AnniZ Lab

Our mission

• Microbes are massively important for human well-being, from industrial production to healthcare.
• Using an AI model built on biological principles, we can design microbes to follow instructions: sensing different environmental molecules and releasing different products accordingly.
• If we solve this problem, it's going to create a trillion-dollar company.

Our sevices

• Who are we?
• We are a team of microbiologists and software engineers previously from MIT and Google.
• We develop biology-informed AI models to predict how proteins interact with DNA and ligands in bacteria.
• We analyze 500,000+ to design the computational blueprints needed for precise microbial engineering.

• What can we do?
• Technical R&D partner for companies in fermentation, therapeutics, drug discovery, and agriculture.
• Our platform identifies the specific "genetic switches" (regulatory motifs and binding sites) required to control and optimize bacterial behavior.

• Core industrial domains
• Industrial fermentation: designing genetic circuits that respond to environmental triggers to improve production yield
• Live biotherapeutics: identifying "localization switches" to ensure engineered probiotics release medicine only at the target site
• Drug discovery: predicting how to activate "silent" genes in bacteria to discover new antibiotics
• Agricultural biotech: engineering soil microbes to remain active in diverse conditions for better plant growth and nitrogen fixation.

Our tools

DNA-protein binding prediction via biology informed AI models!

DNA-protein binding prediction via biology informed AI models!

MicrobiomeConnectome: knowledge graph for druggable target prediction!

MicrobiomeConnectome: knowledge graph for druggable target prediction!

• Our github

Our research

• Biology-informed AI models for precise bacterial engineering
• Protein-ligand binding prediction: to sense different molecules
• Protein-DNA binding prediction: to change bacterial behavior accordingly

• Microbiome druggable target design via knowledge graph link prediction
• LLM-powered microbiome knowledge graph: extracting existing knowledge
• Link prediction: generating new hypotheses
• Rule-based microbial gene/metabolite prioritization: identifying microbiome-derived drug leads

• More research and thoughts shared on LinkedIn and bluesky!

AnniZ Lab Members

What to expect as our teammate

• Receive training in critical thinking and hypothesis testing
• Gain a solid foundation in statistics, microbiology, and AI computation
• Develop skills in coding, analysis, visualization, and public speaking
• Practice collaboration and communication
• More importantly, we care about your future! We are happy to mentor you in career development.

News!

• 2026 Jun Dr. Fatima Aysha Hussain at Tufts University visited our lab at NTU.
• 2026 May Anni is invited to give a talk on Microbiome R&D and Business Collaboration Congress Asia.
• 2026 Apr BIG NEWS: we are awarded SG$1M Singapore MOE Tier 2 grant for protein-DNA binding prediction!
• 2026 Apr Zhiqi talked about "Detection of HGT and selective sweeps in human gut microbiome via large data analysis and population genomics" in Bug seminars - great job.
• 2026 Mar Anni was invited as a working group member for National Infectious Disease Research Strategy, Singapore!
• 2026 Jan Dr. Xiaoqian Yu (University of Montreal) visited our lab at NTU.
• 2025 Dec Qiwen talked about "Biology-guided AI models to predict bacteria protein DNA binding" in BUG seminars - great job.
• 2025 Dec Dr. Klas Udekwu (University of Idaho & SLU) visited our lab at NTU.
• 2025 Nov Anni talked about "Uncovering diverse microbial mechanisms via computation" in SCELSE Retreat.
• 2025 Aug Jinxuan joined our lab as a PhD student. Welcome :)
• 2025 Aug Zhiqi switched to a PhD student. Congrats!
• 2025 July-Aug Aolin and Yifang transitioned to future paths. Good luck!
• 2025 July Anni gave a talk at Gordon Conference AEM 2025
• 2025 July Jeff presented work at Gordon Conference AEM 2025 :)
• 2025 July Anni presented work at Gordon Conference MPB 2025 :)
• 2025 June Anni gave a talk at MIT BE Seminar!
• 2025 March Aolin joined us as a research associate.
• 2025 Jan Qiwen joined us as a PhD student. Welcome to AnniZLab!
• 2025 Jan Zhiqi joined us as a research associate.
• 2024 Nov Yifang joined us as a research associate.

Highlighted discoveries

Case 1 of 6: published on Genome Biology, reported by NTU News Sequence aligners use fixed-size k-mers in their algorithms, with the choice of k-mer size significantly impacting alignment quality. Our tool, Mapper, overcomes this by dynamically incorporating k-mers of various sizes and gaps (gapped x-mers).

Case 2 of 6: published on Cell Genomics, reported by MIT News Gut microbiomes share CRISPR systems through horizontal gene transfer to update their phage defense.

Case 3 of 6: published on BMC Biology A novel algorithm for genetic variant identification that is 9x faster than existing tools, while producing fewer false positives and false negatives.

Case 4 of 6: published on Nature Communications Antibiotic resistance genes (ARGs) are everywhere, but most high-risk ARGs (Rank I, red bars) are only present in human-associated environments but not in natural environments.

Case 5 of 6: published on Microbiome Antibiotic resistance genes are highly prevalent (red and blue dots) in almost all bacterial genomes. Specifically, ARGs in Gammaproteobacteria (red dots) are spreading by horizontal gene transfer via class 1 integrons (i.e. intI1).

Case 6 of 6: Gut microbiomes are actively adapting in healthy human subjects through accumulating beneficial mutations in carbohydrate transport regulators.