AI-Assisted Genomic Sequence Alignment for Disease Surveillance
What it is for
Healthcare
- Used in public health laboratory workflows to align genomic sequencing data from biological samples, supporting pathogen characterization and outbreak response — not for individual clinical care decisions.
Research & Development
- Supports genomic epidemiology research including phylogenetic tree construction and distance tree analysis for disease surveillance programs.
- Run by
- Department of Health and Mental Hygiene (DOHMH)
- Where
- No fixed location
- Kept
- Not stated by the NYC Office of Technology and Innovation.
- Shared with
- Accountable organization
- Your copy
- You cannot see the data it holds about you. What you can do
What it is for
This tool aligns multiple genomic sequences from laboratory samples using mathematical algorithms, helping public health scientists identify genetic differences between pathogens. It is used 3–4 times per week by the NYC Department of Health and Mental Hygiene's Public Health Laboratory to support outbreak investigations and phylogenetic analysis. No personal identifying information is collected or disclosed — the system processes biological sequence data only.
What it collects and what happens to it
Data taken in
About a measurement
Anonymized data
- Homologous nucleotide or protein sequences generated from laboratory sequencing of biological samples or validated reference databases. No personal identifying information is attached to these sequences.
Processing
Optimization
- Uses Fast Fourier Transform-based methods along with guided tree construction and scoring matrices to efficiently compute optimal multiple sequence alignments across large sets of genomic sequences.
What it does
Deciding (Analytical AI)
Human decides
- Applies guided tree, scoring matrices, and sequence alignment algorithms to realign genomic data; outputs are reviewed and interpreted by laboratory scientists before any public health decisions are made.
Outputs
About a measurement
Anonymized data
- Aligned sequences identifying conserved and variable genomic regions, used as input for phylogenetic and outbreak analyses. No personal identifiers are present in the output.
Run by
Department of Health and Mental Hygiene (DOHMH)
- The NYC Department of Health and Mental Hygiene's Disease Control division, Public Health Laboratory, deploys and operates this sequence alignment tool for genomic surveillance and outbreak investigation.
Built by
Not stated by the NYC Office of Technology and Innovation.
Kept for
Not stated by the NYC Office of Technology and Innovation.
Shared with
Not available to me
- The genomic sequence data processed by this system is not available to the public or individuals whose biological samples may have contributed to the analysis.
Available to the accountable organization
- Aligned sequence outputs are available to laboratory scientists and epidemiologists within the NYC Department of Health and Mental Hygiene for internal disease surveillance and outbreak investigation use.
Stored
Not stated by the NYC Office of Technology and Innovation.
How to read the colours
Can it identify you?
- Anonymized data
- Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
- Pseudonymous data
- Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
- Identifiable data
- The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.
Who completes the loop?
- Human decides
- This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
- Human executes
- This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
- Autonomous
- This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.
Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.
- AI registerNYC Automated Decision Systems — MAFFT Register EntryNYC Department of Health and Mental Hygiene, Disease Control – Public Health Laboratory. Reported 2025, first used January 2021.
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- AI registerNYC Automated Decision Systems — MAFFT Register Entry
- Register entryPublished by the NYC Office of Technology and Innovation. Reference 81936079. This disclosure was drafted with AI assistance.Schema: ai@2026-05-06-beta
What you can do
Ask about this system
Questions go to the NYC Office of Technology and Innovation, not the vendor.
Your rights
- Right to Algorithmic TransparencyMAFFT is open-source software (available at https://github.com/GSLBiotech/mafft) and its algorithmic methods — including guided tree construction, scoring matrices, and Fast Fourier Transform-based alignment — are publicly documented in peer-reviewed literature and the project repository.
Risks and safeguards
No risks or safeguards have been published for this system yet.