Bioinformatics Infrastructure in Nigeria
Engineering Excellence & Technical Support
Bioinformatics Infrastructure solutions for Digital & Analytical. High-standard technical execution following OEM protocols and local regulatory frameworks.
Cloud-Powered Genomic Analysis Platforms
Deployment of scalable cloud infrastructure for high-throughput genomic data processing, enabling researchers across Nigerian institutions to perform complex analyses like genome assembly, variant calling, and population genetics studies with reduced computational bottlenecks and enhanced collaboration capabilities.
National Bioinformatics Supercomputing Clusters
Establishment and optimization of national supercomputing clusters equipped with specialized hardware (GPUs, high-speed interconnects) and optimized bioinformatics software stacks, providing researchers with powerful, on-premises computational resources for large-scale omics data integration, machine learning in biology, and drug discovery.
Federated Biological Data Repositories
Development of secure, interoperable, and federated biological data repositories that adhere to FAIR principles, consolidating and standardizing diverse datasets (genomic, proteomic, transcriptomic) from Nigerian research initiatives, facilitating data sharing, reproducibility, and the creation of national reference datasets for disease surveillance and agricultural research.
What Is Bioinformatics Infrastructure In Nigeria?
Bioinformatics infrastructure in Nigeria refers to the integrated collection of computational resources, data repositories, analytical tools, and skilled human capital necessary for the acquisition, storage, processing, analysis, and interpretation of biological data. This infrastructure supports research, development, and application in fields such as genomics, proteomics, transcriptomics, metagenomics, and drug discovery, crucial for addressing national health challenges, agricultural advancements, and environmental sustainability. The service involves establishing and maintaining high-performance computing clusters, secure data storage solutions, access to curated biological databases, specialized software for various analyses, and robust network connectivity. Furthermore, it encompasses training and capacity building for researchers, technicians, and bioinformaticians to effectively utilize these resources.
| Who Needs Bioinformatics Infrastructure? | Typical Use Cases | ||||
|---|---|---|---|---|---|
| Academic Researchers (Universities, Research Institutes) | Genomic sequencing and analysis for disease research (e.g., infectious diseases, cancer, genetic disorders). | Transcriptomic studies to understand gene regulation and disease mechanisms. | Proteomic analysis to identify biomarkers and therapeutic targets. | Phylogenetic analysis for understanding evolutionary relationships of pathogens and species. | Metagenomic studies to explore microbial communities in diverse environments (soil, water, human gut). |
| Healthcare Professionals & Public Health Agencies | Disease surveillance and outbreak investigation through pathogen genomics. | Diagnostic tool development for genetic diseases and infectious agents. | Personalized medicine initiatives for tailored treatment strategies. | Drug resistance monitoring and development. | |
| Agricultural Scientists & Biotechnology Companies | Crop improvement through genomic selection and marker-assisted breeding. | Livestock breeding and disease resistance studies. | Development of genetically modified organisms (GMOs) for enhanced yield and resilience. | Analysis of soil microbiomes for sustainable agriculture. | |
| Environmental Scientists & Conservationists | Biodiversity assessment and monitoring through DNA barcoding and environmental DNA (eDNA). | Ecological studies of microbial communities and their roles in ecosystems. | Bioremediation research using microbial genomics. | ||
| Pharmaceutical & Drug Discovery Companies | Target identification and validation for new drug development. | Virtual screening and in silico drug design. | Pharmacogenomics for understanding drug response variability. | ||
| Students & Trainees | Learning fundamental bioinformatics principles and tools. | Conducting research projects and theses. | Developing a skilled workforce for the growing biotech sector. |
Key Components of Bioinformatics Infrastructure in Nigeria:
- High-Performance Computing (HPC) clusters for rapid data processing.
- Secure and scalable data storage solutions (local and cloud-based).
- Access to global and national biological databases (e.g., GenBank, UniProt, EBI).
- Specialized bioinformatics software and analytical pipelines (e.g., for sequence alignment, variant calling, phylogenetic analysis, gene expression analysis).
- Robust network infrastructure for data transfer and remote access.
- Data management frameworks and standards.
- Skilled bioinformatics personnel and technical support.
- Training and education programs for capacity building.
Who Needs Bioinformatics Infrastructure In Nigeria?
Bioinformatics infrastructure in Nigeria is crucial for advancing research, diagnostics, and agricultural development. It provides the computational power, data storage, and specialized software needed to analyze complex biological datasets. This infrastructure is not a luxury but a necessity for organizations aiming to contribute to and benefit from the global genomics and life sciences revolution. Its presence empowers Nigerian scientists and researchers to tackle local health challenges, improve crop yields, and foster innovation in the biotechnology sector.
| Customer Type | Primary Departments/Units | Key Needs/Applications |
|---|---|---|
| Academic and Research Institutions | Genetics, Genomics, Molecular Biology, Biochemistry, Bioinformatics Departments, Research Centers | Genomic sequencing analysis, transcriptomics, proteomics, phylogenetics, drug discovery research, training future bioinformaticians. |
| Healthcare and Diagnostic Laboratories | Pathology, Microbiology, Clinical Genetics, Public Health Laboratories, Molecular Diagnostics Units | Pathogen identification and tracking (e.g., for infectious diseases like Lassa fever, Malaria, COVID-19), personalized medicine, genetic disease screening, antimicrobial resistance surveillance. |
| Agricultural Research and Development Agencies | Crop Science, Plant Breeding, Animal Husbandry, Agronomy, Biotechnology Divisions | Genomic selection for crop improvement (disease resistance, yield enhancement, climate resilience), livestock breeding optimization, pest and disease management, food security initiatives. |
| Biotechnology and Pharmaceutical Companies | Research & Development (R&D), Drug Discovery, Product Development, Quality Control | Drug target identification, vaccine development, biopharmaceutical production optimization, molecular modeling, data-driven product innovation. |
| Government Ministries and Agencies | Federal Ministry of Health (Epidemiology, Public Health), Federal Ministry of Agriculture and Rural Development (Research & Development), Federal Ministry of Science, Technology & Innovation (Research Support) | Public health surveillance, disease outbreak response, national food security strategy, research funding and policy formulation, development of national bio-databases. |
| Non-Governmental Organizations (NGOs) | Public Health Programs, Environmental Research, Disease Control Initiatives | Epidemiological data analysis for health interventions, environmental impact assessment using molecular markers, community health research, advocacy based on scientific data. |
Target Customers and Departments
- Academic and Research Institutions
- Healthcare and Diagnostic Laboratories
- Agricultural Research and Development Agencies
- Biotechnology and Pharmaceutical Companies
- Government Ministries and Agencies (Health, Agriculture, Science & Technology)
- Non-Governmental Organizations (NGOs) in Health and Environment
Bioinformatics Infrastructure Process In Nigeria
The bioinformatics infrastructure process in Nigeria, from an initial inquiry to the execution of a project, involves a series of structured steps. This workflow aims to ensure efficient resource allocation, project feasibility, and successful scientific outcomes. It encompasses initial conceptualization, proposal development, resource assessment, ethical considerations, technical execution, and data management. The process is often driven by researchers' needs and the availability of national and international collaborative opportunities.
| Stage | Key Activities | Responsible Parties/Bodies | Expected Outcomes/Deliverables | Potential Challenges |
|---|---|---|---|---|
| Inquiry & Conceptualization | Researcher identifies a biological question requiring bioinformatics analysis. Initial brainstorming and literature review. | Individual researcher(s), Research groups, Universities, Research Institutes | Clear research hypothesis, preliminary project idea. | Lack of awareness about bioinformatics capabilities, limited access to expertise. |
| Needs Assessment & Feasibility Study | Determining the specific bioinformatics tools, datasets, and computational resources required. Assessing technical feasibility and potential impact. | Researcher(s), Bioinformatics unit/department, Potential collaborators | Defined project scope, identified data requirements, initial assessment of computational needs. | Underestimation of resource needs, difficulty in accessing existing datasets. |
| Proposal Development & Funding Acquisition | Writing detailed project proposals, outlining methodology, budget, timeline, and expected outcomes. Seeking funding from national (e.g., TETFund, national research grants) and international sources. | Researcher(s), Grant writing specialists, University research offices, Funding agencies | Approved project proposal, secured funding. | Competition for limited funding, complex grant application processes, insufficient local funding. |
| Ethical & Regulatory Approvals | Obtaining necessary ethical clearances (e.g., Institutional Review Boards - IRBs) and adhering to data privacy regulations, especially for human or sensitive biological data. | Researcher(s), Institutional Review Boards (IRBs), National Health Research Ethics Committee (NHREC), Relevant regulatory bodies | Ethical approval certificate, compliance with data protection laws. | Lengthy approval processes, varying ethical guidelines, lack of awareness of specific regulations. |
| Resource Allocation & Infrastructure Setup | Provision of computational resources (servers, cloud computing), software licenses, and access to databases. Establishing data storage and security protocols. | IT departments, Bioinformatics units, National research infrastructure centers (e.g., VNRI), Cloud providers | Access to computational power, software, and databases. Secure data storage. | Limited computational capacity, high cost of software and hardware, unreliable internet connectivity, inadequate skilled personnel for maintenance. |
| Data Generation & Acquisition | Collecting raw biological data (e.g., sequencing data, imaging data) or acquiring existing datasets from public repositories or collaborators. | Research labs, Sequencing facilities, Data providers, National biorepositories | High-quality, well-formatted raw biological data. | Poor data quality, challenges in data standardization, accessibility issues for proprietary data. |
| Bioinformatics Analysis & Interpretation | Applying specialized bioinformatics tools and pipelines for data processing, analysis (e.g., genomics, transcriptomics, proteomics), and statistical interpretation. | Bioinformaticians, Computational biologists, Researchers with bioinformatics expertise | Analyzed datasets, identification of significant biological patterns and insights. | Lack of trained bioinformaticians, selection of inappropriate analytical tools, computational bottlenecks. |
| Results Dissemination & Archiving | Publishing findings in peer-reviewed journals, presenting at conferences, and depositing data and analyses in public repositories for reproducibility and future use. | Researcher(s), Journals, Conference organizers, Data repositories (e.g., GenBank, PRIDE) | Publications, conference presentations, deposited data and code. | Publication bias, challenges in making data FAIR (Findable, Accessible, Interoperable, Reusable). |
| Training & Capacity Building | Ongoing workshops, courses, and mentorship programs to upskill researchers and students in bioinformatics methodologies and tools. | Universities, Research institutes, Bioinformatics units, International organizations | Increased pool of skilled bioinformatics personnel in Nigeria. | Lack of sustainable funding for training, difficulty in attracting and retaining skilled trainers. |
| Project Closure & Evaluation | Formal closure of the project, including financial reporting, outcome evaluation, and lessons learned for future projects. | Researcher(s), Funding agencies, Project management teams | Final project report, documented lessons learned, potential for follow-up projects. | Lack of robust project evaluation frameworks, insufficient feedback mechanisms. |
Bioinformatics Infrastructure Process Workflow in Nigeria
- Inquiry & Conceptualization
- Needs Assessment & Feasibility Study
- Proposal Development & Funding Acquisition
- Ethical & Regulatory Approvals
- Resource Allocation & Infrastructure Setup
- Data Generation & Acquisition
- Bioinformatics Analysis & Interpretation
- Results Dissemination & Archiving
- Training & Capacity Building
- Project Closure & Evaluation
Bioinformatics Infrastructure Cost In Nigeria
Bioinformatics infrastructure in Nigeria is a critical enabler for research, healthcare, and agricultural advancements, but its cost can be a significant hurdle. Several factors influence pricing, making it difficult to provide exact figures without specific project requirements. These factors include the type and scale of infrastructure, hardware specifications, software licensing, maintenance contracts, and the vendor or service provider chosen. The exchange rate of the Nigerian Naira (NGN) against major currencies like the US Dollar (USD) also plays a pivotal role in determining the final cost, especially for imported hardware and software. Local expertise in installation, configuration, and ongoing support can also affect pricing, with specialized services often commanding higher fees. Furthermore, government grants, academic discounts, and bulk purchasing agreements can significantly reduce the overall expenditure.
| Infrastructure Type/Service | Estimated Range (NGN) | Notes |
|---|---|---|
| High-Performance Computing (HPC) Cluster (Small-to-Medium Scale) | ₦15,000,000 - ₦150,000,000+ | Includes servers, networking, storage. Costs vary greatly with node count and specifications. Considerations include power, cooling, and rack space. |
| High-End Workstation (for individual researcher/lab) | ₦1,500,000 - ₦10,000,000 | Powerful CPUs, ample RAM, fast storage (SSDs), and potentially a professional GPU. Good for analysis of moderate-sized datasets. |
| Storage Solutions (NAS/SAN, dedicated servers) | ₦500,000 - ₦50,000,000+ | Scalability, performance (HDD vs. SSD, RAID configurations), and capacity are key determinants. Includes hardware and potentially software for management. |
| Cloud Computing (e.g., AWS, Azure, Google Cloud - estimated monthly) | ₦50,000 - ₦5,000,000+ | Highly variable based on compute instances, storage used, data transfer, and service usage. Effective for elastic scaling and avoiding upfront hardware costs. |
| Proprietary Bioinformatics Software Licenses (Annual) | ₦200,000 - ₦5,000,000+ per license/module | Examples include commercial sequencing analysis pipelines, molecular modeling software. Per-user or per-core licensing models. Open-source alternatives are often free. |
| Installation & Configuration Services | ₦200,000 - ₦2,000,000+ | Depends on complexity and vendor. Can be a one-time cost or part of a support package. |
| Annual Maintenance & Support Contracts | 10-20% of initial hardware/software cost annually | Essential for ensuring uptime, receiving updates, and accessing technical support. Negotiable. |
| Dedicated Bioinformatics Analyst/Support (Monthly Salary) | ₦300,000 - ₦1,000,000+ | Reflects the demand for skilled personnel. Can be a significant recurring cost for institutions. |
Key Pricing Factors for Bioinformatics Infrastructure in Nigeria
- Hardware Specifications (CPU, RAM, Storage, GPUs)
- Software Licensing (Proprietary vs. Open-source, Perpetual vs. Subscription)
- Cloud Computing Services (Compute, Storage, Data Transfer)
- Network Infrastructure (Bandwidth, Connectivity)
- Installation, Configuration, and Deployment Services
- Ongoing Maintenance and Support Contracts
- Training and Skill Development
- Import Duties and Taxes (for imported hardware/software)
- Vendor Margins and Negotiation Power
- Project Scale and Duration
Affordable Bioinformatics Infrastructure Options
Building and maintaining bioinformatics infrastructure can be a significant investment. However, numerous affordable options exist, allowing research institutions and individual researchers to access powerful computational resources and software without breaking the bank. This section explores value bundles and cost-saving strategies to make bioinformatics infrastructure more accessible.
| Strategy/Bundle | Description | Value Proposition | Cost-Saving Mechanism | Typical Use Cases |
|---|---|---|---|---|
| Cloud Provider Bundles (AWS, GCP, Azure) | Integrated packages of compute, storage, networking, and managed services tailored for scientific workloads. Often include discounts for research or academic use. | Scalable, on-demand resources, access to high-performance computing (HPC) instances, specialized AI/ML services, and robust data storage solutions. | Pay-as-you-go pricing, reduced hardware maintenance, economies of scale, potential for academic grants and discounts. | Large-scale genomics, transcriptomics, proteomics analysis; machine learning for drug discovery; big data analytics. |
| HPC Clusters (On-Premise or Hosted) | Dedicated clusters of interconnected computers optimized for parallel processing. Can be owned and managed in-house or leased from a provider. | High throughput for computationally intensive tasks, efficient parallelization of complex algorithms. | Long-term cost savings for continuous high-demand workloads compared to cloud for some scenarios; shared resource utilization. | Large-scale simulations, molecular dynamics, genome assembly, population genetics analyses. |
| Open-Source Bioinformatics Platforms (Galaxy, Snakemake, Nextflow) | Web-based or workflow management systems that provide a user-friendly interface and reproducible pipelines for various bioinformatics analyses. | Democratizes access to complex tools, promotes reproducibility, reduces the need for individual script development and maintenance. | Zero licensing fees for software, reduced training overhead for standard analyses. | Routine genomic data analysis, comparative genomics, variant calling, RNA-Seq analysis. |
| Containerization Services (Docker Hub, Quay.io, Cloud Container Registries) | Repositories and tools for building, sharing, and deploying software in isolated, portable containers. | Ensures consistent software environments across different machines, simplifies dependency management, speeds up deployment. | Reduced troubleshooting due to environment inconsistencies, efficient resource allocation, easier collaboration. | Reproducible research, deployment of custom analysis pipelines, integration with cloud and HPC environments. |
| Research Data Storage Solutions (e.g., Globus Online, University Data Lakes) | Specialized services and infrastructure for storing, managing, and transferring large research datasets. | Secure, reliable, and high-performance data access; integration with computational resources; often compliant with data privacy regulations. | Cost-effective compared to building and maintaining custom storage solutions, potential for bulk storage discounts. | Archiving genomic data, managing large experimental outputs, facilitating data sharing among collaborators. |
Key Considerations for Affordable Bioinformatics Infrastructure
- Cloud Computing: Offers flexible, pay-as-you-go access to powerful computing resources, eliminating the need for large upfront hardware investments.
- Open-Source Software: A vast ecosystem of free and open-source bioinformatics tools and platforms significantly reduces licensing costs.
- Containerization (Docker, Singularity): Simplifies software deployment, ensures reproducibility, and allows for efficient resource utilization, often leading to cost savings.
- Shared Resources and Collaborations: Pooling resources with other institutions or joining collaborative projects can distribute costs and provide access to specialized infrastructure.
- Virtualization: Allows for multiple operating systems and environments to run on a single physical server, maximizing hardware utilization.
- Managed Services: Outsourcing specific infrastructure management tasks to specialized providers can be more cost-effective than building in-house expertise for every component.
- Prioritization and Scalability: Focusing on essential infrastructure needs and choosing solutions that can scale up or down as required prevents over-provisioning and unnecessary costs.
Verified Providers In Nigeria
In Nigeria's dynamic healthcare landscape, identifying trustworthy and skilled medical professionals is paramount for ensuring quality patient care. "Verified Providers" signifies a commitment to excellence, rigorous vetting, and adherence to the highest medical standards. Franance Health has established itself as a leading platform for connecting individuals with these elite healthcare practitioners. Their stringent credentialing process ensures that every provider listed on their platform has undergone thorough background checks, verification of their licenses, educational qualifications, and professional experience. This meticulous approach builds trust and confidence, making Franance Health the premier choice for those seeking reliable and competent healthcare services in Nigeria. By prioritizing verified professionals, Franance Health not only elevates the standard of healthcare delivery but also empowers patients with the knowledge that they are receiving care from the most qualified individuals available.
| Credential Category | Franance Health Verification Process | Why it Matters for Patients |
|---|---|---|
| Medical Licenses & Registrations | Mandatory verification with relevant Nigerian medical regulatory bodies (e.g., MDCN). | Ensures providers are legally qualified and authorized to practice medicine. |
| Educational Qualifications | Verification of degrees, diplomas, and certifications from accredited institutions. | Confirms providers have received formal training and possess the necessary theoretical knowledge. |
| Professional Experience | Review of work history, including previous hospital affiliations and practice durations. | Indicates practical application of skills and exposure to diverse medical cases. |
| Specialty Certifications | Confirmation of any specialized training and board certifications. | Guarantees expertise in specific medical fields, leading to more targeted and effective treatment. |
| Good Standing & Disciplinary Records | Checks for any past disciplinary actions or professional misconduct. | Ensures providers maintain ethical standards and have a clean professional history. |
| Continuing Medical Education (CME) | Encourages and sometimes verifies participation in ongoing professional development. | Demonstrates commitment to staying updated with the latest medical advancements and practices. |
Key Benefits of Choosing Franance Health Verified Providers
- Unwavering commitment to quality and patient safety.
- Access to a curated network of highly qualified and experienced professionals.
- Thorough credentialing and background checks for all listed providers.
- Peace of mind knowing you are receiving care from licensed and accredited practitioners.
- Streamlined and efficient access to reliable healthcare services.
- Enhanced trust and confidence in the medical professionals you choose.
- A commitment to continuous improvement and adherence to ethical medical practices.
Scope Of Work For Bioinformatics Infrastructure
This document outlines the Scope of Work (SOW) for the establishment and maintenance of a robust bioinformatics infrastructure. It details the technical deliverables required and their corresponding standard specifications. The infrastructure is designed to support advanced genomic and proteomic data analysis for research and development purposes.
| Deliverable | Description | Standard Specifications | Quantity/Capacity | Timeline |
|---|---|---|---|---|
| HPC Cluster Compute Nodes | Physical servers dedicated to computational tasks. | CPUs: Intel Xeon Gold/Platinum or AMD EPYC (latest generation), minimum 32 cores per node. RAM: Minimum 256 GB DDR4/DDR5 ECC per node. Accelerators (Optional): NVIDIA Tesla V100/A100 or equivalent GPUs (specify number and type per node if required). | 10 nodes (expandable) | Month 1-3 |
| HPC Cluster Interconnect | High-speed network connecting compute nodes for parallel processing. | InfiniBand HDR/EDR or 100 GbE RoCE. | 10 Gbps minimum per node (aggregate bandwidth) | Month 1-3 |
| High-Capacity Storage - Scratch Space | Fast, temporary storage for active analysis datasets. | NVMe SSD array or parallel file system (e.g., Lustre, BeeGFS). IOPS: Minimum 100,000 read/write IOPS per TB. Latency: <1ms. | 50 TB | Month 1-4 |
| High-Capacity Storage - Archive/Project Space | Reliable, long-term storage for raw and processed data. | NAS or SAN solution, SATA/SAS HDD with enterprise-grade reliability. Redundancy: RAID 6 or equivalent. Data Integrity: Checksums and error correction. | 200 TB (expandable) | Month 1-4 |
| Bioinformatics Software Suite Licenses | Commercial and open-source software for genomics, proteomics, and data analysis. | Includes: BWA, Bowtie2, STAR, GATK, FreeBayes, SAMtools, BEDTools, PLINK, FastQC, MultiQC, Trimmomatic, etc. Specific commercial licenses (e.g., commercial sequencing analysis pipelines, visualization tools) as identified in project requirements. | Unlimited or per-core licenses as applicable. | Month 2-5 |
| Job Scheduler | Software for managing and allocating computational resources. | Slurm or LSF. | 1 instance | Month 1-3 |
| Centralized Logging and Monitoring System | Aggregates logs and monitors system performance. | ELK Stack (Elasticsearch, Logstash, Kibana) or Prometheus/Grafana. | 1 instance | Month 1-3 |
| Network Infrastructure | High-speed, secure network for data transfer and user access. | 10 Gbps/40 Gbps Ethernet switches, firewalls, VPN capabilities. | As per site assessment | Month 1-2 |
| Data Management Platform | System for organizing, tracking, and versioning data. | Open-source (e.g., Nextflow Tower) or commercial Data Lake/Warehouse solutions. | 1 instance | Month 3-6 |
| Security Hardening and Access Control | Implementation of robust security measures. | Multi-factor authentication (MFA), Role-Based Access Control (RBAC), regular security audits, intrusion detection systems. | As per organizational policy | Ongoing |
| User Training Materials and Sessions | Documentation and training for end-users. | User manuals, tutorials, workshops on using the infrastructure and software. | Comprehensive library and scheduled sessions | Month 4 onwards |
Key Components of Bioinformatics Infrastructure
- High-Performance Computing (HPC) Cluster
- High-Capacity Storage Solution
- Bioinformatics Software Suite
- Networking Infrastructure
- Data Management and Security Framework
- User Support and Training
Service Level Agreement For Bioinformatics Infrastructure
This Service Level Agreement (SLA) outlines the expected response times and uptime guarantees for the Bioinformatics Infrastructure. This agreement is established between the service provider (Bioinformatics Core Facility) and the service consumer (Researchers and affiliated departments).
| Service Component | Severity Level | Response Time Target | Resolution Time Target | Uptime Guarantee |
|---|---|---|---|---|
| HPC Cluster Access (Login Nodes, Job Scheduler) | Critical (Service Unusable) | 15 minutes | 4 hours | 99.9% |
| HPC Cluster Access (Login Nodes, Job Scheduler) | High (Degraded Performance, Intermittent Access) | 30 minutes | 8 hours | 99.9% |
| Primary Data Storage (Project/Analysis Data) | Critical (Data Inaccessible) | 30 minutes | 4 hours | 99.9% |
| Primary Data Storage (Project/Analysis Data) | High (Slow Access, Intermittent Issues) | 1 hour | 8 hours | 99.9% |
| Essential Bioinformatics Tools/Software (e.g., BLAST, STAR) | Critical (Tool Unavailable) | 1 hour | 8 hours | 99.5% |
| Web-based Portals (e.g., Galaxy, Web UIs) | Critical (Portal Unavailable) | 2 hours | 12 hours | 99.0% |
| Auxiliary Services (e.g., Git Repositories, Email Notifications) | Medium (Service Interrupted, Impact Limited) | 4 hours | 24 hours | 98.0% |
Key Definitions
- Service Hours: 24 hours a day, 7 days a week, 365 days a year, unless otherwise specified for scheduled maintenance.
- Scheduled Maintenance: Planned downtime for updates, upgrades, or hardware maintenance. Notification will be provided at least 48 hours in advance.
- Unscheduled Downtime: Any unplanned interruption to the service.
- Response Time: The maximum time allowed for the Bioinformatics Core Facility to acknowledge and begin addressing a reported incident.
- Resolution Time: The maximum time allowed to restore the service to its expected operational state.
- Uptime: The percentage of time the Bioinformatics Infrastructure is available and operational.
- Critical Service: Core bioinformatics services including HPC cluster access, primary data storage, and essential workflow execution engines.
Frequently Asked Questions
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