1
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Kiran A, Hanachi M, Alsayed N, Fassatoui M, Oduaran OH, Allali I, Maslamoney S, Meintjes A, Zass L, Rocha JD, Kefi R, Benkahla A, Ghedira K, Panji S, Mulder N, Fadlelmola FM, Souiai O. The African Human Microbiome Portal: a public web portal of curated metagenomic metadata. Database (Oxford) 2024; 2024:baad092. [PMID: 38204360 PMCID: PMC10782148 DOI: 10.1093/database/baad092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/03/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024]
Abstract
There is growing evidence that comprehensive and harmonized metadata are fundamental for effective public data reusability. However, it is often challenging to extract accurate metadata from public repositories. Of particular concern is the metagenomic data related to African individuals, which often omit important information about the particular features of these populations. As part of a collaborative consortium, H3ABioNet, we created a web portal, namely the African Human Microbiome Portal (AHMP), exclusively dedicated to metadata related to African human microbiome samples. Metadata were collected from various public repositories prior to cleaning, curation and harmonization according to a pre-established guideline and using ontology terms. These metadata sets can be accessed at https://microbiome.h3abionet.org/. This web portal is open access and offers an interactive visualization of 14 889 records from 70 bioprojects associated with 72 peer reviewed research articles. It also offers the ability to download harmonized metadata according to the user's applied filters. The AHMP thereby supports metadata search and retrieve operations, facilitating, thus, access to relevant studies linked to the African Human microbiome. Database URL: https://microbiome.h3abionet.org/.
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Affiliation(s)
| | - Mariem Hanachi
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (LR16IPT09), Institute Pasteur of Tunis, University Tunis El Manar, Tunis 1002, Tunisia
- Faculty of Science of Bizerte, University of Carthage, Tunis, Tunisia
| | - Nihad Alsayed
- Kush Centre for Genomics and Biomedical Informatics, Biotechnology Perspectives Organization, Khartoum, Sudan
| | - Meriem Fassatoui
- Laboratory of Biomedical Genomics & Oncogenetics, Institut Pasteur de Tunis, University Tunis El Manar, Tunis 1002, Tunisia
| | - Ovokeraye H Oduaran
- The Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Imane Allali
- Laboratory of Human Pathologies Biology, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Rabat, Morocco
| | - Suresh Maslamoney
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Ayton Meintjes
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Lyndon Zass
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Jorge Da Rocha
- The Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Rym Kefi
- Laboratory of Biomedical Genomics & Oncogenetics, Institut Pasteur de Tunis, University Tunis El Manar, Tunis 1002, Tunisia
| | - Alia Benkahla
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (LR16IPT09), Institute Pasteur of Tunis, University Tunis El Manar, Tunis 1002, Tunisia
| | - Kais Ghedira
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (LR16IPT09), Institute Pasteur of Tunis, University Tunis El Manar, Tunis 1002, Tunisia
| | - Sumir Panji
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town 7925, South Africa
| | - Faisal M Fadlelmola
- Kush Centre for Genomics and Biomedical Informatics, Biotechnology Perspectives Organization, Khartoum, Sudan
| | - Oussema Souiai
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (LR16IPT09), Institute Pasteur of Tunis, University Tunis El Manar, Tunis 1002, Tunisia
- Malawi-Liverpool-Wellcome Trust, Blantyre 3, Malawi
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool CH64 7TE, UK
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2
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Sengupta D, Botha G, Meintjes A, Mbiyavanga M, Hazelhurst S, Mulder N, Ramsay M, Choudhury A. Performance and accuracy evaluation of reference panels for genotype imputation in sub-Saharan African populations. Cell Genom 2023; 3:100332. [PMID: 37388906 PMCID: PMC10300601 DOI: 10.1016/j.xgen.2023.100332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 02/11/2023] [Accepted: 05/02/2023] [Indexed: 07/01/2023]
Abstract
Based on evaluations of imputation performed on a genotype dataset consisting of about 11,000 sub-Saharan African (SSA) participants, we show Trans-Omics for Precision Medicine (TOPMed) and the African Genome Resource (AGR) to be currently the best panels for imputing SSA datasets. We report notable differences in the number of single-nucleotide polymorphisms (SNPs) that are imputed by different panels in datasets from East, West, and South Africa. Comparisons with a subset of 95 SSA high-coverage whole-genome sequences (WGSs) show that despite being about 20-fold smaller, the AGR imputed dataset has higher concordance with the WGSs. Moreover, the level of concordance between imputed and WGS datasets was strongly influenced by the extent of Khoe-San ancestry in a genome, highlighting the need for integration of not only geographically but also ancestrally diverse WGS data in reference panels for further improvement in imputation of SSA datasets. Approaches that integrate imputed data from different panels could also lead to better imputation.
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Affiliation(s)
- Dhriti Sengupta
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Gerrit Botha
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Ayton Meintjes
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Mamana Mbiyavanga
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | | | | | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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3
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Ahmed AE, Mpangase PT, Panji S, Baichoo S, Souilmi Y, Fadlelmola FM, Alghali M, Aron S, Bendou H, De Beste E, Mbiyavanga M, Souiai O, Yi L, Zermeno J, Armstrong D, O'Connor BD, Mainzer LS, Crusoe MR, Meintjes A, Van Heusden P, Botha G, Joubert F, Jongeneel CV, Hazelhurst S, Mulder N. Organizing and running bioinformatics hackathons within Africa: The H3ABioNet cloud computing experience. AAS Open Res 2019; 1:9. [PMID: 32382696 PMCID: PMC7194140 DOI: 10.12688/aasopenres.12847.1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/18/2019] [Indexed: 08/20/2023] Open
Abstract
The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous computing environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. Building on the network's Standard Operating Procedures (SOPs) for common genomic analyses, H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Hackathon in 2016, with the purpose of translating those SOPs into analysis pipelines able to run on heterogeneous computing environments and meeting the needs of H3Africa research projects. This paper describes the preparations for this hackathon and reflects upon the lessons learned about its impact on building the technical and scientific expertise of African researchers. The workflows developed were made publicly available in GitHub repositories and deposited as container images on Quay.io.
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Affiliation(s)
- Azza E. Ahmed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
- Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
| | - Phelelani T. Mpangase
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Sumir Panji
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Shakuntala Baichoo
- Department of Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Yassine Souilmi
- Australian Centre for Ancient DNA, University of Adelaide, Adelaide, Australia
| | - Faisal M. Fadlelmola
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Mustafa Alghali
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Hocine Bendou
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Eugene De Beste
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Mamana Mbiyavanga
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Oussema Souiai
- Institut Pasteur De Tunis and Institut Superieur des Technologies Médicales de Tunis, University Tunis Al Manar, Tunis, Tunisia
| | - Long Yi
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Jennie Zermeno
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Don Armstrong
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Brian D. O'Connor
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Liudmila Sergeevna Mainzer
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Ayton Meintjes
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Peter Van Heusden
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Gerrit Botha
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Fourie Joubert
- Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa
| | - C. Victor Jongeneel
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Nicola Mulder
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
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4
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Ahmed AE, Mpangase PT, Panji S, Baichoo S, Souilmi Y, Fadlelmola FM, Alghali M, Aron S, Bendou H, De Beste E, Mbiyavanga M, Souiai O, Yi L, Zermeno J, Armstrong D, O'Connor BD, Mainzer LS, Crusoe MR, Meintjes A, Van Heusden P, Botha G, Joubert F, Jongeneel CV, Hazelhurst S, Mulder N. Organizing and running bioinformatics hackathons within Africa: The H3ABioNet cloud computing experience. AAS Open Res 2019; 1:9. [PMID: 32382696 PMCID: PMC7194140 DOI: 10.12688/aasopenres.12847.2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/18/2019] [Indexed: 11/20/2022] Open
Abstract
The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous computing environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. Building on the network’s Standard Operating Procedures (SOPs) for common genomic analyses, H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Hackathon in 2016, with the purpose of translating those SOPs into analysis pipelines able to run on heterogeneous computing environments and meeting the needs of H3Africa research projects. This paper describes the preparations for this hackathon and reflects upon the lessons learned about its impact on building the technical and scientific expertise of African researchers. The workflows developed were made publicly available in GitHub repositories and deposited as container images on Quay.io.
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Affiliation(s)
- Azza E Ahmed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.,Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
| | - Phelelani T Mpangase
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Sumir Panji
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Shakuntala Baichoo
- Department of Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Yassine Souilmi
- Australian Centre for Ancient DNA, University of Adelaide, Adelaide, Australia
| | - Faisal M Fadlelmola
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Mustafa Alghali
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Hocine Bendou
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Eugene De Beste
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Mamana Mbiyavanga
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Oussema Souiai
- Institut Pasteur De Tunis and Institut Superieur des Technologies Médicales de Tunis, University Tunis Al Manar, Tunis, Tunisia
| | - Long Yi
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Jennie Zermeno
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Don Armstrong
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Brian D O'Connor
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Liudmila Sergeevna Mainzer
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Ayton Meintjes
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Peter Van Heusden
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Gerrit Botha
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
| | - Fourie Joubert
- Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa
| | - C Victor Jongeneel
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa.,School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Nicola Mulder
- Computational Biology Division, Integrative Medical Biosciences, University of Cape Town, Cape Town, South Africa
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5
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Baichoo S, Souilmi Y, Panji S, Botha G, Meintjes A, Hazelhurst S, Bendou H, Beste ED, Mpangase PT, Souiai O, Alghali M, Yi L, O'Connor BD, Crusoe M, Armstrong D, Aron S, Joubert F, Ahmed AE, Mbiyavanga M, Heusden PV, Magosi LE, Zermeno J, Mainzer LS, Fadlelmola FM, Jongeneel CV, Mulder N. Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics. BMC Bioinformatics 2018; 19:457. [PMID: 30486782 PMCID: PMC6264621 DOI: 10.1186/s12859-018-2446-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 10/23/2018] [Indexed: 12/30/2022] Open
Abstract
Background The Pan-African bioinformatics network, H3ABioNet, comprises 27 research institutions in 17 African countries. H3ABioNet is part of the Human Health and Heredity in Africa program (H3Africa), an African-led research consortium funded by the US National Institutes of Health and the UK Wellcome Trust, aimed at using genomics to study and improve the health of Africans. A key role of H3ABioNet is to support H3Africa projects by building bioinformatics infrastructure such as portable and reproducible bioinformatics workflows for use on heterogeneous African computing environments. Processing and analysis of genomic data is an example of a big data application requiring complex interdependent data analysis workflows. Such bioinformatics workflows take the primary and secondary input data through several computationally-intensive processing steps using different software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly challenging. Results H3ABioNet has built four workflows to support (1) the calling of variants from high-throughput sequencing data; (2) the analysis of microbial populations from 16S rDNA sequence data; (3) genotyping and genome-wide association studies; and (4) single nucleotide polymorphism imputation. A week-long hackathon was organized in August 2016 with participants from six African bioinformatics groups, and US and European collaborators. Two of the workflows are built using the Common Workflow Language framework (CWL) and two using Nextflow. All the workflows are containerized for improved portability and reproducibility using Docker, and are publicly available for use by members of the H3Africa consortium and the international research community. Conclusion The H3ABioNet workflows have been implemented in view of offering ease of use for the end user and high levels of reproducibility and portability, all while following modern state of the art bioinformatics data processing protocols. The H3ABioNet workflows will service the H3Africa consortium projects and are currently in use. All four workflows are also publicly available for research scientists worldwide to use and adapt for their respective needs. The H3ABioNet workflows will help develop bioinformatics capacity and assist genomics research within Africa and serve to increase the scientific output of H3Africa and its Pan-African Bioinformatics Network.
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Affiliation(s)
- Shakuntala Baichoo
- Department of Digital Technologies, University of Mauritius, Reduit, Mauritius.
| | - Yassine Souilmi
- Australian Centre for Ancient DNA, University of Adelaide, Adelaide, South Australia, Australia
| | - Sumir Panji
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Gerrit Botha
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Ayton Meintjes
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Scott Hazelhurst
- School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa.,Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Hocine Bendou
- South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.,Natural Sciences, University of the Western Cape, Bellville, Cape Town, South Africa
| | - Eugene de Beste
- South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.,Natural Sciences, University of the Western Cape, Bellville, Cape Town, South Africa
| | - Phelelani T Mpangase
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Oussema Souiai
- Institut Pasteur De Tunis, University Tunis El manar, Tunis, Tunisia.,Institut Superieur des Technologies Medicales de Tunis, University Tunis El manar, Tunis, Tunisia
| | - Mustafa Alghali
- Center for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.,Department of Electrical & Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
| | - Long Yi
- South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.,Natural Sciences, University of the Western Cape, Bellville, Cape Town, South Africa
| | - Brian D O'Connor
- Genomics Institute, University of California, Santa Cruz, California, USA
| | - Michael Crusoe
- Common Workflow Language project, Software Freedom Conservancy, New York City, NY, USA
| | - Don Armstrong
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Fourie Joubert
- Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa
| | - Azza E Ahmed
- Center for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan.,Department of Electrical & Electronic Engineering, Faculty of Engineering, University of Khartoum, Khartoum, Sudan
| | - Mamana Mbiyavanga
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Peter van Heusden
- South African National Bioinformatics Institute, University of the Western Cape, Bellville, Cape Town, South Africa.,Natural Sciences, University of the Western Cape, Bellville, Cape Town, South Africa
| | - Lerato E Magosi
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jennie Zermeno
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Liudmila Sergeevna Mainzer
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Faisal M Fadlelmola
- Center for Bioinformatics & Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - C Victor Jongeneel
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Medical Biosciences, IDM, University of Cape Town, Cape Town, South Africa
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6
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Ben Hamda C, Sangeda R, Mwita L, Meintjes A, Nkya S, Panji S, Mulder N, Guizani-Tabbane L, Benkahla A, Makani J, Ghedira K. A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study. PLoS One 2018; 13:e0199461. [PMID: 29979707 PMCID: PMC6034806 DOI: 10.1371/journal.pone.0199461] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Accepted: 06/07/2018] [Indexed: 12/16/2022] Open
Abstract
A chronic inflammatory state to a large extent explains sickle cell disease (SCD) pathophysiology. Nonetheless, the principal dysregulated factors affecting this major pathway and their mechanisms of action still have to be fully identified and elucidated. Integrating gene expression and genome-wide association study (GWAS) data analysis represents a novel approach to refining the identification of key mediators and functions in complex diseases. Here, we performed gene expression meta-analysis of five independent publicly available microarray datasets related to homozygous SS patients with SCD to identify a consensus SCD transcriptomic profile. The meta-analysis conducted using the MetaDE R package based on combining p values (maxP approach) identified 335 differentially expressed genes (DEGs; 224 upregulated and 111 downregulated). Functional gene set enrichment revealed the importance of several metabolic pathways, of innate immune responses, erythrocyte development, and hemostasis pathways. Advanced analyses of GWAS data generated within the framework of this study by means of the atSNP R package and SIFT tool identified 60 regulatory single-nucleotide polymorphisms (rSNPs) occurring in the promoter of 20 DEGs and a deleterious SNP, affecting CAMKK2 protein function. This novel database of candidate genes, transcription factors, and rSNPs associated with SCD provides new markers that may help to identify new therapeutic targets.
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Affiliation(s)
- Cherif Ben Hamda
- Laboratory of Bioinformatics, Biomathematics and Biostatistics, Institute Pasteur of Tunis, Tunis, Tunisia
- University of Tunis El Manar, Tunis, Tunisia
- Faculty of Science of Bizerte, Jarzouna, University of Carthage, Tunisia
- * E-mail: (KG); (CBH)
| | - Raphael Sangeda
- Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Liberata Mwita
- Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | | | - Siana Nkya
- Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Sumir Panji
- University of Cape Town, Cape Town, South Africa
| | | | - Lamia Guizani-Tabbane
- University of Tunis El Manar, Tunis, Tunisia
- Laboratory of Medical Parasitology, Biotechnology and Biomolecules, Institute Pasteur of Tunis, Tunis, Tunisia
| | - Alia Benkahla
- Laboratory of Bioinformatics, Biomathematics and Biostatistics, Institute Pasteur of Tunis, Tunis, Tunisia
- University of Tunis El Manar, Tunis, Tunisia
| | - Julie Makani
- Faculty of Science of Bizerte, Jarzouna, University of Carthage, Tunisia
| | - Kais Ghedira
- Laboratory of Bioinformatics, Biomathematics and Biostatistics, Institute Pasteur of Tunis, Tunis, Tunisia
- University of Tunis El Manar, Tunis, Tunisia
- * E-mail: (KG); (CBH)
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Mulder NJ, Adebiyi E, Adebiyi M, Adeyemi S, Ahmed A, Ahmed R, Akanle B, Alibi M, Armstrong DL, Aron S, Ashano E, Baichoo S, Benkahla A, Brown DK, Chimusa ER, Fadlelmola FM, Falola D, Fatumo S, Ghedira K, Ghouila A, Hazelhurst S, Isewon I, Jung S, Kassim SK, Kayondo JK, Mbiyavanga M, Meintjes A, Mohammed S, Mosaku A, Moussa A, Muhammd M, Mungloo-Dilmohamud Z, Nashiru O, Odia T, Okafor A, Oladipo O, Osamor V, Oyelade J, Sadki K, Salifu SP, Soyemi J, Panji S, Radouani F, Souiai O, Tastan Bishop Ö. Development of Bioinformatics Infrastructure for Genomics Research. Glob Heart 2017; 12:91-98. [PMID: 28302555 DOI: 10.1016/j.gheart.2017.01.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 01/05/2017] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community. OBJECTIVES H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis. METHODS AND RESULTS Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. CONCLUSIONS For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa.
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Affiliation(s)
- Nicola J Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa.
| | - Ezekiel Adebiyi
- Department of Computer and Information Sciences, Covenant University, Ota, Nigeria; Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Marion Adebiyi
- Department of Computer and Information Sciences, Covenant University, Ota, Nigeria; Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Seun Adeyemi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria; Center for System and Information Service, Covenant University, Ota, Nigeria
| | - Azza Ahmed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Rehab Ahmed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Bola Akanle
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria; Center for System and Information Service, Covenant University, Ota, Nigeria
| | - Mohamed Alibi
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institut Pasteur de Tunis, Tunis, Tunisia
| | - Don L Armstrong
- Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Efejiro Ashano
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria; H3Africa Bioinformatics Network (H3ABioNet) Node, National Biotechnology Development Agency (NABDA), Federal Ministry of Science and Technology (FMST), Abuja, Nigeria
| | | | - Alia Benkahla
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institut Pasteur de Tunis, Tunis, Tunisia
| | - David K Brown
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, South Africa
| | - Emile R Chimusa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa; Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Faisal M Fadlelmola
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan; Future University of Sudan, Khartoum, Sudan
| | - Dare Falola
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Segun Fatumo
- H3Africa Bioinformatics Network (H3ABioNet) Node, National Biotechnology Development Agency (NABDA), Federal Ministry of Science and Technology (FMST), Abuja, Nigeria
| | - Kais Ghedira
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institut Pasteur de Tunis, Tunis, Tunisia
| | - Amel Ghouila
- Institut Pasteur de Tunis, LR11IPT02, Laboratory of Transmission, Control and Immunobiology of Infections (LTCII), Tunis-Belvédère, Tunisia
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Itunuoluwa Isewon
- Department of Computer and Information Sciences, Covenant University, Ota, Nigeria; Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Segun Jung
- Computation Institute, University of Chicago and Argonne National Laboratory, Chicago, IL, USA
| | - Samar Kamal Kassim
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Abbaseya, Cairo, Egypt
| | | | - Mamana Mbiyavanga
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Ayton Meintjes
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Somia Mohammed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | - Abayomi Mosaku
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Ahmed Moussa
- LAbTIC Laboratory, ENSA, Abdelmalek Essaadi University, Tangier, Morocco
| | - Mustafa Muhammd
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Khartoum, Sudan
| | | | - Oyekanmi Nashiru
- H3Africa Bioinformatics Network (H3ABioNet) Node, National Biotechnology Development Agency (NABDA), Federal Ministry of Science and Technology (FMST), Abuja, Nigeria
| | - Trust Odia
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Adaobi Okafor
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Olaleye Oladipo
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria; Center for System and Information Service, Landmark University, Omu-Aran, Nigeria
| | - Victor Osamor
- Department of Computer and Information Sciences, Covenant University, Ota, Nigeria; Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Jellili Oyelade
- Department of Computer and Information Sciences, Covenant University, Ota, Nigeria; Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Khalid Sadki
- School of Sciences, Mohammed V University of Rabat, Rabat, Morocco
| | - Samson Pandam Salifu
- Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; Kumasi Centre for Collaborative Research, South End Asougya Road, KNUST Campus, Kumasi, Ghana
| | - Jumoke Soyemi
- Department of Computer Science, Ilaro Polytechnic, Ilaro, Nigeria
| | - Sumir Panji
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Fouzia Radouani
- Chlamydiae and Mycoplasma Laboratory, Institut Pasteur du Maroc, Casablanca, Morocco
| | - Oussama Souiai
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institut Pasteur de Tunis, Tunis, Tunisia
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, South Africa
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Choudhury A, Hazelhurst S, Meintjes A, Achinike-Oduaran O, Aron S, Gamieldien J, Jalali Sefid Dashti M, Mulder N, Tiffin N, Ramsay M. Population-specific common SNPs reflect demographic histories and highlight regions of genomic plasticity with functional relevance. BMC Genomics 2014; 15:437. [PMID: 24906912 PMCID: PMC4092225 DOI: 10.1186/1471-2164-15-437] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2013] [Accepted: 05/19/2014] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Population differentiation is the result of demographic and evolutionary forces. Whole genome datasets from the 1000 Genomes Project (October 2012) provide an unbiased view of genetic variation across populations from Europe, Asia, Africa and the Americas. Common population-specific SNPs (MAF > 0.05) reflect a deep history and may have important consequences for health and wellbeing. Their interpretation is contextualised by currently available genome data. RESULTS The identification of common population-specific (CPS) variants (SNPs and SSV) is influenced by admixture and the sample size under investigation. Nine of the populations in the 1000 Genomes Project (2 African, 2 Asian (including a merged Chinese group) and 5 European) revealed that the African populations (LWK and YRI), followed by the Japanese (JPT) have the highest number of CPS SNPs, in concordance with their histories and given the populations studied. Using two methods, sliding 50-SNP and 5-kb windows, the CPS SNPs showed distinct clustering across large genome segments and little overlap of clusters between populations. iHS enrichment score and the population branch statistic (PBS) analyses suggest that selective sweeps are unlikely to account for the clustering and population specificity. Of interest is the association of clusters close to recombination hotspots. Functional analysis of genes associated with the CPS SNPs revealed over-representation of genes in pathways associated with neuronal development, including axonal guidance signalling and CREB signalling in neurones. CONCLUSIONS Common population-specific SNPs are non-randomly distributed throughout the genome and are significantly associated with recombination hotspots. Since the variant alleles of most CPS SNPs are the derived allele, they likely arose in the specific population after a split from a common ancestor. Their proximity to genes involved in specific pathways, including neuronal development, suggests evolutionary plasticity of selected genomic regions. Contrary to expectation, selective sweeps did not play a large role in the persistence of population-specific variation. This suggests a stochastic process towards population-specific variation which reflects demographic histories and may have some interesting implications for health and susceptibility to disease.
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Affiliation(s)
- Ananyo Choudhury
- />Sydney Brenner Institute of Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- />Division of Human Genetics, National Health Laboratory Service, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Scott Hazelhurst
- />Sydney Brenner Institute of Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- />School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Ayton Meintjes
- />Department Clinical Laboratory Sciences, Computational Biology Group, IDM, University of Cape Town, Cape Town, South Africa
| | - Ovokeraye Achinike-Oduaran
- />Sydney Brenner Institute of Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- />Division of Human Genetics, National Health Laboratory Service, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Shaun Aron
- />Sydney Brenner Institute of Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Junaid Gamieldien
- />South African National Bioinformatics Institute/Medical Research Council of South Africa Bioinformatics Unit, University of the Western Cape, Bellville, South Africa
| | - Mahjoubeh Jalali Sefid Dashti
- />South African National Bioinformatics Institute/Medical Research Council of South Africa Bioinformatics Unit, University of the Western Cape, Bellville, South Africa
| | - Nicola Mulder
- />Department Clinical Laboratory Sciences, Computational Biology Group, IDM, University of Cape Town, Cape Town, South Africa
| | - Nicki Tiffin
- />South African National Bioinformatics Institute/Medical Research Council of South Africa Bioinformatics Unit, University of the Western Cape, Bellville, South Africa
| | - Michèle Ramsay
- />Sydney Brenner Institute of Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
- />Division of Human Genetics, National Health Laboratory Service, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Salazar GA, Meintjes A, Mazandu GK, Rapanoël HA, Akinola RO, Mulder NJ. A web-based protein interaction network visualizer. BMC Bioinformatics 2014; 15:129. [PMID: 24885165 PMCID: PMC4029974 DOI: 10.1186/1471-2105-15-129] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Accepted: 04/24/2014] [Indexed: 01/18/2023] Open
Abstract
Background Interaction between proteins is one of the most important mechanisms in the execution of cellular functions. The study of these interactions has provided insight into the functioning of an organism’s processes. As of October 2013, Homo sapiens had over 170000 Protein-Protein interactions (PPI) registered in the Interologous Interaction Database, which is only one of the many public resources where protein interactions can be accessed. These numbers exemplify the volume of data that research on the topic has generated. Visualization of large data sets is a well known strategy to make sense of information, and protein interaction data is no exception. There are several tools that allow the exploration of this data, providing different methods to visualize protein network interactions. However, there is still no native web tool that allows this data to be explored interactively online. Results Given the advances that web technologies have made recently it is time to bring these interactive views to the web to provide an easily accessible forum to visualize PPI. We have created a Web-based Protein Interaction Network Visualizer: PINV, an open source, native web application that facilitates the visualization of protein interactions (http://biosual.cbio.uct.ac.za/pinv.html). We developed PINV as a set of components that follow the protocol defined in BioJS and use the D3 library to create the graphic layouts. We demonstrate the use of PINV with multi-organism interaction networks for a predicted target from Mycobacterium tuberculosis, its interacting partners and its orthologs. Conclusions The resultant tool provides an attractive view of complex, fully interactive networks with components that allow the querying, filtering and manipulation of the visible subset. Moreover, as a web resource, PINV simplifies sharing and publishing, activities which are vital in today’s research collaborative environments. The source code is freely available for download at https://github.com/4ndr01d3/biosual.
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Affiliation(s)
- Gustavo A Salazar
- Computational Biology Group, IDM, Faculty of Health Sciences, University of Cape Town, Anzio Road, Cape Town, South Africa.
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Abstract
SUMMARY We present two web-based components for the display of Protein-Protein Interaction networks using different self-organizing layout methods: force-directed and circular. These components conform to the BioJS standard and can be rendered in an HTML5-compliant browser without the need for third-party plugins. We provide examples of interaction networks and how the components can be used to visualize them, and refer to a more complex tool that uses these components. AVAILABILITY http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7753.
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Affiliation(s)
- Gustavo A Salazar
- Computational Biology Group, University of Cape Town, Cape Town, South Africa
| | - Ayton Meintjes
- Computational Biology Group, University of Cape Town, Cape Town, South Africa
| | - Nicola Mulder
- Computational Biology Group, University of Cape Town, Cape Town, South Africa
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Riedel A, Mofolo B, Avota E, Schneider-Schaulies S, Meintjes A, Mulder N, Kneitz S. Accumulation of splice variants and transcripts in response to PI3K inhibition in T cells. PLoS One 2013; 8:e50695. [PMID: 23383294 PMCID: PMC3562341 DOI: 10.1371/journal.pone.0050695] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2012] [Accepted: 10/23/2012] [Indexed: 12/17/2022] Open
Abstract
Background Measles virus (MV) causes T cell suppression by interference with phosphatidylinositol-3-kinase (PI3K) activation. We previously found that this interference affected the activity of splice regulatory proteins and a T cell inhibitory protein isoform was produced from an alternatively spliced pre-mRNA. Hypothesis Differentially regulated and alternatively splice variant transcripts accumulating in response to PI3K abrogation in T cells potentially encode proteins involved in T cell silencing. Methods To test this hypothesis at the cellular level, we performed a Human Exon 1.0 ST Array on RNAs isolated from T cells stimulated only or stimulated after PI3K inhibition. We developed a simple algorithm based on a splicing index to detect genes that undergo alternative splicing (AS) or are differentially regulated (RG) upon T cell suppression. Results Applying our algorithm to the data, 9% of the genes were assigned as AS, while only 3% were attributed to RG. Though there are overlaps, AS and RG genes differed with regard to functional regulation, and were found to be enriched in different functional groups. AS genes targeted extracellular matrix (ECM)-receptor interaction and focal adhesion pathways, while RG genes were mainly enriched in cytokine-receptor interaction and Jak-STAT. When combined, AS/RG dependent alterations targeted pathways essential for T cell receptor signaling, cytoskeletal dynamics and cell cycle entry. Conclusions PI3K abrogation interferes with key T cell activation processes through both differential expression and alternative splicing, which together actively contribute to T cell suppression.
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Affiliation(s)
- Alice Riedel
- Institute for Virology and Immunobiology, University of Wuerzburg, Versbacher, Wuerzburg, Germany
| | - Boitumelo Mofolo
- Computational Biology Group, Department of Clinical Laboratory Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Elita Avota
- Institute for Virology and Immunobiology, University of Wuerzburg, Versbacher, Wuerzburg, Germany
| | | | - Ayton Meintjes
- Computational Biology Group, Department of Clinical Laboratory Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Nicola Mulder
- Computational Biology Group, Department of Clinical Laboratory Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Susanne Kneitz
- Department of Physiological Chemistry I, Biocenter, University of Wuerzburg, Wuerzburg, Germany
- * E-mail:
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Tiffin N, Meintjes A, Ramesar R, Bajic VB, Rayner B. Computational analysis of candidate disease genes and variants for salt-sensitive hypertension in indigenous Southern Africans. PLoS One 2010; 5:e12989. [PMID: 20886000 PMCID: PMC2946338 DOI: 10.1371/journal.pone.0012989] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2010] [Accepted: 08/29/2010] [Indexed: 01/06/2023] Open
Abstract
Multiple factors underlie susceptibility to essential hypertension, including a significant genetic and ethnic component, and environmental effects. Blood pressure response of hypertensive individuals to salt is heterogeneous, but salt sensitivity appears more prevalent in people of indigenous African origin. The underlying genetics of salt-sensitive hypertension, however, are poorly understood. In this study, computational methods including text- and data-mining have been used to select and prioritize candidate aetiological genes for salt-sensitive hypertension. Additionally, we have compared allele frequencies and copy number variation for single nucleotide polymorphisms in candidate genes between indigenous Southern African and Caucasian populations, with the aim of identifying candidate genes with significant variability between the population groups: identifying genetic variability between population groups can exploit ethnic differences in disease prevalence to aid with prioritisation of good candidate genes. Our top-ranking candidate genes include parathyroid hormone precursor (PTH) and type-1angiotensin II receptor (AGTR1). We propose that the candidate genes identified in this study warrant further investigation as potential aetiological genes for salt-sensitive hypertension.
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Affiliation(s)
- Nicki Tiffin
- Division of Nephrology and Hypertension, University of Cape Town/Groote Schuur Hospital, Cape Town, South Africa.
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Van der Walt JG, Boomker EA, Meintjes A, Schultheiss WA. Effect of water intake on the nitrogen balance of sheep fed a low or a medium protein diet. S AFR J ANIM SCI 2009. [DOI: 10.4314/sajas.v29i3.44206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Bruskiewich R, Senger M, Davenport G, Ruiz M, Rouard M, Hazekamp T, Takeya M, Doi K, Satoh K, Costa M, Simon R, Balaji J, Akintunde A, Mauleon R, Wanchana S, Shah T, Anacleto M, Portugal A, Ulat VJ, Thongjuea S, Braak K, Ritter S, Dereeper A, Skofic M, Rojas E, Martins N, Pappas G, Alamban R, Almodiel R, Barboza LH, Detras J, Manansala K, Mendoza MJ, Morales J, Peralta B, Valerio R, Zhang Y, Gregorio S, Hermocilla J, Echavez M, Yap JM, Farmer A, Schiltz G, Lee J, Casstevens T, Jaiswal P, Meintjes A, Wilkinson M, Good B, Wagner J, Morris J, Marshall D, Collins A, Kikuchi S, Metz T, McLaren G, van Hintum T. The generation challenge programme platform: semantic standards and workbench for crop science. Int J Plant Genomics 2008; 2008:369601. [PMID: 18483570 PMCID: PMC2375972 DOI: 10.1155/2008/369601] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2007] [Accepted: 12/14/2007] [Indexed: 05/26/2023]
Abstract
The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform; (ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive, high-throughput analyses of project data; (iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making.
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