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APBA/ABI Joint Agri-informatics ‘Bring Your Own Data’ (BYOD) Hackathon 2026

Hackathon overview/description:

Advances in genome sequencing technology and bioinformatics are revolutionizing crop improvement by enabling faster, more accurate, and cost-effective breeding. However, many breeding programmes across Africa continue to face significant capacity gaps in translating genomic data into actionable breeding decisions. These challenges are often linked not to data generation, but to limitations in data analysis, interpretation, and reporting.

Recognizing this gap, the African Plant Breeders Association (APBA), in partnership with the African Bioinformatics Institute (ABI), is expanding its efforts to strengthen practical genomics and bioinformatics capacity among plant breeders. This collaboration builds on the success of a recent genomics and bioinformatics data hackathon, which demonstrated strong demand for hands-on, data-driven support and resulted in tangible analytical and publication outcomes for participating researchers.

To extend the reach and impact of this approach, APBA and ABI are jointly convening the ‘APBA/ABI Agri-informatics Bring Your Own Data Hackathon’, designed to support a broader cohort of breeders and researchers in analyzing, interpreting, and reporting genomics-related datasets. The hackathon model emphasizes collaborative problem-solving, mentorship, and real-world data analysis, with the goal of accelerating the translation of genomic data into improved crop varieties and strengthening publication output across African breeding programmes.

Intended audience: This hackathon is aimed at senior postgraduate students (MSc, PhD), postdoctoral researchers, researchers, and academic staff in the agricultural sciences who have existing biological or genomic datasets and wish to advance these data through analysis, interpretation, synthesis, and publication.

Prerequisites:

  • Participants should be actively working in, or studying towards a degree in, the agricultural sector.
  • Participants should have existing datasets that they can access and bring to the workshop for hands-on analysis.
  • Participants should have basic familiarity with the Unix command line and some experience using R or Python for data analysis.

Objectives/Outcomes: After this hackathon participants should be able to:

  • Apply appropriate bioinformatics, statistical, and quantitative genetics approaches to analyse their own agricultural genomics or breeding-related datasets.
  • Interpret genomic, phenotypic, and breeding-related data outputs to support evidence-based decision-making in crop improvement programmes.
  • Select and use suitable analytical workflows for applications such as experimental design, GWAS, genomic selection, machine learning, and quantitative genetics.
  • Work collaboratively with mentors and peers to troubleshoot analytical challenges and improve the quality, reproducibility, and interpretation of their analyses.
  • Develop clear analysis outputs, project summaries, presentations, or publication outlines that communicate key findings, limitations, and next steps.

Keywords: Hackathon, Genomics, NGS, GWAS, Data Analysis

Skill level: Intermediate - advanced

Language: English

Credential awarded: Hackathon certificate

Type of event: Face-to-face

Venue: Faculty of Health Sciences, University of Cape Town, South Africa (TBC)

Dates for the hackathon: The hackathon will take place across 5 days, 5-9 October 2026

Hackathon organisers: APBA, ABI

Registration opens: Friday, 19 June 2026

Registration closes: Thursday, 16 July 2026; no late applications can unfortunately be accepted

Notification date: 30 July 2026 (TBC)

Link to application form: https://redcap.h3abionet.org/redcap/surveys/?s=XDX93YWKTCDXWJKT

Workshop sponsors: African Plant Breeders Association; African Bioinformatics Institute

Trainers:

  • Bruce Walsh
  • Réka Howard
  • Nelson Lubanga
  • Vamsi Manthena
  • Ranjana Bhattacharjee
  • Alexander Lipka

Syllabus/Tools: Coming soon!

Schedule/Programme: Coming soon!

Preceding lecture series:

The trainers/facilitators of the hackathon will be presenting a modularised lecture series in the months leading up to the hackathon. While limited places will be available for the in-person hackathon, the lecture series is 100% free and open to anyone interested in joining.

Planned lecture topics are listed below (please note, these are subject to change and confirmation):

ModuleSessionPresenter(s)DateTimeZoom Registration Link/Lecture recording
Module 1: Experimental Design
Lecture 1: Why is Experimental Design Important?Réka Howard8 June 20263pm CATRecording: https://youtu.be/fPqz62Fb3LU
Lecture 2: A Practical Introduction to Factorial DesignsRéka Howard9 June 20263pm CATRecording: https://youtu.be/kVDWgwJ9IIM
Lecture 3: Block It, Repeat It, Analyze It!Réka Howard16 June 20263pm CATRecording: https://youtu.be/KVy8oB51tVQ
Module 2: Quantitative Genetics
Lecture 4: Quantitative Genetics: An overviewBruce Walsh3 July 2026 Recording:  https://youtu.be/Q-ysKpWI1-0
Lecture 5: Quantitative Genetics: Single trait selectionBruce Walsh6 July 20263pm CATRecording: https://youtu.be/TdFQ6uPEjD8
Lecture 6: Quantitative Genetics: Matrices and multiple trait selectionBruce Walsh7 July 20263pm CATRecording: https://youtu.be/gmTmv4Gu-yY
Lecture 7: Quantitative Genetics: index selectionBruce Walsh8 July 20263pm CATRecording: https://youtu.be/8oqgxAPNWYM
Module 3: Applications of Quantitative Genetics
Lecture 8: Data QC, file formats & ConversionsNelson Lubanga23 July 202612pm CATRegister Here

Lecture 9: Applications of Quantitative Genetics (Part I-III*)

*Part III will not be presented live, a link to the lecture will be provided.

Alex Lipka27 - 28 July 20264pm CATRegister Here
Lecture 10: Introduction to GAPIT and other R packagesAlex Lipka29 July 20264pm CATRegister Here
Module 4: Machine Learning
Lecture 11: Machine Learning Foundations for Genomic PredictionVamsi Manthena04 August 20264pm CATcoming soon!
Lecture 12: Integrating AI into Genomic PredictionVamsi Manthena06 August 20264pm CATcoming soon!
Module 5: Genomic selectionLecture 13: Applied genomic predictionNelson Lubanga11 August12pm CATcoming soon!