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AI-Assisted Genomic Sequence Alignment for Disease Surveillance

What it is for

Environmental Health
  • Used for COVID-19 and monkeypox virus genomic characterization and public health surveillance by aligning sequencing reads to reference genomes.

NYC Automated Decision Systems Use Case — Minimap2

Research & Development
  • Supports genomic surveillance and characterization research, enabling the laboratory to piece together complete genomic sequences from fragmented sequencing data generated by instruments such as Oxford Nanopore Technologies.

NYC Automated Decision Systems Use Case — Minimap2

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
Not available to vendor, Accountable organization

What it is for

Environmental HealthResearch & Development

Minimap2 is a bioinformatics tool used by the NYC Department of Health and Mental Hygiene's Public Health Laboratory to align DNA sequencing data to reference genomes for COVID-19 and monkeypox virus surveillance. It processes laboratory-generated sequencing reads — not personal information — to reconstruct complete genomic sequences used for public health characterization. No identifying information about individuals is collected or disclosed; the system operates on biological samples, not patient records. It runs approximately three to four times per week under laboratory staff supervision.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Laboratory-generated sequencing reads (e.g., Oxford Nanopore Technologies long-read data) derived from biological samples. No personally identifying information is included in these inputs.

NYC Automated Decision Systems Use Case — Minimap2

Operational data
Anonymized data
  • An appropriate reference genome sequence used as the alignment target. This is publicly available scientific data, not associated with any individual.

NYC Automated Decision Systems Use Case — Minimap2

Processing

Optimization
  • Uses optimal chaining scores to efficiently align sequencing reads to a reference genome, optimized specifically for long-read sequencing technologies such as Oxford Nanopore Technologies data.

NYC Automated Decision Systems Use Case — Minimap2

What it does

Deciding (Analytical AI)
Human decides
  • Minimap2 uses optimal chaining scores to compute sequence alignments and predict fragment ordering; results are reviewed by laboratory staff for genomic characterization and surveillance decisions.

NYC Automated Decision Systems Use Case — Minimap2

Outputs

About a measurement
Anonymized data
  • Sequence alignments identifying genomic similarities and differences, used for genomic characterization and surveillance of COVID-19 and monkeypox virus. No personally identifying information is contained in these outputs.

NYC Automated Decision Systems Use Case — Minimap2

Run by

Department of Health and Mental Hygiene (DOHMH)
  • The Public Health Laboratory within the Disease Control division deploys and operates Minimap2 for genomic sequencing analyses of COVID-19 and monkeypox virus samples.

NYC Automated Decision Systems Use Case — Minimap2

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 vendor
  • No vendor is involved; Minimap2 is open-source software with no vendor data access. The tool is operated exclusively by DOHMH laboratory staff.

NYC Automated Decision Systems Use Case — Minimap2

Available to the accountable organization
  • Output sequence alignments are available to DOHMH Disease Control / Public Health Laboratory staff for genomic characterization and surveillance purposes.

NYC Automated Decision Systems Use Case — Minimap2

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.

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 TransparencyMinimap2 is open-source software publicly available at https://github.com/lh3/minimap2. Its algorithms and methodology are fully documented and openly inspectable by anyone. This system processes anonymized biological samples and does not make decisions about individuals.

Risks and safeguards

No risks or safeguards have been published for this system yet.