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Cavalleri E, Cabri A, Soto-Gomez M, Bonfitto S, Perlasca P, Gliozzo J, Callahan TJ, Reese J, Robinson PN, Casiraghi E, Valentini G, Mesiti M. An ontology-based knowledge graph for representing interactions involving RNA molecules. Sci Data 2024; 11:906. [PMID: 39174566 PMCID: PMC11341713 DOI: 10.1038/s41597-024-03673-7] [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: 12/22/2023] [Accepted: 07/23/2024] [Indexed: 08/24/2024] Open
Abstract
The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.
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Affiliation(s)
- Emanuele Cavalleri
- AnacletoLab, Computer Science Department, University of Milan, Milan, 20133, Italy
| | - Alberto Cabri
- AnacletoLab, Computer Science Department, University of Milan, Milan, 20133, Italy
| | - Mauricio Soto-Gomez
- AnacletoLab, Computer Science Department, University of Milan, Milan, 20133, Italy
| | - Sara Bonfitto
- AnacletoLab, Computer Science Department, University of Milan, Milan, 20133, Italy
| | - Paolo Perlasca
- AnacletoLab, Computer Science Department, University of Milan, Milan, 20133, Italy
| | - Jessica Gliozzo
- AnacletoLab, Computer Science Department, University of Milan, Milan, 20133, Italy
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Peter N Robinson
- Berlin Institute of Health - Charité, Universitätsmedizin, Berlin, 13353, Germany
- ELLIS, European Laboratory for Learning and Intelligent Systems, Munich, Germany
| | - Elena Casiraghi
- AnacletoLab, Computer Science Department, University of Milan, Milan, 20133, Italy
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- ELLIS, European Laboratory for Learning and Intelligent Systems, Munich, Germany
| | - Giorgio Valentini
- AnacletoLab, Computer Science Department, University of Milan, Milan, 20133, Italy
- ELLIS, European Laboratory for Learning and Intelligent Systems, Munich, Germany
| | - Marco Mesiti
- AnacletoLab, Computer Science Department, University of Milan, Milan, 20133, Italy.
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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Goodman SJ, Carr IM. Drawing mitochondrial genomes with circularMT. Bioinformatics 2024; 40:btae450. [PMID: 39002115 PMCID: PMC11272167 DOI: 10.1093/bioinformatics/btae450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/05/2024] [Accepted: 07/11/2024] [Indexed: 07/15/2024] Open
Abstract
SUMMARY Mitochondrial DNA sequences are used extensively in phylogeographic and phylogenetic studies for a wide range of organisms. With the advent of low-cost, high throughput 'next generation' DNA sequencing, and user-friendly bioinformatics pipelines for generating and annotating whole mitochondrial genome assemblies, the analysis of whole mitochondrial genomes has become an important component of phylogenomic studies for taxa with high species diversity but limited coverage for other genomic resources. An important step in characterizing de novo mitochondrial genome assemblies is to evaluate and describe structural rearrangements relative to reference taxa. Accessible tools are needed to help visualize gene and noncoding feature complement, their order and strand orientation. However, there are few dedicated applications that generate high quality genome diagrams. Here we present circularMT and circularMT-console that allow users to create highly customizable, publication quality images, of linear and circular mitochondrial genome maps, either individually, or integrated into an analysis pipeline. AVAILABILITY AND IMPLEMENTATION Both applications are implemented in C#, with binaries, source code and user guides available on GitHub (https://github.com/msjimc/circularMT). An archive of the published version is available on Zenodo (https://zenodo.org/records/10912319). SUPPLEMENTARY INFORMATION This paper has no supplementary data.
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Affiliation(s)
- Simon J Goodman
- School of Biology, Faculty of Biological Science, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Ian M Carr
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds LS9 7TF, United Kingdom
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Guo X, She Y, Liu Q, Qin J, Wang L, Xu A, Qi B, Sun C, Xie Y, Ma Y, Zhu L, Tao W, Wei X, Zhang Y. Osteoporosis and depression in perimenopausal women: From clinical association to genetic causality. J Affect Disord 2024; 356:371-378. [PMID: 38608764 DOI: 10.1016/j.jad.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 03/24/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Osteoporosis and major depressive disorder (MDD) represent two significant health challenges globally, particularly among perimenopausal women. This study utilizes NHANES data and Mendelian randomization (MR) analysis to explore the link between them, aiming to provide a basis for intervention strategies for this group. METHODS The study analyzed NHANES 2007-2018 data using weighted logistic regression in R software to evaluate the link between MDD and osteoporosis risk. Then, a two-sample MR analysis with GWAS summary statistics was performed, mainly using the IVW method. Additional validation included MR Egger, Weighted Median, Mode, and MR-PRESSO methods. RESULTS The research analysis indicated a significant link between MDD and the risk of osteopenia/osteoporosis. Our analysis revealed a significant positive relationship between MDD and both femoral neck osteoporosis (OR = 6.942 [95 % CI, 1.692-28.485]) and trochanteric osteoporosis (OR = 4.140 [95 % CI, 1.699-10.089]). In analyses related to osteopenia, a significant positive correlation was observed between MDD and both total femoral osteopenia (OR = 3.309 [95 % CI, 1.577-6.942]) and trochanteric osteopenia (OR = 2.467 [95 % CI, 1.004-6.062]). Furthermore, in the MR analysis, genetically predicted MDD was causally associated with an increased risk of osteoporosis via the IVW method (P = 0.013). LIMITATIONS Our study was limited by potential selection bias due to excluding subjects with missing data, and its applicability was primarily to European and American populations. CONCLUSION Integrating NHANES and MR analyses, a robust correlation between MDD and osteoporosis was identified, emphasizing the significance of addressing this comorbidity within clinical practice and meriting further investigation.
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Affiliation(s)
- Xiangyun Guo
- School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Yun She
- The First Clinical Medical College of Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Qingqing Liu
- School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Jinran Qin
- School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Liang Wang
- School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China
| | - Aili Xu
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China
| | - Baoyu Qi
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China
| | - Chuanrui Sun
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China
| | - Yanming Xie
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Yong Ma
- School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China; Jiangsu CM Clinical Innovation Center of Degenerative Bone & Joint Disease, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, China
| | - Liguo Zhu
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China
| | - Weiwei Tao
- School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China; Jiangsu CM Clinical Innovation Center of Degenerative Bone & Joint Disease, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, China.
| | - Xu Wei
- Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China.
| | - Yili Zhang
- School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China; Jiangsu CM Clinical Innovation Center of Degenerative Bone & Joint Disease, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214071, China.
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Arizmendi-Izazaga A, Navarro-Tito N, Jiménez-Wences H, Evaristo-Priego A, Priego-Hernández VD, Dircio-Maldonado R, Zacapala-Gómez AE, Mendoza-Catalán MÁ, Illades-Aguiar B, De Nova Ocampo MA, Salmerón-Bárcenas EG, Leyva-Vázquez MA, Ortiz-Ortiz J. Bioinformatics Analysis Reveals E6 and E7 of HPV 16 Regulate Metabolic Reprogramming in Cervical Cancer, Head and Neck Cancer, and Colorectal Cancer through the PHD2-VHL-CUL2-ELOC-HIF-1α Axis. Curr Issues Mol Biol 2024; 46:6199-6222. [PMID: 38921041 PMCID: PMC11202971 DOI: 10.3390/cimb46060370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/06/2024] [Accepted: 06/15/2024] [Indexed: 06/27/2024] Open
Abstract
Human papillomavirus 16 (HPV 16) infection is associated with several types of cancer, such as head and neck, cervical, anal, and penile cancer. Its oncogenic potential is due to the ability of the E6 and E7 oncoproteins to promote alterations associated with cell transformation. HPV 16 E6 and E7 oncoproteins increase metabolic reprogramming, one of the hallmarks of cancer, by increasing the stability of hypoxia-induced factor 1 α (HIF-1α) and consequently increasing the expression levels of their target genes. In this report, by bioinformatic analysis, we show the possible effect of HPV 16 oncoproteins E6 and E7 on metabolic reprogramming in cancer through the E6-E7-PHD2-VHL-CUL2-ELOC-HIF-1α axis. We proposed that E6 and E7 interact with VHL, CUL2, and ELOC in forming the E3 ubiquitin ligase complex that ubiquitinates HIF-1α for degradation via the proteasome. Based on the information found in the databases, it is proposed that E6 interacts with VHL by blocking its interaction with HIF-1α. On the other hand, E7 interacts with CUL2 and ELOC, preventing their binding to VHL and RBX1, respectively. Consequently, HIF-1α is stabilized and binds with HIF-1β to form the active HIF1 complex that binds to hypoxia response elements (HREs), allowing the expression of genes related to energy metabolism. In addition, we suggest an effect of E6 and E7 at the level of PHD2, VHL, CUL2, and ELOC gene expression. Here, we propose some miRNAs targeting PHD2, VHL, CUL2, and ELOC mRNAs. The effect of E6 and E7 may be the non-hydroxylation and non-ubiquitination of HIF-1α, which may regulate metabolic processes involved in metabolic reprogramming in cancer upon stabilization, non-degradation, and translocation to the nucleus.
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Affiliation(s)
- Adán Arizmendi-Izazaga
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Napoleón Navarro-Tito
- Laboratorio de Biología Celular del Cáncer, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
| | - Hilda Jiménez-Wences
- Laboratorio de Investigación en Biomoléculas, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
- Laboratorio de Investigación Clínica, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
| | - Adilene Evaristo-Priego
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Víctor Daniel Priego-Hernández
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Roberto Dircio-Maldonado
- Laboratorio de Investigación Clínica, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
| | - Ana Elvira Zacapala-Gómez
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Miguel Ángel Mendoza-Catalán
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Berenice Illades-Aguiar
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Mónica Ascención De Nova Ocampo
- Escuela Nacional de Medicina y Homeopatía, Programa Institucional de Biomedicina Molecular, Instituto Politécnico Nacional, Guillermo Massieu Helguera No. 239 Col. Fracc. La Escalera-Ticomán, Ciudad de Mexico C.P. 07320, Mexico;
| | - Eric Genaro Salmerón-Bárcenas
- Departamento de Biomedicina Molecular, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Ciudad de México C.P. 07360, Mexico;
| | - Marco Antonio Leyva-Vázquez
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
| | - Julio Ortiz-Ortiz
- Laboratorio de Biomedicina Molecular, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico; (A.A.-I.); (A.E.-P.); (V.D.P.-H.); (A.E.Z.-G.); (M.Á.M.-C.); (B.I.-A.)
- Laboratorio de Investigación en Biomoléculas, Facultad de Ciencias Químico Biológicas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Ciudad Universitaria, Colonia La Haciendita, Chilpancingo C.P. 39090, Guerrero, Mexico;
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Chen P, Qin J, Wang Y, Yuan J, Pan Y, Zhu B. The causal relationship between sleep and risk of psychiatric disorders: A two-sample mendelian randomization study. Front Genet 2024; 15:1380544. [PMID: 38952712 PMCID: PMC11215123 DOI: 10.3389/fgene.2024.1380544] [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: 02/01/2024] [Accepted: 05/31/2024] [Indexed: 07/03/2024] Open
Abstract
Introduction Sleep is associated with psychiatric disorders. However, their causality remains unknown. Methods The study explored the causal relationship between seven sleep parameters (sleep duration, insomnia, sleep apnea, chronotype, daytime dozing, napping during the day, and snoring) and three psychiatric disorders including major depressive disorder (MDD), schizophrenia, and attention-deficit/hyperactivity disorder (ADHD) using two-sample Mendelian randomization (MR). Genome-wide association study (GWAS) summary data for sleep parameters were obtained from the United Kingdom biobank, FinnGen biobank, and EBI databases. MR-Egger, weighted median, inverse-variance weighted (IVW), simple mode, weighted mode, maximum likelihood, penalized weighted median, and IVW(fixed effects) were used to perform the MR analysis. The heterogeneity was detected by Cochran's Q statistic. The horizontal pleiotropy was detected by MR Egger. The sensitivity was investigated by the leave-one-out analysis. Results Insomnia (OR = 2.02, 95%CI = 1.34-3.03, p = 0.001, False-discovery rate (FDR) corrected p-value = 0.011) and napping during the day (OR = 1.81, 95%CI = 1.34-2.44, FDR corrected p-value<0.001) were associated with an increased risk of MDD. Longer sleep duration (OR = 2.20, 95%CI = 1.24-3.90, FDR corrected p-value = 0.049) had an association with the increased risk of schizophrenia, while daytime dozing (OR = 4.44, 95%CI = 1.20-16.41, corrected p-value = 0.088)and napping during the day (OR = 2.11, 95%CI = 1.11-4.02, FDR corrected p-value = 0.088) had a suggestive association with an increased risk of schizophrenia. Longer sleep duration had a suggestive association with a decreased risk of ADHD (OR = 0.66, 95%CI = 0.42-0.93, FDR corrected p-value = 0.088). Conclusion This study provides further evidence for a complex relationship between sleep and psychiatric disorders. Our findings highlight the potential benefits of addressing sleep problems in the prevention of psychiatric disorders.
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Affiliation(s)
- Pei Chen
- College of Nursing, University of Illinois Chicago, Chicago, IL, United States
| | - Jiuhang Qin
- Department of Mathematics, Statistics, and Computer Science, University of Illinois Chicago, Chicago, IL, United States
| | - Yueying Wang
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
| | - Jinjin Yuan
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago, Chicago, IL, United States
| | - Bingqian Zhu
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
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Li W, Lin X, Liang H, Wu Z, Wang M, Sun J, Li X, He W, Gao X, Hu T, Xiao L, Zou Y. Genomic and functional diversity of the human-derived isolates of Faecalibacterium. Front Microbiol 2024; 15:1379500. [PMID: 38873165 PMCID: PMC11169845 DOI: 10.3389/fmicb.2024.1379500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/06/2024] [Indexed: 06/15/2024] Open
Abstract
Introduction Faecalibacterium is one of the most abundant bacteria in the gut microbiota of healthy adults, highly regarded as a next-generation probiotic. However, the functions of Faecalibacterium genomes from cultured strains and the distribution of different species in populations may differ among different sources. Methods We here performed an extensive analysis of pan-genomes, functions, and safety evaluation of 136 Faecalibacterium genomes collected from 10 countries. Results The genomes are clustered into 11 clusters, with only five of them were characterized and validly nomenclated. Over 80% of the accessory genes and unique genes of Faecalibacterium are found with unknown function, which reflects the importance of expanding the collection of Faecalibacterium strains. All the genomes have the potential to produce acetic acid and butyric acid. Nine clusters of Faecalibacterium are found significantly enriched in the healthy individuals compared with patients with type II diabetes.. Discussion This study provides a comprehensive view of genomic characteristic and functions and of culturable Faecalibacterium bacterium from human gut, and enables clinical advances in the future.
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Affiliation(s)
- Wenxi Li
- BGI-Shenzhen, Shenzhen, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Xiaoqian Lin
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Hewei Liang
- BGI-Shenzhen, Shenzhen, China
- BGI Research, Wuhan, China
| | - Zhinan Wu
- BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Mengmeng Wang
- BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Jingxi Sun
- BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaofang Li
- BGI-Shenzhen, Shenzhen, China
- BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | | | | | - Tongyuan Hu
- BGI-Shenzhen, Shenzhen, China
- BGI Research, Wuhan, China
| | - Liang Xiao
- BGI-Shenzhen, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, BGI-Shenzhen, Shenzhen, China
| | - Yuanqiang Zou
- BGI-Shenzhen, Shenzhen, China
- BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
- Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome, BGI-Shenzhen, Shenzhen, China
- Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Universitetsparken, Copenhagen, Denmark
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7
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Zhang S, Sheng B, Xia S, Gao Y, Yan J. Cholecystitis may decrease the risk of sudden death: A 2-sample Mendelian randomization study. Medicine (Baltimore) 2024; 103:e38240. [PMID: 38787985 PMCID: PMC11124735 DOI: 10.1097/md.0000000000038240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/25/2024] [Indexed: 05/26/2024] Open
Abstract
Some observational studies have highlighted a significant association between cholecystitis and factors leading to sudden death; however, the specific relationship between the 2 has not been fully elucidated. The primary objective of this study was to elucidate the causal interplay between cholecystitis and augmented risk of sudden cardiac death. We used large-scale genetic summary data from genome-wide association study, genetic summary statistics were sourced from 3 eminent repositories: the UK Biobank (N = 463,010), the FinnGen consortium (N = 215,027), and the European Bioinformatics Institute (N = 471,251). By employing 2-sample Mendelian randomization (MR) to decipher the causal interplay between cholecystitis and sudden death etiologies, a meta-analytical approach was employed to amalgamate the findings derived from these disparate data sources. The primary MR methodologies used included inverse variance weighting with random effects, inverse variance weighting with fixed effects, maximum likelihood, MR-Egger, and weighted median. Subsequently, we performed heterogeneity testing, polyvalency examination, and sensitivity analysis to bolster the robustness of causal relationship assessments. Meta-analysis and amalgamating variegated data sources revealed a statistically significant inverse correlation between cholecystitis and ventricular arrhythmias (odds ratio, 0.896; 95% confidence interval: 0.826-0.971; P = .008). Similarly, an inverse association was observed between cholecystitis and aortic aneurysm (odds ratio, 0.899; 95% confidence interval: 0.851-0.951, P < .001). This study substantiates the absence of a direct causal link between cholecystitis and cerebrovascular accidents (P = .771), pulmonary embolism (P = .071), and acute myocardial infarction (P = .388). A direct causal correlation existed between cholecystitis and sudden death associated with ventricular arrhythmias and aortic aneurysms. The onset of cholecystitis may mitigate the risk of sudden death due to ventricular arrhythmias and aortic aneurysms.
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Affiliation(s)
- Shina Zhang
- School of Traditional Chinese Medicine and School of Informatics are two departments of Hunan University of Chinese Medicine
| | - Boyang Sheng
- School of Traditional Chinese Medicine and School of Informatics are two departments of Hunan University of Chinese Medicine
| | - Shuaishuai Xia
- School of Traditional Chinese Medicine and School of Informatics are two departments of Hunan University of Chinese Medicine
| | - Yuan Gao
- School of Traditional Chinese Medicine and School of Informatics are two departments of Hunan University of Chinese Medicine
| | - Junfeng Yan
- School of Informatics, Hunan University of Chinese Medicine, Changsha, China
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Qiu Z, Jia X, Fu Y, Yang Y. Screen time in the development of cardiovascular diseases: A two-sample Mendelian randomization study. Nutr Metab Cardiovasc Dis 2024; 34:706-717. [PMID: 37996370 DOI: 10.1016/j.numecd.2023.09.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/27/2023] [Accepted: 09/29/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND AND AIMS Coronary artery disease (CAD), heart failure (HF), and ischemic heart disease (IHD) are three common cardiovascular diseases that are closely associated with metabolic activity. The global incidence and prevalence of these conditions are on the rise, primarily due to unhealthy lifestyles, aging populations, and the increasing prevalence of obesity and diabetes. Excessive screen time has emerged as a potential risk factor for various adverse health outcomes, although limited research has explored its relationship with cardiovascular disease outcomes. METHODS AND RESULTS A Mendelian randomization (MR) study was conducted, employing exposure-associated genetic variants as instrumental variables to explore the causal relationship between screen time use and cardiovascular disease outcomes. Single nucleotide polymorphisms (SNPs) were utilized as pooled data for the genetic variable instrument, investigating the association between screen use duration and three types of cardiovascular diseases: coronary artery disease (CAD), heart failure (HF), and ischemic heart disease (IHD). Through the MR analysis, it was revealed that the use of mobile phones and TV screens exhibited a significant causal association with the occurrence of CAD, heart failure, and IHD. However, no significant association was observed between the use of computers and these three types of cardiovascular diseases. CONCLUSION Our study suggests that excessive screen time use is associated with the development of cardiovascular disease. However, it should be noted that the consequences of screen time can vary depending on the reasons and purposes for its use. Implementing reasonable control over screen time, particularly for entertainment purposes, holds promise as a potential approach to mitigating cardiovascular disease.
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Affiliation(s)
- Zhengqi Qiu
- Faculty of Medicine, Macau University of Science and Technology, Room PP-R203, Est. Seak Pai Van Praia Park, Rés-Do-Chão R, Coloane, Macau 999078, China.
| | - Xueyuan Jia
- Faculty of Medicine, Macau University of Science and Technology, Room PP-R203, Est. Seak Pai Van Praia Park, Rés-Do-Chão R, Coloane, Macau 999078, China
| | - Yancheng Fu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Health Science Center, Shenzhen University, Shenzhen 518060, China
| | - Yanru Yang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Health Science Center, Shenzhen University, Shenzhen 518060, China
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9
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Rahman N, O'Cathail C, Zyoud A, Sokolov A, Oude Munnink B, Grüning B, Cummins C, Amid C, Nieuwenhuijse DF, Visontai D, Yuan DY, Gupta D, Prasad DK, Gulyás GM, Rinck G, McKinnon J, Rajan J, Knaggs J, Skiby JE, Stéger J, Szarvas J, Gueye K, Papp K, Hoek M, Kumar M, Ventouratou MA, Bouquieaux MC, Koliba M, Mansurova M, Haseeb M, Worp N, Harrison PW, Leinonen R, Thorne R, Selvakumar S, Hunt S, Venkataraman S, Jayathilaka S, Cezard T, Maier W, Waheed Z, Iqbal Z, Aarestrup FM, Csabai I, Koopmans M, Burdett T, Cochrane G. Mobilisation and analyses of publicly available SARS-CoV-2 data for pandemic responses. Microb Genom 2024; 10:001188. [PMID: 38358325 PMCID: PMC10926692 DOI: 10.1099/mgen.0.001188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 01/14/2024] [Indexed: 02/16/2024] Open
Abstract
The COVID-19 pandemic has seen large-scale pathogen genomic sequencing efforts, becoming part of the toolbox for surveillance and epidemic research. This resulted in an unprecedented level of data sharing to open repositories, which has actively supported the identification of SARS-CoV-2 structure, molecular interactions, mutations and variants, and facilitated vaccine development and drug reuse studies and design. The European COVID-19 Data Platform was launched to support this data sharing, and has resulted in the deposition of several million SARS-CoV-2 raw reads. In this paper we describe (1) open data sharing, (2) tools for submission, analysis, visualisation and data claiming (e.g. ORCiD), (3) the systematic analysis of these datasets, at scale via the SARS-CoV-2 Data Hubs as well as (4) lessons learnt. This paper describes a component of the Platform, the SARS-CoV-2 Data Hubs, which enable the extension and set up of infrastructure that we intend to use more widely in the future for pathogen surveillance and pandemic preparedness.
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Affiliation(s)
- Nadim Rahman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Colman O'Cathail
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Ahmad Zyoud
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Alexey Sokolov
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Bas Oude Munnink
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Björn Grüning
- University of Freiburg, Friedrichstr. 39, 79098 Freiburg, Germany
| | - Carla Cummins
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Clara Amid
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | | | - Dávid Visontai
- Eötvös Loránd University, H-1053 Budapest, Egyetem tér 1-3, Hungary
| | - David Yu Yuan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Dipayan Gupta
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Divyae K. Prasad
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Gábor Máté Gulyás
- Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - Gabriele Rinck
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Jasmine McKinnon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Jeena Rajan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Jeff Knaggs
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Jeffrey Edward Skiby
- Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - József Stéger
- Eötvös Loránd University, H-1053 Budapest, Egyetem tér 1-3, Hungary
| | - Judit Szarvas
- Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - Khadim Gueye
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Krisztián Papp
- Eötvös Loránd University, H-1053 Budapest, Egyetem tér 1-3, Hungary
| | - Maarten Hoek
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Manish Kumar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Marianna A. Ventouratou
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | | | - Martin Koliba
- Technical University of Denmark, Anker Engelunds Vej 101, 2800 Kongens Lyngby, Denmark
| | - Milena Mansurova
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Muhammad Haseeb
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Nathalie Worp
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Peter W. Harrison
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Rasko Leinonen
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Ross Thorne
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Sandeep Selvakumar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Sarah Hunt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Sundar Venkataraman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Suran Jayathilaka
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Timothée Cezard
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Wolfgang Maier
- University of Freiburg, Friedrichstr. 39, 79098 Freiburg, Germany
| | - Zahra Waheed
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Zamin Iqbal
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | | | - Istvan Csabai
- Eötvös Loránd University, H-1053 Budapest, Egyetem tér 1-3, Hungary
| | - Marion Koopmans
- Erasmus Medical Center, Wytemaweg 80, 3015 CN Rotterdam, Netherlands
| | - Tony Burdett
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Guy Cochrane
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
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10
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Wang X, Sheng Y, Cui H, Qiao J, Song Y, Li X, Huang H. Corner Engineering: Tailoring Enzymes for Enhanced Resistance and Thermostability in Deep Eutectic Solvents. Angew Chem Int Ed Engl 2024; 63:e202315125. [PMID: 38010210 DOI: 10.1002/anie.202315125] [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: 10/08/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 11/29/2023]
Abstract
Deep eutectic solvents (DESs), heralded for their synthesis simplicity, economic viability, and reduced volatility and flammability, have found increasing application in biocatalysis. However, challenges persist due to a frequent diminution in enzyme activity and stability. Herein, we developed a general protein engineering strategy, termed corner engineering, to acquire DES-resistant and thermostable enzymes via precise tailoring of the transition region in enzyme structure. Employing Bacillus subtilis lipase A (BSLA) as a model, we delineated the engineering process, yielding five multi-DESs resistant variants with highly improved thermostability, such as K88E/N89 K exhibited up to a 10.0-fold catalytic efficiency (kcat /KM ) increase in 30 % (v/v) choline chloride (ChCl): acetamide and 4.1-fold in 95 % (v/v) ChCl: ethylene glycol accompanying 6.7-fold thermal resistance improvement than wild type at ≈50 °C. The generality of the optimized approach was validated by two extra industrial enzymes, endo-β-1,4-glucanase PvCel5A (used for biofuel production) and esterase Bs2Est (used for plastics degradation). The molecular investigations revealed that increased water molecules at substrate binding cleft and finetuned helix formation at the corner region are two dominant determinants governing elevated resistance and thermostability. This study, coupling corner engineering with obtained molecular insights, illuminates enzyme-DES interaction patterns and fosters the rational design of more DES-resistant and thermostable enzymes in biocatalysis and biotransformation.
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Affiliation(s)
- Xinyue Wang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Yijie Sheng
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Haiyang Cui
- RWTH Aachen University, Templergraben 55, 52062, Aachen, Germany
- Current address: Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana, IL 61801, USA
| | - Jie Qiao
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Yibo Song
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - Xiujuan Li
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
| | - He Huang
- School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, No. 2 Xuelin Road, Nanjing, 210097, China
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11
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Wang G, Wu S, Xiong Z, Qu H, Fang X, Bao Y. CROST: a comprehensive repository of spatial transcriptomics. Nucleic Acids Res 2024; 52:D882-D890. [PMID: 37791883 PMCID: PMC10773281 DOI: 10.1093/nar/gkad782] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/07/2023] [Accepted: 09/15/2023] [Indexed: 10/05/2023] Open
Abstract
The development of spatial transcriptome sequencing technology has revolutionized our comprehension of complex tissues and propelled life and health sciences into an era of spatial omics. However, the current availability of databases for accessing and analyzing spatial transcriptomic data is limited. In response, we have established CROST (https://ngdc.cncb.ac.cn/crost), a comprehensive repository of spatial transcriptomics. CROST encompasses high-quality samples and houses 182 spatial transcriptomic datasets from diverse species, organs, and diseases, comprising 1033 sub-datasets and 48 043 tumor-related spatially variable genes (SVGs). Additionally, it encompasses a standardized spatial transcriptome data processing pipeline, integrates single-cell RNA sequencing deconvolution spatial transcriptomics data, and evaluates correlation, colocalization, intercellular communication, and biological function annotation analyses. Moreover, CROST integrates the transcriptome, epigenome, and genome to explore tumor-associated SVGs and provides a comprehensive understanding of their roles in cancer progression and prognosis. Furthermore, CROST provides two online tools, single-sample gene set enrichment analysis and SpatialAP, for users to annotate and analyze the uploaded spatial transcriptomics data. The user-friendly interface of CROST facilitates browsing, searching, analyzing, visualizing, and downloading desired information. Collectively, CROST offers fresh and comprehensive insights into tissue structure and a foundation for understanding multiple biological mechanisms in diseases, particularly in tumor tissues.
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Affiliation(s)
- Guoliang Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Song Wu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuang Xiong
- Interdisciplinary Institute for Medical Engineering, Fuzhou University, Fuzhou 350002, China
| | - Hongzhu Qu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiangdong Fang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiming Bao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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12
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Zhang K, Liang J, Fu Y, Chu J, Fu L, Wang Y, Li W, Zhou Y, Li J, Yin X, Wang H, Liu X, Mou C, Wang C, Wang H, Dong X, Yan D, Yu M, Zhao S, Li X, Ma Y. AGIDB: a versatile database for genotype imputation and variant decoding across species. Nucleic Acids Res 2024; 52:D835-D849. [PMID: 37889051 PMCID: PMC10767904 DOI: 10.1093/nar/gkad913] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/05/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023] Open
Abstract
The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.
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Affiliation(s)
- Kaili Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Jiete Liang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuhua Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Jinyu Chu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Liangliang Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
- The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, China
| | - Yongfei Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Wangjiao Li
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - You Zhou
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Jinhua Li
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaoxiao Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
| | - Haiyan Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaolei Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Chunyan Mou
- College of Animal Science and Technology, Southwest University, Chongqing 402460, China
| | - Chonglong Wang
- Key Laboratory of Pig Molecular Quantitative Genetics of Anhui Academy of Agricultural Sciences, Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering, Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Heng Wang
- College of Animal Science and Technology, Shandong Agricultural University, Taian 271018, China
| | - Xinxing Dong
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Dawei Yan
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Mei Yu
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
- Lingnan Modern Agricultural Science and Technology Guangdong Laboratory, Guangzhou 510642, China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Yunlong Ma
- Key Laboratory of Agricultural Animal Genetics, Breeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of Agriculture, Huazhong Agricultural University, Wuhan 430070, China
- Lingnan Modern Agricultural Science and Technology Guangdong Laboratory, Guangzhou 510642, China
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Vasileiou D, Karapiperis C, Baltsavia I, Chasapi A, Ahrén D, Janssen PJ, Iliopoulos I, Promponas VJ, Enright AJ, Ouzounis CA. CGG toolkit: Software components for computational genomics. PLoS Comput Biol 2023; 19:e1011498. [PMID: 37934729 PMCID: PMC10629618 DOI: 10.1371/journal.pcbi.1011498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 09/07/2023] [Indexed: 11/09/2023] Open
Abstract
Public-domain availability for bioinformatics software resources is a key requirement that ensures long-term permanence and methodological reproducibility for research and development across the life sciences. These issues are particularly critical for widely used, efficient, and well-proven methods, especially those developed in research settings that often face funding discontinuities. We re-launch a range of established software components for computational genomics, as legacy version 1.0.1, suitable for sequence matching, masking, searching, clustering and visualization for protein family discovery, annotation and functional characterization on a genome scale. These applications are made available online as open source and include MagicMatch, GeneCAST, support scripts for CoGenT-like sequence collections, GeneRAGE and DifFuse, supported by centrally administered bioinformatics infrastructure funding. The toolkit may also be conceived as a flexible genome comparison software pipeline that supports research in this domain. We illustrate basic use by examples and pictorial representations of the registered tools, which are further described with appropriate documentation files in the corresponding GitHub release.
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Affiliation(s)
- Dimitrios Vasileiou
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece
| | - Christos Karapiperis
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece
- Biological Computation & Computational Biology Group, AIIA Lab, School of Informatics, Aristotle University of Thessalonica, Thessalonica, Greece
| | - Ismini Baltsavia
- Computational Biology Group, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Anastasia Chasapi
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece
| | - Dag Ahrén
- Department of Biology, Microbial Ecology Group, Lund University, Lund, Sweden
| | - Paul J. Janssen
- Nuclear Medical Applications, Belgian Nuclear Research Centre SCK CEN, Mol, Belgium
| | - Ioannis Iliopoulos
- Computational Biology Group, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Vasilis J. Promponas
- Bioinformatics Research Laboratory, Department of Biological Sciences, New Campus, University of Cyprus, Nicosia, Cyprus
| | - Anton J. Enright
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, United Kingdom
| | - Christos A. Ouzounis
- Biological Computation & Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece
- Biological Computation & Computational Biology Group, AIIA Lab, School of Informatics, Aristotle University of Thessalonica, Thessalonica, Greece
- SysBioBio.info (SBBI), Thessalonica, Greece
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14
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Ma L, Zou D, Liu L, Shireen H, Abbasi AA, Bateman A, Xiao J, Zhao W, Bao Y, Zhang Z. Database Commons: A Catalog of Worldwide Biological Databases. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:1054-1058. [PMID: 36572336 PMCID: PMC10928426 DOI: 10.1016/j.gpb.2022.12.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 12/25/2022]
Abstract
Biological databases serve as a global fundamental infrastructure for the worldwide scientific community, which dramatically aid the transformation of big data into knowledge discovery and drive significant innovations in a wide range of research fields. Given the rapid data production, biological databases continue to increase in size and importance. To build a catalog of worldwide biological databases, we curate a total of 5825 biological databases from 8931 publications, which are geographically distributed in 72 countries/regions and developed by 1975 institutions (as of September 20, 2022). We further devise a z-index, a novel index to characterize the scientific impact of a database, and rank all these biological databases as well as their hosting institutions and countries in terms of citation and z-index. Consequently, we present a series of statistics and trends of worldwide biological databases, yielding a global perspective to better understand their status and impact for life and health sciences. An up-to-date catalog of worldwide biological databases, as well as their curated meta-information and derived statistics, is publicly available at Database Commons (https://ngdc.cncb.ac.cn/databasecommons/).
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Affiliation(s)
- Lina Ma
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Dong Zou
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Lin Liu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Huma Shireen
- National Center for Bioinformatics, Programme of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Amir A Abbasi
- National Center for Bioinformatics, Programme of Comparative and Evolutionary Genomics, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wenming Zhao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiming Bao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhang Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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15
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Lim HGM, Fann YC, Lee YCG. COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2. Brief Bioinform 2023; 24:bbad280. [PMID: 37738400 PMCID: PMC10516370 DOI: 10.1093/bib/bbad280] [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: 12/30/2022] [Revised: 07/15/2023] [Accepted: 07/19/2023] [Indexed: 09/24/2023] Open
Abstract
Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://cgc.sbgenomics.com) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://github.com/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly; it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic.
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Affiliation(s)
- Hendrick Gao-Min Lim
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan 11031
- Department of Medical Research, Tzu Chi Hospital Indonesia, Pantai Indah Kapuk, Greater Jakarta, Indonesia 14470
| | - Yang C Fann
- IT and Bioinformatics Program, Division of Intramural, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA 20892
| | - Yuan-Chii Gladys Lee
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan 11031
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16
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Stefancsik R, Balhoff JP, Balk MA, Ball RL, Bello SM, Caron AR, Chesler EJ, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA)-computational traits for the life sciences. Mamm Genome 2023; 34:364-378. [PMID: 37076585 PMCID: PMC10382347 DOI: 10.1007/s00335-023-09992-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/06/2023] [Indexed: 04/21/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK.
| | - James P Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, 27517, USA
| | - Meghan A Balk
- Natural History Museum, University of Oslo, Oslo, Norway
| | - Robyn L Ball
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | | | - Anita R Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Laura W Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Nicole Vasilevsky
- Data Collaboration Center, Critical Path Institute, Tucson, AZ, 85718, USA
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17
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Vedi M, Smith JR, Thomas Hayman G, Tutaj M, Brodie KC, De Pons JL, Demos WM, Gibson AC, Kaldunski ML, Lamers L, Laulederkind SJF, Thota J, Thorat K, Tutaj MA, Wang SJ, Zacher S, Dwinell MR, Kwitek AE. 2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource. Genetics 2023; 224:iyad042. [PMID: 36930729 PMCID: PMC10474928 DOI: 10.1093/genetics/iyad042] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/19/2023] Open
Abstract
The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.
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Affiliation(s)
- Mahima Vedi
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jennifer R Smith
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - G Thomas Hayman
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Monika Tutaj
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Kent C Brodie
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jeffrey L De Pons
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Wendy M Demos
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Adam C Gibson
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Mary L Kaldunski
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Logan Lamers
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Stanley J F Laulederkind
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Jyothi Thota
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Ketaki Thorat
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Marek A Tutaj
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Shur-Jen Wang
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Stacy Zacher
- Finance and Administration, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Melinda R Dwinell
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Anne E Kwitek
- The Rat Genome Database, Department of Physiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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18
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Stefancsik R, Balhoff JP, Balk MA, Ball R, Bello SM, Caron AR, Chessler E, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525742. [PMID: 36747660 PMCID: PMC9900877 DOI: 10.1101/2023.01.26.525742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - James P. Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27517, USA
| | - Meghan A. Balk
- National Ecological Observatory Network, Battelle, Boulder, CO 80301, USA
| | - Robyn Ball
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Anita R. Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Laura W. Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L. Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A. McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Christopher J. Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Nicole Vasilevsky
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
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19
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UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 2023; 51:D523-D531. [PMID: 36408920 PMCID: PMC9825514 DOI: 10.1093/nar/gkac1052] [Citation(s) in RCA: 1537] [Impact Index Per Article: 1537.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/05/2022] [Accepted: 10/25/2022] [Indexed: 11/22/2022] Open
Abstract
The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins.
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20
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Fahlgren N, Kapoor M, Yordanova G, Papatheodorou I, Waese J, Cole B, Harrison P, Ware D, Tickle T, Paten B, Burdett T, Elsik CG, Tuggle CK, Provart NJ. Toward a data infrastructure for the Plant Cell Atlas. PLANT PHYSIOLOGY 2023; 191:35-46. [PMID: 36200899 PMCID: PMC9806565 DOI: 10.1093/plphys/kiac468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.
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Affiliation(s)
- Noah Fahlgren
- Donald Danforth Plant Science Center, Saint Louis, Missouri 63132, USA
| | - Muskan Kapoor
- Bioinformatics and Computational Biology Program, Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA
| | | | | | - Jamie Waese
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario M5S 3B2, Canada
| | - Benjamin Cole
- DOE-Joint Genome Institute, Lawrence Berkeley National Laboratory, 1, Cyclotron Road, Berkeley, California 94720, USA
| | - Peter Harrison
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Doreen Ware
- Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, New York 11724, USA
- USDA ARS NAA Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853, USA
| | - Timothy Tickle
- Data Sciences Platform, The Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, Massachusetts 02142, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, Baskin School of Engineering, 1156 High Street, Santa Cruz, California 95064, USA
| | - Tony Burdett
- EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Christine G Elsik
- Division of Animal Sciences/Division of Plant Science & Technology/Institute for Data Science & Informatics, University of Missouri, Columbia, Missouri 65211, USA
| | - Christopher K Tuggle
- Bioinformatics and Computational Biology Program, Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA
| | - Nicholas J Provart
- Department of Cell and Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto, Toronto, Ontario M5S 3B2, Canada
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21
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Song DJ, Fan B, Li GY. Blood cell traits and risk of glaucoma: A two-sample mendelian randomization study. Front Genet 2023; 14:1142773. [PMID: 37124610 PMCID: PMC10130872 DOI: 10.3389/fgene.2023.1142773] [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: 01/12/2023] [Accepted: 04/03/2023] [Indexed: 05/02/2023] Open
Abstract
Importance: Glaucoma is the second leading cause of blindness in the world. The causal direction and magnitude of the association between blood cell traits and glaucoma is uncertain because of the susceptibility of observational studies to confounding and reverse causation. Objective: To explore whether there is a causal relationship of blood cell traits including white blood cell (WBC) count (WBCC) and its subtypes [basophil cell count (BASO), monocyte cell count (MONO), lymphocyte cell count (LYMPH), eosinophil cell count (EOS), neutrophil cell count (NEUT)], red blood cell (RBC) count (RBCC), red blood distribution width (RDW), platelet count (PLT), and plateletcrit (PCT) on glaucoma risk. Methods: A two-sample Mendelian randomization (MR) analysis was conducted. Genome-wide significant single nucleotide polymorphisms (SNPs) from published genome-wide association studies (GWAS) on human blood cell traits were utilized as exposure instruments and the dataset for outcome was from the GWAS summary data of glaucoma. In the univariable MR analysis, we examined the association between genetic evidence of blood cell traits and glaucoma. To further investigate the potential causal mechanisms underlying the observed association, we performed multivariable MR analysis with three models, taking into account the mediator effect of inflammation and oxidative stress. According to Bonferroni-corrected for the 10 exposures in 3 methods, the MR study yielded a statistically significant p-value of 0.0017. Results: Genetically BASO, PCT, LYMPH, and PLT were potentially positively associated with glaucoma in the European ancestry [BASO: Odds ratio (OR) = 1.00122, 95% confidence interval (CI), 1.00003-1.00242, p = 0.045; PCT: OR = 1.00078, 95% CI, 1.00012-1.00143, p = 0.019; LYMPH: OR = 1.00076, 95% CI, 1.00002-1.00151, p = 0.045; PLT: OR = 1.00065, 95% CI, 1.00006-1.00123, p = 0.030], There was insufficient evidence to support a causal association of MONO, NEUT, EOS, WBCC, RBCC and RDW (MONO: OR = 1.00050, p = 0.098; NEUT: OR = 1.00028, p = 0.524; EOS: OR = 1.00020, p = 0.562; WBCC: OR = 1.00008, p = 0.830; RBCC: OR = 0.99996, p = 0.920; RDW: OR = 0.99987, p = 0.734) with glaucoma. The multivariable MR with model 1, 2, and 3 demonstrated that BASO, PCT, LYMPH, and PLT were still potentially genetically associated with the risk of glaucoma. Conclusion: Our study reveals a genetic predisposition to higher LYMPH, BASO, PLT, and PCT are associated with a higher risk of glaucoma, whereas WBCC, MONO, EOS, NEUT, RBCC, and RDW are not associated with the occurrence of glaucoma. This finding also supports previous observational studies associating immune components with glaucoma, thus provide guidance on the predication and prevention for glaucoma.
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22
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Arend D, Scholz U, Lange M. The Plant Phenomics and Genomics Research Data Repository: An On-Premise Approach for FAIR-Compliant Data Acquisition. Methods Mol Biol 2023; 2703:3-22. [PMID: 37646933 DOI: 10.1007/978-1-0716-3389-2_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. However, although many established infrastructures provide comprehensive and long-term stable services and platforms, a large quantity of research data is still hidden. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, for example, time series of images or high-resolution hyperspectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institutional boundaries. To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements an on-premise approach, which allows research data to be kept in place and wrapped in FAIR-aware software infrastructure. In this chapter, the e!DAL infrastructure software and the PGP repository are presented as best practice on how to easily setup FAIR-compliant and intuitive research data services.
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Affiliation(s)
- Daniel Arend
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, OT Gatersleben, Germany.
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, OT Gatersleben, Germany
| | - Matthias Lange
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, OT Gatersleben, Germany
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23
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Evolutionary Conserved Short Linear Motifs Provide Insights into the Cellular Response to Stress. Antioxidants (Basel) 2022; 12:antiox12010096. [PMID: 36670957 PMCID: PMC9854524 DOI: 10.3390/antiox12010096] [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: 08/24/2022] [Revised: 11/22/2022] [Accepted: 12/22/2022] [Indexed: 01/03/2023] Open
Abstract
Short linear motifs (SLiMs) are evolutionarily conserved functional modules of proteins composed of 3 to 10 residues and involved in multiple cellular functions. Here, we performed a search for SLiMs that exert sequence similarity to two segments of alpha-fetoprotein (AFP), a major mammalian embryonic and cancer-associated protein. Biological activities of the peptides, LDSYQCT (AFP14-20) and EMTPVNPGV (GIP-9), have been previously confirmed under in vitro and in vivo conditions. In our study, we retrieved a vast array of proteins that contain SLiMs of interest from both prokaryotic and eukaryotic species, including viruses, bacteria, archaea, invertebrates, and vertebrates. Comprehensive Gene Ontology enrichment analysis showed that proteins from multiple functional classes, including enzymes, transcription factors, as well as those involved in signaling, cell cycle, and quality control, and ribosomal proteins were implicated in cellular adaptation to environmental stress conditions. These include response to oxidative and metabolic stress, hypoxia, DNA and RNA damage, protein degradation, as well as antimicrobial, antiviral, and immune response. Thus, our data enabled insights into the common functions of SLiMs evolutionary conserved across all taxonomic categories. These SLiMs can serve as important players in cellular adaptation to stress, which is crucial for cell functioning.
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24
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He L, He W, Luo J, Xu M. Upregulated ENC1 predicts unfavorable prognosis and correlates with immune infiltration in endometrial cancer. Front Cell Dev Biol 2022; 10:919637. [PMID: 36531950 PMCID: PMC9751423 DOI: 10.3389/fcell.2022.919637] [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: 04/13/2022] [Accepted: 11/17/2022] [Indexed: 07/30/2023] Open
Abstract
A better knowledge of the molecular process behind uterine corpus endometrial carcinoma (UCEC) is important for prognosis prediction and the development of innovative targeted gene therapies. The purpose of this research is to discover critical genes associated with UCEC. We analyzed the gene expression profiles of TCGA-UCEC and GSE17025, respectively, using Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. From four sets of findings, a total of 95 overlapping genes were retrieved. On the 95 overlapping genes, KEGG pathway and GO enrichment analysis were conducted. Then, we mapped the PPI network of 95 overlapping genes using the STRING database. Twenty hub genes were evaluated using the Cytohubba plugin, including NR3C1, ATF3, KLF15, THRA, NR4A1, FOSB, PER3, HLF, NTRK3, EGR3, MAPK13, ARNTL2, PKM2, SCD, EIF5A, ADHFE1, RERGL, TUB, and ENC1. The expression levels of NR3C1, PKM2, and ENC1 were shown to be adversely linked with the survival time of UCEC patients using univariate Cox regression analysis and Kaplan-Meier survival calculation. ENC1 were also overexpressed in UCEC tumor tissues or cell lines, as shown by quantitative real-time PCR and Western blotting. Then we looked into it further and discovered that ENC1 expression was linked to tumor microenvironment and predicted various immunological checkpoints. In conclusion, our data indicate that ENC1 may be required for the development of UCEC and may serve as a future biomarker for diagnosis and therapy.
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Affiliation(s)
- Lingling He
- Department of Obstetrics and Gynecology, Ganzhou People's Hospital, Ganzhou, China
- Department of Obstetrics and Gynecology, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China
- Department of Obstetrics and Gynecology, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, China
| | - Wenjing He
- Department of Endocrinology, Baoji Gaoxin Hospital, Baoji, China
| | - Ji Luo
- Department of Obstetrics and Gynecology, Ganzhou People's Hospital, Ganzhou, China
- Department of Obstetrics and Gynecology, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China
- Department of Obstetrics and Gynecology, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, China
| | - Minjuan Xu
- Department of Obstetrics and Gynecology, Ganzhou People's Hospital, Ganzhou, China
- Department of Obstetrics and Gynecology, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China
- Department of Obstetrics and Gynecology, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou, China
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25
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Fu Y, Liu H, Dou J, Wang Y, Liao Y, Huang X, Tang Z, Xu J, Yin D, Zhu S, Liu Y, Shen X, Liu H, Liu J, Yang X, Zhang Y, Xiang Y, Li J, Zheng Z, Zhao Y, Ma Y, Wang H, Du X, Xie S, Xu X, Zhang H, Yin L, Zhu M, Yu M, Li X, Liu X, Zhao S. IAnimal: a cross-species omics knowledgebase for animals. Nucleic Acids Res 2022; 51:D1312-D1324. [PMID: 36300629 PMCID: PMC9825575 DOI: 10.1093/nar/gkac936] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/23/2022] [Accepted: 10/11/2022] [Indexed: 01/30/2023] Open
Abstract
With the exponential growth of multi-omics data, its integration and utilization have brought unprecedented opportunities for the interpretation of gene regulation mechanisms and the comprehensive analyses of biological systems. IAnimal (https://ianimal.pro/), a cross-species, multi-omics knowledgebase, was developed to improve the utilization of massive public data and simplify the integration of multi-omics information to mine the genetic mechanisms of objective traits. Currently, IAnimal provides 61 191 individual omics data of genome (WGS), transcriptome (RNA-Seq), epigenome (ChIP-Seq, ATAC-Seq) and genome annotation information for 21 species, such as mice, pigs, cattle, chickens, and macaques. The scale of its total clean data has reached 846.46 TB. To better understand the biological significance of omics information, a deep learning model for IAnimal was built based on BioBERT and AutoNER to mine 'gene' and 'trait' entities from 2 794 237 abstracts, which has practical significance for comprehending how each omics layer regulates genes to affect traits. By means of user-friendly web interfaces, flexible data application programming interfaces, and abundant functional modules, IAnimal enables users to easily query, mine, and visualize characteristics in various omics, and to infer how genes play biological roles under the influence of various omics layers.
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Affiliation(s)
- Yuhua Fu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Hong Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Jingwen Dou
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yue Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yong Liao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Xin Huang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Zhenshuang Tang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - JingYa Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Dong Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Shilin Zhu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yangfan Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Xiong Shen
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Hengyi Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Jiaqi Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Xin Yang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yi Zhang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei 430070, PR China
| | - Yue Xiang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Jingjin Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Zhuqing Zheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Yunxia Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Yunlong Ma
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Haiyan Wang
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Xiaoyong Du
- Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Shengsong Xie
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Haohao Zhang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei 430070, PR China
| | - Lilin Yin
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Mengjin Zhu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Mei Yu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Key Laboratory of Swine Genetics and Breeding, Ministry of Agriculture, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China,Frontiers Science Center for Animal Breeding and Sustainable Production, Wuhan, Hubei 430070, PR China
| | - Xiaolei Liu
- Correspondence may also be addressed to Xiaolei Liu.
| | - Shuhong Zhao
- To whom correspondence should be addressed. Tel: +86 27 87387480;
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26
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Qi C, Cai Y, Qian K, Li X, Ren J, Wang P, Fu T, Zhao T, Cheng L, Shi L, Zhang X. gutMDisorder v2.0: a comprehensive database for dysbiosis of gut microbiota in phenotypes and interventions. Nucleic Acids Res 2022; 51:D717-D722. [PMID: 36215029 PMCID: PMC9825589 DOI: 10.1093/nar/gkac871] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/16/2022] [Accepted: 09/28/2022] [Indexed: 01/30/2023] Open
Abstract
Gut microbiota plays a significant role in maintaining host health, and conversely, disorders potentially lead to dysbiosis, an imbalance in the composition of the gut microbial community. Intervention approaches, such as medications, diets, and several others, also alter the gut microbiota in either a beneficial or harmful direction. In 2020, the gutMDisorder was developed to facilitate researchers in the investigation of dysbiosis of gut microbes as occurs in various disorders as well as with therapeutic interventions. The database has been updated this year, following revision of previous publications and newly published reports to manually integrate confirmed associations under multitudinous conditions. Additionally, the microbial contents of downloaded gut microbial raw sequencing data were annotated, the metadata of the corresponding hosts were manually curated, and the interactive charts were developed to enhance visualization. The improvements have assembled into gutMDisorder v2.0, a more advanced search engine and an upgraded web interface, which can be freely accessed via http://bio-annotation.cn/gutMDisorder/.
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Affiliation(s)
| | | | | | - Xuefeng Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang, China
| | - Jialiang Ren
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang, China
| | - Ping Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang, China
| | - Tongze Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang, China
| | - Tianyi Zhao
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China
| | - Liang Cheng
- To whom correspondence should be addressed. Tel: +86 153 0361 4540;
| | - Lei Shi
- Correspondence may also be addressed to Lei Shi.
| | - Xue Zhang
- Correspondence may also be addressed to Xue Zhang.
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27
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Shi S, Wang Q, Shang Y, Bu C, Lu M, Jiang M, Zhang H, Yu S, Zeng J, Zhang Z, Du Z, Xiao J. TSomVar: a tumor-only somatic and germline variant identification method with random forest. Brief Bioinform 2022; 23:6695269. [DOI: 10.1093/bib/bbac381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Somatic variants act as critical players during cancer occurrence and development. Thus, an accurate and robust method to identify them is the foundation of cutting-edge cancer genome research. However, due to low accessibility and high individual-/sample-specificity of the somatic variants in tumor samples, the detection is, to date, still crammed with challenges, particularly when lacking paired normal samples as control. To solve this burning issue, we developed a tumor-only somatic and germline variant identification method (TSomVar) using the random forest algorithm established on sample-specific variant datasets derived from genotype imputation, reads-mapping level annotation and functional annotation. We trained TSomVar by using genomic variant datasets of three major cancer types: colorectal cancer, hepatocellular carcinoma and skin cutaneous melanoma. Compared with existing tumor-only somatic variant identification tools, TSomVar shows excellent performances in somatic variant detection with higher accuracy and better capability of recalling for test datasets from colorectal cancer and skin cutaneous melanoma. In addition, TSomVar is equipped with the competence of accurately identifying germline variants in tumor samples. Taken together, TSomVar will undoubtedly facilitate and revolutionize somatic variant explorations in cancer research.
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Affiliation(s)
- Shuo Shi
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- University of Chinese Academy of Sciences , Beijing 100049, China
- Burning Rock Biotech Ltd , Shanghai 201114, China
| | - Qi Wang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- University of Chinese Academy of Sciences , Beijing 100049, China
- Qujiang Culture Finance Holding (Group) Co., Ltd , Xian 710061, China
| | - Yunfei Shang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- University of Chinese Academy of Sciences , Beijing 100049, China
| | - Congfan Bu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
| | - Mingming Lu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- University of Chinese Academy of Sciences , Beijing 100049, China
| | - Meiye Jiang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- University of Chinese Academy of Sciences , Beijing 100049, China
| | - Hao Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- University of Chinese Academy of Sciences , Beijing 100049, China
| | - Shuhuan Yu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- University of Chinese Academy of Sciences , Beijing 100049, China
| | - Jingyao Zeng
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
| | - Zaichao Zhang
- Department of Biology, The University of Western Ontario , London, Ontario N6A 5B7, Canada
| | - Zhenglin Du
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation , Beijing 100101, China
- University of Chinese Academy of Sciences , Beijing 100049, China
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28
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Fernández-Torras A, Duran-Frigola M, Bertoni M, Locatelli M, Aloy P. Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque. Nat Commun 2022; 13:5304. [PMID: 36085310 PMCID: PMC9463154 DOI: 10.1038/s41467-022-33026-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/30/2022] [Indexed: 12/25/2022] Open
Abstract
Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., 'drug treats disease', 'gene interacts with gene'). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.
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Affiliation(s)
- Adrià Fernández-Torras
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
- Ersilia Open Source Initiative, Cambridge, UK
| | - Martino Bertoni
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Martina Locatelli
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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29
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Qiao T, Zhang L, Yu Y, Pang Y, Tang X, Wang X, Li L, Li B, Sun Q. Identification and expression analysis of xyloglucan endotransglucosylase/hydrolase (XTH) family in grapevine ( Vitis vinifera L.). PeerJ 2022; 10:e13546. [PMID: 35722264 PMCID: PMC9202548 DOI: 10.7717/peerj.13546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/16/2022] [Indexed: 01/17/2023] Open
Abstract
Xyloglucan endotransglucosylases/hydrolases (XTH) are key enzymes in cell wall reformulation. They have the dual functions of catalyzing xyloglucan endotransglucosylase (XET) and xyloglucan endonuclease (XEH) activity and play a crucial role in the responses against abiotic stresses, such as drought, salinity, and freezing. However, a comprehensive analysis of the XTH family and its functions in grapevine (Vitis vinifera L.) has not yet been completed. In this study, 34 XTHs were identified in the whole grapevine genome and then named according to their distribution on chromosomes. Based on a phylogenetic analysis including Arabidopsis XTHs, the VvXTHs were classified into three groups. Cis-element analysis indicated that these family members are related to most abiotic stresses. We further selected 14 VvXTHs from different groups and then examined their transcription levels under drought and salt stress. The results indicated that the transcription levels of selected VvXTHs in the leaves and roots presented the largest changes, suggesting that VvXTHs are likely to take part in the responses to drought and salt stress in grapevines. These results provide useful evidence for the further investigation of VvXTHs function in response to abiotic stresses in grapevine.
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Affiliation(s)
- Tian Qiao
- State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China
| | - Lei Zhang
- State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China
| | - Yanyan Yu
- State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China
| | - Yunning Pang
- State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China
| | - Xinjie Tang
- State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China
| | - Xiao Wang
- State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China
| | - Lijian Li
- State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China
| | - Bo Li
- Shandong Academy of Grape, Shandong Academy of Agricultural Sciences, Jinan, Shandong, China
| | - Qinghua Sun
- State Key Laboratory of Crop Biology, College of Life Science, Shandong Agricultural University, Taian, Shandong, China
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30
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Khan AM, Ranganathan S, Suravajhala P. Editorial: Bioinformatics and the Translation of Data-Driven Discoveries. Front Genet 2022; 13:902940. [PMID: 35620463 PMCID: PMC9127959 DOI: 10.3389/fgene.2022.902940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Asif M Khan
- School of Data Sciences, Perdana University, Kuala Lumpur, Malaysia.,Beykoz Institute of Life Sciences and Biotechnology, Bezmialem Vakif University, Istanbul, Turkey.,APBioNET.org, Singapore, Singapore
| | - Shoba Ranganathan
- APBioNET.org, Singapore, Singapore.,Applied BioSciences, Macquarie University, Sydney, NSW, Australia
| | - Prashanth Suravajhala
- APBioNET.org, Singapore, Singapore.,Bioclues.org, Hyderabad, India.,Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham University, Kollam, India
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31
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Rigden DJ, Fernández XM. The 2022 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 2022; 50:D1-D10. [PMID: 34986604 PMCID: PMC8728296 DOI: 10.1093/nar/gkab1195] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.
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Affiliation(s)
- Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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