Journal:Building infrastructure for African human genomic data management - Revision history

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	\|download = [https://datascience.codata.org/articles/10.5334/dsj-2019-047/galley/894/download/ https://datascience.codata.org/articles/10.5334/dsj-2019-047/galley/894/download/] (PDF)		\|download = [https://datascience.codata.org/articles/10.5334/dsj-2019-047/galley/894/download/ https://datascience.codata.org/articles/10.5334/dsj-2019-047/galley/894/download/] (PDF)
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	==Abstract==		==Abstract==
	Human [[Genomics\|genomic]] data are large and complex, and require adequate infrastructure for secure storage and transfer. The [[National Institutes of Health]] (NIH) and The Wellcome Trust have funded multiple projects on genomic research, including the Human Heredity and Health in Africa (H3Africa) initiative, and data are required to be deposited into the public domain. The European Genome-phenome Archive (EGA) is a repository for [[Sequencing\|sequence]] and genotype data where data access is controlled by access committees. Access is determined by a formal application procedure for the purpose of secure storage and distribution, which must be in line with the informed consent of the study participants. H3Africa researchers based in Africa and generating their own data can benefit tremendously from the data sharing capabilities of the internet by using the appropriate technologies. The H3Africa Data Archive is an effort between the H3Africa data generating projects, H3ABioNet, and the EGA to store and submit genomic data to public repositories. H3ABioNet maintains the security of the H3Africa Data Archive, ensures ethical security compliance, supports users with data submission, and facilitates data transfers. The goal is to ensure efficient data flow between researchers, the archive, and the EGA or other public repositories. To comply with the H3Africa data sharing and release policy, nine months after the data is in secure storage, H3ABioNet converts the data into an Extensible Markup Language (XML) format ready for submission to EGA. This article describes the infrastructure that has been developed for African human genomic data management.		Human [[Genomics\|genomic]] data are large and complex, and require adequate infrastructure for secure storage and transfer. The [[National Institutes of Health]] (NIH) and The Wellcome Trust have funded multiple projects on genomic research, including the Human Heredity and Health in Africa (H3Africa) initiative, and data are required to be deposited into the public domain. The European Genome-phenome Archive (EGA) is a repository for [[Sequencing\|sequence]] and genotype data where data access is controlled by access committees. Access is determined by a formal application procedure for the purpose of secure storage and distribution, which must be in line with the informed consent of the study participants. H3Africa researchers based in Africa and generating their own data can benefit tremendously from the data sharing capabilities of the internet by using the appropriate technologies. The H3Africa Data Archive is an effort between the H3Africa data generating projects, H3ABioNet, and the EGA to store and submit genomic data to public repositories. H3ABioNet maintains the security of the H3Africa Data Archive, ensures ethical security compliance, supports users with data submission, and facilitates data transfers. The goal is to ensure efficient data flow between researchers, the archive, and the EGA or other public repositories. To comply with the H3Africa data sharing and release policy, nine months after the data is in secure storage, H3ABioNet converts the data into an Extensible Markup Language (XML) format ready for submission to EGA. This article describes the infrastructure that has been developed for African human genomic data management.
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	Data submitted via the internet is ingested into the Vault from the Landing Area (Table 2, step 1). Likewise, data that is due to be submitted to the EGA is moved from the Vault to the Landing Area. Due to network design, the only method of moving data between the Vault and Landing Area is via the network. It is not currently possible to connect external USB storage to the Landing area. The slowest data transfer speeds are recorded when transferring data from a local server via the internal network to the EGA. This is largely due to the various firewalls and packet inspection tools implemented along the data transfer path. As expected, connecting a high speed USB storage device directly to the Vault server yields higher transfer rates. The data transfer rate shown in Table 2 (step 3) was conducted using a USB 3.0 enabled storage device. The Landing Area is located in the institutional DMZ, as data transfers to and from this server have been optimized for internet based data transfers which yields the fastest transfer speeds.		Data submitted via the internet is ingested into the Vault from the Landing Area (Table 2, step 1). Likewise, data that is due to be submitted to the EGA is moved from the Vault to the Landing Area. Due to network design, the only method of moving data between the Vault and Landing Area is via the network. It is not currently possible to connect external USB storage to the Landing area. The slowest data transfer speeds are recorded when transferring data from a local server via the internal network to the EGA. This is largely due to the various firewalls and packet inspection tools implemented along the data transfer path. As expected, connecting a high speed USB storage device directly to the Vault server yields higher transfer rates. The data transfer rate shown in Table 2 (step 3) was conducted using a USB 3.0 enabled storage device. The Landing Area is located in the institutional DMZ, as data transfers to and from this server have been optimized for internet based data transfers which yields the fastest transfer speeds.

	As is evident from Table 2, data transfers are a challenge when internet speeds are slow and resources are limited. The data archive’s preferred online data transfer mechanism is “Globus Online,” which uses GridFTP (Foster ~~et al~~. 2011), while EGA uses Aspera. The Aspera and Globus Online (GO) data transfer applications are optimized to efficiently and securely transfer data between two points on a public or private network. (Madduri et al. 2014) Both applications have security and fault recovery built into the system, which sets it apart from traditional data transfer methods such as FTP (file transfer protocol). The fault recovery measures work by setting checkpoints as data is successfully delivered to its destination. In the event of a network failure, when the transfer is restarted, GO or Aspera will pick up from the last checkpoint, compared to FTP, which would require restarting the entire copy.<ref name="LappalainenTheEuro15" />		As is evident from Table 2, data transfers are a challenge when internet speeds are slow and resources are limited. The data archive’s preferred online data transfer mechanism is “Globus Online,” which uses GridFTP<ref name="FosterGlobus11">{{cite journal \|title=Globus Online: Accelerating and Democratizing Science through Cloud-Based Services \|journal=IEEE Internet Computing \|author=Foster, I. \|volume=15 \|issue=3 \|pages=70–73 \|year=2011 \|doi=10.1109/MIC.2011.64}}</ref>, while EGA uses Aspera. The Aspera and Globus Online (GO) data transfer applications are optimized to efficiently and securely transfer data between two points on a public or private network.<ref name="MadduriExperi14">{{cite journal \|title=Experiences Building Globus Genomics: A Next-Generation Sequencing Analysis Service using Galaxy, Globus, and Amazon Web Services \|journal=Concurrency and Computing: Practice & Experience \|author=Madduri, R.K.; Sulakhe, D.; Lacinski, L. et al. \|volume=26 \|issue=13 \|pages=2266-2279 \|year=2014 \|doi=10.1002/cpe.3274 \|pmid=25342933 \|pmc=PMC4203657}}</ref> Both applications have security and fault recovery built into the system, which sets it apart from traditional data transfer methods such as FTP (file transfer protocol). The fault recovery measures work by setting checkpoints as data is successfully delivered to its destination. In the event of a network failure, when the transfer is restarted, GO or Aspera will pick up from the last checkpoint, compared to FTP, which would require restarting the entire copy.<ref name="LappalainenTheEuro15" />

			===Encryption===
			The primary aim of data encryption is to secure the genomic data.<ref name="MahdiSecure18">{{cite journal \|title=Secure Similar Patients Query on Encrypted Genomic Data \|journal=IEEE Journal of Biomedical and Health Informatics \|author=Mahdi, M.S.R.; Aziz, M.M.A.; Alhadidi, D. et al. \|volume=23 \|issue=6 \|pages=2611-2618 \|year=2019 \|doi=10.1109/JBHI.2018.2881086 \|pmid=30442622}}</ref> Encryption works by encoding data using a secret private key. The data is only accessible or readable when using the matching public key to decrypt it. Without the private key, the data is inaccessible, making it a suitable method of securing data whilst in transit across public networks such as the internet. Encryption to EGA has a separate public key which is built into the EGACryptor tool. A major challenge of encryption is that it takes longer to encrypt or decrypt a file compared to using a standard password to protect the file. It also requires additional storage space, up to three times the size of the raw data. This is not much of an issue when working with small data sets, but for larger data sets, such as genomic data, storage space for encryption becomes an important factor. To encrypt a terabyte of data can take approximately an hour and thirty minutes. This varies depending on the file type and amount of resources used on the server at a particular time.

			===Archive dashboard===
			The Archive Dashboard is a web application that was built to keep track of data submissions from the data submitters. The dashboard tracks all the progress of the submissions in an intuitive user friendly interface. A user is able to register, log in, and fill in a data submission request form. The HDAT will respond by assisting the data submitter with the submission pack and file formats. Funders or project managers can log in to the dashboard with different access rights to view the progress of data from first engagement to submission to the EGA.

			===Current status of the Archive===
			At the time of writing, a total of nine data sets have been submitted to the H3Africa Archive and the EGA. The total size of all the data sets submitted is 118.7 terabytes, with the average data set being 13.2 terabytes. There are currently two studies that have been submitted to the EGA. The AWI-Gen Pilot Study (accession number: EGAS00001002482) is accessible via the EGA, and the H3Africa Chip (accession ID: EGAS00001002976) is currently under embargo and expected to be accessible to the greater scientific community soon. More datasets are expected to be submitted to the H3Africa Data Archive in the near future.

			==Discussion and conclusions==
			In order to implement the H3Africa data sharing policies, we developed what to our knowledge is the first human genomic data archive in Africa. There were many challenges encountered in the development of the infrastructure, most notably in data transfers when moving data around the globe and specifically across the African continent. Common challenges include:

			* Researchers or data owners not wanting to share their data
			* Communication issues
			* Technical issues, such as slow internet speeds or expensive bandwidth
			* Available server compute resources, available storage space, or familiarity with data transfer technologies
			* Data governance which restricts the movement of genomic data across borders

			The H3Africa Archive, though developed to address internal needs of the consortium, provides a useful proof of concept for the possibility of establishing local EGA facilities. It was designed based on the EGA architecture, and it ensures data security and conversion into EGA formats. A similar infrastructure could be used for other genomic data, where an archive of files is required with built-in secure storage, off-site replication, and data transfer procedures. Our experience has demonstrated that significant long-term resources are required for such an infrastructure, including both human and computational. We also recognize the value in data sharing initiatives as researchers and funders move increasingly to an open science ethos. Researchers from the project sites can benefit tremendously from the data sharing capabilities of the internet. In addition to having access to international data sets, by submitting their data to public data archives such as the EGA, they expose their research to the greater scientific community, which in itself holds many benefits.

			==Acknowledgements==
			===Competing interests===
			The authors have no competing interests to declare.

	==References==		==References==

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	==Introduction==		==Introduction==
	Advances in high-throughput [[Genomics\|genomic]] technologies are laying the foundations for the goal of precision medicine to be realized.<ref name="ChristensenAssessing15">{{cite journal \|title=Assessing the Costs and Cost-Effectiveness of Genomic Sequencing \|journal=Journal of Personalized Medicine \|author=Christensen, K.D.; Dukhovny, D.; Siebert, U. et al. \|volume=5 \|issue=4 \|pages=470–86 \|year=2015 \|doi=10.3390/jpm5040470 \|pmid=26690481 \|pmc=PMC4695866}}</ref><ref name="AronsonBuilding15">{{cite journal \|title=Building the foundation for genomics in precision medicine \|journal=Nature \|author=Aronson, S.J.; Rehm, H.L. \|volume=526 \|issue=7573 \|pages=336–42 \|year=2015 \|doi=10.1038/nature15816 \|pmid=26469044 \|pmc=PMC5669797}}</ref> Decreasing costs and the capacity to generate larger volumes of human genomic data at faster rates are enabling population-level genomics studies to be conducted.<ref name="GoldfederHuman17">{{cite journal \|title=Human Genome Sequencing at the Population Scale: A Primer on High-Throughput DNA Sequencing and Analysis \|journal=American Journal of Epidemiology \|author=Goldfeder, R.L.; Wall, D.P.; Khoury, M.J. et al. \|volume=186 \|issue=8 \|pages=1000–1009 \|year=2017 \|doi=10.1093/aje/kww224 \|pmid=29040395 \|pmc=PMC6250075}}</ref><ref name="ProkopGenome18">{{cite journal \|title=Genome sequencing in the clinic: the past, present, and future of genomic medicine \|journal=Physiological Genomics \|author=Prokop. J.W.; May, T.; Strong, K. et al. \|volume=50 \|issue=8 \|pages=563–79 \|year=2018 \|doi=10.1152/physiolgenomics.00046.2018 \|pmid=29727589 \|pmc=PMC6139636}}</ref> However, most of the current population-level genomics studies and data generated to date have a significant population representational bias, with the majority of genome sequences being derived from European and North American ancestry, regions that have been early adopters of genomic technologies.<ref name="ProkopGenome18" /><ref name="PopejoyGenomics16">{{cite journal \|title=Genomics is failing on diversity \|journal=Nature \|author=Popejoy, A.B.; Fullerton, S.M. \|volume=538 \|issue=7624 \|pages=161–64 \|year=2016 \|doi=10.1038/538161a \|pmid=27734877 \|pmc=PMC5089703}}</ref> African researchers, in general, have been late adopters of high-throughput technologies for use in population genomics due to more limited resources and funding. To address this critical gap in scientific knowledge about African genomics and population variation, and inspired by the African Society for Human Genetics, the [[National Institutes of Health]] (NIH) and The Wellcome Trust, through the Human Hereditary and Health in Africa (H3Africa) program, have funded multiple genomics projects led by African investigators.<ref name="H3AfricaResearch14">{{cite journal \|title=Research capacity. Enabling the genomic revolution in Africa \|journal=Science \|author=H3Africa Consortium; Rotimi, C.; Abayomi, A. et al. \|volume=344 \|issue=6190 \|pages=1346–8 \|year=2014 \|doi=10.1126/science.1251546 \|pmid=24948725 \|pmc=PMC4138491}}</ref><ref name="MulderH3Africa18">{{cite journal \|title=H3Africa: Current perspectives \|journal=Pharmacogenomics and Personalized Medicine \|author=Mulder, N.; Abimiku, A.; Adebamowo, S.N. et al. \|volume=11 \|pages=59–86 \|year=2018 \|doi=10.2147/PGPM.S141546 \|pmid=29692621 \|pmc=PMC5903476}}</ref> To support the H3Africa projects in terms of provisioning of infrastructure for secure data storage, management, and computing, the NIH has also funded a Pan-African [[Bioinformatics]] Network for H3Africa ([http://www.h3abionet.org H3ABioNet]).<ref name="MulderH3ABioNet16">{{cite journal \|title=H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa \|journal=Genome Research \|author=Mulder, N.J.; Adebiyi, E.; Alami, R. et al. \|volume=26 \|issue=2 \|pages=271–7 \|year=2016 \|doi=10.1101/gr.196295.115 \|pmid=26627985 \|pmc=PMC4728379}}</ref>		Advances in high-throughput [[Genomics\|genomic]] technologies are laying the foundations for the goal of precision medicine to be realized.<ref name="ChristensenAssessing15">{{cite journal \|title=Assessing the Costs and Cost-Effectiveness of Genomic Sequencing \|journal=Journal of Personalized Medicine \|author=Christensen, K.D.; Dukhovny, D.; Siebert, U. et al. \|volume=5 \|issue=4 \|pages=470–86 \|year=2015 \|doi=10.3390/jpm5040470 \|pmid=26690481 \|pmc=PMC4695866}}</ref><ref name="AronsonBuilding15">{{cite journal \|title=Building the foundation for genomics in precision medicine \|journal=Nature \|author=Aronson, S.J.; Rehm, H.L. \|volume=526 \|issue=7573 \|pages=336–42 \|year=2015 \|doi=10.1038/nature15816 \|pmid=26469044 \|pmc=PMC5669797}}</ref> Decreasing costs and the capacity to generate larger volumes of human genomic data at faster rates are enabling population-level genomics studies to be conducted.<ref name="GoldfederHuman17">{{cite journal \|title=Human Genome Sequencing at the Population Scale: A Primer on High-Throughput DNA Sequencing and Analysis \|journal=American Journal of Epidemiology \|author=Goldfeder, R.L.; Wall, D.P.; Khoury, M.J. et al. \|volume=186 \|issue=8 \|pages=1000–1009 \|year=2017 \|doi=10.1093/aje/kww224 \|pmid=29040395 \|pmc=PMC6250075}}</ref><ref name="ProkopGenome18">{{cite journal \|title=Genome sequencing in the clinic: the past, present, and future of genomic medicine \|journal=Physiological Genomics \|author=Prokop. J.W.; May, T.; Strong, K. et al. \|volume=50 \|issue=8 \|pages=563–79 \|year=2018 \|doi=10.1152/physiolgenomics.00046.2018 \|pmid=29727589 \|pmc=PMC6139636}}</ref> However, most of the current population-level genomics studies and data generated to date have a significant population representational bias, with the majority of genome [[Sequencing\|sequences]] being derived from European and North American ancestry, regions that have been early adopters of genomic technologies.<ref name="ProkopGenome18" /><ref name="PopejoyGenomics16">{{cite journal \|title=Genomics is failing on diversity \|journal=Nature \|author=Popejoy, A.B.; Fullerton, S.M. \|volume=538 \|issue=7624 \|pages=161–64 \|year=2016 \|doi=10.1038/538161a \|pmid=27734877 \|pmc=PMC5089703}}</ref> African researchers, in general, have been late adopters of high-throughput technologies for use in population genomics due to more limited resources and funding. To address this critical gap in scientific knowledge about African genomics and population variation, and inspired by the African Society for Human Genetics, the [[National Institutes of Health]] (NIH) and The Wellcome Trust, through the Human Hereditary and Health in Africa (H3Africa) program, have funded multiple genomics projects led by African investigators.<ref name="H3AfricaResearch14">{{cite journal \|title=Research capacity. Enabling the genomic revolution in Africa \|journal=Science \|author=H3Africa Consortium; Rotimi, C.; Abayomi, A. et al. \|volume=344 \|issue=6190 \|pages=1346–8 \|year=2014 \|doi=10.1126/science.1251546 \|pmid=24948725 \|pmc=PMC4138491}}</ref><ref name="MulderH3Africa18">{{cite journal \|title=H3Africa: Current perspectives \|journal=Pharmacogenomics and Personalized Medicine \|author=Mulder, N.; Abimiku, A.; Adebamowo, S.N. et al. \|volume=11 \|pages=59–86 \|year=2018 \|doi=10.2147/PGPM.S141546 \|pmid=29692621 \|pmc=PMC5903476}}</ref> To support the H3Africa projects in terms of provisioning of infrastructure for secure data storage, management, and computing, the NIH has also funded a Pan-African [[Bioinformatics]] Network for H3Africa ([http://www.h3abionet.org H3ABioNet]).<ref name="MulderH3ABioNet16">{{cite journal \|title=H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa \|journal=Genome Research \|author=Mulder, N.J.; Adebiyi, E.; Alami, R. et al. \|volume=26 \|issue=2 \|pages=271–7 \|year=2016 \|doi=10.1101/gr.196295.115 \|pmid=26627985 \|pmc=PMC4728379}}</ref>

	The H3Africa Consortium consists of multiple projects and sites distributed across Africa, most of which are generating genomic data linked to clinical data for specific diseases. The principal H3Africa funders (NIH and the Wellcome Trust) require any project data generated to be deposited into a data repository accessible by the scientific community.<ref name="NIHSharing19">{{cite web \|url=https://grants.nih.gov/policy/sharing.htm \|title=NIH Sharing Policies and Related Guidance on NIH-Funded Research Resources \|work=Grants & Funding \|publisher=National Institutes of Health \|date=2019}}</ref><ref name="WallcomeData17">{{cite web \|url=https://wellcome.ac.uk/funding/guidance/data-software-materials-management-and-sharing-policy \|title=Data, software and materials management and sharing policy \|work=Funding \|publisher=The Wellcome Trust \|date=10 July 2017}}</ref> In order to facilitate the storage and accessibility of H3Africa genomics data, significant infrastructure, procedures, and policies were established. Part of H3ABioNet’s mandate is to develop processes and implement an infrastructure that will enable the ingestion, validation, annotation, secure storage, and submission of the African genomics data to the controlled access European Genome-phenome Archive (EGA).<ref name="LappalainenTheEuro15">{{cite journal \|title=The European Genome-phenome Archive of human data consented for biomedical research \|journal=Nature Genetics \|author=Lappalainen, I.; Almeida-King, J.; Kumanduri, V. et al. \|volume=47 \|issue=7 \|pages=692–5 \|year=2015 \|doi=10.1038/ng.3312 \|pmid=26111507 \|pmc=PMC5426533}}</ref> This has been achieved through the development of the H3Africa Data Archive, which also ensures a copy of the genomic data is securely stored and retained on the African continent.<ref name="MulderH3ABioNet16" /><ref name="MulderDevelop17">{{cite journal \|title=Development of Bioinformatics Infrastructure for Genomics Research \|journal=Global Heart \|author=Mulder, N.J.; Adebiyi, E.; Adebiyi, M. et al. \|volume=12 \|issue=2 \|pages=91–98 \|year=2017 \|doi=10.1016/j.gheart.2017.01.005 \|pmid=28302555 \|pmc=PMC5582980}}</ref> This article describes the infrastructure that has been developed, which to our knowledge, is the first formalized human genomic data archive on the continent.		The H3Africa Consortium consists of multiple projects and sites distributed across Africa, most of which are generating genomic data linked to clinical data for specific diseases. The principal H3Africa funders (NIH and the Wellcome Trust) require any project data generated to be deposited into a data repository accessible by the scientific community.<ref name="NIHSharing19">{{cite web \|url=https://grants.nih.gov/policy/sharing.htm \|title=NIH Sharing Policies and Related Guidance on NIH-Funded Research Resources \|work=Grants & Funding \|publisher=National Institutes of Health \|date=2019}}</ref><ref name="WallcomeData17">{{cite web \|url=https://wellcome.ac.uk/funding/guidance/data-software-materials-management-and-sharing-policy \|title=Data, software and materials management and sharing policy \|work=Funding \|publisher=The Wellcome Trust \|date=10 July 2017}}</ref> In order to facilitate the storage and accessibility of H3Africa genomics data, significant infrastructure, procedures, and policies were established. Part of H3ABioNet’s mandate is to develop processes and implement an infrastructure that will enable the ingestion, validation, annotation, secure storage, and submission of the African genomics data to the controlled access European Genome-phenome Archive (EGA).<ref name="LappalainenTheEuro15">{{cite journal \|title=The European Genome-phenome Archive of human data consented for biomedical research \|journal=Nature Genetics \|author=Lappalainen, I.; Almeida-King, J.; Kumanduri, V. et al. \|volume=47 \|issue=7 \|pages=692–5 \|year=2015 \|doi=10.1038/ng.3312 \|pmid=26111507 \|pmc=PMC5426533}}</ref> This has been achieved through the development of the H3Africa Data Archive, which also ensures a copy of the genomic data is securely stored and retained on the African continent.<ref name="MulderH3ABioNet16" /><ref name="MulderDevelop17">{{cite journal \|title=Development of Bioinformatics Infrastructure for Genomics Research \|journal=Global Heart \|author=Mulder, N.J.; Adebiyi, E.; Adebiyi, M. et al. \|volume=12 \|issue=2 \|pages=91–98 \|year=2017 \|doi=10.1016/j.gheart.2017.01.005 \|pmid=28302555 \|pmc=PMC5582980}}</ref> This article describes the infrastructure that has been developed, which to our knowledge, is the first formalized human genomic data archive on the continent.
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	===Data submission and access policy===		===Data submission and access policy===
	Genomic data associated with phenotype data enables the possibility of re-identification of study participants; hence all human genomic data and its accompanying phenotypic data need to be governed by a controlled access policy.<ref name="ShabaniControl15">{{cite journal \|title=Controlled Access under Review: Improving the Governance of Genomic Data Access \|journal=PLoS Biology \|author=Shabani, M.; Dyke, S.O.; Joly, Y. et al. \|volume=13 \|issue=12 \|pages=e1002339 \|year=2015 \|doi=10.1371/journal.pbio.1002339 \|pmid=26720729 \|pmc=PMC4697814}}</ref><ref name="DykeRegist18">{{cite journal \|title=Registered access: Authorizing data access \|journal=European Journal of Human Genetics \|author=Dyke, S.O.M.; Linden, M.; Lappalainen, I. et al. \|volume=26 \|issue=12 \|pages=1721-1731 \|year=2018 \|doi=10.1038/s41431-018-0219-y \|pmid=30069064 \|pmc=PMC6244209}}</ref> H3Africa is distinguished as biospecimens are also being collected and stored at one of the three H3Africa biorepositories, so researchers can request access to genomic data and/or biospecimens. As a consortium, H3Africa has developed its own data submission and access policy, which takes into account the genomic and phenotype data generated, as well as policies for the access to and transfer of biospecimens.<ref name="deVriesTheH3Africa15">{{cite journal \|title=The H3Africa policy framework: negotiating fairness in genomics \|journal=Trends in Genetics \|author=de Vries, J.; Tindana, P;. Littler, K. et al. \|volume=31 \|issue=3 \|pages=117-9 \|year=2015 \|doi=10.1016/j.tig.2014.11.004 \|pmid=25601285 \|pmc=PMC4471134}}</ref> A single H3Africa Data and Biospecimen Access committee has been established to oversee the secondary use of both the data, which is being deposited in the EGA, and biospecimens in the H3Africa biorepositories. The sharing and access policies and the H3Africa Data and Biospecimen Access Committee guidelines seek to provide a balance between protecting the rights of individuals and their data, while at the same time not acting as a barrier to advancing scientific knowledge. A data requester will need to identify the data in the EGA and apply for data access.<ref name="LappalainenTheEuro15" /> The data access request is routed to the Data and Biospecimen Access Committee (DBAC), who review it to determine whether the intended research use is inline with the H3Africa data and access policy, and ensure the requester is a bona fide researcher. Once the data request has been reviewed, the H3Africa DBAC will provide a decision to approve or reject it.		Genomic data associated with phenotype data enables the possibility of re-identification of study participants; hence all human genomic data and its accompanying phenotypic data need to be governed by a controlled access policy.<ref name="ShabaniControl15">{{cite journal \|title=Controlled Access under Review: Improving the Governance of Genomic Data Access \|journal=PLoS Biology \|author=Shabani, M.; Dyke, S.O.; Joly, Y. et al. \|volume=13 \|issue=12 \|pages=e1002339 \|year=2015 \|doi=10.1371/journal.pbio.1002339 \|pmid=26720729 \|pmc=PMC4697814}}</ref><ref name="DykeRegist18">{{cite journal \|title=Registered access: Authorizing data access \|journal=European Journal of Human Genetics \|author=Dyke, S.O.M.; Linden, M.; Lappalainen, I. et al. \|volume=26 \|issue=12 \|pages=1721-1731 \|year=2018 \|doi=10.1038/s41431-018-0219-y \|pmid=30069064 \|pmc=PMC6244209}}</ref> H3Africa is distinguished as biospecimens are also being collected and stored at one of the three H3Africa biorepositories, so researchers can request access to genomic data and/or biospecimens. As a consortium, H3Africa has developed its own data submission and access policy, which takes into account the genomic and phenotype data generated, as well as policies for the access to and transfer of biospecimens.<ref name="deVriesTheH3Africa15">{{cite journal \|title=The H3Africa policy framework: negotiating fairness in genomics \|journal=Trends in Genetics \|author=de Vries, J.; Tindana, P;. Littler, K. et al. \|volume=31 \|issue=3 \|pages=117-9 \|year=2015 \|doi=10.1016/j.tig.2014.11.004 \|pmid=25601285 \|pmc=PMC4471134}}</ref> A single H3Africa Data and Biospecimen Access committee has been established to oversee the secondary use of both the data, which is being deposited in the EGA, and biospecimens in the H3Africa biorepositories. The sharing and access policies and the H3Africa Data and Biospecimen Access Committee guidelines seek to provide a balance between protecting the rights of individuals and their data, while at the same time not acting as a barrier to advancing scientific knowledge. A data requester will need to identify the data in the EGA and apply for data access.<ref name="LappalainenTheEuro15" /> The data access request is routed to the Data and Biospecimen Access Committee (DBAC), who review it to determine whether the intended research use is inline with the H3Africa data and access policy, and ensure the requester is a bona fide researcher. Once the data request has been reviewed, the H3Africa DBAC will provide a decision to approve or reject it.

			===Types of data being accepted===
			The principal data types being collected for submission to the H3Africa Data Archive and the EGA include genomic sequence data, genotype array files, the associated phenotypes and metadata that is collected along with the samples, and results of any analysis conducted. Genomic sequence data mainly comprises of short DNA sequence reads in FASTQ format.<ref name="CockTheSanger10">{{cite journal \|title=The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants \|journal=Nucleic Acids Research \|author=Cock, P.J.; Fields, C.J.; Goto, N. et al. \|volume=38 \|issue=6 \|pages=1767–71 \|year=2010 \|doi=10.1093/nar/gkp1137 \|pmid=20015970 \|pmc=PMC2847217}}</ref><ref name="Hsi-YangEffic11">{{cite journal \|title=Efficient storage of high throughput DNA sequencing data using reference-based compression \|journal=Genome Research \|author=Hsi-Yang Fritz, M.; Leinonen, R.; Cochrane, G. et al. \|volume=21 \|issue=5 \|pages=734–40 \|year=2011 \|doi=10.1101/gr.114819.110 \|pmid=21245279 \|pmc=PMC3083090}}</ref> The types of data and associated files for the H3Africa research projects are summarized in Table 1.

			{\|
			\| STYLE="vertical-align:top;"\|
			{\| class="wikitable" border="1" cellpadding="5" cellspacing="0" width="100%"
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;" colspan="3"\|'''Table 1.''' Description of data types for submission
			\|-
			! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" \|Exome/Whole Genome Sequence
			! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" \|16S rRNA Microbiome studies
			! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" \|Genome Wide Association studies/genotyping arrays
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Study type and description
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Study type and description
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Study type and description
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Sequencing platform and technology used
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Sequencing platform and technology used
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Genotyping array model/name and description of the software and version used for calling the genotypes
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|FASTQ files linked with de-identified participant ID (minus technical reads such as adapters, linkers, barcodes)
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|FASTQ files linked with de-identified participant ID (minus technical reads such as adapters, linkers, barcodes)
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Raw intensity files linked with de-identified participant IDs (IDATs, CELs)
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Binary Alignment files (BAMs, de-multiplexed) – linked with participant de-identified ID
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Manifest file describing SNP or probe content on the genotyping array
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Associated phenotypic data collected
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Associated phenotypic data collected
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Associated phenotypic data collected
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Variant calling files (VCFs)
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Final analyses BIOM files (at minimum must contain OTUs)
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Final reports and analysis files generated
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Mapping file indicating the relationship between the submitted files
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Mapping file indicating the relationship between the submitted files
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Mapping file indicating the relationship between the submitted files (completed Array Format template)
			\|-
			\|}
			\|}

	==References==		==References==

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	The H3Africa Consortium consists of multiple projects and sites distributed across Africa, most of which are generating genomic data linked to clinical data for specific diseases. The principal H3Africa funders (NIH and the Wellcome Trust) require any project data generated to be deposited into a data repository accessible by the scientific community.<ref name="NIHSharing19">{{cite web \|url=https://grants.nih.gov/policy/sharing.htm \|title=NIH Sharing Policies and Related Guidance on NIH-Funded Research Resources \|work=Grants & Funding \|publisher=National Institutes of Health \|date=2019}}</ref><ref name="WallcomeData17">{{cite web \|url=https://wellcome.ac.uk/funding/guidance/data-software-materials-management-and-sharing-policy \|title=Data, software and materials management and sharing policy \|work=Funding \|publisher=The Wellcome Trust \|date=10 July 2017}}</ref> In order to facilitate the storage and accessibility of H3Africa genomics data, significant infrastructure, procedures, and policies were established. Part of H3ABioNet’s mandate is to develop processes and implement an infrastructure that will enable the ingestion, validation, annotation, secure storage, and submission of the African genomics data to the controlled access European Genome-phenome Archive (EGA).<ref name="LappalainenTheEuro15">{{cite journal \|title=The European Genome-phenome Archive of human data consented for biomedical research \|journal=Nature Genetics \|author=Lappalainen, I.; Almeida-King, J.; Kumanduri, V. et al. \|volume=47 \|issue=7 \|pages=692–5 \|year=2015 \|doi=10.1038/ng.3312 \|pmid=26111507 \|pmc=PMC5426533}}</ref> This has been achieved through the development of the H3Africa Data Archive, which also ensures a copy of the genomic data is securely stored and retained on the African continent.<ref name="MulderH3ABioNet16" /><ref name="MulderDevelop17">{{cite journal \|title=Development of Bioinformatics Infrastructure for Genomics Research \|journal=Global Heart \|author=Mulder, N.J.; Adebiyi, E.; Adebiyi, M. et al. \|volume=12 \|issue=2 \|pages=91–98 \|year=2017 \|doi=10.1016/j.gheart.2017.01.005 \|pmid=28302555 \|pmc=PMC5582980}}</ref> This article describes the infrastructure that has been developed, which to our knowledge, is the first formalized human genomic data archive on the continent.		The H3Africa Consortium consists of multiple projects and sites distributed across Africa, most of which are generating genomic data linked to clinical data for specific diseases. The principal H3Africa funders (NIH and the Wellcome Trust) require any project data generated to be deposited into a data repository accessible by the scientific community.<ref name="NIHSharing19">{{cite web \|url=https://grants.nih.gov/policy/sharing.htm \|title=NIH Sharing Policies and Related Guidance on NIH-Funded Research Resources \|work=Grants & Funding \|publisher=National Institutes of Health \|date=2019}}</ref><ref name="WallcomeData17">{{cite web \|url=https://wellcome.ac.uk/funding/guidance/data-software-materials-management-and-sharing-policy \|title=Data, software and materials management and sharing policy \|work=Funding \|publisher=The Wellcome Trust \|date=10 July 2017}}</ref> In order to facilitate the storage and accessibility of H3Africa genomics data, significant infrastructure, procedures, and policies were established. Part of H3ABioNet’s mandate is to develop processes and implement an infrastructure that will enable the ingestion, validation, annotation, secure storage, and submission of the African genomics data to the controlled access European Genome-phenome Archive (EGA).<ref name="LappalainenTheEuro15">{{cite journal \|title=The European Genome-phenome Archive of human data consented for biomedical research \|journal=Nature Genetics \|author=Lappalainen, I.; Almeida-King, J.; Kumanduri, V. et al. \|volume=47 \|issue=7 \|pages=692–5 \|year=2015 \|doi=10.1038/ng.3312 \|pmid=26111507 \|pmc=PMC5426533}}</ref> This has been achieved through the development of the H3Africa Data Archive, which also ensures a copy of the genomic data is securely stored and retained on the African continent.<ref name="MulderH3ABioNet16" /><ref name="MulderDevelop17">{{cite journal \|title=Development of Bioinformatics Infrastructure for Genomics Research \|journal=Global Heart \|author=Mulder, N.J.; Adebiyi, E.; Adebiyi, M. et al. \|volume=12 \|issue=2 \|pages=91–98 \|year=2017 \|doi=10.1016/j.gheart.2017.01.005 \|pmid=28302555 \|pmc=PMC5582980}}</ref> This article describes the infrastructure that has been developed, which to our knowledge, is the first formalized human genomic data archive on the continent.

			==Methods==
			In order to establish the H3Africa Archive, and to make the submission process seamless, a data storage infrastructure had to be created and new processes and policies developed and adhered to.

			===Data submission and access policy===
			Genomic data associated with phenotype data enables the possibility of re-identification of study participants; hence all human genomic data and its accompanying phenotypic data need to be governed by a controlled access policy.<ref name="ShabaniControl15">{{cite journal \|title=Controlled Access under Review: Improving the Governance of Genomic Data Access \|journal=PLoS Biology \|author=Shabani, M.; Dyke, S.O.; Joly, Y. et al. \|volume=13 \|issue=12 \|pages=e1002339 \|year=2015 \|doi=10.1371/journal.pbio.1002339 \|pmid=26720729 \|pmc=PMC4697814}}</ref><ref name="DykeRegist18">{{cite journal \|title=Registered access: Authorizing data access \|journal=European Journal of Human Genetics \|author=Dyke, S.O.M.; Linden, M.; Lappalainen, I. et al. \|volume=26 \|issue=12 \|pages=1721-1731 \|year=2018 \|doi=10.1038/s41431-018-0219-y \|pmid=30069064 \|pmc=PMC6244209}}</ref> H3Africa is distinguished as biospecimens are also being collected and stored at one of the three H3Africa biorepositories, so researchers can request access to genomic data and/or biospecimens. As a consortium, H3Africa has developed its own data submission and access policy, which takes into account the genomic and phenotype data generated, as well as policies for the access to and transfer of biospecimens.<ref name="deVriesTheH3Africa15">{{cite journal \|title=The H3Africa policy framework: negotiating fairness in genomics \|journal=Trends in Genetics \|author=de Vries, J.; Tindana, P;. Littler, K. et al. \|volume=31 \|issue=3 \|pages=117-9 \|year=2015 \|doi=10.1016/j.tig.2014.11.004 \|pmid=25601285 \|pmc=PMC4471134}}</ref> A single H3Africa Data and Biospecimen Access committee has been established to oversee the secondary use of both the data, which is being deposited in the EGA, and biospecimens in the H3Africa biorepositories. The sharing and access policies and the H3Africa Data and Biospecimen Access Committee guidelines seek to provide a balance between protecting the rights of individuals and their data, while at the same time not acting as a barrier to advancing scientific knowledge. A data requester will need to identify the data in the EGA and apply for data access.<ref name="LappalainenTheEuro15" /> The data access request is routed to the Data and Biospecimen Access Committee (DBAC), who review it to determine whether the intended research use is inline with the H3Africa data and access policy, and ensure the requester is a bona fide researcher. Once the data request has been reviewed, the H3Africa DBAC will provide a decision to approve or reject it.

	==References==		==References==

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	==Introduction==		==Introduction==
	Advances in high-throughput [[Genomics\|genomic]] technologies are laying the foundations for the goal of precision medicine to be realized.<ref name="ChristensenAssessing15">{{cite journal \|title=Assessing the Costs and Cost-Effectiveness of Genomic Sequencing \|journal=Journal of Personalized Medicine \|author=Christensen, K.D.; Dukhovny, D.; Siebert, U. et al. \|volume=5 \|issue=4 \|pages=470–86 \|year=2015 \|doi=10.3390/jpm5040470 \|pmid=26690481 \|pmc=PMC4695866}}</ref><ref name="AronsonBuilding15">{{cite journal \|title=Building the foundation for genomics in precision medicine \|journal=Nature \|author=Aronson, S.J.; Rehm, H.L. \|volume=526 \|issue=7573 \|pages=336–42 \|year=2015 \|doi=10.1038/nature15816 \|pmid=26469044 \|pmc=PMC5669797}}</ref> Decreasing costs and the capacity to generate larger volumes of human genomic data at faster rates are enabling population-level genomics studies to be conducted.<ref name="GoldfederHuman17">{{cite journal \|title=Human Genome Sequencing at the Population Scale: A Primer on High-Throughput DNA Sequencing and Analysis \|journal=American Journal of Epidemiology \|author=Goldfeder, R.L.; Wall, D.P.; Khoury, M.J. et al. \|volume=186 \|issue=8 \|pages=1000–1009 \|year=2017 \|doi=10.1093/aje/kww224 \|pmid=29040395 \|pmc=PMC6250075}}</ref><ref name="ProkopGenome18">{{cite journal \|title=Genome sequencing in the clinic: the past, present, and future of genomic medicine \|journal=Physiological Genomics \|author=Prokop. J.W.; May, T.; Strong, K. et al. \|volume=50 \|issue=8 \|pages=563–79 \|year=2018 \|doi=10.1152/physiolgenomics.00046.2018 \|pmid=29727589 \|pmc=PMC6139636}}</ref> However, most of the current population-level genomics studies and data generated to date have a significant population representational bias, with the majority of genome sequences being derived from European and North American ancestry, regions that have been early adopters of genomic technologies.<ref name="ProkopGenome18" /><ref name="PopejoyGenomics16">{{cite journal \|title=Genomics is failing on diversity \|journal=Nature \|author=Popejoy, A.B.; Fullerton, S.M. \|volume=538 \|issue=7624 \|pages=161–64 \|year=2016 \|doi=10.1038/538161a \|pmid=27734877 \|pmc=PMC5089703}}</ref> African researchers, in general, have been late adopters of high-throughput technologies for use in population genomics due to more limited resources and funding. To address this critical gap in scientific knowledge about African genomics and population variation, and inspired by the African Society for Human Genetics, the [[National Institutes of Health]] (NIH) and The Wellcome Trust, through the Human Hereditary and Health in Africa (H3Africa) program, have funded multiple genomics projects led by African investigators.<ref name="H3AfricaResearch14">{{cite journal \|title=Research capacity. Enabling the genomic revolution in Africa \|journal=Science \|author=H3Africa Consortium; Rotimi, C.; Abayomi, A. et al. \|volume=344 \|issue=6190 \|pages=1346–8 \|year=2014 \|doi=10.1126/science.1251546 \|pmid=24948725 \|pmc=PMC4138491}}</ref><ref name="MulderH3Africa18">{{cite journal \|title=H3Africa: Current perspectives \|journal=Pharmacogenomics and Personalized Medicine \|author=Mulder, N.; Abimiku, A.; Adebamowo, S.N. et al. \|volume=11 \|pages=59–86 \|year=2018 \|doi=10.2147/PGPM.S141546 \|pmid=29692621 \|pmc=PMC5903476}}</ref> To support the H3Africa projects in terms of provisioning of infrastructure for secure data storage, management, and computing, the NIH has also funded a Pan-African [[Bioinformatics]] Network for H3Africa ([http://www.h3abionet.org H3ABioNet]).<ref name="MulderH3ABioNet16">{{cite journal \|title=H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa \|journal=Genome Research \|author=Mulder, N.J.; Adebiyi, E.; Alami, R. et al. \|volume=26 \|issue=2 \|pages=271–7 \|year=2016 \|doi=10.1101/gr.196295.115 \|pmid=26627985 \|pmc=PMC4728379}}</ref>		Advances in high-throughput [[Genomics\|genomic]] technologies are laying the foundations for the goal of precision medicine to be realized.<ref name="ChristensenAssessing15">{{cite journal \|title=Assessing the Costs and Cost-Effectiveness of Genomic Sequencing \|journal=Journal of Personalized Medicine \|author=Christensen, K.D.; Dukhovny, D.; Siebert, U. et al. \|volume=5 \|issue=4 \|pages=470–86 \|year=2015 \|doi=10.3390/jpm5040470 \|pmid=26690481 \|pmc=PMC4695866}}</ref><ref name="AronsonBuilding15">{{cite journal \|title=Building the foundation for genomics in precision medicine \|journal=Nature \|author=Aronson, S.J.; Rehm, H.L. \|volume=526 \|issue=7573 \|pages=336–42 \|year=2015 \|doi=10.1038/nature15816 \|pmid=26469044 \|pmc=PMC5669797}}</ref> Decreasing costs and the capacity to generate larger volumes of human genomic data at faster rates are enabling population-level genomics studies to be conducted.<ref name="GoldfederHuman17">{{cite journal \|title=Human Genome Sequencing at the Population Scale: A Primer on High-Throughput DNA Sequencing and Analysis \|journal=American Journal of Epidemiology \|author=Goldfeder, R.L.; Wall, D.P.; Khoury, M.J. et al. \|volume=186 \|issue=8 \|pages=1000–1009 \|year=2017 \|doi=10.1093/aje/kww224 \|pmid=29040395 \|pmc=PMC6250075}}</ref><ref name="ProkopGenome18">{{cite journal \|title=Genome sequencing in the clinic: the past, present, and future of genomic medicine \|journal=Physiological Genomics \|author=Prokop. J.W.; May, T.; Strong, K. et al. \|volume=50 \|issue=8 \|pages=563–79 \|year=2018 \|doi=10.1152/physiolgenomics.00046.2018 \|pmid=29727589 \|pmc=PMC6139636}}</ref> However, most of the current population-level genomics studies and data generated to date have a significant population representational bias, with the majority of genome sequences being derived from European and North American ancestry, regions that have been early adopters of genomic technologies.<ref name="ProkopGenome18" /><ref name="PopejoyGenomics16">{{cite journal \|title=Genomics is failing on diversity \|journal=Nature \|author=Popejoy, A.B.; Fullerton, S.M. \|volume=538 \|issue=7624 \|pages=161–64 \|year=2016 \|doi=10.1038/538161a \|pmid=27734877 \|pmc=PMC5089703}}</ref> African researchers, in general, have been late adopters of high-throughput technologies for use in population genomics due to more limited resources and funding. To address this critical gap in scientific knowledge about African genomics and population variation, and inspired by the African Society for Human Genetics, the [[National Institutes of Health]] (NIH) and The Wellcome Trust, through the Human Hereditary and Health in Africa (H3Africa) program, have funded multiple genomics projects led by African investigators.<ref name="H3AfricaResearch14">{{cite journal \|title=Research capacity. Enabling the genomic revolution in Africa \|journal=Science \|author=H3Africa Consortium; Rotimi, C.; Abayomi, A. et al. \|volume=344 \|issue=6190 \|pages=1346–8 \|year=2014 \|doi=10.1126/science.1251546 \|pmid=24948725 \|pmc=PMC4138491}}</ref><ref name="MulderH3Africa18">{{cite journal \|title=H3Africa: Current perspectives \|journal=Pharmacogenomics and Personalized Medicine \|author=Mulder, N.; Abimiku, A.; Adebamowo, S.N. et al. \|volume=11 \|pages=59–86 \|year=2018 \|doi=10.2147/PGPM.S141546 \|pmid=29692621 \|pmc=PMC5903476}}</ref> To support the H3Africa projects in terms of provisioning of infrastructure for secure data storage, management, and computing, the NIH has also funded a Pan-African [[Bioinformatics]] Network for H3Africa ([http://www.h3abionet.org H3ABioNet]).<ref name="MulderH3ABioNet16">{{cite journal \|title=H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa \|journal=Genome Research \|author=Mulder, N.J.; Adebiyi, E.; Alami, R. et al. \|volume=26 \|issue=2 \|pages=271–7 \|year=2016 \|doi=10.1101/gr.196295.115 \|pmid=26627985 \|pmc=PMC4728379}}</ref>

			The H3Africa Consortium consists of multiple projects and sites distributed across Africa, most of which are generating genomic data linked to clinical data for specific diseases. The principal H3Africa funders (NIH and the Wellcome Trust) require any project data generated to be deposited into a data repository accessible by the scientific community.<ref name="NIHSharing19">{{cite web \|url=https://grants.nih.gov/policy/sharing.htm \|title=NIH Sharing Policies and Related Guidance on NIH-Funded Research Resources \|work=Grants & Funding \|publisher=National Institutes of Health \|date=2019}}</ref><ref name="WallcomeData17">{{cite web \|url=https://wellcome.ac.uk/funding/guidance/data-software-materials-management-and-sharing-policy \|title=Data, software and materials management and sharing policy \|work=Funding \|publisher=The Wellcome Trust \|date=10 July 2017}}</ref> In order to facilitate the storage and accessibility of H3Africa genomics data, significant infrastructure, procedures, and policies were established. Part of H3ABioNet’s mandate is to develop processes and implement an infrastructure that will enable the ingestion, validation, annotation, secure storage, and submission of the African genomics data to the controlled access European Genome-phenome Archive (EGA).<ref name="LappalainenTheEuro15">{{cite journal \|title=The European Genome-phenome Archive of human data consented for biomedical research \|journal=Nature Genetics \|author=Lappalainen, I.; Almeida-King, J.; Kumanduri, V. et al. \|volume=47 \|issue=7 \|pages=692–5 \|year=2015 \|doi=10.1038/ng.3312 \|pmid=26111507 \|pmc=PMC5426533}}</ref> This has been achieved through the development of the H3Africa Data Archive, which also ensures a copy of the genomic data is securely stored and retained on the African continent.<ref name="MulderH3ABioNet16" /><ref name="MulderDevelop17">{{cite journal \|title=Development of Bioinformatics Infrastructure for Genomics Research \|journal=Global Heart \|author=Mulder, N.J.; Adebiyi, E.; Adebiyi, M. et al. \|volume=12 \|issue=2 \|pages=91–98 \|year=2017 \|doi=10.1016/j.gheart.2017.01.005 \|pmid=28302555 \|pmc=PMC5582980}}</ref> This article describes the infrastructure that has been developed, which to our knowledge, is the first formalized human genomic data archive on the continent.

	==References==		==References==

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	==Introduction==		==Introduction==
	Advances in high-throughput [[Genomics\|genomic]] technologies are laying the foundations for the goal of precision medicine to be realized.<ref name="ChristensenAssessing15">{{cite journal \|title=Assessing the Costs and Cost-Effectiveness of Genomic Sequencing \|journal=Journal of Personalized Medicine \|author=Christensen, K.D.; Dukhovny, D.; Siebert, U. et al. \|volume=5 \|issue=4 \|pages=470–86 \|year=2015 \|doi=10.3390/jpm5040470 \|pmid=26690481 \|pmc=PMC4695866}}</ref><ref name="AronsonBuilding15">{{cite journal \|title=Building the foundation for genomics in precision medicine \|journal=Nature \|author=Aronson, S.J.; Rehm, H.L. \|volume=526 \|issue=7573 \|pages=336–42 \|year=2015 \|doi=10.1038/nature15816 \|pmid=26469044 \|pmc=PMC5669797}}</ref> Decreasing costs and the capacity to generate larger volumes of human genomic data at faster rates are enabling population-level genomics studies to be conducted. (Goldfeder et al. 2017; ~~Prokop~~ et al. 2018) However, most of the current population-level genomics studies and data generated to date have a significant population representational bias, with the majority of genome sequences being derived from European and North American ancestry, regions that have been early adopters of genomic technologies. (Popejoy ~~and~~ Fullerton 2016~~; Prokop et al~~. ~~2018)~~ African researchers, in general, have been late adopters of high-throughput technologies for use in population genomics due to more limited resources and funding. To address this critical gap in scientific knowledge about African genomics and population variation, and inspired by the African Society for Human Genetics, the [[National Institutes of Health]] (NIH) and The Wellcome Trust, through the Human Hereditary and Health in Africa (H3Africa) program, have funded multiple genomics projects led by African investigators. (H3Africa Consortium et al. 2014; ~~Mulder~~ et al. 2018) To support the H3Africa projects in terms of provisioning of infrastructure for secure data storage, management, and computing, the NIH has also funded a Pan-African [[Bioinformatics]] Network for H3Africa ([http://www.h3abionet.org H3ABioNet]). (Mulder et al. 2016)		Advances in high-throughput [[Genomics\|genomic]] technologies are laying the foundations for the goal of precision medicine to be realized.<ref name="ChristensenAssessing15">{{cite journal \|title=Assessing the Costs and Cost-Effectiveness of Genomic Sequencing \|journal=Journal of Personalized Medicine \|author=Christensen, K.D.; Dukhovny, D.; Siebert, U. et al. \|volume=5 \|issue=4 \|pages=470–86 \|year=2015 \|doi=10.3390/jpm5040470 \|pmid=26690481 \|pmc=PMC4695866}}</ref><ref name="AronsonBuilding15">{{cite journal \|title=Building the foundation for genomics in precision medicine \|journal=Nature \|author=Aronson, S.J.; Rehm, H.L. \|volume=526 \|issue=7573 \|pages=336–42 \|year=2015 \|doi=10.1038/nature15816 \|pmid=26469044 \|pmc=PMC5669797}}</ref> Decreasing costs and the capacity to generate larger volumes of human genomic data at faster rates are enabling population-level genomics studies to be conducted.<ref name="GoldfederHuman17">{{cite journal \|title=Human Genome Sequencing at the Population Scale: A Primer on High-Throughput DNA Sequencing and Analysis \|journal=American Journal of Epidemiology \|author=Goldfeder, R.L.; Wall, D.P.; Khoury, M.J. et al. \|volume=186 \|issue=8 \|pages=1000–1009 \|year=2017 \|doi=10.1093/aje/kww224 \|pmid=29040395 \|pmc=PMC6250075}}</ref><ref name="ProkopGenome18">{{cite journal \|title=Genome sequencing in the clinic: the past, present, and future of genomic medicine \|journal=Physiological Genomics \|author=Prokop. J.W.; May, T.; Strong, K. et al. \|volume=50 \|issue=8 \|pages=563–79 \|year=2018 \|doi=10.1152/physiolgenomics.00046.2018 \|pmid=29727589 \|pmc=PMC6139636}}</ref> However, most of the current population-level genomics studies and data generated to date have a significant population representational bias, with the majority of genome sequences being derived from European and North American ancestry, regions that have been early adopters of genomic technologies.<ref name="ProkopGenome18" /><ref name="PopejoyGenomics16">{{cite journal \|title=Genomics is failing on diversity \|journal=Nature \|author=Popejoy, A.B.; Fullerton, S.M. \|volume=538 \|issue=7624 \|pages=161–64 \|year=2016 \|doi=10.1038/538161a \|pmid=27734877 \|pmc=PMC5089703}}</ref> African researchers, in general, have been late adopters of high-throughput technologies for use in population genomics due to more limited resources and funding. To address this critical gap in scientific knowledge about African genomics and population variation, and inspired by the African Society for Human Genetics, the [[National Institutes of Health]] (NIH) and The Wellcome Trust, through the Human Hereditary and Health in Africa (H3Africa) program, have funded multiple genomics projects led by African investigators.<ref name="H3AfricaResearch14">{{cite journal \|title=Research capacity. Enabling the genomic revolution in Africa \|journal=Science \|author=H3Africa Consortium; Rotimi, C.; Abayomi, A. et al. \|volume=344 \|issue=6190 \|pages=1346–8 \|year=2014 \|doi=10.1126/science.1251546 \|pmid=24948725 \|pmc=PMC4138491}}</ref><ref name="MulderH3Africa18">{{cite journal \|title=H3Africa: Current perspectives \|journal=Pharmacogenomics and Personalized Medicine \|author=Mulder, N.; Abimiku, A.; Adebamowo, S.N. et al. \|volume=11 \|pages=59–86 \|year=2018 \|doi=10.2147/PGPM.S141546 \|pmid=29692621 \|pmc=PMC5903476}}</ref> To support the H3Africa projects in terms of provisioning of infrastructure for secure data storage, management, and computing, the NIH has also funded a Pan-African [[Bioinformatics]] Network for H3Africa ([http://www.h3abionet.org H3ABioNet]).<ref name="MulderH3ABioNet16">{{cite journal \|title=H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa \|journal=Genome Research \|author=Mulder, N.J.; Adebiyi, E.; Alami, R. et al. \|volume=26 \|issue=2 \|pages=271–7 \|year=2016 \|doi=10.1101/gr.196295.115 \|pmid=26627985 \|pmc=PMC4728379}}</ref>

	==References==		==References==

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	'''Keywords''': genomic data, data archive, H3Africa data, African genomic data		'''Keywords''': genomic data, data archive, H3Africa data, African genomic data

			==Introduction==
			Advances in high-throughput [[Genomics\|genomic]] technologies are laying the foundations for the goal of precision medicine to be realized.<ref name="ChristensenAssessing15">{{cite journal \|title=Assessing the Costs and Cost-Effectiveness of Genomic Sequencing \|journal=Journal of Personalized Medicine \|author=Christensen, K.D.; Dukhovny, D.; Siebert, U. et al. \|volume=5 \|issue=4 \|pages=470–86 \|year=2015 \|doi=10.3390/jpm5040470 \|pmid=26690481 \|pmc=PMC4695866}}</ref><ref name="AronsonBuilding15">{{cite journal \|title=Building the foundation for genomics in precision medicine \|journal=Nature \|author=Aronson, S.J.; Rehm, H.L. \|volume=526 \|issue=7573 \|pages=336–42 \|year=2015 \|doi=10.1038/nature15816 \|pmid=26469044 \|pmc=PMC5669797}}</ref> Decreasing costs and the capacity to generate larger volumes of human genomic data at faster rates are enabling population-level genomics studies to be conducted. (Goldfeder et al. 2017; Prokop et al. 2018) However, most of the current population-level genomics studies and data generated to date have a significant population representational bias, with the majority of genome sequences being derived from European and North American ancestry, regions that have been early adopters of genomic technologies. (Popejoy and Fullerton 2016; Prokop et al. 2018) African researchers, in general, have been late adopters of high-throughput technologies for use in population genomics due to more limited resources and funding. To address this critical gap in scientific knowledge about African genomics and population variation, and inspired by the African Society for Human Genetics, the [[National Institutes of Health]] (NIH) and The Wellcome Trust, through the Human Hereditary and Health in Africa (H3Africa) program, have funded multiple genomics projects led by African investigators. (H3Africa Consortium et al. 2014; Mulder et al. 2018) To support the H3Africa projects in terms of provisioning of infrastructure for secure data storage, management, and computing, the NIH has also funded a Pan-African [[Bioinformatics]] Network for H3Africa ([http://www.h3abionet.org H3ABioNet]). (Mulder et al. 2016)

	==References==		==References==

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{{Infobox journal article
|name =
|image =
|alt = 
|caption =
|title_full = Building infrastructure for African human genomic data management
|journal = ''Data Science Journal''
|authors = Parker, Ziyaad; Maslamoney, Suresh; Meintjes, Ayton; Botha, Gerrit; Panji, Sumir; Hazelhurst, Scott; Mulder, Nicola
|affiliations = University of Cape Town, University of the Witwatersrand
|contact = Email: ziyaad dot parker at uct dot ac dot za
|editors =
|pub_year = 2019
|vol_iss = '''18'''(1)
|pages = 47
|doi = [http://dx.doi.org/10.5334/dsj-2019-047 10.5334/dsj-2019-047]
|issn = 1683-1470
|license = [http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International]
|website = [https://datascience.codata.org/articles/10.5334/dsj-2019-047/ https://datascience.codata.org/articles/10.5334/dsj-2019-047/]
|download = [https://datascience.codata.org/articles/10.5334/dsj-2019-047/galley/894/download/ https://datascience.codata.org/articles/10.5334/dsj-2019-047/galley/894/download/] (PDF)
}}
{{ombox
| type = notice
| image = [[Image:Emblem-important-yellow.svg|40px]]
| style = width: 500px;
| text = This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed.
}}
==Abstract==
Human [[Genomics|genomic]] data are large and complex, and require adequate infrastructure for secure storage and transfer. The [[National Institutes of Health]] (NIH) and The Wellcome Trust have funded multiple projects on genomic research, including the Human Heredity and Health in Africa (H3Africa) initiative, and data are required to be deposited into the public domain. The European Genome-phenome Archive (EGA) is a repository for [[Sequencing|sequence]] and genotype data where data access is controlled by access committees. Access is determined by a formal application procedure for the purpose of secure storage and distribution, which must be in line with the informed consent of the study participants. H3Africa researchers based in Africa and generating their own data can benefit tremendously from the data sharing capabilities of the internet by using the appropriate technologies. The H3Africa Data Archive is an effort between the H3Africa data generating projects, H3ABioNet, and the EGA to store and submit genomic data to public repositories. H3ABioNet maintains the security of the H3Africa Data Archive, ensures ethical security compliance, supports users with data submission, and facilitates data transfers. The goal is to ensure efficient data flow between researchers, the archive, and the EGA or other public repositories. To comply with the H3Africa data sharing and release policy, nine months after the data is in secure storage, H3ABioNet converts the data into an Extensible Markup Language (XML) format ready for submission to EGA. This article describes the infrastructure that has been developed for African human genomic data management.

'''Keywords''': genomic data, data archive, H3Africa data, African genomic data

==References==
{{Reflist|colwidth=30em}}

==Notes==
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The original paper listed references alphabetically; this wiki lists them by order of appearance, by design. The two footnotes were turned into inline references for convenience.


[[Category:LIMSwiki journal articles (added in 2019)‎]]
[[Category:LIMSwiki journal articles (all)‎]]
[[Category:LIMSwiki journal articles on data management and sharing]]
[[Category:LIMSwiki journal articles on genome informatics]]
[[Category:LIMSwiki journal articles on health informatics]]

← Older revision		Revision as of 21:22, 11 November 2019
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	==Introduction==		==Introduction==
	Advances in high-throughput [[Genomics\|genomic]] technologies are laying the foundations for the goal of precision medicine to be realized.<ref name="ChristensenAssessing15">{{cite journal \|title=Assessing the Costs and Cost-Effectiveness of Genomic Sequencing \|journal=Journal of Personalized Medicine \|author=Christensen, K.D.; Dukhovny, D.; Siebert, U. et al. \|volume=5 \|issue=4 \|pages=470–86 \|year=2015 \|doi=10.3390/jpm5040470 \|pmid=26690481 \|pmc=PMC4695866}}</ref><ref name="AronsonBuilding15">{{cite journal \|title=Building the foundation for genomics in precision medicine \|journal=Nature \|author=Aronson, S.J.; Rehm, H.L. \|volume=526 \|issue=7573 \|pages=336–42 \|year=2015 \|doi=10.1038/nature15816 \|pmid=26469044 \|pmc=PMC5669797}}</ref> Decreasing costs and the capacity to generate larger volumes of human genomic data at faster rates are enabling population-level genomics studies to be conducted.<ref name="GoldfederHuman17">{{cite journal \|title=Human Genome Sequencing at the Population Scale: A Primer on High-Throughput DNA Sequencing and Analysis \|journal=American Journal of Epidemiology \|author=Goldfeder, R.L.; Wall, D.P.; Khoury, M.J. et al. \|volume=186 \|issue=8 \|pages=1000–1009 \|year=2017 \|doi=10.1093/aje/kww224 \|pmid=29040395 \|pmc=PMC6250075}}</ref><ref name="ProkopGenome18">{{cite journal \|title=Genome sequencing in the clinic: the past, present, and future of genomic medicine \|journal=Physiological Genomics \|author=Prokop. J.W.; May, T.; Strong, K. et al. \|volume=50 \|issue=8 \|pages=563–79 \|year=2018 \|doi=10.1152/physiolgenomics.00046.2018 \|pmid=29727589 \|pmc=PMC6139636}}</ref> However, most of the current population-level genomics studies and data generated to date have a significant population representational bias, with the majority of genome sequences being derived from European and North American ancestry, regions that have been early adopters of genomic technologies.<ref name="ProkopGenome18" /><ref name="PopejoyGenomics16">{{cite journal \|title=Genomics is failing on diversity \|journal=Nature \|author=Popejoy, A.B.; Fullerton, S.M. \|volume=538 \|issue=7624 \|pages=161–64 \|year=2016 \|doi=10.1038/538161a \|pmid=27734877 \|pmc=PMC5089703}}</ref> African researchers, in general, have been late adopters of high-throughput technologies for use in population genomics due to more limited resources and funding. To address this critical gap in scientific knowledge about African genomics and population variation, and inspired by the African Society for Human Genetics, the [[National Institutes of Health]] (NIH) and The Wellcome Trust, through the Human Hereditary and Health in Africa (H3Africa) program, have funded multiple genomics projects led by African investigators.<ref name="H3AfricaResearch14">{{cite journal \|title=Research capacity. Enabling the genomic revolution in Africa \|journal=Science \|author=H3Africa Consortium; Rotimi, C.; Abayomi, A. et al. \|volume=344 \|issue=6190 \|pages=1346–8 \|year=2014 \|doi=10.1126/science.1251546 \|pmid=24948725 \|pmc=PMC4138491}}</ref><ref name="MulderH3Africa18">{{cite journal \|title=H3Africa: Current perspectives \|journal=Pharmacogenomics and Personalized Medicine \|author=Mulder, N.; Abimiku, A.; Adebamowo, S.N. et al. \|volume=11 \|pages=59–86 \|year=2018 \|doi=10.2147/PGPM.S141546 \|pmid=29692621 \|pmc=PMC5903476}}</ref> To support the H3Africa projects in terms of provisioning of infrastructure for secure data storage, management, and computing, the NIH has also funded a Pan-African [[Bioinformatics]] Network for H3Africa ([http://www.h3abionet.org H3ABioNet]).<ref name="MulderH3ABioNet16">{{cite journal \|title=H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa \|journal=Genome Research \|author=Mulder, N.J.; Adebiyi, E.; Alami, R. et al. \|volume=26 \|issue=2 \|pages=271–7 \|year=2016 \|doi=10.1101/gr.196295.115 \|pmid=26627985 \|pmc=PMC4728379}}</ref>		Advances in high-throughput [[Genomics\|genomic]] technologies are laying the foundations for the goal of precision medicine to be realized.<ref name="ChristensenAssessing15">{{cite journal \|title=Assessing the Costs and Cost-Effectiveness of Genomic Sequencing \|journal=Journal of Personalized Medicine \|author=Christensen, K.D.; Dukhovny, D.; Siebert, U. et al. \|volume=5 \|issue=4 \|pages=470–86 \|year=2015 \|doi=10.3390/jpm5040470 \|pmid=26690481 \|pmc=PMC4695866}}</ref><ref name="AronsonBuilding15">{{cite journal \|title=Building the foundation for genomics in precision medicine \|journal=Nature \|author=Aronson, S.J.; Rehm, H.L. \|volume=526 \|issue=7573 \|pages=336–42 \|year=2015 \|doi=10.1038/nature15816 \|pmid=26469044 \|pmc=PMC5669797}}</ref> Decreasing costs and the capacity to generate larger volumes of human genomic data at faster rates are enabling population-level genomics studies to be conducted.<ref name="GoldfederHuman17">{{cite journal \|title=Human Genome Sequencing at the Population Scale: A Primer on High-Throughput DNA Sequencing and Analysis \|journal=American Journal of Epidemiology \|author=Goldfeder, R.L.; Wall, D.P.; Khoury, M.J. et al. \|volume=186 \|issue=8 \|pages=1000–1009 \|year=2017 \|doi=10.1093/aje/kww224 \|pmid=29040395 \|pmc=PMC6250075}}</ref><ref name="ProkopGenome18">{{cite journal \|title=Genome sequencing in the clinic: the past, present, and future of genomic medicine \|journal=Physiological Genomics \|author=Prokop. J.W.; May, T.; Strong, K. et al. \|volume=50 \|issue=8 \|pages=563–79 \|year=2018 \|doi=10.1152/physiolgenomics.00046.2018 \|pmid=29727589 \|pmc=PMC6139636}}</ref> However, most of the current population-level genomics studies and data generated to date have a significant population representational bias, with the majority of genome [[Sequencing\|sequences]] being derived from European and North American ancestry, regions that have been early adopters of genomic technologies.<ref name="ProkopGenome18" /><ref name="PopejoyGenomics16">{{cite journal \|title=Genomics is failing on diversity \|journal=Nature \|author=Popejoy, A.B.; Fullerton, S.M. \|volume=538 \|issue=7624 \|pages=161–64 \|year=2016 \|doi=10.1038/538161a \|pmid=27734877 \|pmc=PMC5089703}}</ref> African researchers, in general, have been late adopters of high-throughput technologies for use in population genomics due to more limited resources and funding. To address this critical gap in scientific knowledge about African genomics and population variation, and inspired by the African Society for Human Genetics, the [[National Institutes of Health]] (NIH) and The Wellcome Trust, through the Human Hereditary and Health in Africa (H3Africa) program, have funded multiple genomics projects led by African investigators.<ref name="H3AfricaResearch14">{{cite journal \|title=Research capacity. Enabling the genomic revolution in Africa \|journal=Science \|author=H3Africa Consortium; Rotimi, C.; Abayomi, A. et al. \|volume=344 \|issue=6190 \|pages=1346–8 \|year=2014 \|doi=10.1126/science.1251546 \|pmid=24948725 \|pmc=PMC4138491}}</ref><ref name="MulderH3Africa18">{{cite journal \|title=H3Africa: Current perspectives \|journal=Pharmacogenomics and Personalized Medicine \|author=Mulder, N.; Abimiku, A.; Adebamowo, S.N. et al. \|volume=11 \|pages=59–86 \|year=2018 \|doi=10.2147/PGPM.S141546 \|pmid=29692621 \|pmc=PMC5903476}}</ref> To support the H3Africa projects in terms of provisioning of infrastructure for secure data storage, management, and computing, the NIH has also funded a Pan-African [[Bioinformatics]] Network for H3Africa ([http://www.h3abionet.org H3ABioNet]).<ref name="MulderH3ABioNet16">{{cite journal \|title=H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa \|journal=Genome Research \|author=Mulder, N.J.; Adebiyi, E.; Alami, R. et al. \|volume=26 \|issue=2 \|pages=271–7 \|year=2016 \|doi=10.1101/gr.196295.115 \|pmid=26627985 \|pmc=PMC4728379}}</ref>

	The H3Africa Consortium consists of multiple projects and sites distributed across Africa, most of which are generating genomic data linked to clinical data for specific diseases. The principal H3Africa funders (NIH and the Wellcome Trust) require any project data generated to be deposited into a data repository accessible by the scientific community.<ref name="NIHSharing19">{{cite web \|url=https://grants.nih.gov/policy/sharing.htm \|title=NIH Sharing Policies and Related Guidance on NIH-Funded Research Resources \|work=Grants & Funding \|publisher=National Institutes of Health \|date=2019}}</ref><ref name="WallcomeData17">{{cite web \|url=https://wellcome.ac.uk/funding/guidance/data-software-materials-management-and-sharing-policy \|title=Data, software and materials management and sharing policy \|work=Funding \|publisher=The Wellcome Trust \|date=10 July 2017}}</ref> In order to facilitate the storage and accessibility of H3Africa genomics data, significant infrastructure, procedures, and policies were established. Part of H3ABioNet’s mandate is to develop processes and implement an infrastructure that will enable the ingestion, validation, annotation, secure storage, and submission of the African genomics data to the controlled access European Genome-phenome Archive (EGA).<ref name="LappalainenTheEuro15">{{cite journal \|title=The European Genome-phenome Archive of human data consented for biomedical research \|journal=Nature Genetics \|author=Lappalainen, I.; Almeida-King, J.; Kumanduri, V. et al. \|volume=47 \|issue=7 \|pages=692–5 \|year=2015 \|doi=10.1038/ng.3312 \|pmid=26111507 \|pmc=PMC5426533}}</ref> This has been achieved through the development of the H3Africa Data Archive, which also ensures a copy of the genomic data is securely stored and retained on the African continent.<ref name="MulderH3ABioNet16" /><ref name="MulderDevelop17">{{cite journal \|title=Development of Bioinformatics Infrastructure for Genomics Research \|journal=Global Heart \|author=Mulder, N.J.; Adebiyi, E.; Adebiyi, M. et al. \|volume=12 \|issue=2 \|pages=91–98 \|year=2017 \|doi=10.1016/j.gheart.2017.01.005 \|pmid=28302555 \|pmc=PMC5582980}}</ref> This article describes the infrastructure that has been developed, which to our knowledge, is the first formalized human genomic data archive on the continent.		The H3Africa Consortium consists of multiple projects and sites distributed across Africa, most of which are generating genomic data linked to clinical data for specific diseases. The principal H3Africa funders (NIH and the Wellcome Trust) require any project data generated to be deposited into a data repository accessible by the scientific community.<ref name="NIHSharing19">{{cite web \|url=https://grants.nih.gov/policy/sharing.htm \|title=NIH Sharing Policies and Related Guidance on NIH-Funded Research Resources \|work=Grants & Funding \|publisher=National Institutes of Health \|date=2019}}</ref><ref name="WallcomeData17">{{cite web \|url=https://wellcome.ac.uk/funding/guidance/data-software-materials-management-and-sharing-policy \|title=Data, software and materials management and sharing policy \|work=Funding \|publisher=The Wellcome Trust \|date=10 July 2017}}</ref> In order to facilitate the storage and accessibility of H3Africa genomics data, significant infrastructure, procedures, and policies were established. Part of H3ABioNet’s mandate is to develop processes and implement an infrastructure that will enable the ingestion, validation, annotation, secure storage, and submission of the African genomics data to the controlled access European Genome-phenome Archive (EGA).<ref name="LappalainenTheEuro15">{{cite journal \|title=The European Genome-phenome Archive of human data consented for biomedical research \|journal=Nature Genetics \|author=Lappalainen, I.; Almeida-King, J.; Kumanduri, V. et al. \|volume=47 \|issue=7 \|pages=692–5 \|year=2015 \|doi=10.1038/ng.3312 \|pmid=26111507 \|pmc=PMC5426533}}</ref> This has been achieved through the development of the H3Africa Data Archive, which also ensures a copy of the genomic data is securely stored and retained on the African continent.<ref name="MulderH3ABioNet16" /><ref name="MulderDevelop17">{{cite journal \|title=Development of Bioinformatics Infrastructure for Genomics Research \|journal=Global Heart \|author=Mulder, N.J.; Adebiyi, E.; Adebiyi, M. et al. \|volume=12 \|issue=2 \|pages=91–98 \|year=2017 \|doi=10.1016/j.gheart.2017.01.005 \|pmid=28302555 \|pmc=PMC5582980}}</ref> This article describes the infrastructure that has been developed, which to our knowledge, is the first formalized human genomic data archive on the continent.
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	===Data submission and access policy===		===Data submission and access policy===
	Genomic data associated with phenotype data enables the possibility of re-identification of study participants; hence all human genomic data and its accompanying phenotypic data need to be governed by a controlled access policy.<ref name="ShabaniControl15">{{cite journal \|title=Controlled Access under Review: Improving the Governance of Genomic Data Access \|journal=PLoS Biology \|author=Shabani, M.; Dyke, S.O.; Joly, Y. et al. \|volume=13 \|issue=12 \|pages=e1002339 \|year=2015 \|doi=10.1371/journal.pbio.1002339 \|pmid=26720729 \|pmc=PMC4697814}}</ref><ref name="DykeRegist18">{{cite journal \|title=Registered access: Authorizing data access \|journal=European Journal of Human Genetics \|author=Dyke, S.O.M.; Linden, M.; Lappalainen, I. et al. \|volume=26 \|issue=12 \|pages=1721-1731 \|year=2018 \|doi=10.1038/s41431-018-0219-y \|pmid=30069064 \|pmc=PMC6244209}}</ref> H3Africa is distinguished as biospecimens are also being collected and stored at one of the three H3Africa biorepositories, so researchers can request access to genomic data and/or biospecimens. As a consortium, H3Africa has developed its own data submission and access policy, which takes into account the genomic and phenotype data generated, as well as policies for the access to and transfer of biospecimens.<ref name="deVriesTheH3Africa15">{{cite journal \|title=The H3Africa policy framework: negotiating fairness in genomics \|journal=Trends in Genetics \|author=de Vries, J.; Tindana, P;. Littler, K. et al. \|volume=31 \|issue=3 \|pages=117-9 \|year=2015 \|doi=10.1016/j.tig.2014.11.004 \|pmid=25601285 \|pmc=PMC4471134}}</ref> A single H3Africa Data and Biospecimen Access committee has been established to oversee the secondary use of both the data, which is being deposited in the EGA, and biospecimens in the H3Africa biorepositories. The sharing and access policies and the H3Africa Data and Biospecimen Access Committee guidelines seek to provide a balance between protecting the rights of individuals and their data, while at the same time not acting as a barrier to advancing scientific knowledge. A data requester will need to identify the data in the EGA and apply for data access.<ref name="LappalainenTheEuro15" /> The data access request is routed to the Data and Biospecimen Access Committee (DBAC), who review it to determine whether the intended research use is inline with the H3Africa data and access policy, and ensure the requester is a bona fide researcher. Once the data request has been reviewed, the H3Africa DBAC will provide a decision to approve or reject it.		Genomic data associated with phenotype data enables the possibility of re-identification of study participants; hence all human genomic data and its accompanying phenotypic data need to be governed by a controlled access policy.<ref name="ShabaniControl15">{{cite journal \|title=Controlled Access under Review: Improving the Governance of Genomic Data Access \|journal=PLoS Biology \|author=Shabani, M.; Dyke, S.O.; Joly, Y. et al. \|volume=13 \|issue=12 \|pages=e1002339 \|year=2015 \|doi=10.1371/journal.pbio.1002339 \|pmid=26720729 \|pmc=PMC4697814}}</ref><ref name="DykeRegist18">{{cite journal \|title=Registered access: Authorizing data access \|journal=European Journal of Human Genetics \|author=Dyke, S.O.M.; Linden, M.; Lappalainen, I. et al. \|volume=26 \|issue=12 \|pages=1721-1731 \|year=2018 \|doi=10.1038/s41431-018-0219-y \|pmid=30069064 \|pmc=PMC6244209}}</ref> H3Africa is distinguished as biospecimens are also being collected and stored at one of the three H3Africa biorepositories, so researchers can request access to genomic data and/or biospecimens. As a consortium, H3Africa has developed its own data submission and access policy, which takes into account the genomic and phenotype data generated, as well as policies for the access to and transfer of biospecimens.<ref name="deVriesTheH3Africa15">{{cite journal \|title=The H3Africa policy framework: negotiating fairness in genomics \|journal=Trends in Genetics \|author=de Vries, J.; Tindana, P;. Littler, K. et al. \|volume=31 \|issue=3 \|pages=117-9 \|year=2015 \|doi=10.1016/j.tig.2014.11.004 \|pmid=25601285 \|pmc=PMC4471134}}</ref> A single H3Africa Data and Biospecimen Access committee has been established to oversee the secondary use of both the data, which is being deposited in the EGA, and biospecimens in the H3Africa biorepositories. The sharing and access policies and the H3Africa Data and Biospecimen Access Committee guidelines seek to provide a balance between protecting the rights of individuals and their data, while at the same time not acting as a barrier to advancing scientific knowledge. A data requester will need to identify the data in the EGA and apply for data access.<ref name="LappalainenTheEuro15" /> The data access request is routed to the Data and Biospecimen Access Committee (DBAC), who review it to determine whether the intended research use is inline with the H3Africa data and access policy, and ensure the requester is a bona fide researcher. Once the data request has been reviewed, the H3Africa DBAC will provide a decision to approve or reject it.

			===Types of data being accepted===
			The principal data types being collected for submission to the H3Africa Data Archive and the EGA include genomic sequence data, genotype array files, the associated phenotypes and metadata that is collected along with the samples, and results of any analysis conducted. Genomic sequence data mainly comprises of short DNA sequence reads in FASTQ format.<ref name="CockTheSanger10">{{cite journal \|title=The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants \|journal=Nucleic Acids Research \|author=Cock, P.J.; Fields, C.J.; Goto, N. et al. \|volume=38 \|issue=6 \|pages=1767–71 \|year=2010 \|doi=10.1093/nar/gkp1137 \|pmid=20015970 \|pmc=PMC2847217}}</ref><ref name="Hsi-YangEffic11">{{cite journal \|title=Efficient storage of high throughput DNA sequencing data using reference-based compression \|journal=Genome Research \|author=Hsi-Yang Fritz, M.; Leinonen, R.; Cochrane, G. et al. \|volume=21 \|issue=5 \|pages=734–40 \|year=2011 \|doi=10.1101/gr.114819.110 \|pmid=21245279 \|pmc=PMC3083090}}</ref> The types of data and associated files for the H3Africa research projects are summarized in Table 1.

			{\|
			\| STYLE="vertical-align:top;"\|
			{\| class="wikitable" border="1" cellpadding="5" cellspacing="0" width="100%"
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;" colspan="3"\|'''Table 1.''' Description of data types for submission
			\|-
			! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" \|Exome/Whole Genome Sequence
			! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" \|16S rRNA Microbiome studies
			! style="background-color:#e2e2e2; padding-left:10px; padding-right:10px;" \|Genome Wide Association studies/genotyping arrays
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Study type and description
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Study type and description
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Study type and description
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Sequencing platform and technology used
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Sequencing platform and technology used
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Genotyping array model/name and description of the software and version used for calling the genotypes
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|FASTQ files linked with de-identified participant ID (minus technical reads such as adapters, linkers, barcodes)
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|FASTQ files linked with de-identified participant ID (minus technical reads such as adapters, linkers, barcodes)
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Raw intensity files linked with de-identified participant IDs (IDATs, CELs)
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Binary Alignment files (BAMs, de-multiplexed) – linked with participant de-identified ID
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Manifest file describing SNP or probe content on the genotyping array
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Associated phenotypic data collected
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Associated phenotypic data collected
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Associated phenotypic data collected
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Variant calling files (VCFs)
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Final analyses BIOM files (at minimum must contain OTUs)
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Final reports and analysis files generated
			\|-
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Mapping file indicating the relationship between the submitted files
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Mapping file indicating the relationship between the submitted files
			\| style="background-color:white; padding-left:10px; padding-right:10px;"\|Mapping file indicating the relationship between the submitted files (completed Array Format template)
			\|-
			\|}
			\|}

	==References==		==References==