2334. [PMID: 32047225 PMCID: PMC7012867 DOI: 10.1038/s41598-020-59344-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 01/22/2020] [Indexed: 12/18/2022] Open
Abstract
Apart from some model organisms, the interactome of most organisms is largely unidentified. High-throughput experimental techniques to determine protein-protein interactions (PPIs) are resource intensive and highly susceptible to noise. Computational methods of PPI determination can accelerate biological discovery by identifying the most promising interacting pairs of proteins and by assessing the reliability of identified PPIs. Here we present a first in-depth study describing a global view of the ant Camponotus floridanus interactome. Although several ant genomes have been sequenced in the last eight years, studies exploring and investigating PPIs in ants are lacking. Our study attempts to fill this gap and the presented interactome will also serve as a template for determining PPIs in other ants in future. Our C. floridanus interactome covers 51,866 non-redundant PPIs among 6,274 proteins, including 20,544 interactions supported by domain-domain interactions (DDIs), 13,640 interactions supported by DDIs and subcellular localization, and 10,834 high confidence interactions mediated by 3,289 proteins. These interactions involve and cover 30.6% of the entire C. floridanus proteome.
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Affiliation(s)
- Shishir K Gupta
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany.,Department of Microbiology, Biocenter, Am Hubland, D-97074, Würzburg, Germany
| | - Mugdha Srivastava
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany
| | - Özge Osmanoglu
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany. .,EMBL Heidelberg, BioComputing Unit, Meyerhofstraße 1, 69117, Heidelberg, Germany.
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48
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Kishore R, Arnaboldi V, Van Slyke CE, Chan J, Nash RS, Urbano JM, Dolan ME, Engel SR, Shimoyama M, Sternberg PW, Genome Resources TAO. Automated generation of gene summaries at the Alliance of Genome Resources. Database (Oxford) 2020; 2020:baaa037. [PMID: 32559296 PMCID: PMC7304461 DOI: 10.1093/database/baaa037] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/06/2020] [Accepted: 04/29/2020] [Indexed: 12/28/2022]
Abstract
Short paragraphs that describe gene function, referred to as gene summaries, are valued by users of biological knowledgebases for the ease with which they convey key aspects of gene function. Manual curation of gene summaries, while desirable, is difficult for knowledgebases to sustain. We developed an algorithm that uses curated, structured gene data at the Alliance of Genome Resources (Alliance; www.alliancegenome.org) to automatically generate gene summaries that simulate natural language. The gene data used for this purpose include curated associations (annotations) to ontology terms from the Gene Ontology, Disease Ontology, model organism knowledgebase (MOK)-specific anatomy ontologies and Alliance orthology data. The method uses sentence templates for each data category included in the gene summary in order to build a natural language sentence from the list of terms associated with each gene. To improve readability of the summaries when numerous gene annotations are present, we developed a new algorithm that traverses ontology graphs in order to group terms by their common ancestors. The algorithm optimizes the coverage of the initial set of terms and limits the length of the final summary, using measures of information content of each ontology term as a criterion for inclusion in the summary. The automated gene summaries are generated with each Alliance release, ensuring that they reflect current data at the Alliance. Our method effectively leverages category-specific curation efforts of the Alliance member databases to create modular, structured and standardized gene summaries for seven member species of the Alliance. These automatically generated gene summaries make cross-species gene function comparisons tenable and increase discoverability of potential models of human disease. In addition to being displayed on Alliance gene pages, these summaries are also included on several MOK gene pages.
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Affiliation(s)
- Ranjana Kishore
- WormBase, Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA
| | - Valerio Arnaboldi
- WormBase, Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA
| | - Ceri E Van Slyke
- ZFIN, The Institute of Neuroscience, 222 Huestis Hall, University of Oregon, Eugene, OR 97403-1254, USA
| | - Juancarlos Chan
- WormBase, Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA
| | - Robert S Nash
- Saccharomyces Genome Database, Department of Genetics, Stanford University, 3165 Porter Drive, Palo Alto, CA 94304, USA
| | - Jose M Urbano
- FlyBase, Department of Physiology, Development and Neuroscience, 7 Downing Pl, University of Cambridge, Cambridge CB2 3DY, UK
| | - Mary E Dolan
- MGI, The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | - Stacia R Engel
- Saccharomyces Genome Database, Department of Genetics, Stanford University, 3165 Porter Drive, Palo Alto, CA 94304, USA
| | - Mary Shimoyama
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin and Marquette University, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA
| | - Paul W Sternberg
- WormBase, Division of Biology and Biological Engineering, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA
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49
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Tsaramirsis K, Tsaramirsis G, Khan FQ, Ahmad A, Khadidos AO, Khadidos A. More Agility to Semantic Similarities Algorithm Implementations. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:ijerph17010267. [PMID: 31905999 PMCID: PMC6982023 DOI: 10.3390/ijerph17010267] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 12/22/2019] [Accepted: 12/28/2019] [Indexed: 02/07/2023]
Abstract
Algorithms for measuring semantic similarity between Gene Ontology (GO) terms has become a popular area of research in bioinformatics as it can help to detect functional associations between genes and potential impact to the health and well-being of humans, animals, and plants. While the focus of the research is on the design and improvement of GO semantic similarity algorithms, there is still a need for implementation of such algorithms before they can be used to solve actual biological problems. This can be challenging given that the potential users usually come from a biology background and they are not programmers. A number of implementations exist for some well-established algorithms but these implementations are not generic enough to support any algorithm other than the ones they are designed for. The aim of this paper is to shift the focus away from implementation, allowing researchers to focus on algorithm’s design and execution rather than implementation. This is achieved by an implementation approach capable of understanding and executing user defined GO semantic similarity algorithms. Questions and answers were used for the definition of the user defined algorithm. Additionally, this approach understands any direct acyclic digraph in an Open Biomedical Ontologies (OBO)-like format and its annotations. On the other hand, software developers of similar applications can also benefit by using this as a template for their applications.
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Affiliation(s)
- Kostandinos Tsaramirsis
- Infosuccess3D, 55 Navarxou Kountourgiotou Road, Aigaleo, 122 42 Athens, Greece
- Correspondence: (K.T.); (G.T.)
| | - Georgios Tsaramirsis
- Department of Information Technology, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (F.Q.K.); (A.K.)
- Correspondence: (K.T.); (G.T.)
| | - Fazal Qudus Khan
- Department of Information Technology, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (F.Q.K.); (A.K.)
| | - Awais Ahmad
- Dipartimento di informatica, universita’ degli Studi di Milano, 20122 Milan, Italy;
| | - Alaa Omar Khadidos
- Department of Information Systems, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Adil Khadidos
- Department of Information Technology, Faculty of Computing And IT, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (F.Q.K.); (A.K.)
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50
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Brionne A, Juanchich A, Hennequet-Antier C. ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity. BioData Min 2019; 12:16. [PMID: 31406507 PMCID: PMC6685253 DOI: 10.1186/s13040-019-0204-1] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 07/25/2019] [Indexed: 12/24/2022] Open
Abstract
The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. ViSEAGO is publicly available on https://bioconductor.org/packages/ViSEAGO .
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